Basic Marketing Research with Excel, 3E Alvin C Burns Ronald F Bush Solution Manual

Type:

Solution Manual

Resource:

Basic Marketing Research with Excel

Edition:

3rd Edition

Author(s):

Alvin C. Burns Ronald F. Bush

Instructor’s Manual for

Basic Marketing Research 3rd Edition by Alvin C. Burns and Ronald F. Bush Pearson Prentice Hall, Publishers

Prepared by

Alvin C. Burns Department of Marketing E. J. Ourso College of Business Louisiana State University

.

TABLE OF CONTENTS Chapter

Section Title What Makes Basic Marketing Research: Using Microsoft® Excel Data Analysis, 3rd Edition, Unique?…………………………………...…...

Page

Sample Course Syllabi: Content vs. Project…………………………...

8

1

An Introduction to Marketing Research………………………………...

12

2

The Marketing Research Industry………………………………….…...

22

3

The Marketing Research Process—Defining the Problem and the Research Objectives…………………………………………...…...…...

40

4

Research Design Alternatives and Qualitative Research………….…...

57

5

Information Types and Sources: Secondary Data and Standardized Information……………………………………………………………...

79

6

Data Collection Methods…………………………………………...…...

91

7

Measurement Scales………………………………………………..…...

108

8

Designing Data Collection Forms………………………………….…...

127

9

Determining Sample Size and the Sample Plan………………………...

141

10

Data Issues and Inputting Data into XL Data Analyst……………..…...

159

11

Summarizing Your Data………………………………………………...

171

12

Generalizing Your Findings………………………………………..…...

188

13

Finding Differences………………………………………………...…...

212

14

Determining Relationships……………………………………………...

233

15

Preparing and Presenting Your Research Report……………………....

277

WHAT MAKES BASIC MARKETING RESEARCH: USING .1

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MICROSOFT® EXCEL DATA ANALYSIS, 3RD EDITION, UNIQUE? This book provides: • A concise presentation of the fundamentals of marketing research.

• An improved software package, XL Data Analyst™, which runs using Microsoft® Excel 2010 or earlier versions.

• Input from many professionals in the marketing research industry.

• An integrated case complete with a data set that gives students an experiential learning exercise throughout the course.

Why Excel For Data Analysis?

Most students will not become marketing researchers and only a small percentage of them, in their future careers, will have access to powerful software programs designed specifically for data analysis. By having this book, they will continually have access to our Excel add-in program, XL Data Analyst™. In this course students will learn how to use this powerful software program, which they can access as long as they can access Excel. Instructors told us they want to teach students a software program they will have and use in the future. Once students learn to use XL Data Analyst™ they can use it with their Excel programs for years to come.

Microsoft Excel is a powerful computing tool that is widely used and understood by students. Developers commonly use program applications, called add-ins, that simplify Excel spreadsheet operations. Our add-in, XL Data Analyst™, opens up Excel’s computing capabilities for marketing research applications in an easy-to-use format.

Many features of XL Data Analyst™ make it more desirable than some of the most widely used dedicated stat packages because it takes the mystery and confusion out of data analysis.

Who Should Use This Textbook?

This book is written for the introductory marketing research course at the undergraduate level. We assume students have not had a prior course in marketing research, and that they have had at least one elementary statistics course. We focus on teaching the process of marketing research so that students will be better users of marketing research. They should be able to evaluate the need for marketing research and also determine the adequacy of research proposals. At the same time, we give the students of this book the tools to conduct basic analysis techniques on their own.

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A Concise Presentation We wanted to provide a book with the basics of marketing research. Adopters have told us they want to teach the basics of marketing research in depth as opposed to covering a large amount of material superficially. Many professors desire to teach a course with less text material, allowing them to supplement the course with projects or to spend more time on the basics. Basic Marketing Research: Using Microsoft® Excel Data Analysis is shorter in length but covers the essential, basic components of marketing research. We made every effort to write a shorter book without sacrificing knowledge on what we consider the “basics.”

Features of XL Data Analyst™ XL Data Analyst™ is unique in that it only requires Excel, to which many students have access, and it is written expressly for the purpose of conducting marketing research data analysis. When we wrote the first edition of this book we knew we didn’t want to just write a shorter version of a marketing research book. We wanted a new approach to data analysis. Specifically, we wanted a program that would operate without statistical terms that are difficult for students to navigate. We wanted the program to operate in a userfriendly format that was intuitive. Secondly, with many years of teaching marketing research experience, we wanted our program to offer output in a way that allowed students to interpret the output correctly and more easily. Those who have studied statistics realize that many of the presentations of statistical output are based upon tradition. We offer users an alternative. The XL Data Analyst™ has both traditional and classical statistical formats as well as outputs in our new easy-to-interpret format.

However, the essence of our new software is output, which students can immediately interpret without a need to consult the statistical values; our program generates polished tables with “plain English” presentations of the various findings. This allows students to have greater focus on using marketing research to make decisions—the purpose of marketing research. The XL Data Analyst™ has been tested and is fully compatible with Excel 2010. Students may download XL Data Analyst™ at www.xldataanalyst.com.

About the Text: Key Strengths Aside from being the first marketing research text to fully integrate Excel for data analysis, this book offers several key strengths.

New in the 3rd Edition Here is a bullet list of the new and improved features of our textbook: •

Much more information about qualitative research. We added a section on research design to cover qualitative versus quantitative research and we have new material discussing several methods used in qualitative research.

•

Combined secondary data analysis with standardized information into one chapter and we have extensive coverage of the new, annual census information available .3

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through the American Community Survey. We feature a complete illustration of how to use the ACS for a marketing research objective.

There is a new chapter on data issues and inputting data into XL Data Analyst.

Chapter 10 describes data matrices and data coding plus data quality issues. We also describe the organization of data and variables in the XL Data Analyst.

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New integrated case and dataset (see below).

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Flow charts on data analysis—our flow charts identify key considerations such as categorical or metric data and provide guides to the selection of proper analyses.

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Numerous tweaks and small improvements. We have meticulously examined every section of material and sought to make the presentation as understandable and useful as possible.

Time-Tested, 11-Step Approach The framework of our best-selling SPSS® text is the same framework for our Excel version. Our logical 11-step process is a time-tested process used throughout this book.

New Examples In every chapter we searched for new examples for opening vignettes that would awaken the students’ interest and understanding of marketing research. Several of these vignettes were supplied from our professional contacts in the marketing research industry. Several of them reflect current marketing research practices. In addition to these all-new chapteropening vignettes, new examples, many from marketing research industry sources, are integrated throughout the text.

(New) Integrated Case with Data Set As with our previous textbooks, we wanted an integrated case that relates to students’ interests and which was realistic. Consequently, for the 3rd edition, we developed “Advanced Automotive Concepts,” a fictitious case about a major automobile manufacturer attempting to develop fuel-efficient and environmentally friendly vehicles.

The case addresses consumer concerns about rising gasoline prices, global warming, and their reactions to automobile concepts the company is capable of manufacturing. The case is integrated throughout the textbook. The case resonates with students’ interests and, at the same time, is an excellent example of teaching the marketing research process.

With the exception of Chapter 15 where we have two end-of-chapter cases pertaining to Advanced Automotive Concept, the integrated case is located as the second case at the end of the chapter. Thus, the integrated case is case 1.2, 2.2, 3.2 etc.

Also, we use the Advanced Automobile Concepts case data set to illustrate all of our data analyses procedures discussed in our four data analyses chapters. Of course, we have an Advanced Automobile Concepts XL Data Analyst data set for students to use in applying the various types of data analysis covered in the textbook.

Our Approach to Teaching Data Analysis .4

When we introduced the first edition of this book we said “Finally, there is an alternative!” After many years of teaching marketing research and talking with dozens of colleagues who do the same, the authors decided it was time to do some things a different way. Weary of students struggling with levels of measurement, we present measurement in terms of categorical or metric variables. Instead of having students baffled by data analysis, we present data analysis in an easy-to-learn process. In this edition, we have provided flow charts that instruct students on the key factors to consider when deciding what analysis to use. In addition, data analysis keystrokes are illustrated through colorful, annotated screen captures. Experience has shown us that the students, using XL Data Analyst™, quickly learn the tools of data analysis and complete their projects much faster than with traditional software programs. They focus more on getting the answers and writing their reports instead of staring at hard-to-interpret output.

Datasets In addition to the Advanced Automobile Concepts dataset (AAConcepts.xlsm), we have a dataset on retail store target marketing, Case 14.1, “Friendly Market Versus Circle K” (Friendlymarket.xlsm). There is a recoded version of the integrated case (AAConcepts.Recoded.xlsm) where categorical demographic variables such as age are coded using midpoints of the category ranges, making them metric and thereby appropriate to be used as independent variables in multiple regression analysis. This recoding process is described in Chapter 14 in the section on multiple regression analysis.

Chapter 10 describes how students can set up their own datasets, such as those obtained with a team marketing research project, in the XL Data Analyst. The description makes note of the “Import Data” utility feature of the XL Data Analyst which can be used to import a CSV (comma separated variable) data set with column labels into an XL Data Analyst file, which can then be modified to fit the new dataset.

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Ethics, Global Marketing Research, and Practical Applications As we did in the first edition, when we touch on ethical issues or give examples of the global use of research, we use icons to alert readers to these special topics. Whereas in our previous edition, we highlighted references to the use of online research, it is so dominant in the industry now that is it no longer a “special” aspect of marketing research so it is not identified with any special icon. Finally, when we use a practical application we denote this with an icon.

Marginal Notes, Key Terms, Review Questions, Application Questions, and Case Studies These proven pedagogical aids are included in Basic Marketing Research: Using Microsoft® Excel Data Analysis, 3rd Edition.

Teaching Aids Instructor’s Manual A complete instructor’s manual, prepared by the authors, can be used to prepare lecture or class presentations, find answers to end-of-chapter questions and case studies, and even to design the course syllabus.

Test Item File The test bank for the 3rd Edition contains over 50 questions for each chapter. Questions are provided in both multiple-choice and true/false format. Page numbers corresponding to answers to the questions are provided for each question.

Instructor’s Resource Center All your teaching resources in one place. Electronic versions of the instructor’s manual, test item file, TestGen test generating software, plus PowerPoints are available online at www.pearsonhighered.com/burns (select Instructor Resource Center). All of these resources (with the exception of TestGen) are also available on CD-Rom for your convenience.

Companion Website From the www.pearsonhighered.com/burns portal, students will be able to take self-study quizzes on each chapter in the 3rd Edition and have them automatically graded. Students should study the chapter first, and then take the sample test to assess how well they have learned chapter material.

As always, we wish to thank our life partners who put up with our book writing exploits and, no matter what, always smile. Thank you, Jeanne and Libbo, for your steadfast support of our professional endeavors.

Al Burns .6

Louisiana State University alburns@lsu.edu Ron Bush University of West Florida rbush@uwf.edu

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SAMPLE COURSE SYLLABI: CONTENT VS. PROJECT Although other approaches exist, there are two basic approaches to teaching marketing research: content-based or project-based approach. A content-based approach is based on the premise that knowledge is acquired by reading, memorization, and application to case studies, while a project-based approach is based on the belief that students learn by “doing.” That is, the project-based approach relies on experiential learning; whereas, the content-based approach is based on vicarious learning. As a quick reference and comparison, we offer the following exhibit: Two Approaches to Teaching Marketing Research Content-Based Approach Project-Based Approach Objective: Cover as many marketing research concepts as possible over the term.

Objective: Cover marketing research concepts related primarily to survey research.

Learning Basis: Vicarious learning, meaning that students learn by reading about marketing research and memorize definitions and concepts.

Learning Basis: Experiential learning, meaning that students learn by applying marketing research techniques to real-life or simulated situations.

Strategy: Cover all material thoroughly, and test students frequently on these concepts.

Strategy: Have students apply survey methods concepts with a pilot survey project.

Example: Cover sample methods in a single class with lecture and examples; test students on sample method definitions and the steps for each method.

Example: Cover sample methods over two classes with examples and in-class exercises; students draw a representative sample in the team research project

To elaborate on what we call the “content-based” approach to teaching marketing research, it pertains to Instructors whose primary objective is to cover much marketing research content and to frequently test their students’ command of it. This approach is akin to a “survey course” teaching strategy where the Instructor wishes to expose students to the complete range of marketing research topics so that at the end of the course, students are familiar with basic terminology and they can describe key concepts.

With the content-based approach, the Instructor must cover most concepts efficiently as there are many concepts and little time per concept. Lectures, examples, and exercises are the primary delivery vehicles for the content-based approach.

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In contrast is the “project-based” approach to teaching marketing research. This approach is used by the Instructor who has a team project as a major aspect of the marketing research course. Students are set up in teams, and each student team is assigned the task of completing a pilot survey for a sponsor such as a local business. On the other hand, the Instructor may define the survey project task for students without the use of a sponsor. Regardless, with the project-based approach, the primary emphasis of the course is survey research methods, and the overriding objective is to have students apply survey research techniques in their pilot studies or a simulated marketing research situation. Because of this orientation and time constraints, the project-based Instructor will tend to omit marketing research concepts that are less relevant to survey research, and he/she will use in-class or other exercises to entice students to apply the concepts to real-world situations. Of course, students will be tested on their command of the related concepts during the course, but the project will have a large role in determining the Instructor’s assessment of student ability to apply survey research techniques.

The course syllabus reflects the Instructor’s orientation. We offer two sample syllabi. The first one is for the “content-based” approach, while the second one is for the “projectbased” approach to teaching marketing research.

Sample Syllabus for “Content-Based” Approach to Teaching Marketing Research This example is based on a 15-week semester, and it assumes that the Instructor will give four examinations during the regular semester plus a comprehensive final examination.

Week Title Chapter Exam (Coverage) 1 An Introduction to Marketing Research 1 2 The Marketing Research Industry 2 3 The Marketing Research Process Including Defining 3 the Problem and Research Objectives 4

Research Design Alternatives and Qualitative Research

4

5

Information Types and Sources: Secondary Data and Standardized Information

5

6 7 8 9 10 11 12 13 14 15

Data Collection Methods Measurement Scales Designing Data Collection Forms Determining Sample Size and the Sample Plan Data Issues and Inputting Data into XL Data Analyst Summarizing Your Data Generalizing Your Findings Finding Differences Determining Relationships Preparing and Presenting the Research Report

6 7 8 9 10 11 12 13 14 15

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Exam 1 (1–4)

Exam 2 (5–8)

Exam 3 (9–11)

Exam 4 (12–14) Final Exam (1–15)

Sample Syllabus for “Project-Based” Approach To Teaching Marketing Research This example is based on a 15-week semester, and it assumes that the Instructor will give three examinations during the regular semester, plus a comprehensive team project.

Week

Title

Chapter

Project Progress and Exam (Coverage)

1 2 3

An Introduction to Marketing Research The Marketing Research Industry The Marketing Research Process Including Defining the Problem and Research Objectives

1 2 3

4

Research Design Alternatives and Qualitative Research

4

Meet with sponsor

5

Information Types and Sources: Secondary Data and Standardized Information

5

Exam 1 (1–5)

6 7

Data Collection Methods Measurement Scales

6 7

8 9

Designing Data Collection Forms Determining Sample Size and the Sample Plan

8 9

10

Data Issues and Inputting Data into XL Data Analyst

10

Define project problem Identify information objectives Project research design Project questionnaire design Project sample design

11

Summarizing Your Data

11

12 13*

Generalizing Your Findings Preparing and Presenting Your Research Report

12 15

14

Finding Differences

13

15

Determining Relationships

14

Exam 2 (5–10) Project data collection Data input Project analysis Project analysis and report writing Project analysis and report writing Project writing and presentation Final—Exam 3(11–15)

*Note that the report writing chapter is covered in week 13 to give students proper structure for this activity at the appropriate time in the project execution process.

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A Compromise: The Integrated Case Approach Basic Marketing Research has within it an integrated case study, Advanced Automobile Concepts, a fictitious, yet lifelike, case about a major automobile manufacturer attempting to develop fuel-efficient and environmentally friendly vehicles. The case addresses consumer concerns about rising gasoline prices, global warming, and their reactions to automobile concepts the company is capable of manufacturing. The case is integrated throughout the textbook. The case resonates with students’ interests and, at the same time, is an excellent example of teaching the marketing research process.

With the Advanced Automobile Concepts integrated case, the Instructor can approximate a research project without the many issues and headaches associated with a sponsored project (problems such as communication, scheduling meetings, working with multiple sponsors, money matters, quality control, survey nonresponse, etc.). With the Advanced Automobile Concepts case integrated through the textbook, every chapter has an end-ofchapter case study that pertains to it, and Instructors may use these cases as individual case study assignments or organize student teams to undertake the solutions to the various cases. In other words, the Advanced Automobile Concepts case is a simulated marketing research project, and the Instructor may use it as they wish to test or reinforce student learning of the concepts in our textbook.

With respect to a schedule, we recommend the “content-based” approach schedule with the integrated chapter cases strategically assigned and included as a significant part of the course evaluation system.

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CHAPTER 1 An Introduction to Marketing Research LEARNING OBJECTIVES •

To know the relationship of marketing research to marketing, the marketing concept, and marketing strategy

•

To define marketing research

•

To understand the purpose and uses of marketing research

•

To classify different types of marketing research studies

•

To describe a marketing information system (MIS) and understand why marketing research occupies a place in an MIS

CHAPTER OUTLINE Marketing Research: Part of Marketing?

The Marketing Concept: The “Right” Philosophy The “Right” Marketing Strategy How Do We Define Marketing Research?

What Is the Purpose of Marketing Research?

What Are the Uses of Marketing Research?

Identifying Market Opportunities and Problems Generate, Refine, and Evaluate Potential Marketing Actions Monitor Marketing Performance Improve Marketing as a Process A Classification of Marketing Research Studies The Marketing Information System Components of the MIS Internal Reports System

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Marketing Intelligence System Marketing Decision Support System (DSS) Marketing Research System Before Reading Further…

KEY TERMS Applied research

Marketing decision support system (DSS)

Basic research

Marketing information system (MIS)

Internal reports system

Marketing intelligence system

Market research

Marketing research

Marketing

Marketing research system

Marketing concept

Marketing strategy

TEACHING SUGGESTIONS 1. The purpose of the first section in the chapter is to review marketing, the marketing concept, marketing strategy, and the environments that affect marketing activities. A way to test students (and to bring their thinking back to school as this will be the first week in a new term) is to have them role play as marketing managers for a new brand of a particular product. In the following examples, what would be the target market, salient marketing activities, positioning strategy, and environmental factors?

•

Launching a new brand of sparkling water fruit drink

•

Marketing an online apartment finder that assists college students in finding an apartment. The finder would search for apartments based on criteria, check on vacancies, and let the user place a refundable deposit to hold the apartment for 24 hours while he/she checked it out.

•

Being the new vice president of the Microsoft Xbox division

•

A movie company that uses inexpensive talent (such as actors with little acting ability) and specializes in horror and science fiction “made for video” films

2. The AMA definition makes it clear that marketing research exists to create information to be used in marketing decisions. Product and service organization failures are good class examples of how marketing information should have been used to design products, packaging, or services. Students can often recall products or . 13

services that were disappointing or otherwise failures in their eyes. Class discussion can be stimulated with the question, “What product or service can you think of, or have you encountered that would have benefited from good marketing research?” On the flip side, students may relate products that they believe are especially successful.

College student-oriented products are a good category as all students can relate to them. The objective with successful product/service examples is the same.

3. To drive home the importance of marketing research information in marketing decisions, ask students what type of restaurant they would build and operate off campus. They will probably opt for one that is appropriate for college students— maybe a late night hangout or a takeout service. Then ask what type they would build if the location was beside a university in a distant state. They will most likely give the same answer. Now, move the location to a foreign university (say mainland China where all college students live in dorms, eat in dining halls, have free tuition, don’t drive cars, etc.). If possible, use a country that you are familiar with and is quite different from your students’ situations. They will come to realize that they need information in order to make reasonable decisions about the restaurant.

4. Hold a class brainstorming session on marketing opportunities. Select one of the examples below or use your own to generate several different directions that these companies could go in. Ask the class what marketing research information would be useful to evaluate the success potential of each opportunity brainstormed.

What marketing opportunities are there for the following?

• • •

A zoo A family restaurant A pet food company

5. Perhaps using one of your own applied research experiences such as a consultation, illustrate the various characteristics of marketing research (applied rather than basic, sometimes inaccurate, and shaped by budget and time constraints). Note: If you use class projects where surveys are done for sponsors, you can bring one of these into the class and describe how it illustrates the characteristics. You may want to bring a recent copy of an academic marketing journal to class to provide some examples of basic research.

6. Because it is a complicated and abstract concept, it is worthwhile to go over the components of an MIS. To make it more real, use a company example and ask students what they think would be involved with each component. Possible examples include: a hotel chain such as Marriott, a fast-food chain such as Taco Bell, or a periodical such as People magazine. Indicate or generate class discussion to identify the various specific examples that would go into each of the components of an MIS. For instance, marketing intelligence might be a “clipping” service on relevant competitors, internal reports might be salespeople’s reports and notes, marketing research might be an annual customer satisfaction survey, and so forth.

ANSWERS TO REVIEW QUESTIONS 1. Explain how marketing research can be used in the leisure and sports industry. .

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The introductory vignette describes how the Sports & Leisure Research Group works with sports and leisure companies in the golf, cruise line, and other recreation industries to: • • • •

Effectively communicate with the target market Optimize product offerings Understand what customers want Position their products and services

2. What is marketing? Explain the role of marketing research in the process of marketing management.

The American Marketing Association has defined marketing as an organizational function and a set of processes for creating, communicating, and delivering value to customers and for managing customer relationships in ways that benefit the organization and its stakeholders.

The purpose of marketing research is to link the consumer to the marketer by providing information that can be used in making marketing decisions.

3. Give some examples of products that have failed.

Listed are: Irridium telephone, General Motors EVI, McDonald’s veggie burger, IncrEdibles, Hey! There’s A Monster in My Room Spray. Marketing Research Application 1.1 describes the failures: Ice Breakers Pacs, Coca-Cola Blak, Coca-Cola C2, Wolfgang Puck Self-Heating Latte.

4. Why are philosophies important to decision makers? What is the marketing concept?

A philosophy is a system of values, or principles, by which you live. Thus, one’s philosophy dictates the decisions you make every day. There are many different philosophies that managers may use to guide them in their decision making.

The marketing concept is a business philosophy, which holds that the key to achieving organizational goals consists of the company being more effective than competitors in creating, delivering, and communicating customer value to its chosen target markets.

5. What is strategy, and why is marketing research important to strategy makers?

A marketing strategy consists of selecting a segment of the market as the company's target market and designing the proper “mix” of product/service, price, promotion, and distribution system to meet the wants and needs of the consumers within the target market.

Marketing research is a process that results in reporting information and that information can be used to solve a marketing problem such as determining price, how to advertise, and so on. That is, marketing research is the basis of marketing strategy.

6. Define marketing research. Define market research. . 15

Marketing research is the process of designing, gathering, analyzing, and reporting information that may be used to solve a specific marketing problem.

Market research refers to applying marketing research to a specific market area.

One definition of market research is: The systematic gathering, recording, and analyzing of data with respect to a particular market, where market refers to a specific customer group in a specific geographic area.

7. What is the purpose of marketing research?

The purpose of marketing research is to link the consumer to the marketer by providing information that can be used in making marketing decisions.

8. Name the uses of marketing research.

The chapter describes four uses: • • • •

Identifying market opportunities and problems Generate, refine, and evaluate potential marketing actions Monitor marketing performance Improve marketing as a process

9. Which use of marketing research is considered basic research?

Basic research is conducted to expand our knowledge rather than to solve a specific problem. Research conducted to solve specific problems is called applied research; hence, “Improve Marketing as a Process” pertains to basic research.

10. Give two examples of the types of studies in each of the four classes of marketing research studies provided in this chapter.

A classification of studies is provided in Table 1.1. Students can provide examples from the table or come up with their own.

11. Distinguish among MIS (marketing information system), marketing research, and DSS (decision support system).

A marketing information system (MIS) is a system consisting of people, equipment, and procedures to gather, sort, analyze, evaluate, and distribute needed, timely, and accurate information to marketing decision makers. Whereas, a marketing decision support system (DSS) is defined as collected data that may be accessed and analyzed using tools and techniques that assist managers in decision making.

ANSWERS TO APPLICATIONS QUESTIONS 12. Go to your library, either in person or online, and look through several business periodicals such as Advertising Age, Business Week, Fortune, and Forbes. Find three examples of companies using marketing research. . 16

Students will have varying degrees of success depending on their efforts and available sources. There is no set answer to this question as it depends on current business periodicals.

13. Select a company in a field in which you have a career interest and look up information on this firm in your library or on the Internet. After gaining some knowledge of this company, its products and services, customers, and competitors, list five different types of decisions that you believe this company’s management may have made within the last two years. For each decision, list the information the company’s executives would have needed to make these decisions.

Results will vary greatly based on students’ efforts and their career interests. Typical marketing decisions are: product development, price, promotion of all types, distribution, market targeting, going global, and facing competition.

14. Think of the following situations. What component of the marketing information system would a manager use to find the necessary information? a. A manager of an electric utilities firm hears a friend at lunch talk about a new breakthrough in solar panel technology she read about in a science publication.

The marketing intelligence system should have information about new developments. b. A manager wants to know how many units of each of three of the company’s products were sold during each month for the past three years.

This information should be in the internal reports system. c. A manager wants to estimate the contribution to company ROI (return on investment) earned by ten different products in the company product line.

This would be in the realm of the decision support system. d. A manager is considering producing a totally new type of health food. But first, he would like to know if consumers are likely to purchase the new food, at which meal they would most likely eat the food, and how they would prefer the food to be packaged.

The marketing research system would address this in the form of a survey or some sort of marketing research effort.

CASE SOLUTIONS Case 1.1 Starlight Films Case Objective: This case illustrates the need for marketing research to estimate the demand (sales forecast) for a movie. Specifically, there is a very large . 17

investment (cost of making the movie) at stake, and the director/producer must convince potential investors that the movie will make a significant profit (ROI).

Answers to Case Questions 1. Do you think Daniel Yarbough needs to conduct marketing research? Why or why not?

Because the purpose of marketing research is to “…link the consumer to the marketer by providing information that can be used in marketing decisions,” this situation certainly calls for marketing research information. Based on his experience, Yarbough has a good idea of the costs, but although he thinks the movie script will be attractive, he does not have objective evidence. Using the “profit = sales – costs” formula to illustrate the value of marketing research, Yarbough knows only one-half of the formula—the costs. He certainly needs sales estimate information gained from marketing research to accurately predict the profit.

2. Based on the case material alone, list decisions that Daniel needs to make.

Here is a list of decisions and how they will affect the ROI estimate.

Decision How much to pay for the script?

Use “hot” actors or unknown actors?

Film on location or in a studio?

Use marketing research or not?

Effect on ROI More cost means less ROI, and vice versa.

“Hot” actors means more cost, but “hot” talent will increase sales so ROI might be higher, and vice versa On location means more cost and less ROI; in studio means less cost and more ROI. (The movie will be about a soccer team, so it is unlikely that any “spectacular” location would be used to increase sales.)

Research will increase costs, but not significantly.

It will result in more certain sales estimates which, if positive, will be more convincing to potential investors.

3. For each decision you list in question 2, provide a description of the information that you think Daniel needs in order to make the decision.

Here are the decisions and the information that is needed: Decision How much to pay for the script?

1.

2.

3.

Use “hot” actors or unknown actors?

1.

18

Information Needed How much has been paid for similar scripts in recent years?

What does St. John expect to be paid for the script?

How much are competing producers willing to pay for the script?

What “hot” actors are available and interested?

Film on location or in a studio?

Use marketing research or not?

2. How much were they paid in similar recent movies?

3. How much will the “hot” actors increase the movie’s sales versus the sales associated with unknown talent?

1. What is the estimated cost of the studio production?

2. What is the estimated cost of the “on location” production?

3. How much additional sales revenue is associated with the on location versus the in studio production?

1. How much will the marketing research cost?

2. Who or what company will do the research?

Case 1.2 Integrated Case: Advanced Automobile Concepts Case Objective: This is the introduction to the integrated case that is used throughout the textbook. It describes how an automobile manufacturer that has built its reputation and profit on large automobiles senses a change in consumer sentiment as a result of environmental (global warming) and practical (high gasoline prices) factors. In addition to innovative new designs, the company may move to alternative fuel models. Whether or not prospective automobile buyers are willing to buy the new models is the issue.

Answers to Case Questions 1. Should Nick Thomas use marketing research?

Yes, absolutely. This is a multimillion dollar investment that may take 3 to 5 years to implement. The marketing decision definitely needs general as well as specific information to be supplied by marketing research.

2. What components of ZEN’s marketing information system will Nick Thomas need?

Nick will need all aspects of the MIS. Each component and its associated value are listed below: •

Internal reports system—useful to gather past sales patterns by model, country, and within countries such as by major cities or regions.

•

Marketing intelligence system—useful for gaining an understanding of what competitors are doing, to learn about market trends, to see about global warming and/or gasoline issues and automobiles, and to understand consumer sentiments.

•

Marketing decision support system—depending on its features, perhaps use the tools to do “what if” scenarios such as drops in market share with key or all models; might be useful in doing break-even analyses of possible new models.

19

•

Marketing research system—definitely a specific situation, and there will be a great need for marketing research on consumer attitudes toward global warming and alternative fuels as well as specific reactions to possible new automobile designs.

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CHAPTER 2

The Marketing Research Industry

LEARNING OBJECTIVES •

To learn about the history and characteristics of the marketing research industry, including the professional organizations in the industry

•

To learn the leading marketing research firms and their place in the industry structure

•

To know how to classify marketing research firms

•

To be aware of the challenges facing the industry and what has been suggested to improve the industry, including the Professional Researcher Certification (PRC)

•

To understand how a researcher’s philosophy might dictate behavior in ethically sensitive situations

•

To learn the ethical codes and standards developed by professional associations serving the marketing research industry

•

To learn important ethical issues facing the marketing research industry today

CHAPTER OUTLINE The Marketing Research Industry Evolution of the Industry The Early Days Growth of the Need A Maturing Industry Honomichl Global Top 25 Worldwide Spending on Marketing Research Honomichl Top 50 Revenues of U.S. Firms Stronger Competition Through M&A and Strategic Alliances Classifying Firms in the Marketing Research Industry Internal Suppliers .

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How Do Internal Suppliers Organize the Research Function?

Organizing the Formal Department of Internal Suppliers Organizing When There Is No Formal Department External Suppliers How Do External Suppliers Organize?

Classifying External Supplier Firms Full-Service Supplier Firms Syndicated Data Service Firms Standardized Service Firms Customized Service Firms Online Research Services Firms Limited-Service Supplier Firms Challenges Facing the Marketing Research Industry Issues with the Economy The Lifeblood of the Industry—Consumer Cooperation Marketing Research No Longer Represents “Voice of the Consumer” Marketing Research is Parochial Marketing Research Operates in a “Silo” Marketing Research Is Tool Oriented Using IT to Speed Up Marketing Research Other Criticisms of Marketing Research Certification and Education: Means to Improving the Industry Certification Education Ethics and Marketing Research Ethical Views Are Shaped by Philosophy: Deontology or Teleology Ethical Behavior in Marketing Research is a Worldwide Issue .

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Codes of Ethics Sugging and Frugging Research Integrity Treating Others Fairly Buyers Suppliers Failure to Honor Time and Money Agreements The Public Respondents Deception of Respondents Confidentiality and Anonymity Invasions of Privacy

KEY TERMS Anonymity

Online research

Certification

Online research services firms

Code of ethical behavior

Online survey research

Confidentiality

Panel equity

Customized service firms

Charles Coolidge Parlin

Data analysis services

Phone banks

Deception

Professional Certified Marketer (PCM)

Deontology

Requests for proposals (RFP’s)

Ethics

Research integrity

External suppliers

Research suppliers

Field service firms

Sample design and distribution

Frugging

Spam

Full-service supplier firms

Specialized research technique firms .

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Honomichl Global Top 25

Standardized service firms

Honomichl Top 50

Strategic alliances

Internal supplier

Sugging

Limited-service supplier firms

Syndicated data service firms

Market segment specialists

Teleology

Mystery shopping

Web-based research

TEACHING SUGGESTIONS 1. Ask students if they can detect the three main causes for the changes in marketing research over its history. One is evolution of markets, which moved from local markets where buyers and sellers were neighbors to global ones where buyers and sellers are from completely different cultures. This drives the need for information about the marketing environment. Another cause is changes in technology ranging from questionnaire design to computerization, which has been adopted by marketing research. The third cause is the combination of computer technology (namely the Internet) and globalization effected by online research. Class discussion and examples of one or all three of these forces can be fruitful. Select a company that has global marketing aspirations and have students identify what research questions are relevant as it moves into different countries and cultures. It may be enlightening to illustrate how marketing research companies are now on the Internet by using companies identified in Chapter 2.

2. Some topics that you might want to point out to students to illustrate the impact of technology on marketing research are the following: (1) single source data where purchases of a family’s (such as grocery products) are captured across several weeks, (2) focus groups that take place over the Internet using video cameras and chat software, or (3) handheld computers that administer customer satisfaction questionnaires to existing customers at (for example) automobile dealership service centers.

3. To delve into the “Honomichl Top 50” look at the Marketing News Marketing Research Report. Unless there is a change in policy, the Marketing Research Report appears annually in a June issue, and it can be used to update the figures. The Report also profiles each of the 50 top companies, and has an introduction to the industry.

Have students select companies and summarize the profiles in class presentations or discussion.

4. The Honomichl Top 50 and references to some marketing research companies in Chapter 2 include the website addresses of the top marketing research companies. If you have multimedia capability with Internet capability in your classroom, use one or a number of them to show students the number and variety of products and services that these companies offer. Alternatively, have student volunteer to visit top company websites and “show and tell” some interesting products and/or services that they find.

5. Students may not understand the distinctions between the three internal research .

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suppliers: (a) formal department, (b) single individual, and (c) no one responsible. If possible, have a representative from each organizational type come to the class and discuss how marketing research takes place in his/her company. Alternatively, describe the situations based on your knowledge of representative companies.

6. Most students will think that all marketing research companies are full service. One way to help them understand about the limited service companies is to say that they are practicing niche marketing. They have found niches in the research industry, and they specialize in performing their functions very well.

7. Save copies of Marketing News or Quirk’s Marketing Research Review and bring them to class. After reviewing full-service and limited-service marketing research firms, hand out the newspapers and have students look at the marketing research company ads. Let selected students summarize the services of companies they have singled out. Alternatively, select the ads yourself, make overhead transparencies, or PowerPoint slides, and use them when you cover this topic in your class presentation.

Another approach, if you have multimedia classroom capability, is to find the Internet sites for various types of marketing research firms and illustrate the products and services of these different firms during class.

8. The chapter indicates that formal marketing research departments are typically only found in large companies, and often they are very small. Ask students what this implies about a career in marketing research. You might tie this discussion in with the careers in the marketing research appendix. Points to be made are: (1) it will probably take a master’s degree to break into the management level; (2) you will probably have to locate in a major metropolitan area; but (3) there will be opportunities for sharp managers who know something about marketing research in medium- and small-sized companies because they don’t have marketing research personnel.

9. It is important that students come to realize the ethical issues in marketing research early on, and this is why this topic is introduced in Chapter 2. Additionally, they need to be made aware that philosophical differences exist. Going over the various ethical issue areas is worthwhile, but sugging and frugging invariably generate the most class interest and story-telling.

10. The right to privacy issue is a major worry in the research industry. Ask students what they believe are their personal privacy rights and how they enforce them when they are violated. Then turn the tables by telling them that they have taken on a job as a telephone interviewer where they will be compensated $5 for each completed interview. What would they do to make money? Sometimes students do an about face on privacy when they are worried about making enough money to pay their rent.

11. It is always fun to ask students if they have been “sugged” or “frugged” recently.

Those who have not read the chapter will be immediately obvious, as sugging and frugging invariably stick in students’ minds. Those who have no clue obviously did not read the chapter before class.

12. There is considerable information about the Professional Researcher Certification program operated by the Marketing Research Association on its website (www.mranet.org/prc/). Either use it in class or assign it to a student or student team for presentation to the class.

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ANSWERS TO REVIEW QUESTIONS 1. Describe the PRC as discussed in the opening vignette by the COO of the Marketing Research Association.

The Professional Researcher Certification (PRC) came about in 2005 when the Marketing Research Association (MRA) began certifying U.S. individuals involved in research. Over 1000 individuals have become certified. The program is based on competencies in experience, education, and ethics. Certification lasts two years whereupon the individual must be re-vetted for renewal.

2. Who is credited for conducting the first continuous and organized marketing research? (He is also known as the “father of marketing research.”)

This person is Charles Coolidge Parlin, who pioneered marketing research for the Curtis Publishing Company and Saturday Evening Post.

3. Explain why marketing research was not widespread prior to the Industrial Revolution.

Prior to the Industrial Revolution, businesses and their customers were in close proximity, meaning that businesses had much more direct contact with their customers and did not need to research them.

4. Roughly, how much is the worldwide marketing research industry worth? How much is the U.S. marketing research industry worth?

Worldwide, it was a $32.5 billion industry in 2008. Due to the worldwide recession, it is a $30 billion industry in 2010. The chapter states that the top 50 United States research firms accounted for about $7.8 billion in 2009. When CASRO firms are added, that total is $8.6 billion.

5. What is the difference between the Honomichl Global Top 25 and the Honomichl Top 50 reports?

The Honomichl Global Top 25 is a report of the top 25 marketing/advertising/public opinion research services firms ranked in terms of worldwide revenues received. The Honomichl Top 50 is a report ranking the top 50 U.S.-based firms in terms of the revenues these firms earn from U.S. operations only.

6. What is meant by a “strategic alliance”? Give an example of one.

A strategic alliance allows firms with strong expertise in one area to form partnerships with firms offering expertise in other areas. Examples given in the text are: GfK and Arbor Strategy Group; Kantar Group and TNSplc, which expanded capabilities of GfK and Kantar Group.

7. We categorized firms as internal and external suppliers of marketing research information. Explain what is meant by each, and give an example of each type of firm.

An internal supplier is an entity inside a firm that supplies marketing research. An example would be a marketing research department such as those noted—Kraft Foods, IBM, Kodak, General Mills, and Ford. An external supplier is an outside or . 27

independent firm hired by another firm to fulfill its marketing research needs. All of the marketing research companies on the Honomichl list are external suppliers.

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8. Distinguish among full-service, limited-service firms, syndicated data services, standardized services, customized service, and online research services firms.

Full-service supplier firms have the ability to conduct the entire marketing research project for the buyer firms.

Limited-service supplier firms specialize in one, or at most, a few marketing research activities.

Syndicated data service firms collect information that is made available to multiple subscribers.

Standardized service firms provide syndicated marketing research services, as opposed to syndicated data, to clients.

Customized service firms offer a variety of research services that are tailored to meet the client’s specific needs.

Online research services firms specialize in providing services online.

9. How would you categorize the following firms? a. One specializing in marketing to kids (ages six to twelve years) Limited-service firm. b. One that specializes in a computerized scent generator for testing reactions to smells Standardized service firm c. One that offers a method for conducting “mock trials” Standardized service firm d. One that offers clients samples drawn according to the client’s sample plan Standardized service firm e. One that collects data over the Internet Online research services firm 10. What makes an online marketing research firm different from other marketing research firms?

Online research firms specialize in providing services online. We define online research as the use of computer networks, including the Internet, to assist in any phase of the marketing research process, including development of the problem, research design, data gathering, analysis, and report distribution. Thus, other marketing research firms do not use computer networks, including the Internet, for these phases. . 29

11. What is the advantage in a firm having its own formal marketing research department? Explain three different ways such a department may be internally organized.

The advantage is that the marketing research department staff is fully aware of the company’s operations and the industry, so they will have greater insight into opportunities and problems.

The three internal organization alternatives are: (1) area of application such as industrial consumers and ultimate consumers, (2) marketing function such as pricing, advertising, etc., or (3) the marketing research process such as data analysis, data collection, and so forth.

12. What are four challenges to the marketing research industry?

The challenges are: • Issues with the economy meaning fallouts, alliances, and the need to demonstrate value-added services.

• Declining consumer cooperation to take part in surveys due to telemarketing and desire for privacy.

• Loss of prominence of marketing with top executives as they focus on supplychain economies to keep prices low.

• Parochial or narrow focus of marketing research, meaning it is not involved in the planning process sufficiently.

• Operating in a silo or being isolated from other departments in the company.

• Too tool oriented (see question 13).

• Need to speed up marketing research by using information technology.

13. Explain how being “too tool oriented” may hurt the marketing research industry.

Researchers too readily apply a tool instead of focusing on the more complex strategic issues facing the firm. The end result is that marketing researchers are used to supplying “ingredients” instead of being involved in making strategic decisions.

14. Explain how certification and education may improve the marketing research industry.

Certification such as the PRC is a system that guarantees minimum standards will be maintained by those marketing researchers who are certified. Education occurs in a variety of ways and it serves to develop the research skills of marketing researchers.

15. How would you define ethics?

Ethics may be defined as a field of inquiry into determining what behaviors are deemed appropriate under certain circumstances as prescribed by codes of behavior that are set by society.

16. What are the two fundamental philosophies that can be used as a basis for making ethical decisions?

Deontology is concerned with the rights of the individual. If an individual's rights are violated, then the behavior is not ethical. . 30

Teleology analyzes a given behavior in terms of its benefits and costs to society. If there are individual costs but group benefits, then there are net gains and the behavior is judged to be ethical.

17. List where you can find some codes of ethics applicable to the marketing research industry.

Various associations and organizations such as American Marketing Association, Council of American Survey Organizations, Qualitative Research Consultants Association, Marketing Research Association, Professional Market Research Society, and the European Society for Opinion and Marketing Research have codes of ethics.

18. Name some of the ethical issues facing the marketing research industry.

Some of the ethical issues noted are: worldwide issues, codes of ethics, sugging and frugging, research integrity, treating others fairly (buyers, suppliers and the public), respondents, and respondent fairness (deception, confidentiality and anonymity, privacy, unsolicited telephone calls, and spam).

19. Explain why sugging and frugging are bad for marketing researchers.

Sugging refers to “selling under the guise of a survey.” Frugging refers to “fundraising under the guise of a survey.” Both practices cause consumers to think that all marketing research is deceptive so they do not cooperate with any marketing research requests.

ANSWERS TO APPLICATION QUESTIONS 20. Go to the websites of either CASRO, MRA, or IMRO and look up their codes of ethics/professional standards. What do they have to say about doing online surveys?

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31

CASRO refers to “active agent technology” such as spyware, which is strictly prohibited.

MRA has a complete set of ethical standards for online research—“Use of the Internet for Conducting Opinion and Marketing Research.”

IMRO also has a complete set—“Use of the Internet for Conducting Opinion and Marketing Research Ethical Guidelines.”

21. Look up “marketing research” in your Yellow Pages directory. Given the information provided there, can you classify the research firms in your area according to the classification system of research firms we used in this chapter?

There is no set answer to this exercise. Instructors who are located in small cities or places where there is little industry or commerce should refrain from assigning this exercise as students will find few or no marketing research companies in their directories. An alternative is to have students use an online business directory services such as SmartPages.com or MSNYellow Pages (yellowpages.msn.com) and specify a major city such as Chicago, Atlanta, Los Angeles, or New York.

22. Comment on each practice in the following list. Is it ethical? Indicate your reasoning in each case. a. A research company conducts a telephone survey and gathers information that it uses later to send a salesperson to the home of potential buyers for the purpose of selling a product. It makes no attempt to sell the product over the telephone.

This is ethical as long as the respondents were informed that information was being gathered to qualify them for the sales call; it is unethical if there were promises of confidentiality or anonymity, or if the respondent was otherwise lead to believe that this was purely a research effort in which case the approach would be sugging. b. Would your answer to (a) change if you found out that the information gathered during the telephone survey was used as part of a “legitimate” marketing research report?

If the company is doing research, and it is confidential and/or anonymous, the use of the information to send a salesperson is unethical. However, if the survey did not assure those conditions, and respondents agreed to a sales call, it would be an ethical practice. c. A door-to-door salesperson finds that by telling people that he is conducting a survey they are more likely to listen to his sales pitch.

This is sugging—an unethical practice. d. Greenpeace sends out a direct-mail piece described as a survey and asks for donations as the last question.

If it is not a bona fide survey, this is frugging, an unethical practice. e. In the appendix of the final report, the researcher lists the names of all .

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respondents who took part in the survey and places an asterisk beside the names of those who indicated a willingness to be contacted by the client’s sales personnel.

This is ethical as those identified agreed to the contact. f. A list of randomly generated telephone numbers is drawn in order to conduct a telephone survey.

These will result in unsolicited telephone calls, but it is not an unethical practice. g.

A list of randomly generated e-mail addresses is generated using a “Spambot” (an electronic “robot” that searches the Internet looking for and retaining e-mail addresses) in order to conduct a random online research project.

According to the CASRO code of ethics, this is an unethical practice.

h. Students conducting a marketing research project randomly select e-mail addresses of other students from the student directory in order to conduct their term project.

Not only would this be unethical, it would be illegal as it would be considered SPAM. The rule is that a respondent to an e-mail survey must have some sort of established relationship with the person sending the e-mail request to complete the survey. In the example cited above, “randomly” selecting e-mail addresses would mean there is no relationship between the sender and receiver.

Consider the following examples of established relationships: • •

You buy a Die-Hard battery at Sears. In the paperwork you provide your e-mail address. A month later, it would be perfectly legitimate for Sears to send you a survey regarding your satisfaction with their service.

The Dean of your college decides to send an e-mail to all students attending the school asking them to complete a survey about preferences for future courses. This would be ethical because students have a "relationship" with the college (represented by the Dean) and could reasonably expect communications from the Dean.

CASE SOLUTIONS Case 2.1 ABR Marketing Research Case Objective: This case study deals with ethical issues in marketing research.

Answers to Case Questions The following case solution is provided by Professor Harriet Bettis-Outland, who wrote the case.

Case Questions: . 33

1. After you have thoroughly read the case, write down what you believe are the issues in the case.

2. For each issue in your list, rate its importance, from 7 (very important) to 1 (unimportant).

This original case scenario was developed by John R. Sparks and Shelby D. Hunt to assist in a project designed to measure ethical sensitivity. Their work was reported in an excellent journal article, John R. Sparks and Shelby D. Hunt (1998), “Marketing Researcher Ethical Sensitivity: Conceptualization, Measurement, and Exploratory Investigation,” Journal of Marketing, v62(April), 92–109. Though some details of the case that originally appeared in the Sparks and Hunt article have been updated in this version, the case still offers the ability to measure students’ ethical sensitivity against those groups represented in the journal article. The case discussion was written by Dean Allmon, Professor of Marketing, and Harriet Bettis-Outland, Assistant Professor of Marketing, both at the University of West Florida. We thank them for their contribution and highly recommend this case for your use.

Ethical sensitivity has been described as certain personal characteristics that enable one to recognize the presence of an ethical issue. This is perhaps the most important part of ethical decision-making—if no problem or ethical situation is perceived, then no ethical evaluation process is activated. The purpose of this case is to help students become aware of their own ethical sensitivity. The Sparks and Hunt study was among the first to measure the ethical sensitivity of marketers. After designing and pretesting the case, Sparks and Hunt presented it to three different sample populations—Introductory Marketing Students, Senior Marketing Students, and Research Practitioners.

An ethical issue exists when there is a decision situation that involves alternate courses of action in which the situation and/or courses of action are inconsistent with guides such as rules, codes, or norms. These guides can come from many sources; some are formal, such as those reported in the text by organizations such as CASRO, MRA, ESOMAR, and AMA. Others are less formal but are nevertheless powerful, such as your own value set.

Should you lie, cheat, and steal to make a living? Society’s morals, the glue that holds society together, says that this type of behavior is not right, therefore unethical. But, as noted previously, unless we perceive an issue to be of an ethical nature, we never even embark on the road to ethical evaluation.

This case attempts to measure students’ ethical sensitivity. They are asked to list issues they see in the case. Some issues will not deal with ethics. The subtle purpose of this case is two-fold. First, how many ethical issues will the students identify? Secondly, how important are the issues rated? Even if an individual recognizes an ethical issue, there may be wide divergence in the evaluation of the importance of the issue. In the eyes of most everyone, we find agreement that all ethical issues are not of equal value; there are misdemeanors and felonies. Given the audience and the players, there may be wide divergence in both the recognition and the evaluation of an ethical issue. Students are asked to rate the importance of each issue they identify. How will their importance ratings differ from those of the three groups tested by Sparks and Hunt?

How Many Ethical Issues Did Your Students Identify? What Level of Importance Did Your Students Assign the Issue(s)?

The original Sparks and Hunt case as well as this updated version, were both developed .

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with a great deal of effort aimed at meeting seven criteria. First, rather than being an “ethics case,” both ethical and nonethical issues are raised. Second, the ethical issues are those commonly faced by marketing researchers. Third, the case is realistic. Fourth, the case is brief. Fifth, no ethical issues beyond those intended are in the case. Sixth, the ethical issues vary in terms of importance from moderate to severe. Finally, because ethical sensitivity is an antecedent of ethical behavior, the case only implies or subtly suggests that some potential behavior is consistent or inconsistent with a rule, code, or norm.

There are five ethical issues identified in this version of the case—research integrity, fair treatment of vendors, research confidentiality, incomplete reporting, and misleading reporting. Each will be discussed. Additionally, for the first three ethical issues listed previously, the scores of the three populations tested by Sparks and Hunt (Introductory Marketing Students, Senior Marketing Students, and Research Practitioners) are shown.

In the “percentage identified” row are the percentages of each population that correctly identified the ethical issue. In the “average importance” row are the means derived from the 7-point importance scale.

Issue 1 (Research Integrity)—Barbara believes her boss wants her to produce a statistical analysis consistent with those recommendations already made to Precision Grooming Products.

Research Integrity

Introductory Students n = 142

Senior Students n = 178

Research Practitioners n = 188

Percentage Identified

30

38

63

Average Importance

4.8

4.8

5.8

What is truth? According to one old sage, “There are lies, damned lies, and statistics.”

The implication is that each researcher could tell his or her own story, depending upon the slant they wanted to take. So where is the problem? First of all, why was ABR Marketing retained in the first place? Precision Grooming most likely retained ABR to research the market, and, based on these results, Precision would derive a media plan.

That is what they were expecting and that is what they should get. How do they know if the presentation of the data was manipulated to match the plan? They don’t. This is where professionalism enters the picture. ABR must be true to the process and the profession. If they are not, then, in the long run, everyone suffers.

Declaring the results before a study is performed or completed happens at times. By introducing biased or leading questions, one can steer the results of research. In this situation, Precision wanted to know the attitude, media habits, and demographic characteristics of its market. Consequently, the design and implementation of the study should reflect that.

Issue 2 (Vendor Fairness)—Barbara’s assistant left out media habit questions from the questionnaire when she gave it to the vendor. Michelle (Barbara’s boss) and Phillip (of Precision Grooming) both thought it was the vendor’s fault. After they got angry with the vendor, they decided to finish out the project. Barbara said nothing about responsibility . 35

or cause of the problem.

Vendor Fairness

Introductory Students n = 142

Senior Students n = 178

Research Practitioners n = 188

Percentage Identified

4

2

35

Average Importance

4.8

5.6

5.9

Some would say that Barbara didn’t do anything wrong. In fact, she didn’t do anything— Sins of Commission vs. Sins of Omission—Barbara didn’t actually lie, but she let a lie that she helped to create and could have corrected, stand. Letting others take the punishment as a result of your errors generally reflects lack of character values such as integrity and moral strength.

Barbara wanted this job. She was working furiously to meet the deadlines. We do not know her personal situation. We do not know if she was behind in her house payments, had high credit card bills or other debts—nothing, other than what was in the case.

Myriad of things could impact her behavior. What was she thinking when Michelle and Phillip were fuming about the mistake? If it got bad enough, would she have stepped forward? We don’t know, do we? Barbara probably didn’t either. Some would say that she stole, lied, and cheated the vendor by her silence. Note the small percentage of student respondents who identified this as an ethical issue in the Sparks and Hunt study.

Issue 3 (Confidentiality)—David Miller, who worked for the advertising agency, previously handled the Village Toiletries’ account, which was Precision’s main competitor. Now he is using knowledge attained while working with Village Toiletries, to work against them.

Confidentiality

Introductory Students n = 142

Senior Students n = 178

Research Practitioners n = 188

Percentage Identified

13

12

35

Average Importance

5.7

5.7

5.4

Like both Issues 1 and 2, this issue was identified as a problem with a much greater percentage of practitioners as compared to students. We are back to the question of professional ethics. How long do you have to be true to a past client? David didn’t lie, cheat, or steal. He didn’t remain silent when he should have spoken out. Why then is this a problem? Broken promises. When a professional frequently works with a client, he is inevitably made privy to sensitive information. So when someone such as David works with and for a client, even if he doesn’t make explicit promises, there is an implicit . 36

promise not to use any information he discovers against that company. This includes the exposure of information that would change the competitive environment.

Can research findings for one client be used for a competitor? Could past findings be used in another setting if they did not harm the first client? These are privacy questions and issues that need to be addressed when examining the ethical dimensions of marketing research.

Though not included as part of the original data collection in the Sparks and Hunt study, in addition to the three previous issues, the following ethical issues (4 and 5) are identified in the ABR case: Issue 4 (Incomplete Reporting)—Barbara’s assistant accidentally deleted all questions pertaining to media habits that should have been included in the phone interviews of almost 75 percent of all participants in the sample. Consequently, any suggestion that they found conclusive evidence regarding certain media habits typical of hair gel users would constitute incomplete reporting. Only by admitting that the majority of study participants did not even answer questions regarding media habits, could ABR truthfully share results with its clients.

Incomplete reporting enables an organization to skew results in their favor, for instance, by neglecting to mention the composition of the sample, or the types of questions asked on the survey. By choosing participants based on certain criteria that just happen to coincide with the “desired” outcome, a research firm may be engaged in the practice of incomplete reporting.

Issue 5 (Misleading Reporting)—closely related to incomplete reporting, the practice of misleading reporting occurs when research results are presented in such a manner that the intended audience draws conclusions that are not entirely justified. For instance, even though Precision Grooming Products considered attitude, demographics, and media habits to be critical information needed in developing a new hair gel for men, a majority of participants in the study did not even participate in the media habit questions.

Furthermore, Barbara did not disclose that conclusions were drawn based on a much smaller study than originally planned; Barbara had initially asked for 250 participants from 15 different metropolitan areas. Due to time and financial constraints this was cut to 200 participants from 11 metropolitan areas. However, as a result of the survey administration error, only three cities were included in the final study. To try to draw conclusive decisions based on such a small sample could easily be misleading.

Case 2.2 Integrated Case: Advanced Automobile Concepts Case Objective: Students are asked to think about: (1) using an internal versus an external supplier of marketing research and (2) the concerns marketing research clients have about the confidentiality of their projects, namely, whether or not the marketing research company will be ethical and not divulge information to the client’s competition.

Answers to Case Questions

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1. Why should Nick Thomas use the internal supplier, his own parent company’s marketing research department? Why should he not use them?

Zen has its own formally organized marketing research department. The major advantages of using such an internal supplier are: (1) knowledge of the company’s operations and policies, (2) knowledge of the company’s industry, and (3) experience with doing research in the industry. How this department is organized is unknown.

From the description of Zen Motors, it is clear that the company has not done much innovative for many years. Nick needs marketing research for radical new automobile technology and designs. If there is this expertise in the Zen marketing research department, Nick should seriously consider using it.

However, if not, then he should review potential custom research providers such as CMG to find a provider that can provide the proper expertise.

2. Nick Thomas is concerned about using an external supplier of marketing research services, CMG Research. Go to the MRA’s Code of Ethics and read what they have to say about researchers sharing confidential information. (at www.mra-­net.org, go to “Resources” and then “Codes, Standards, Guidelines”; then go to “MRA Expanded Code of Market Research, Section A”).

Section A has several ethical items that MRA members must agree to by way of signature. Item 3. states: “Will protect and preserve the confidentiality of all research techniques and/or methodologies and of information considered confidential or proprietary.” Item 12, states: “Will protect the confidentiality of anything learned about a client’s business as a result of access to proprietary information.” Item 25, states: “Will ensure that the results of the research are the sole property of the End User(s). At no time will results be shared with other clients.”

If CMG Research belongs to the MRA (and yes, it does), Nick is assured that it knows about and has agreed to ensuring the confidentiality of any work done for Advanced Automobile Concepts.

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CHAPTER 3 The Marketing Research Process—Defining the Problem and the Research Objectives LEARNING OBJECTIVES •

To gain insights into marketing research by learning the steps in the marketing research process

•

To understand when marketing research is not needed

•

To understand the difference between the “problem” and the “research objective”

•

To know the importance of properly defining the problem

•

To appreciate a process for defining the problem and research objectives including knowing the varied sources of problems

•

To understand the role of symptoms in problem recognition

•

To realize that problems occur when management must make a decision and that decisions are statements of decision alternatives

•

To understand the researcher’s role in problem definition

•

To understand that marketing research is needed when managers are uncertain of their assumptions needed to specify consequences of decision alternatives

•

To know the criteria for properly wording a research objective

•

To know what an action standard is and why it is needed

•

To know common impediments to properly defining the problem

•

To learn what is contained in the marketing research proposal

CHAPTER OUTLINE The Marketing Research Process The Process: Eleven Steps Step-by-Step Process: Words of Caution Why Eleven Steps? .

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Not All Studies Use Every Step Steps Are Not Sequential Introducing “Where We Are” Step 1: Establish the Need for Marketing Research Company Policy Regarding the Use of Marketing Research When Is Marketing Research Not Needed The Information Is Already Available The Timing Is Wrong Funds Are Not Available Costs Outweigh the Value Step 2: Define the Problem Step 3: Establish Research Objectives Step 4: Determine Research Design Step 5: Identify Information Types and Sources Step 6: Determine Methods of Accessing Data Step 7: Design Data-Collection Forms Step 8: Determine Sample Plan and Size Step 9: Collect Data Step 10: Analyze Data Step 11: Prepare and Present the Final Research Report Defining the Problem What Is the “Problem” and the “Research Objective”?

The Problem The Research Objective The Importance of Properly Defining the Problem A Process for Defining the Problem and the Research Objectives .

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Problem Sources Failure to Meet an Objective Opportunity Recognizing the Problem Systems Needed to Recognize Problem Sources Control System Opportunity Identification System The Role of Symptoms in Problem Recognition Problem Definition The Role of the Researcher in Problem Definition When Management Defines the Problem in Terms of a Decision to Be Made The Role of ITBs and RFPs When Management Does Not Define the Problem in Terms of a Decision to Be Made Conduct a Situation Analysis Validate Symptoms of the Problem Determine Probable Cause of the Symptom Specification of the Decision Specify Decision Alternatives to Alleviate the Symptom Consequences of the Alternatives Identify the Manager’s Assumptions About the Consequences of the Alternatives Assess the Adequacy of Information on Hand to Specify Research Objectives Research Objectives Defining Research Objectives From Whom Are We Gathering Information?

What Construct Do We Wish to Measure? . 42

What Is the Unit of Measurement?

Word the Information Requested in the Respondents’ Frame of Reference Completing the Process Action Standards Impediments to Problem Definition Failure to Change Behavior for Problem Definition Situations Differences Between Managers and Researchers Formulate the Marketing Research Proposal

KEY TERMS

Action standard

Market opportunity analysis (MOA)

Assumptions

Marketing opportunity

Causal research

Marketing research proposal

Cause

Operational definition

Consequences

Opportunity

Construct

Opportunity Identification

Criteria for research objectives Data analysis

Possible causes

Decision alternatives Descriptive research

Primary information Probable causes

Problems

Eleven steps

Research objectives

Exploratory research

RFPs

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Failure to meet an objective

Sample plans

Hypotheses

Sample size

Information gaps

Secondary information

Information state

Situation analysis

ITBs

Symptoms

TEACHING SUGGESTIONS 1. This chapter is an overview of the steps in the marketing research process. One effective method of teaching these steps is to describe a real marketing research project. Possible approaches are: instructors using applied research they have done themselves, class projects done in prior classes, or some other real-world marketing research example with which they are familiar. Use the example to describe how each step was accomplished. Reading excerpts from final reports is an effective method.

Plus, if you have a team project-based class, it will show students what the final product will look like.

2. If you have former students available, who have participated in a marketing research project in a previous semester, consider inviting the team or members of the team to represent their project (assuming that they did a presentation previously). If the team used PowerPoint or some other presentation software, consider obtaining the file(s) at the end of the term either for the former students to use or for your use if the former students are not available in the present term.

3. Because the chapter is an overview of, basically, the rest of the textbook, it is important to avoid the temptation to go into detail on any one step. Use Figure 3.1 and present it as a flow chart of the steps in the marketing research process. Indicate that the steps are arranged in logical order (define the problem, set objectives, decide the method, collect the data, analyze, and write up). However, sometimes you will need to iterate back to previous steps in the process as discoveries are made in later steps. Also, the decisions made in each step will impact what will or will not be done in subsequent steps.

4. Here is a “Research Phase” scheme that might help students better understand the various steps: Phase: Figure out what to research Step 1: Establish Need Step 2: Define Problem Step 3: Determine Research Objectives Phase: Design the mechanics of the research .

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Step 4: Determine Design Step 5: Information Source Step 6: Data Collection Method Step 7: Questionnaire Design Step 8: Sample Size and Plan Phase: Gather data from respondents Step 9: Collect Data Phase: Generate findings and interpret them Step 10: Analyze Data Step 11: Write and Present Report 5. Connecting the marketing research process with the course syllabus and material coverage schedule is useful. One approach is to note the step and then indicate the chapters that will be covered and the weeks in the course schedule where those chapters are assigned. If you are using a team project-based approach to teaching the course, indicate when students will be taking up each step in the process as they work through their research projects.

6. If available, a marketing research practitioner is an effective guest at this point in the course. One approach is to have him/her describe the services of his company, slanted toward a full-service operation, and if possible the speaker will be able to comment specifically on all steps in the marketing research process. Another approach is to have him/her describe a typical day or week worth of activities.

7. Several items are new in this chapter for the 3rd edition, Figure 3.2, Recognizing the Problem, the researcher’s role in problem definition, situation analysis, validation of the symptoms, and action standards. Plus, defining the research objectives has been expanded. The chapter is much more realistic and useful as a result of Ron Bush’s close association and work with Ron Tatham who is featured in the chapter.

ANSWERS TO REVIEW QUESTIONS 1. What are the steps in the marketing research process?

The steps are: (1) establishing the need for marketing research, (2) defining the problem, (3) establishing research objectives, (4) determining research design, (5) identifying information types and sources, (6) determining methods of accessing data, (7) designing data collection forms, (8) determining the sample plan and size, (9) collecting data, (10) analyzing data, and (11) preparing and presenting the final research report.

2. Use an example to illustrate that the steps in the marketing research process are not always taken in sequence.

Early in the data collection step, a researcher may realize that the research objectives should be changed.

3. Explain why firms may not have a need for marketing research. . 45

Marketing research is not needed when: (1) information is already available, (2) the timing is wrong, (3) funds are not available, and (4) costs outweigh the value of the marketing research.

4. Why is defining the problem the most important step in the marketing research process?

If the problem is not defined correctly, all of the following steps in the marketing research process will be wasted. That is, the wrong problem will be investigated, and the findings will not be useful to the marketing manager 5. Explain why research objectives differ from the definition of the problem.

Research objectives, when achieved, provide the information necessary to choose between the identified decisions alternatives. Research objectives tell the researcher exactly what he/she must do in order to obtain the information necessary to allow the manager to choose between the decision alternatives. The “problem” is a statement of decision alternatives.

6. What are the three types of research that constitute research design?

There are three widely recognized research designs: exploratory, descriptive and causal designs. Exploratory research is a form of casual, informal research that is undertaken to learn more about the research problem, learn terms and definitions, or identify research priorities. Descriptive research refers to research that describes the phenomena of interest. Causal studies attempt to uncover what factor or factors cause some event.

7. Which part of the research process ensures that the sample is representative?

Step 8: Determine the sample plan and size. The plan describes how sample units are drawn from the population, or are representative.

8. Which part of the research process ensures the accuracy of the results?

Certainly we could argue that all the steps in the marketing research process affect accuracy of the results. However, there is one step that specifically addresses accuracy—setting the sample size. Assuming that there is a representative sample, which is determined by the sampling plan, the sample size formulas are used to determine “the minimum size sample needed, given the amount of variance in the population and the degree of confidence management wishes to maintain, the DESIRED LEVEL OF ACCURACY OF THE RESULTS.” 9. What is meant by the “problem”?

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A problem is a situation calling for a manager to make choices among alternatives.

10. What is the research objective?

A research objective states specifically what information must be produced by the researcher so that the manager can choose the correct alternative to solve the problem.

11. Briefly overview the process presented in the chapter for defining both the problem and the research objective.

The process is: • •

•

Recognize the problem Define the problem with ❧ Situation analysis ❧ Validate symptoms ❧ Determine probable cause(s) of the symptoms ❧ Specify the decision ❧ Specify decision alternatives that may alleviate the symptom ❧ Identify the manager’s assumptions about the consequences of the alternatives ❧ Assess the adequacy of information on hand to specify research objectives Specify research objectives

12. What are the two sources of marketing problems?

A problem occurs when a gap exists between what was supposed to happen and what did happen. This is called a “failure to meet an objective.” This second type of problem, called an opportunity, occurs when there is a gap between what did happen and what could have happened.

13. Explain how managers should recognize they have a problem.

Unless managers have a control system they will not likely identify problems arising from failure to meet objectives. Managers must also be aware of opportunities and unless they have a process for monitoring opportunities, called market opportunity analysis, they will not likely identify these problems.

14. What is the role of symptoms in problem recognition?

Symptoms are changes in the level of some key monitor that measures the achievement of an objective, so a failure is indicated by the level of the monitor. Or the symptom may imply some current or upcoming opportunity.

15. What is the role of the researcher when management has already defined the problem?

Here is the quote by definition expert Lawrence D. Gibson: “Researchers must resist the temptation to ‘go along’ with the first definition suggested. They should take the . 47

time to conduct their own investigation and to develop and consider alternative definitions.”

16. What is a situation analysis and when would it likely be used when defining the problem?

A situation analysis is a form of exploratory research undertaken to gather background information and gather data pertinent to the problem area that may be helpful in properly defining the problem decision.

17. How do ITBs and RFPs influence the problem definition process?

In either case, the roles of the researcher and manager in the problem definition process are changed. As we have noted, when a company uses an ITB or RFP it has already defined the problem and, in some cases, the research objectives. At the very least management has thought through many of the issues involved with defining the problem. This means that much of the dialogue normally necessary between researchers and managers may be circumvented.

18. What is the role of the researcher when management has NOT already defined the problem?

The researcher should undertake a situation analysis and apply other steps in the process to arrive at research objectives.

19. What is meant by the researcher validating the symptoms?

The researcher should study the control system and/or conduct a situation analysis to ensure that the symptoms really exist and require corrective action.

20. What is the difference between (a) all possible causes and (b) probable causes?

Through situation analysis, the researcher determines all possible causes. If only a partial list of causes is compiled, it is possible that the real cause will be overlooked and an incorrect decision will be indicated. Probable causes are defined as the most likely factors giving rise to the symptom.

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21. Discuss why “defining the problem” is really stating the decision to be made along with the decision alternatives.

Students should refer to Figure 3.2, Problem Definition, to see that it includes “problem defined by management” and “specify decision alternatives.”

22. What is meant by “consequences” of the decision alternatives?

Consequences are the results of marketing actions.

23. Explain how assumptions play a role in the problem definition process.

Assumptions are assertions that certain conditions exist or that certain reactions will take place if the considered alternatives are implemented.

2 4. Use Figure 3.2 and explain what happens when the information on hand is adequate.

With adequate information on hand, there is no need to perform marketing research.

The manager should use the on-hand information to choose the decision alternative and implement it.

25. Explain the information state when there are information gaps.

This is the state of “uncertainty.”

26. What is needed to close information gaps?

Research objectives state specifically what information must be produced by the researcher so that the manager can choose the correct decision alternative to solve the problem—they close the gaps.

27. What is the role of a hypothesis in defining the problem?

Hypotheses are statements that are taken for true for the purposes of argument or investigation. Sometimes hypotheses are used to guide the development of the research objective.

28 . What are some factors considered to be important in determining research objectives?

The factors are: • From whom are we going to gather information?

• What construct do we wish to measure?

• What is the unit of measurement?

• Word the information requested of the respondent in his/her frame of reference.

29. What role do constructs play in the problem definition/research objectives process? .

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A construct is an abstract idea inferred from specific instances that are thought to be related. They are variables or concepts to be researched.

30. What is an operational definition and where would it likely be used?

An operational definition defines a construct, such as intention to buy or satisfaction that describes the operations to be carried out in order for the construct to be measured empirically. It is the response scale to be used to measure the construct.

31. What is an action standard?

An action standard is the predesignation in a research objective of some quantity of the attribute or characteristic being measured that must be achieved in order for a predetermined action to take place. The purpose of the action standard is to define what action will be taken given the results of the research findings 32 . Discuss impediments to problem definition.

The impediments discussed are: (1) managers sometimes fail to realize that they must change their behavior for problem-definition situations, and (2) differences such as organizational position, responsibilities, and training between managers and researchers.

33. What are the components of the marketing research proposal?

The components are: • Problem statement • Research objectives • Proposed research method • Timetable and budget Here is a detailed description.

Function Defines the marketing management problem

Description The problem statement identifies: (1) the client company and principals, (2) symptoms, (3) possible causes of these symptoms, and (4) anticipated uses of the research.

The proposal itemizes the information objectives agreed to by the manager and researcher.

The proposed research method identifies data collection mode, questionnaire design, sample plan, and other aspects of the anticipated marketing research.

There is a time line, or at least an ETA for the completion and cost and payment

Specifies the research objectives Details the research method proposed by the researcher to accomplish the research objectives Gives a tentative timetable and budget .

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provisions.

ANSWERS TO APPLICATION QUESTIONS 34. Go to the Internet and do a search for marketing research firms. Look through their Web pages. Can you identify examples of what they are presenting to you as relating to steps in the research process?

This exercise’s outcome will vary greatly from student to student as it is idiosyncratic for each company. The task is for students to identify what step, or steps in the process are in the company’s services. Most students will find data collection companies, online panels, focus group companies, mystery shopping companies, questionnaire design, sample firms, and analysis companies. Few students will find companies that specialize only in problem definition.

35. Go to your library’s online database or the Internet and look for examples of firms conducting a marketing research study. There are many examples reported in periodicals such as Advertising Age, Marketing News, Business Week, and Forbes.

Typically, these articles will mention a few details of the research project itself.

Identify as many of the steps in the marketing research process as possible that are referred to in the articles you find.

Again, this exercise will vary by student as each example will be unique. Typically, periodicals such as the ones listed above report the findings and conclusions of marketing research studies. The “mechanics” such as questionnaire design, sample design, data analysis, etc. are rarely reported or reported very succinctly.

36. Observe any business in your community. Examine what it does, what products or services it provides, its pricing policy, its promotions, or any other aspect of its business. Try to determine whether you, as manager of the business, would have conducted research to determine the firm’s products, their design, features, prices, promotion, and so on. If you decide that you would not have conducted marketing research on a given area, explain why.

With this exercise, students typically select a business they patronize meaning that they have first-hand knowledge of its products or services. They may believe that they would not need research for this reason. However, when forced to think about marketing decisions such as price increases, promotion themes and media, product improvements, etc. they will soon realize that they will rely on marketing research for complete understanding.

37. Think of what you may imagine as being an opportunity in the marketplace. Explain how Figure 3.2 would help you in conducting or not conducting marketing research.

Figure 3.2 identifies the recommended steps to follow, so, the task is for the student to address each of the following steps: •

Define the problem by management (select a category, such as marketing a .

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• • • • • • • • •

new recipe, selling a new food item, or producing a new food ingredient) Situation analysis (who will buy, why, and how much) Validate the symptoms (see if others agree that the opportunity exists) Validate the probable cause (assure that the technology and demand are viable) Specify the decision (refine what the opportunity is and what will be marketed) Specify decision alternatives (mass market or target market; high, medium, or low price/quality strategy) Anticipate consequences of the alternatives (what are the expected consumer and competitive reactions to the various decision alternatives) Identify assumptions of the consequences of the alternatives (are the assumptions logical and based on economic or consumer behavior theory) Access adequacy of the information on hand (is it supposition or fact—do a quick Internet search to see what is “out there” on the topic) Research or not—most likely there will be inadequate information and so marketing research will be warranted

CASE SOLUTIONS

Case 3.1 Golf Technologies Inc. Case Objective: This short case requires students to take the role of a marketing researcher and to apply concepts pertaining to the problem definition phase of the marketing research process.

Answers to Case Questions 1. Assuming Pennick agrees with his staff on the choice of Woods or Mickelson, what now is Pennick’s decision in terms of decision alternatives?

Decision alternatives are: • Select Tiger Woods as spokesperson.

• Select Phil Mickelson as spokesperson.

2. Assuming that Pennick is not confident in his assumptions about the consequences of the outcomes associated with your decision alternative, what should Pennick consider doing?

It is at this point that Pennick should consider conducting a marketing research project. When the decision is important and when the manager is uncertain as to the assumptions pertaining to the consequences of the decision alternatives, managers should consider conducting marketing research.

3. Should Pennick decide to conduct marketing research, write the research objective.

Remember that research objectives should: (1) specify from whom information be gathered; (2) specify what information (construct) is needed; (3) specify the unit of . 52

measurement used to gather the information; and (4) should word questions used to gather the information using the respondents’ frame of reference.

From whom—should be those who play golf. Students may also differentiate between male and female golfers and they may want to add another criterion such as “those who are contemplating purchasing new clubs within the next 6 months.”

What—the construct that is of most importance is the credibility of the spokesperson.

Unit of measurement—what is important here is that the students actually specify some measure, i.e. a 5-point scale ranging from “Not at All Credible” to “Very Credible.”

Frame of reference—students should raise the issue of what does credibility mean to the average person? They may explore with measuring terms that have greater clarity such as “believability” or “trustworthiness.” This may also be a good place to discuss multi-item measures of constructs.

Case 3.2 Integrated Case: Advanced Automobile Concepts Case Objective: This case will help students to understand how to define the problem and develop a list of research objectives.

Answers to Case Questions 1. How would you describe the “source” of the problem(s) facing Nick Thomas?

This is clearly an opportunity situation. That is, although the Zen Motors brands have been losing market share, the case relates that the Advanced Automobile Concepts division has been created to reengineer existing Zen models or develop new ones that are more competitive.

According to the chapter, an opportunity represents a “favorable circumstance, or chance for progress or advancement.” The circumstances facing Nick Adams are: • • • • •

Trend toward smaller, fuel-efficient automobiles Rising fuel costs Attitudes toward global warming perhaps affecting demand for more efficient automobiles Hybrid and alternative fuel technologies that show promise (electric, biodesiel, etc.)

Internal and public reports such as the Douglas Report calling for more efficient vehicles

2. Focus just on one area identified in The Douglass Report: demand for the different basic models. How would you write this problem?

The problem can be posed as the following question.

How many of the various hybrid vehicles under consideration for development will .

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be purchased?

3. Given what you wrote in question 2, write the research objective.

The research objectives for “How many of the various hybrid vehicles under consideration for development will be purchased?” are the following: 1. What are (prospective) automobile buyers attitudes toward the following: a. Very small auto (one-seater) with very high mpg ratings b. Small auto (two-seater) with high mpg ratings c. Hybrid compact-size auto with moderately high mpg ratings 2. What are consumer preferences and intentions for various types and combinations of fuel-efficient automobiles? a. Very small (one-seater), no trunk space, and very high mpg b. Small (two-seater), very limited trunk space, and high mpg c. Hybrid models (compact and moderately high mpg) d. Synthetic fuel hybrids e. Electric hybrids f. Alternative fuels models

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CHAPTER 4

Research Design Alternatives and Qualitative Research LEARNING OBJECTIVES •

To understand what research design is and why it is significant

•

To learn how exploratory research design helps the researcher gain a feel for the problem by providing background information, suggesting hypotheses, and prioritizing research objectives

•

To know the fundamental questions addressed by descriptive research and the different types of descriptive research

•

To explain what is meant by causal research and to describe types of experimental research designs

•

To know the different types of test marketing and how to select test market cities

•

To know the difference between qualitative and quantitative research and to know several approaches to conducting qualitative research

CHAPTER OUTLINE Research Design The Significance of Research Design Three Types of Research Design Research Design: A Caution Exploratory Research Uses of Exploratory Research Gain Background Information Define Terms Clarify Problems and Hypotheses Establish Research Priorities Methods of Conducting Exploratory Research .

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Secondary Data Analysis Experience Surveys Case Analysis Descriptive Research Classification of Descriptive Research Studies Causal Research Experiments Experimental Design Before-After with Control Group How Valid Are Experiments?

Types of Experiments Test Marketing Types of Test Markets Selecting Test Market Cities Pros and Cons of Test Marketing Qualitative Research Methods of Conducting Qualitative Research Observation Techniques Types of Observation Direct Observation Indirect Observation Disguised Versus Undisguised Focus Groups How Focus Groups Work Advantages of Focus Groups Disadvantages of Focus Groups .

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When Should Focus Groups Be Used?

When Should Focus Groups Not Be Used?

Depth Interviews Protocol Analysis Projective Techniques Word-Association Test Sentence-Completion Test Cartoon or Balloon Test Role-Playing Activity Ethnographic Research

KEY TERMS Ability to control distribution and promotion “Archives” Balloon test Before-after with control group Brand-switching studies Case analysis Causal research Causality Continuous panels Control group Controlled test markets Cross-sectional studies Degree of isolation

Indirect observations Internal validity Laboratory experiments Laddering Longitudinal studies Market tracking studies Moderators Nontraditional focus group Observation methods Omnibus panels Panels “Physical traces” Pluralistic research

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Dependent variables

Posttest

Depth interviews

Pretest

Descriptive research

Projective techniques

Direct observations

Protocol analysis

Discontinuous panels

Qualitative research

Disguised observation

Qualitative Research Consultants (QRs or QRCs)

Electronic test markets Ethnographic research Experience surveys Experiment Experimental design Experimental group Exploratory research External validity Extraneous variables Field experiments Focus group facility Focus group report Focus groups Independent variables

Quantitative research Quasi-experimental designs Representativeness Research design Role playing Sample surveys Secondary data analysis Sentence-completion test Simulated test markets Standard test market Test marketing Traditional focus group “True” experimental design Undisguised observation Word-association test TEACHING SUGGESTIONS 1. (The following will require giving an assignment to students and having them bring to class their findings.) Although students have not been fully exposed to exploratory research methods, they can perform three of them to some extent. Divide the class up into three groups and assign each group one of the following types of exploratory research: (1) secondary data analysis, (2) experience surveys, and (3) case analysis. . 60

Here are some possible “assignment” situations. It is necessary to keep the situations local.

•

The university is experiencing a decline (increase) in enrollment and wonders why. (Choose “decline” or “increase” to fit the situation of your institution.

Alternatively, consider using a particular major or a college as it suits your situation and students’ abilities.)

•

The university bookstore is considering going to an “all rent” policy for textbooks. (Some universities have this policy in place. Note: The experience survey will pertain to students’ and instructors’ experiences with buying and selling back textbooks.)

•

The director of your “student life” office is concerned about the high use and possible abuse of credit cards by college students. Under consideration is a self-help program for students to learn about credit card abuse and money management.

Have the groups present their findings and use class discussion to generate hypotheses.

2. If available, show a segment of a focus group or a video that will hopefully illustrate: • Physical set up • Role of the moderator • How participants interact 3. Illustrate the result of a cross-sectional descriptive study by presenting findings from such a study to the class. If you have used team projects in the past, you have many from which to pick. Alternatively, you may use a consulting report, or even the descriptive findings from an academic study may suffice. Use the tables and figures to illustrate the descriptive nature of the findings. For instance, show how the demographic profile describes the sample. If there is product use information, use it to show how it describes the types of product usage.

4. Some students may not readily understand the value of a longitudinal panel. A homely analogy is that a cross-sectional survey is like a snapshot; whereas a longitudinal panel is like a video.

5. A tangential discussion can be generated by asking students the implications of attrition with an omnibus panel versus a longitudinal panel. In the former case, the lost panel member can be replaced with someone whose profile is identical or very similar. That is, if a female elderly sole survivor living on the West Coast drops out, you would replace her with an elderly sole survivor who lives on the West Coast.

However, with a drop out in a longitudinal panel, the string of observations over time is broken, so attrition is a much more serious problem.

6. You can have fun with causality because humans have the inborn tendency to make causal attributions without using good experimental design. Here are some examples: •

It has been documented that human births increase as stork populations grow, so storks bring babies. (Actually, rain causes crops to grow, so agrarian people . 61

expand their families; whereas water affords more protection for stork nests so more storks are sighted.)

•

Lizards sometimes jump out of fires, so medieval people believed that lizards were created by fire. (Actually, lizards jump out of logs when lit on fire, because they hide in logs and the fire forces them to leave their hiding places.)

•

The sun revolves around the earth because one can see it rising and setting. (It was believed for centuries that the sun revolved around the earth because of the fallacious assumption that the earth stood still. In truth, the sun’s gravity causes the earth to revolve around it while the earth spins on its axis.)

7. The experimental design with control group can be used to illustrate the role of a control group. Quasi-experimental designs suffer from internal validity problems, namely extraneous effects and changes in subjects in both cases, and measure error and subjects guessing for the before-after design. The way to overcome these problems is to let them happen to both the experimental and the control group. That is, you must have a control group to “capture” the internal validity problem, and then subtract it out. The description of the before-after with control group and the causal statement formula where E = (O2 -­‐ O1) -­‐ (O4 -­‐ O3) shows how the control groups O3 and O4 do the subtracting out.

8. The before-after with control group experimental design can be set up as a convenient straw man to demonstrate the effects of controlling other independent and extraneous variables. Ask students to identify the independent variable, dependent variable, and causal statement in one or more of the following examples.

•

A dry cleaning company institutes a “frequent cleaner” plan where $1 is taken off the price of a dry cleaning order for every ten times a customer uses the dry cleaning company. Sales at the end of three months increase by $10,000.

•

A movie theater adds more parking spaces in its lot, and 300 more tickets are sold in a month.

•

Goodyear adds a line of ultra-high performance radials and sells 200,000 more of them in six months.

As a before-after design, the causal statement is that “X caused O.” That is, the frequent cleaner plan caused $10,000 in sales; the parking spaces caused 300 tickets to be sold, and that addition of the radials caused Goodyear to sell 200,000 more of them. However, students will generate a great number of other possible reasons why the sales were as described. Other independent variables may come into play, for example an especially popular movie shown in the month. Extraneous variables are involved as well such as competitors’ actions or changes in customers.

However, the control group aspect of this design measures the effects of the other independent variables and the extraneous variables. When the before-after amounts for the control groups are subtracted from the before-after amounts of their associated experimental groups, the true effect of the company’s changed independent variable is apparent. . 62

9. The descriptions of internal and external validity in the chapter do not utilize terms used in more comprehensive treatments. For internal validity, the terms are history, maturation, instrumentation, pretest effect, equivalence, and mortality, while for external validity; they are sample representativeness, artificiality, and generalizability. Some instructors may want to introduce these terms to students when reviewing the two types of validity.

10. There are experimental designs that are more complex and which are not covered in the chapter. Some Instructors may want to expand students’ knowledge by bringing these into the classroom.

11. Students can relate to a taste test as an example of a laboratory experiment, and they can be questioned on why, for example, when Diet Pepsi does a blind taste test against Diet Coke, findings show a slight preference for Diet Pepsi. However, Diet Coke sales are more than those for Diet Pepsi. The artificiality of the blind taste test can be exploited. There are extraneous and independent variables affecting brand loyalty, plus who does a blind taste test of two colas to decide which one to buy?

12. Consider doing an abbreviated taste test in class. You can select some students to be the administrators, and two or three to be the taste testers. One approach is to tell the administrators to read up on taste tests and to design and implement it themselves.

Another is to bring the materials to class and select the administrators and tasters from class, but you will need to orient the administrators on how to conduct the test.

Here are the instructions for three colas with six subjects. It is best to use Dixie cupsized cups as subjects must take several drinks. It is fun to use national brands like Coke, or Pepsi along with private brands like Sam’s Choice, or Dr. Thunder.

Instructions for Conducting an In-Class Taste Test 1. Pour the colas into identical large containers suitable for storage and decanting. Assign the three colas letters (A, B, C) 2. Select subjects at random 3. Assign the specific order of the taste tests to each subject, ABC, ACB, BCA, BAC, CAB, CBA 4. Subjects drink bottled water 5. Pour 1st letter cola for each subject; each tastes the cola and rates it 6. Subjects drink bottled water 7. Pour 2nd letter cola for each subject; each tastes the cola and rates it 8. Subjects drink bottled water 9. Pour 3rd letter cola for each subject; each tastes and rates the cola 10. Subjects identify 1st choice with respect to best taste rating .

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For class enjoyment—let the subjects guess which cola brands they have tasted, then divulge the brands to them and the class.

13. There are four types of test markets described: standard, controlled, electronic, and simulated. Ask students who uses each type and why. One factor is the distribution system of the company wishing a test market; another is the type of product being tested, a third factor is the type of information desired.

•

Standard test markets fit companies with fully developed distribution systems that are marketing convenience goods such as grocery products, personal care items, or over-the-counter drugs. This is a “do-it-yourself” approach with the information need being primarily to track the test product’s sales.

•

Controlled test markets fit companies without fully developed distribution systems. The test market company establishes the retail structure during the test. Convenience goods marketers are a good fit. There is more competitive sales information available because all brands are tracked, so the information needs are more specific as to which brand is gaining or losing during the test.

•

Electronic test markets are limited to those companies testing products that are sold in the stores where the electronic tracking system is in place (grocery and drug stores primarily). Because electronic test market customers are in a database, the information would profile the buyers versus nonbuyers and provide market segmentation implications.

•

The simulated test market is suited to a new product where the innovation is tried and the repurchase rate estimated by mathematical model. The distribution system may be nonexistent, or the cost of other marketing variables needed in the test (e.g. training salespeople to demonstrate it, or a large-scale educational advertising campaign) may be prohibitive. There is an implicit evaluative process and adoption decision on the part of the consumer, and its outcome is the primary information need.

14. Principles of marketing textbooks sometimes list 10 to 20+ U.S. test market cities.

One exercise is to take these listings over the past ten years and see what cities have been added or deleted from the list. If one of these is known to students due to its proximity to the university, students may be able to comment on why it was dropped or added.

ANSWERS TO REVIEW QUESTIONS 1. What are the independent, dependent, and possible extraneous variables in our chapter opening vignette written by Herb Sorenson?

The opening vignette describes somewhat generically a number of experiments specific to a snack company’s placement of chips and nuts/seeds in convenience stores. The independent variable is placement (shelf location and merchandise configuration are mentioned), and the dependent variable is sales, although “PathTracker®” measures other shopper aspects such as length of visit, reasons for . 64

coming to that store, etc. Two convenience store chains are matched in the experiments, so the extraneous effect of chain is controlled as are other extraneous factors such as competition, weather, etc.

2. Give some examples illustrating the uses of exploratory research.

Exploratory research is most commonly unstructured, informal research that is undertaken to gain background information about the general nature of the research problem.

Exploratory research is very flexible in that it allows the researcher to investigate whatever sources he or she desires and to the extent he or she feels it is necessary in order to gain a good feel for the problem at hand. So, students may come up with any number of different examples using the methods listed: secondary data, experience surveys, case studies, focus groups, or projective techniques.

3. What type of research design answers the questions of who, what, where, when, and how?

Descriptive research is undertaken to describe answers to questions of who, what, where, when, and how.

4. What are the differences between longitudinal studies and cross-sectional studies?

Cross-sectional studies measure units from a sample of the population at only one point in time, whereas longitudinal studies repeatedly measure the same sample units of a population over a period of time, typically with some type of panel.

5. In what situation would a continuous panel be more suitable than a discontinuous panel? In what situation would a discontinuous panel be more suitable than a continuous panel?

.

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Continuous panels ask panel members the same questions on each panel measurement, whereas discontinuous panels vary questions from one panel measurement to the next.

With continuous panels, panel members are asked to record the same information (grocery store purchases) over and over. The essential point is that panel members are asked to record the same information (grocery store purchases) over and over. So tracking grocery purchases would be a suitable situation for a continuous panel.

The discontinuous panel’s primary usefulness is that it represents a large group— people, stores, or some other entity that is agreeable to providing marketing research information. Therefore, a marketer wanting to know how a large number of consumers feel about two different product concepts may elect to utilize the services of an omnibus (discontinuous) panel.

6. What type of panel is an omnibus panel?

Discontinuous panels are sometimes referred to as omnibus panels. (Omnibus means—including or covering many things or classes.) They may be used for a variety of purposes, and the information collected by a discontinuous panel varies from one panel measurement to the next. How longitudinal data are applied depends on the type of panel used to collect the data. Essentially, the discontinuous panel’s primary usefulness is that it represents a large group—people, stores, or some other entity—and its members are agreeable to providing marketing research information.

7. Explain why studies of the “if-then” variety are considered to be causal studies.

By definition, causality may be thought of as understanding a phenomenon in terms of conditional statements of the form “If X, then Y.” Causality means cause-andeffect, or if X, then y.

8. Define each of the following types of variables and give an example of each in an experiment designed to determine the effects of an advertising campaign: independent, dependent, extraneous, control group, experimental group.

Independent variables are those variables which the researcher has control over and wishes to manipulate. Some independent variables include level of advertising expenditure, type of advertising appeal (humor, prestige), display location, method of compensating salespeople, price, and type of product. Dependent variables, on the other hand, are those variables that we have little or no direct control over, yet we have a strong interest in. Extraneous variables are those that may have some effect on a dependent variable yet are not independent variables. By control group, we mean a group whose subjects have not been exposed to the change in the independent variable. The experimental group, on the other hand, is the group that has been exposed to a change in the independent variable Let’s suppose there are two competing themes for the advertising campaign: A and B. Further, let’s assume that we can do a split viewing of the test themes, A, B, and none (Control), each in a comparable small city (Experimental City A, Experimental City B, and Control City C). The ad campaigns, A and B, are the independent variables, . 66

while the sales of the brand in each city is the dependent variable. The after-before sales differences are the quasi effects of the campaigns and these effects minus the control before-after difference are the true effects. Extraneous variables are all other factors that may have affected the sales (competition, weather, etc.) and we are assuming that these are constant across all three cities, so their effects are measured in the control city and then subtracted to factor them out of the findings.

9. Explain the two types of validity in experimentation and also explain why different types of experiments are better suited for addressing one type of validity versus another.

Two forms of validity are used to assess the validity of an experiment—internal and external. Internal validity is concerned with the extent to which the change in the dependent variable was actually due to the independent variable. External validity refers to the extent that the relationship observed between the independent and dependent variables during the experiment is generalizable to the “real world.”

Laboratory experiments are those in which the independent variable is manipulated and measures of the dependent variable are taken in a contrived, artificial setting for the purpose of controlling the many possible extraneous variables that may affect the dependent variable. So laboratory experiments have good internal validity but weak external validity. Field experiments are those in which the independent variables are manipulated and the measurements of the dependent variable are made on test units in their natural setting. They have good external validity but weak internal validity.

10. Distinguish among the various types of test marketing.

Test marketing is the phrase commonly used to indicate an experiment, study, or test that is conducted in a field setting. Test markets have been classified into four types: standard, controlled, electronic, and simulated.

•

The standard test market is one in which the firm tests the product and/or marketing mix variables through the company’s normal distribution channels.

•

Controlled test markets are conducted by outside research firms that guarantee distribution of the product through prespecified types and numbers of distributors.

•

Electronic test markets are those in which a panel of consumers has agreed to carry identification cards that each consumer presents when buying goods and services.

•

Simulated test markets (STMs) are those in which a limited amount of data on consumer response to a new product is fed into a model containing certain assumptions regarding planned marketing programs, which generates likely product sales volume.

11. How does qualitative research differ from quantitative research?

Quantitative research is defined as research involving the use of structured questions .

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or observations where the response options have been predetermined and a large number of respondents are involved. Quantitative research often involves a sizeable representative sample of the population, and a formalized procedure for gathering data. The purpose of quantitative research is very specific, and the manager and researcher have agreed that precise information is needed. Data format and sources are clear and well defined, and the presentation of the data that has been gathered follows an orderly procedure, which is largely numerical in nature. Qualitative research, in contrast, involves collecting, analyzing, and interpreting data by observing what people do and say. Observations and statements are in a qualitative or nonstandardized form. Because of this fact, qualitative data can be quantified but only after a translation process has taken place.

12. What is meant by pluralistic research?

Pluralistic research is the combination of qualitative and quantitative research methods in order to gain the advantages of both.

13. What are the different methods of observation?

The methods described in the chapter are: •

Focus Groups

•

Depth Interviews

•

Protocol Analysis

•

Projective Techniques ❧ Word-Association Test ❧ Sentence-Completion Test ❧ Cartoon or Balloon Test ❧ Role-Playing Activity

14. Why are focus groups called “focus” groups?

Although focus groups should encourage openness on the part of the participants, the moderator’s task is to ensure that the discussion is “focused” on some general area of interest.

15. What is the difference between traditional and nontraditional focus groups?

Traditional focus groups select about 6 to 12 people and meet in a dedicated room, with a one-way mirror for client viewing, for about two hours. Nontraditional focus groups can differ from traditional focus groups in many ways: they may be online, with clients observing on computer monitors in distant locations; they may have up to 50 respondents; they may allow client interaction with participants; they can last four or five hours; or take part outside dedicated facilities such as in a park.

16. What is the role of a focus group moderator and what is meant by QRC? .

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Focus group participants are interviewed by moderators, often referred to as Qualitative Research Consultants (QRs or QRCs). QRCs have the responsibility of creating an atmosphere that is conducive to openness, yet they must make certain that participants do not stray too far from the central focus of the study. QRCs must also prepare a focus group report.

17. When should focus groups be used? When should focus groups not be used?

Use a focus group when the research objective is to describe, rather than predict. Do not use a focus group when trying to predict something about a population or target market (because the group is too small to be representative).

18. Discuss the advantages and disadvantages of in-depth interviewing.

The advantages are that interviewers have the ability to probe, asking many additional questions, as a result of a respondent’s response. This enables the research technique to generate rich, deep, in-depth, responses. In-depth responses may be more revealing in some research situations than say, responses to predetermined, yes/no questions typical of a structured survey. If used properly, in-depth interviews can offer great insight into consumer behavior. The disadvantages are the lack of structure in the process; unless interviewers are well trained, the results may be too varied to give sufficient insight to the problem.

19. Explain how “laddering” works.

Laddering is a technique used in in-depth interviews in an attempt to discover how product attributes are associated with desired consumer values. Essentially, values that are important to consumers such as “good health” are determined. Next, researchers determine which routes consumers take to achieve their values, i.e. exercise, eating certain foods, stress reduction, and so on. Finally, researchers attempt to determine which specific product attributes are used as a means of achieving the end that is the desired value.

20. What is meant by “protocol analysis”?

Protocol analysis involves placing people in a decision-making situation and having them verbalize everything they consider when making a decision. It is a specialpurpose qualitative research technique that has been developed to peek into the consumer’s decision-making processes.

21. Explain the rationale behind using projective techniques.

Projective techniques involve situations in which participants are placed in (projected into) simulated activities in the hopes that they will divulge things about themselves that they might not reveal under direct questioning. Projective techniques are appropriate in situations in which the researcher is convinced that respondents will be hesitant or unable to relate their true opinions.

22. Why do you think ethnographic research could be ethically sensitive?

Although students may have different opinions, the chapter states, “Ethnographic . 69

research is an area of ethical sensitivity. Researchers’ immersing themselves in others’ homes, schools, places of work and play for purposes of recording behaviors, comments, reactions, and emotions of people naïve to the research is unethical. As the technique grows in marketing research, researchers must be adept in skills necessary to be ‘present and known’ without interfering with normal behavior. Fortunately, most of the behavior marketers are interested in are public behaviors—shopping, cooking, and eating, for example. Such public behaviors are easily observed.”

ANSWERS TO APPLICATION QUESTIONS 23. Think of a past job that you have held. List three areas in which you, or some other person in the organization, could have benefited from having information generated by research. What would be the most appropriate research design for each of the three areas of research you have listed?

Students will have different answers due to different job situations so there is no set answer to this question. The following are some examples that might be used to stimulate class discussion: Area Product or service Advertising

Issue What can be improved?

Price

What happens if we raise (or lower) price?

What theme to use?

Research Design Exploratory research in the form of a focus group of users Descriptive research in the form of a survey asking reactions to prospective advertising themes Field market test where we change price in one region by X% and see what happens to sales

24. You are no doubt familiar with Internet search engine companies that find online sources pertaining to words, phrases, or questions that users type in. Can you identify research problems likely to be addressed by a search engine company such as Google™? What type of research design would you recommend for these problems?

Google and its competitors are search engine services. They provide four types of searches—web, pictures, news, and products. As such, Internet search engines can be useful in exploratory research where the researcher is gaining background (e.g. who are the client’s competitors?). It might help in defining terms as a researcher could quickly visit a number of websites or other “hits” and find terminology common to the phenomenon. The news hits could help to clarify a problem if a news article or release detailing the problem is found.

25. Design an experiment. Select an independent variable and a dependent variable.

What are some possible extraneous variables that may cause problems? Explain how you would control for the effects these variables may have on your dependent variable. Is your experiment a valid one?

The purpose is to have students apply an experimental design and thus pick out independent, dependent, and extraneous variables as well as assess the internal and external validity of the experiment. Class discussion can be generated by having a . 70

student “present” his or her design to the class.

26. The Maximum Company has invented an extra-strength, instant coffee brand to be called “Max-Caff” and positioned to be stronger tasting than any competing brands.

Design a taste test experiment that compares Max-Caff to the two leading instant coffee brands to determine which brand consumers consider to taste the strongest.

Identify and diagram your experiment. Indicate how the experiment is to be conducted, and assess the internal and external validity of your experiment.

There is no need to perform a pretest as there is no baseline to taste tests. Max-Caff is to be compared to the leading brands, A and B (Maxwell House and Folger’s, for instance). With a taste test, the design can be within subjects or across subjects. For the within design, the same subject tastes all three coffees and rates the taste of each.

Typically, a neutral stimulus such as a soda cracker or a drink of water is administered between the randomly ordered stimuli. The panel design would look like the following: X1 O1 N X2 O2 N X3 O3 Where the Xis are the randomly ordered stimuli, the Ois are the measurements, and the Ns are the neutralizers.

With an across subjects design, three groups are used, each with its own stimulus.

Subjects are assigned randomly to each group, and the after-only design diagram looks like this: X1 O1 X2 O2 X3 O3 No control group is used as it does not make sense to have subjects rate the strength of a nonstimulus.

Internal validity is addressed in the following ways: • • • • • •

Extraneous factors—eliminated if the panel design is random and if the three groups take place in the same circumstances and time frame Changes in subjects—obviated by the neutral stimulus in the panel and the short time frame in the three group design Measure error—use the same measure Subjects guessing—could happen but will be random and cancel out Equivalent groups—use random selection or matching criteria Drop-out— should be monitored

External validity is an issue as the taste test is conducted in an artificial setting (perhaps at a shopping mall or some other facility), and the subjects are told that they are to rate the taste so they will be especially vigilant to the taste. Also, the long term taste aspects of the coffees are not tested, so if Max-Caff “stays with” the taster, this factor is not captured by the design. Of course, the sample should be representative of coffee drinkers.

27. Coca-Cola markets PowerAde as a sports drink that competes with Gatorade.

Competition for sports drinks is fierce where they are sold in the coolers of . 71

convenience stores. Coca-Cola is thinking about using a special holder that fits in a standard convenience-store cooler but moves PowerAde to eye level and makes it more conspicuous than Gatorade. Design an experiment that determines whether the special holder increases the sales of PowerAde in convenience stores. Identify and diagram your experiment. Indicate how the experiment is to be conducted and assess the internal and external validity of your experiment.

There is one treatment (the cooler holder for PowerAde) and one control situation (the current stocking of PowerAde without the cooler holder). A before-after with control group or an after-only with control group design would be satisfactory. The after-only with control group design looks like the following: X1 O1 O2 The groups are randomly assigned convenience stores such as Circle K’s or 7Eleven’s, and the Os are store sales, and the effect of the PowerAde cooler holder is O1:O2, and if the cooler holder average PowerAde sales are greater than the control group average PowerAde sales, the experiment has shown that the cooler holder has caused the greater sales.

Internal validity is addressed in the following ways: • • • • • •

Extraneous factors—eliminated groups take place in the same circumstances and time frame Changes in subjects—if groups are matched or random, changes will be the same for all Measure error—use the same measure (sales of PowerAde) Subjects guessing—no pretest, so no problem Equivalent groups—use random selection or matching criteria Drop-out—probably no problem, but should be monitored

External validity is taken care of if the groups are representative of all convenience stores.

The test is realistic and generalizable.

28. SplitScreen is a marketing research company that tests television advertisements.

SplitScreen has an agreement with a cable television company in a medium-size city in Iowa. The cable company can send up to four different television ads simultaneously to different households. SplitScreen also has agreements with the three largest grocery store chains, which will provide scanner data to SplitScreen.

About 25% of the residents have SplitScreen scan cards that are scanned when items are bought at the grocery store and that allow SplitScreen to identify who bought which grocery products. For allowing SplitScreen access to their television hookups and their grocery-purchase information, residents receive bonus points that can be used to buy products in a special points catalog. Identify and diagram the true experimental design possible using the SplitScreen system. Assess the internal and external validity of SplitScreen’s system.

Virtually any true experimental design is possible with the SplitScreen system, but .

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the most powerful ones would be before-after with control group or after-only design.

The system allows for up to four groups—either four experimental groups or three experimental groups and one control group. By combining or not using groups, fewer groups can be used for a less complex design.

All internal validity problems are under control assuming that groups are equivalent, drop-outs are replaced, commercials are seen at the same time, stores maintain constant, etc. The external validity may concern some students who will point out the artificiality of the scanner cards, bonus points, and the use of a medium-sized city in Iowa. If SplitScreen recruits households and does not use them right away and thus lets them settle back into their normal shopping patterns after what might be a period of unusual shopping because they feel “watched,” the artificiality will be minimized.

The medium-sized city in Iowa is consistent with good market test selection criteria.

29. A tire store chain is concerned that women have concerns about bringing their car to tire stores which are typically dominated by men—employees and customers. What type of research would you conduct to determine if this concern is prevalent among female drivers and what techniques the chain may adopt that would alleviate some of these concerns?

This is a sensitive topic because it concerns subtle or deep fears on the part of female customers who are threatened by the male-dominated situation. So, a projective technique of some sort is appropriate. Candidate methods are: word-association test, sentence-completion test, cartoon or balloon test, or role playing.

CASE SOLUTIONS Case 4.1 Memos from a Researcher Case Objective: This case presents the student with three different situations where they must determine the appropriate research design.

Answers to Case Questions 1. Describe what research design you would recommend for each client.

•

The research design for client A should be to use exploratory research.

•

The research design for Client B should be to use descriptive research.

•

The research design for Client C should be to use causal research.

2. For each research design you selected for the three clients, discuss why you believe your choice of design is the correct choice.

The answers are provided in the table below.

Client Client A

Research Design Exploratory research should be used

Reason(s) All we know is that an important symptom of a problem exists (market share has gone down). .

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because we know very little about the problem.

Client B Descriptive research should be used.

Exploratory research is needed to help us determine the source of the problem—product itself? Prices?

Promotions? Distribution problems? A situation analysis, a form of exploratory research is appropriate.

Unlike Client A, we know a good deal more about this situation and we are told that we need to describe sales by SKU for the time period associated with each TV ad campaign. By conducting this descriptive research, management can better assess the different types of TV campaigns and determine the relative effectiveness of the different types of campaigns.

.

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Client C An experiment is needed, the primary tool of causal research.

We need to manipulate the independent variables of package design, i.e. size, shape, color, texture, and measure their effects on the dependent variables of awareness of, preference for, and intention to buy the brand. An experiment will help us determine how each of these independent variables effects the dependent variables while controlling for other, extraneous variables.

Case 4.2 Integrated Case: Advanced Automobile Concepts Case Objective: This case requires students to identify appropriate research designs for the Advanced Automobile Concepts marketing research project.

Answers to Case Questions 1. To deal with the first set of issues—determining how consumers feel about certain issues, how important these issues are to them, and what terms they use to discuss these issues—what research design would you suggest? Why? At this point in the marketing research process, Nick should strongly consider using exploratory research. He is at the beginning of the process where he needs to gain background information, define terms, clarify the situation, and establish research priorities. He definitely should consult secondary sources as there is much written on global warming, gasoline prices, alternative fuels, and so on. Focus groups would also be useful.

2. Consider the second paragraph in the case. What research design would you suggest to determine all of the following: Which attitudes are strongly associated with purchase intentions of different auto models? Will these relationships vary around the country and will they exist for definable market segments? Why? The critical issues are: determining associations between attitudes and intentions and investigating if these vary by market segment. This is definitely the realm of descriptive research requiring a cross-sectional survey using a representative sample of prospective automobile buyers.

3. Nick Thomas’s last question deals with determining how much of an increase in mpg will be needed for consumers to have equal preference for ZEN models as those outselling ZEN today. What research design would you suggest?

.

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The tip-off is the use of the “require to buy” phrase. It strongly infers cause and effect.

So, a causal research design is called for. The treatments can be the mpg increases (such as 10%, 20%, 30%, etc.), and the dependent variables would be the preference for the new Zen models. Most likely a cross- sectional survey would be called for, but the approach is to find out how much of an increase would cause the preferences to rise to a certain level.

.

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CHAPTER 5

Information Types and Sources: Secondary Data and Standardized Information

LEARNING OBJECTIVES •

To learn what secondary data are, and the varied uses; and how we may classify different types of secondary data including internal and external databases

•

To understand the advantages and disadvantages of secondary data

•

To learn how to evaluate secondary data

•

To learn how to use the U.S. Census Bureau’s new American Community Survey

•

To learn how to distinguish standardized information from other types of information

•

To know the differences between syndicated data and standardized services

•

To understand the advantages and disadvantages of standardized information

•

To see some of the various areas in which standardized information may be applied

CHAPTER OUTLINE Secondary Data Primary Versus Secondary Data Uses of Secondary Data Classification of Secondary Data Internal Secondary Data External Secondary Data Published Sources Syndicated Services Data External Databases Advantages of Secondary Data Disadvantages of Secondary Data .

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Incompatible Reporting Units Measurement Units Do Not Match Class Definitions Are Not Usable Data Are Outdated Evaluating Secondary Data What Was the Purpose of the Study?

Who Collected the Information?

What Information Was Collected?

How Was the Information Obtained?

How Consistent Is the Information Among Sources?

Key Sources of Secondary Data for Marketers Census 2010 and American Community Survey American Community Survey What Is Standardized Information?

Advantages and Disadvantages of Standardized Information Syndicated Data Standardized Services Applications of Standardized Information Market Segmentation Monitoring Consumer-Generated Media KEY TERMS

American Community Survey

Internal secondary data

Aspiring Young Families

Metropolitan SAs

Census 2010

Micropolitan SAs

Core-based Statistical Areas (CBSAs)

Online information databases

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Customer Relationship Management

Performer Q

(CRM)

Primary data

Data mining

Published sources

Database

Record

Database marketing

Secondary data

ESRI’s Tapestry Segmentation

Standard subject headings

External databases

Standardized Information

External secondary data

Standardized Services

Fields

Syndicated Data

Geodemographics

Syndicated services data

Internal databases

Top Rung

TEACHING SUGGESTIONS 1. University library staff members are invaluable in orienting students on how to use secondary sources. Contact the business reference librarian and arrange for an orientation session on the use of business databases.

2. Large companies typically have library and secondary data specialists on staff. If you have a local contact, invite the company librarian or secondary information specialist to class to describe the secondary sources used by the company’s employees.

3. One way to introduce students to secondary sources is to give them an assignment that will require them to investigate various secondary sources. Here are some sample assignments to get students in the Library or to compel them to use Internet sources to search for information. a. Determine population size, growth and composition for a particular county in your state. Assign different counties to various students or student teams and have them make class presentations on their findings. b. Determine how many and which states have state lotteries, gambling casinos, video poker, and river boat gambling.

4. Tell students that they have been hired as the corporate intelligence officer for a major garment company. They are to investigate the competition by use of secondary sources. Who are the competitors, how big are they, what are their distinguishing features, and so forth? With a large class, you may want to use several industries, or have student teams concentrate on separate competitors. .

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5. The chapter introduces Internet secondary data sources. Some students will be proficient in the use of the Internet and search engines. Ask those with good skills to provide a demonstration to the class.

7. Table 5.2 lists a large number of secondary sources with brief descriptions of each.

Determine which ones are readily available to your students and assign each student (or student team) the task of finding the source, becoming familiar with it, and reporting to the class what the source is all about including specific examples of information in the source.

8. The U.S. Census website (www.census.gov) is free and reasonably user-friendly. It has a huge amount of information with a great many different aspects. Here is a listing of data available in the “People and Households” section.

• Age • American Community Survey • Ancestry • Births • Children • Computer Ownership and Use • Coverage Measurement • Deaths • Disability • Education • Elderly • Estimates • Families • Fertility • Foreign Born • Grandparents • Health Insurance • Hispanic Origin • Households and Families • Immigration • Income • International Population • Journey to Work

• Labor Force • Language Use • Marital Status and Living Arrangements • Migration • Occupation • Overseas U.S. Population • Population Profile • Poverty • Program Participation • Projections • Puerto Rico and the Island Areas • Race • Small Area Health Insurance Estimates • Small Area Income and Poverty Estimates • School Costs • School Districts • School Enrollment • Veterans Statistics • Voting and Registration • Wealth • Well-Being • Women • Working at Home

Instructors can demonstrate the functionality and data on the U.S. Census website in class and/or assign various topics to students or student teams to investigate and write up or present to the class.

ANSWERS TO REVIEW QUESTIONS 1. What are secondary data, and how do they differ from primary data?

Secondary data have previously been gathered by someone other than the researcher and/or for some other purpose than the research project at hand, whereas primary data refers to information that is developed or gathered by the researcher specifically for the research project at hand. . 82

2. Describe some uses of secondary data.

Suggested applications include economic trends forecasting, insights on industries, corporate intelligence, international data, public opinion, and historical data, among others.

3. How would you classify secondary data?

The classification system is as follows: •

Internal secondary data are data that have been collected within the firm.

Such data include sales records, purchase requisitions, and invoices, including internal databases.

External secondary data are data obtained from outside the firm from three sources: (1) published, (2) syndicated services data, and (3) databases.

4. What is database marketing and what is CRM? •

Kotler defines database marketing as the process of building, maintaining, and using customer (internal) databases and other (internal) databases (products, suppliers, resellers) for the purpose of contacting, transacting, and building relationships. Companies use their internal databases for purposes of direct marketing and to strengthen relationships with customers called customer relationship management (CRM).

5. What are three types of external secondary data?

There are three types of external: •

Published sources are those sources of information that are prepared for public distribution and are normally found in libraries or through a variety of other entities such as trade associations, professional organizations, or companies.

•

Syndicated services data are provided by firms that collect data in a standard format and make them available to subscribing firms.

•

External databases are databases supplied by organizations outside the firm.

6. What are online information databases? Name three of them.

Online information databases are sources of secondary data available by search engines online. Examples include Factiva, Lexis-Nexis, Proquest, and Gale Group.

7. What are the five advantages of secondary data? Discuss the disadvantages of secondary data.

The advantages of secondary data: • • •

Can be obtained quickly Inexpensive relative to primary data Secondary data are usually available . 83

• •

Secondary data enhances primary data Secondary data may achieve the research objective

Four of the problems associated with secondary data include: (1) incompatible reporting units which are not suitable for the researcher’s need, (2) mismatch of the units of measurement as compared to the units needed by the researcher, (3) definitions used to classify the data that are not useful to the researcher, and (4) the timeliness of the secondary data, meaning it might be outdated.

8. How would you go about evaluating secondary data? Why is evaluation important?

In order to properly use secondary data, you must evaluate that information before you use it as a basis for decision making. A user must be very cautious when using an Internet source because few quality standards are applied to most Internet sites.

To determine the reliability of secondary information, marketing researchers must answer the following five questions:

• • • • •

What was the purpose of the study?

Who collected the information?

What information was collected?

How was the information obtained?

How consistent is the information with other information?

9. Describe the American Community Survey in terms of the advantage it will offer as well as how to find it to retrieve data.

The American Community Survey provides current data about communities every year, rather than once every 10 years. The chapter describes the interface created for easy access of the data.

10. What are the two types of standardized information?

Two broad classes of standardized information are syndicated data and standardized services.

11. Clearly distinguish between syndicated data and standardized services.

Syndicated data refers to data being collected in a standard format and being made available to all subscribers. Standardized services refer to a standardized marketing research process that is used to generate information for a particular user.

12. ESRI’s market segmentation service, Tapestry™ Segmentation would be classified as which type of standardized information? Why? It is a standardized service because it uses a set process to profile residential neighborhoods.

13. What are the advantages and disadvantages of syndicated data?

Advantages of syndicated data are shared costs, high quality of the data, and speed .

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with which data are collected and made available for decision making.

Disadvantages of syndicated data are that there is little control over what data are collected, buyers must commit to long-term contracts, and competitors have access to the same information.

14. What are the advantages and disadvantages of standardized services?

With standardized services, the advantages are experience of the company providing the service, reduced cost, and speed of service. The disadvantages are inability to customize services and the service firm not being knowledgeable of the client’s industry.

15. Describe some areas in which standardized information may be applied.

The application areas described are: market segmentation and monitoring consumer generated media.

ANSWERS TO APPLICATION QUESTIONS 16. Access your library’s online databases. Describe how your library helps categorize the many databases such that you would know how to select databases appropriate for business.

This exercise will depend on each adopter’s library system. As an example answer, here is how the Louisiana State University Library system is organized.

Indexes and Databases

By Topic (General Reference: Arts & Humanities; Biological Sciences; Business, etc.

By subtopic (Business: Accounting, Agribusiness, Economics, Finance, Information Systems, Patents & Trademarks, Management, Marketing, Business Reference, Public Administration) By (e.g.) Marketing Databases (Business Searching Interface, Business Source Complete, Choices 3, Simmons, etc.)

Search engine specific to the database (basic search: key words in fields; advanced search) 17. Go online to your library’s website. Can you locate a database that would be a good choice to find publications such as journal articles, trade publications, or newspapers? What are they?

Again, this exercise depends on each adopter’s library system. If students find “Business Source Complete,” for example, they will discover that it has the following potential publication types for each search.

• • • • •

Authors Author Profiles Business Videos Cited References Indexes . 85

• • • • • • • • • • • •

Subjects Publications Academic Journals Books/Monographs Company Profiles Country Reports Industry Profiles Magazines Market Research Reports SWOT Analyses Trade Journals Working Papers

18. Go online to your favorite search engine (e.g., Google, Yahoo) and enter “Demographics.” Visit some of these sites and describe the kind of information you are receiving. Why would this information be considered secondary data?

Most likely, the hits will be a combination of public websites such as the U.S. Census, company websites such as Claritas, marketing research companies that specialize in demographics, databases with demographic data, and others. If students visit publicly available websites such as the U.S. Census, they will find that the data was gathered for the purposes/mission of the organization that gathered it.

19. Find your library’s online databases and check if any of them have the ability to automatically put an article you retrieve in a format suitable for citing, (e.g., MLA, APA).

Your library may or may not have this service, and it will be easy to determine by visiting the website or making a telephone call to the reference librarian. An alternative is to have students look online for such services. There are a few including the following: •

Landmarks Citation Machine

•

BibMe

•

SimpleBib

•

EasyBib

•

OttoBib

• Perrla 20. Go to the website of Nielsen, at http://nielsen.com/. Select the region or country in which you live. Look at the services provided by Nielsen and read about them. Which ones would you classify as (a) syndicated data and (b) standardized services?

For the United States, Nielsen’s services are found under “Measurement.” The services are: •

Consumer Measurement ❧ Retail measurement via electronic point-of-sale (POS) scanning— standardized service . 86

❧ Consumer panel of households using scanners, sales receipts, and manual diaries—standardized data •

Television ❧ Panels o People meter that monitors television viewing in a household— standardized service o Television ratings from these panels—standardized data o “Census” which links television viewing with datasets on lifestyle, consumer behavior, advertising exposure, etc.—standardized service

•

Online ❧ Audience and Internet measurement using “a range of web technologies” that monitor online usage—standardized service ❧ Buzz measurement by culling a large number of blogs, social networks, and other online social networks—standardized service

•

Mobile ❧ Mobile subscriber counts—syndicated data ❧ Bill panel—syndicated data ❧ Drive tests—standardized service ❧ Online surveys—syndicated data ❧ Mobile application testing—standardized service

•

Radio listening via diary measurement—syndicated data

•

Three screens, which measures across television, Internet, and cell phone content viewing—standardized service

•

Data fusion, which merges various Nielsen databases such as TV Panel and Homescan to find insights—standardized service

21. Go to www.maritz.com, and click on “Sales & Marketing Services” then “Marketing Research.” Go to “Case Studies.” Read any case in an industry in which you have an interest for future employment. Was the study conducted by Maritz using either syndicated data or a standardized service? If so, provide a description.

Maritz lists anywhere from a dozen to twenty such case studies with periodic updates.

Because Maritz is a full-service marketing research company, it utilizes a wide range of techniques including syndicated data, standardized services, and custom research.

Students will need to read about the research method in the chosen case study to determine if what technique(s) were employed.

CASE SOLUTIONS Case 5.1 Secondary Data to Find Your Career Industry . 87

Case Objective: This case requires students to use secondary and other data to learn about industries in which they have interests with respect to possible careers.

Answers to Case Questions 1. Go to your library and find publications that give you some assessment of industries.

There are several sources that give you industry overviews. Your reference librarian may be able to help. Describe an industry in which you have a career interest in terms of its future outlook. Properly cite your source. (Hint: Some of the securities ratings services publish industry overviews).

Most students will have a unique experience as their career goals will be different.

The point of the exercise is to make them aware of the several secondary sources of information that are available. Students who find only a small number of secondary sources are not giving the exercise their full effort.

2. For your career industry, access some of the electronic databases available through your library. Find recent publications that describe issues currently facing your industry. Describe those issues and how your industry is trying to address them.

Properly cite your sources.

Most university libraries are under severe budget crunch pressure, and if students dig into the various electronic databases, they will most likely find that the more expensive and more specialized ones are not available to them. Nonetheless, they will be able to identify major issues and trends in their industries from publically-available and “free” databases.

Case 5.2 Integrated Case: Advanced Automobile Concepts Case Objective: Students are required to recommend and investigate secondary data sources and online information sources relevant to the integrated case.

Answers to Case Questions 1. What types of secondary information should Nick Thomas seek?

First, Nick needs three different types of secondary information. He needs scientific information from well-respected sources on the fuel crisis as well as global warming.

He will likely find mixed opinions but he needs something more authoritative than what has been published in the general press. If you were going to make a major decision based upon an assumption, wouldn’t you want that assumption to be grounded in information from an authoritative source? He will likely have to find some industry sales figures in trade association publications in the auto industry to reflect sales of car models. Again, an authoritative source, something stressed in the chapter, is critical.

2. Select one particular topic from those that concern Thomas. Go to your library’s databases and find current articles on the topic. List the database and the search terms you used to find the articles. . 88

See Table 5.1 for information on secondary data sources. This question is open-ended in that each student will select a topic that appeals to him or her. Plus, library systems vary across universities. Instructors will need to investigate these systems before engaging students in dialogue on this topic.

3. Write a short summary of the contents of the articles you found relative to the issue selected in question 2.

One strategy for evaluation here is to ask the students how they know what they’ve found is authentic. They should mention the sponsoring organization of the information. Is it a reputable source? They should do a search on the organization itself. How long has it been in existence? Also, for many sites they may find it difficult to determine authorship. In these cases, validating the information is difficult. If information is cited from another source, that source (e.g., Business Week) would be evaluated. How current is the information? Did they find a date on the information they found?

In general, the chapter relates that weaknesses of secondary data can include incompatible reporting units, mismatch of the units of measurement, differing definitions used to classify the data, and the timeliness of the secondary data.

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CHAPTER 6 Data Collection Methods LEARNING OBJECTIVES •

To learn the four basic alternative modes for gathering survey data

•

To understand the advantages and disadvantages of each of the alternative data gathering modes

•

To become knowledgeable about the details of different types of survey data collection methods such as personal interviews, telephone interviews, and computerassisted interviews, including online surveys

•

To comprehend the factors researchers consider when choosing a particular survey method

CHAPTER OUTLINE Four Alternative Data Collection Modes Person-Administered Surveys Advantages of Person-Administered Surveys Feedback Rapport Quality Control Adaptability Disadvantages of Person-Administered Surveys Humans Error Slowness Cost Interview Evaluation Computer-Assisted Surveys Advantages of Computer-Administered Surveys . 91

Speed Error-Free Interviews Pictures, Videos, and Graphics Real-Time Capture of Data Reduced Interview Evaluation Disadvantages of Computer-Administered Surveys Technical Skills Required High Setup Costs Self-Administered Surveys Advantages of Self-Administered Surveys Reduced Cost Respondent Control No Interview-Evaluation Apprehension Disadvantages of Self-Administered Surveys Respondent Errors Lack of Supervision Stand-alone Questionnaire Mixed-Mode Surveys Advantage of Mixed-Mode Surveys Disadvantage of Mixed-Mode Surveys Descriptions of Data Collection Methods Person-Administered Surveys In-Home Interviews Mall-Intercept Interviews In-Office Interviews Central Location Telephone Interviews .

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Computer-Assisted Surveys Computer-Assisted Telephone Interviews (CATIs) Fully Computerized Interviews (Not Online) Online Interviews Self-Administered Surveys Mail Surveys Group-Administered Surveys Drop-Off Surveys Deciding Which Survey Method to Use How Much Time Do I Have for Data Collection?

How Much Money Do I Have for Data Collection?

What Type of Respondent Interaction Is Required?

Are There Special Considerations to Take into Account?

KEY TERMS Central location telephone interviewing

Internet-based questionnaire

Completely automated telephone survey (CATS)

Interview evaluation

Computer-administered survey

Mail survey

Computer-assisted telephone interviews (CATI)

Mall-intercept interview

Drop-off survey

Mixed-mode survey

Fully computerized interviews

Nonresponse

Group self-administered survey

Person-administered survey

Incidence rate

Self-administered survey

In-home interview

Self-selection bias

In-office interviews

Survey

.

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TEACHING SUGGESTIONS 1. A field trip to a local telephone interview company does a great deal in the way of illustrating how this survey mode operates. If there is an interview service in your city, consider such a field trip, or if this is not feasible, have a representative from the company speak to the class about the company’s operations.

2. It is possible to illustrate self-selection bias in students with a quick exercise. Give students the following set of topics and have them indicate how they would respond to a mail survey request in each case. Use a show of hands or actual count of the responses to illustrate that some topics are inherently interesting and garner more response than uninteresting topics.

Instructions: You pick up your mail, and you find a mail survey in it. How likely would you be to take 15 minutes to fill out the questionnaire and return it if it concerned each of the following topics?

Would you fill out the survey and send it back?

Topic Automobile maintenance

Definitely Would _____

Definitely Would Not _____

Unsure

Dental hygiene

_____

_____

_____

Personal safety on campus

_____

_____

_____

Baby care

_____

_____

_____

Recycling

_____

_____

_____

Foreign trade with Chile

_____

_____

_____

_____

3. A large number of studies have addressed mail survey response rates. Have each student select a single incentive tactic such as an advance postcard or monetary incentive, and perform research on it, ideally, with some database search service maintained by your university library or otherwise available to your students. Instruct students to prepare a 3–5 minute summary class presentation on their topics.

4. Incidence rates can be illustrated with students’ families. With a show of hands, determine the percent of students whose parents now own each of the following: (1) a cat, (2) a Ford pickup truck, (3) a digital video camera, (4) a riding mower, and (5) an American Automobile Association membership. The percentages will be analogous to incidence rates.

5 . Ask students what creative approaches can be taken when a researcher requires a large amount of information from respondents. Suppose, for instance, that a researcher is investigating attitudes toward the preservation of natural habitats, and the survey includes a battery of lifestyle questions, many attitude questions on . 94

wildlife and habitat preservation, extensive demographics, plus many opinion questions on specific state parks, national parks, public lands, and recreational areas.

The questionnaire takes 60 minutes to complete when self-administered, and 1 hour and 30 minutes to complete when administered by an interviewer. What approaches can be used to overcome this amount of information difficulty?

Some suggestions that may arise from class discussion include: • • • • •

Divide the questions into three or four surveys and administer them at separate times to the same respondents Provide a very large incentive (say, a lottery of $500) Pay respondents for their time Send out a huge number of mail surveys and see what comes back Pay interviewers extra compensation for completing over a certain number

6. Describe the survey method selection phase of a recent survey with which you have been involved. Describe the survey’s circumstances, what modes were considered, and how the final selection was made.

7. Privacy issues continually challenge marketing researchers who conduct surveys.

Students protect their privacy just as do typical consumers. Ask students to identify the various ways they protect their telephone privacy (such as answering machines, caller identification, etc.), and for each one have them discuss the challenge(s) posed to marketing researchers. What can researchers do to entice consumers to take part in surveys? This topic can quickly degenerate to telemarketer bashing, so be prepared to divert the discussion toward what strategies ethical marketing researchers might consider in order to increase response rates.

Related to this topic is the National “Do Not Call” Registry (www.donotcall.gov).

Ask how many students are registered and whether or not they have noticed a decrease in telemarketing phone calls. Most students will mistakenly believe that marketing research calls are blocked by the Do Not Call system; however, they will be mistaken as bona fide marketing research companies are not blocked.

8. Computer-assisted interview and web-based formats are steadily advancing and evolving. There may be articles in the Marketing News, Quirk’s Marketing Research Review, or other marketing research practitioner literature that describe variations or the pros and cons of the various types. Alternatively, students can do a simple Internet search and find companies with these products and services. Most have extensive descriptions of their capabilities and many offer trial usage. Have students share their findings either with short presentations or in a discussion format.

9. In this edition, we did not include “traditional telephone interview” as this method is rarely used. If students inquire about the omission, this is the answer. Alternatively, you might ask them to add traditional telephone interviews. The key advantage is fast turnaround relative to face-to-face or mail alternatives, whereas the key disadvantage is control as interviewers cannot be monitored or supervised while working. A comment is that traditional telephone interviewing is an option when other telephone data collection methods are unavailable or too expensive.

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ANSWERS TO REVIEW QUESTIONS 1. What is a survey?

Surveys are interviews with a large number of respondents using a predesigned questionnaire.

2.

What factors are affecting how marketing researchers conduct surveys?

Surveys are being greatly affected by technological advances in communications and the communications preferences of consumers.

3. What are the four basic survey modes?

There are four major modes of collecting survey information from respondents: •

Person-administered surveys—have a person ask the questions, either face to face or voice to voice without any assistance from a computer.

•

Computer-assisted surveys—have a computer assist or direct the questioning in a face-to-face or voice-to-voice survey.

Self-administered surveys—allow respondents to fill out the questionnaire themselves, without computer assistance.

• •

Mixed-mode or “hybrid” surveys—some combination of two or more of the above three modes.

4. What are the advantages of person-administered surveys over computer-administered ones?

A person-administered survey is one in which an interviewer reads questions, either face to face or over the telephone, to the respondent and records his or her answers.

Its advantages are: feedback, rapport, quality control, and adaptability.

A computer-administered survey is one in which computer technology plays an essential role in the interview work. Here either the computer assists an interview or it interacts directly with the respondent. In the case of an Internet-based survey, the person-administered survey is advantageous with respect to feedback, rapport, and adaptability.

5. What is interview evaluation, and which survey mode is most likely to produce it?

The presence of another person may create apprehension, called “interview evaluation” among certain respondents. This is of greatest concern with a personadministered survey as there is an interviewer present.

6. Discuss the advantages and disadvantages of self-administered surveys.

A self-administered survey is one in which the respondent completes the survey on his or her own. Its advantages are reduced cost, respondent control, and no interviewer-evaluation apprehension. On the other hand, its disadvantages are (lack . 96

of) respondent control, lack of monitoring, and high questionnaire requirements.

7.

Discuss why a researcher would or would not use a mixed-mode survey. Give an example to illustrate your points.

Mixed-mode surveys, sometimes referred to as “hybrid” surveys, use multiple data collection methods. A researcher would use a mixed-mode survey approach to gain the benefits of the various survey methods. For example, a researcher would use an Internet-based survey to gain speedy access to respondents who have Internet connections while using a telephone survey to reach those who do not have Internet access. The first method would gain speed, while the second method gains coverage.

8. Indicate the differences among (a) in-home interviews, (b) mall-intercept interviews, and (c) in-office interviews. What do they share in common?

An in-home interview is conducted in the home of the respondent. The mallintercept interview is one in which the respondent is encountered and questioned while he or she is visiting a shopping mall. In-office interviews take place in person while the respondent is in his or her office, or perhaps in a company lounge area.

Thus, the major difference is the location of the interview. They all share the common denominator of a personal interviewer.

9. Why are telephone surveys popular?

Telephone interview are popular because practically everyone has a telephone, and this survey method is fast, relatively inexpensive, and has good quality control.

10. What controls are possible with central location telephone interviewing?

With central location telephone interviewing, a field data collection company installs several telephone lines at one location, and the interviewers make calls from the central location. With central location telephone interviewing, there are controls over recruitment and training of interviewers; interviews can be monitored and checked on the spot; and interviewers’ schedules can be controlled.

11. What does CATI stand for? What does CATS stand for?

Computer-Assisted Telephone Interviews, or CATI, is a case of computerized central location telephone interviewing. CATS, which stands for Completely Automated Telephone Survey is a totally computer administered survey but not completed online.

12. What advantages do online surveys have?

Internet-based online surveys are fast, easy, and questionnaires are fast, easy, and inexpensive. These questionnaires accommodate all of the standard question formats, and they are very flexible, including the ability to present pictures, diagrams, or displays to respondents.

13. What are the major disadvantages of a mail survey? . 97

The mail survey is plagued by two major problems. The first is nonresponse, which refers to questionnaires that are not returned. The second is self-selection bias, which means that those who do respond are probably different from those who do not fill out the questionnaire and return it and, therefore, the sample gained through this method is nonrepresentative of the general population. (Students may also point out that mail surveys are slower than most other survey methods.)

14. How does a drop-off survey differ from a mail survey?

With a drop-off survey, the representative approaches a prospective respondent, introduces the general purpose of the survey to the prospect, and leaves it with the respondent to fill out on his or her own. A mail survey is one in which the questions are mailed to prospective respondents who are asked to fill them out and return them to the researcher by mail. Thus, the major difference is how the prospective respondent is approached and introduced to the survey.

15. What are the major factors to be considered in the choice of the survey method?

The major factors are: • The amount of time for data collection • The amount of money for collection budget • Type of respondent interaction required • Other considerations such as the incidence rate 16. What is the “best” data collection method?

The “best” data collection method is determined by answering the question, “What data collection method will generate the most complete and generalizable information within the time horizon and without exceeding the allowable expenditure for data collection?”

ANSWERS TO APPLICATION QUESTIONS 17. Is a telephone interview inappropriate for a survey that has as one of its objectives a complete listing of all possible advertising media a person was exposed to in the last week? Why or why not?

This question can be answered by applying the survey time horizon and budget constraints. Phone survey can be executed very quickly and inexpensively, so there are no reasons to object to a telephone interview on these criteria. With regard to “type of respondent interaction required,” the question does not specify hard-to-reach individuals or complicated tasks. The questions are straightforward (“Did you read a newspaper in the past week?”). There are not many questions (newspapers, magazines, television, radio, billboards, and mail), and the questions are not sensitive.

So telephone interviewing is appropriate.

18. NAPA Car Parts is a retail chain that specializes in stocking and selling automobile parts. It is interested in learning about its customers, so 100 questionnaires are sent to each of the 2,000 store managers with instructions that they are to give them to “the next” 100 customers. The customer is instructed to answer the questions and to . 98

place the completed questionnaire in the mail in a self-addressed envelope. There is a $500 NAPA coupon random drawing for a lucky respondent who returns the completed survey. What data collection method is being used, and what are its pros and cons in this situation?

This is a variation of a “drop-off” survey research, but instead of dropping the questionnaire, the store manager hands it to customers during the checkout.

Consequently, instead of traveling to customers, the customers travel to the store and then encounter the survey.

A major “pro” is that this data collection approach is inexpensive; in addition there is the possibility of a personal appeal to the prospective respondent to take part in the survey. A “con” is that high nonresponse (and subsequent self-selection) is likely even with the $500 incentives because taking part in the survey is entirely voluntary and out of the control of the researcher.

19. Discuss the feasibility of each of the types of survey modes for each of the following cases: a. Fabergé Inc. wants to test a new fragrance called “Lime Brut.” b. Kelly Services needs to determine how many businesses expect to hire temporary secretaries to replace secretaries who go on vacation during the summer months. c. The Encyclopedia Britannica requires information on the degree to which mothers of elementary-school-aged children see encyclopedia DVDs as worthwhile purchases for their children. d. AT&T Wireless is considering a television screen wireless phone application and wants to know people’s reaction to it a. Lime Brut In-Home Interview—works but will be expensive Mall-Intercept Interview—works and reasonable in cost In-Office Interview—not appropriate for a consumer product test Central Location Telephone Interview—cannot use smell Computer-Assisted Telephone Interview—cannot use smell Fully Computerized Interview—difficult to use smell Online Questionnaire—cannot use smell Mail Survey—works but low response rate Group Self-Administered Survey—could work but administration difficult .

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Drop-Off Survey—works and reasonable in cost b. Kelly Services In-Home Interview—incidence rate too low to work Mall- Intercept Interview—incidence rate too low In-Office Interview—works but relatively expensive Central Location Telephone Interview— works and inexpensive Computer-Assisted Telephone Interview—works but not economical unless national survey Fully Computerized Interview—won’t work due to office “gatekeepers” Online Questionnaire—works if there is a list of individuals at businesses to contact to take part Mail Survey—works but response rate will be low Group Self-Administered Survey—could be used at several national business conventions Drop-Off Survey—works if target companies are in a concise geographic area such as an industrial park or large office building c. Encyclopedia Britannica In-Home Interview—appropriate assuming mothers need to see examples of Britannica materials Mall-Intercept Interview—appropriate and less costly than in-home In-Office Interview— inappropriate as will miss “at-home” mothers Central Location Telephone Interview—works unless examples and materials must be viewed by respondent Computer-Assisted Telephone Interview—works unless examples and materials must be viewed by respondent Fully Computerized Interview—works unless examples and materials must be viewed by respondent Online Questionnaire—works best with an online panel, but could work if there was a list of qualified respondents to contact via e-mail . 100

Mail Survey—could work with proper incentives to increase response rate Group Self-Administered Survey—could work at PTA or PTO meetings Drop-Off Survey—could work but in a limited geographical area d. AT&T In-Home Interview—appropriate as system would need to be viewed Mall-Intercept Interview— appropriate assuming mall intercept company has space to set up equipment and allow respondents to view and/or use it In-Office Interview— inappropriate due to set-up time unless target market was executives who might want visual conference call services Central Location Telephone Interview— would work only as a “concept” test Computer-Assisted Telephone Interview— would work only as a “concept” test Fully Computerized Interview— would work only as a “concept” test Online Questionnaire— would work as graphics and even video can be embedded in an online survey. Also, AT&T has a very diverse market, so the wide scope of online surveys is appropriate Mail Survey would work only as a “concept” test but subject to nonresponse Group Self-Administered Survey—works if the system can be demonstrated in front of a large group of people Drop-Off Survey—too limited in scope, might work as a very limited “concept” test 20. With a telephone survey, when a potential respondent refuses to take part or is found to have changed his or her telephone number or moved away, it is customary to simply try another prospect until a completion is secured. It is not standard practice to report the number of refusals or noncontacts. What are the implications of this policy for the reporting of nonresponse?

The two concepts involved are nonqualifiers and nonresponse. With a changed or nolonger-working phone number, it is a case of nonqualification, meaning that the potential respondent cannot be asked to take part in the survey. With low incidence in the general population, many calls must be made to secure one response; however, this situation is not nonresponse because the nonqualifiers are no longer members of the target group. If the potential respondent has changed numbers or moved, it is appropriate to replace that person with someone who is assumed to be identical.

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Nonresponse is a refusal to take part, and it is related to self-selection that has caused the composition of the respondents to differ from that of the nonrespondents, as was described in the discussion on mail surveys. So, self-selection bias can certainly result in a phone survey.

Severe self-selection degrades generalizability greatly. So a judgment must be made concerning the extent that refusals reflect incidence rate and normal declining to participate versus how much it involved self-selection bias. In the former case, the policy of not reporting nonresponse does little harm, although in the latter case it masks a flaw in the research.

21. Compu-Ask Corporation has developed a stand-alone computerized interview system that can be adapted to almost any type of survey. It can fit on a palm-sized computer, and the respondent directly answers questions using a stylus once the interviewer has started up the program. Indicate the appropriateness of this interviewing system in each of the following cases: a. A survey of plant managers concerning a new type of hazardous-waste disposal system Appropriate—it will save time, keep the managers’ attention, and videos, pictures, diagrams, and tables could be used. b. A survey of high school teachers to see if they are interested in a company’s DVDs of educational public broadcast television programs Appropriate—the videos could be previewed on the computer screen, so this approach could be effective. c. A survey of consumers to determine their reactions to a nonrefrigerated variety of yogurt Inappropriate—the survey would probably require a taste test and Even if did not, the Compu-Ask approach would be overkill. Traditional methods such as mall-intercept are satisfactory.

22. A researcher is pondering what survey mode to use for a client who markets a home security system for apartment dwellers. The system comprises sensors that are pressed onto all of the windows and magnetic strips that are glued to each door.

Once plugged into an electric socket and activated with a switch box, the system emits a loud alarm and simulates a barking guard dog when an intruder trips one of the sensors. The client wants to know how many apartment dwellers in the United States are aware of the system, what they think of it, and how likely they are to buy it in the coming year. Which consideration factors are positive and which ones are negative for each of the following survey modes: (a) in-home interview, (b) mallintercept survey, (c) online survey, (d) drop-off survey, and (e) CATI survey?

Survey data collection time horizon—assuming it is short, it discourages in-home interviews, but mall intercepts, online survey, drop-off survey, and telephone survey are reasonable.

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Survey data collection budget—assuming it is limited, it discourages in-home interviews and perhaps mall intercepts, although online survey, drop-off survey, and telephone survey are candidates.

Type of respondent interaction required—the concept is easily understood with verbal description, so this factor would not enter into the decision.

Other considerations: Incidence rate—the incidence of apartment dwellers needing security systems is low, so in-home interviews and mall intercepts would be inefficient. Because the company wants to generalize to the entire United States, drop-offs to apartment buildings would be difficult nationwide. The online survey will handle low incidence well as respondents self-select, and the CATI survey would be a “brute strength” method making many, many calls to overcome the low incidence, however it would be expensive.

CASE SOLUTIONS Case 6.1 Steward Research Inc. Case Objective: This case requires students to consider the circumstances and unique aspects of three different research situations and recommend appropriate survey data collection methods.

Answers to Case Questions 1. For each of the three clients, suggest one or more data collection methods that would be appropriate.

2. For each data collection method you select in question 1, discuss the rationale for your choice.

3. What disadvantages are inherent in the data collection methods you have recommended?

Answers are provided in the tables below.

Client 1: High Precision Drill Bits Recommended Method Rationale In-office interview Must interview to obtain reactions of industrial buyers Client 2: Regional Bakery TOMA Study Recommended Method Rationale Mall-intercept Quick, easy, and accesses a large portion of the market (cookie and crackers buyers) CATI Fast, accesses large numbers of . 103

Disadvantage(s) High cost

Disadvantage(s) Will miss those who do not frequent malls If the bakery’s market region is small, CATI

Online questionnaire

respondents; appropriate for simple questions like TOMA Fast, accesses large numbers of respondents

Client 3: Sanitized Toothbrush Concept Test Recommended Method Rationale Mall-intercept Quick, easy, and accesses a large portion of the market (people who use toothbrushes) Central telephone interview Quick, easy, relatively low cost

will be relatively expensive If Internet penetration in the region is low, this will miss many customers.

If the bakery’s market region is small, CATI will be relatively expensive

Disadvantage(s) Will miss those who do not frequent malls (but the client does not need high representativeness) Respondents may not comprehend the concept over the phone

Case 6.2 Integrated Case: Advanced Automobile Concepts Case Objective: Students must assess the appropriateness of various data collection methods with the case of Advanced Automobile Concepts. The survey should yield a respondent profile that reflects the demographic and automobile ownership profile of the American public.

Answers to Case Questions 1. If a mail survey was used, what would be the pros, cons, and special considerations associated with achieving the overriding objective of the survey?

Mail surveys are inexpensive and there are nationally representative mailing lists, so a great many could be sent out to obtain a national sample. The slowness of this method would not be a detriment as there is no requirement to do the survey quickly. The major drawback would be nonresponse and consequent self-selection that would render the final sample unrepresentative of the American public.

2. There are many telephone data collection companies that offer national coverage.

Some have centralized telephone interview facilities and some offer CATI services. If a telephone survey were used employing one of these companies, what would be the pros, cons, and special considerations associated with achieving the overriding objective of the survey?

This would be a quick and reasonably priced alternative. There would be controls in place to guarantee that the survey was conducted properly. Unfortunately, telephone surveys suffer from high nonresponse, so unless there were very special controls,

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such as quotas in place the final sample would not be representative of the American public.

3. The following data collection methods are not likely to achieve the overriding objective. For each one, indicate why not. a. Drop-off survey—this is a “local” survey method. It is impossible to do this nationally. b. Group-administered survey—again, this is a local method. It would be impossible to find and go to groups all over the United States. c. Mall-intercept survey—this is a better method, but it would take a large number of malls to represent the entire United States.

4. Compare the use of an in-home method to the use of an online method for the Advanced Automobile Concepts survey. What are the relevant pros and cons of each?

Indicate which one you would recommend and why.

In home interviews would be slow and expensive. It would require sending personal interviewers to randomly selected homes all over the United States or hiring them in their various locales to perform a single in-home interview there because no locale would have more than one respondent household.

The online survey would be fast and much less expensive. With a national e-mail list, it could be broadcast to many thousands of potential respondents almost instantaneously. The actual data collection cost would be minimal as it would all take place electronically. There would be a concern about self-selection and response bias, but with a very large number of respondents, they could cull out the “bad” respondents. With census data, they could select a representative subsample from the large number of respondents.

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CHAPTER 7 Measurement Scales LEARNING OBJECTIVES •

To examine question formats commonly used in marketing research

•

To understand the basic concepts in measurement

•

To learn about three levels of measurement used by marketing researchers

•

To appreciate why the type of measurement scale is important

•

To become familiar with scale types commonly used in marketing research

•

To illustrate common measures of constructs used in marketing research

CHAPTER OUTLINE Question-Response Format Options Open-Ended Response Format Questions Categorical Response Format Questions Metric Response Format Questions Basic Measurement Concepts Open-Ended Measurement Categorical Measurement Metric Measurement Why the Level of a Scale Is Important Commonly Used Synthetic Metric Scales Symmetric Synthetic Scales Nonsymmetric Synthetic Scales Whether to Use a Symmetric or a Nonsymmetric Scale Choosing Which Scale to Use .

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KEY TERMS Aided open-ended format

Objective properties

Anchored n-point scale

One-way labeled scale

Categorical measure

Open-ended response format

Categorical response format

Operational definition

“Check all that apply” question

Properties

Dual choice question

Reliable scale

Halo effect

Response probe

Level of measurement

Scale development

Lifestyle

Semantic differential scale

Likert scale

Stapel scale

Measurement

Subjective properties

Metric measure

Synthetic label metric scale

Metric response question

Synthetic metric format

Multiple choice category question

Synthetic number metric scale

Natural metric response format

Unaided open-ended format

Neutral point

Unanchored n-point scale

N-point scale

Valid scale

TEACHING SUGGESTIONS 1. Here are some principles that one of the authors uses to introduce the notion of information contained in scales in his class.

•

The Information Principle ❧ The easier and faster you can give an answer, ❧ the less information you will provide

•

The Simplicity Principle ❧ The more simple the answer, ❧ the less information in the answer (e.g. a check mark versus a number) .

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•

The Reporting Principle ❧ The less information in the answers, ❧ the less you (researcher) can tell the client about them

•

The Four Scale Properties Principle ❧ A scale may have from one to four properties, and these properties are… ➘ Labels—words or symbols that designate scale differences (e.g. male vs. female, 3 gallons, etc.) ➘ Order—relative size differences between the labels ➘ Distance—equal distances between adjacent labels ➘ Origin—a true zero point (vs. a neutral answer) ❧ Scales with fewer properties have less information ➘ One property— LEAST information While… ❧ Scales with more properties have more information ➘ Four properties—MOST information

2. Measurement of a construct requires an operational definition. Instructors may want to review this concept, which is taken up in this chapter and elaborate on how an operational definition indicates how a construct’s subjective and/or objective properties are measured on a scale. For instance, “purchase intention” may be operationally defined as a respondent’s indication on a 7-point scale where 1 corresponds to “not at all likely” and 7 pertains to “very likely” to purchase the brand at his/her next purchase.

3. Scale development in an academic context is much more rigorous than in an applied context. If an instructor desires to bring this difference to students’ attention, Churchill’s scale development paradigm (Churchill [1979], “A Paradigm for Developing Better Measures of Marketing Constructs,” Journal of Marketing Research, 16, 64–73) is a good vehicle. A slightly different approach would be to relate the scale development steps reported in a recent scale development article.

4. To help students understand the idea of measurable qualities, have them generate as many different rating aspects of one or more of the following: • • •

A convenience store A department store A new automobile

Use class discussion to point out how customers may harbor positive ratings on some qualities, but negative ratings on others, and how this information has important marketing implications.

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5. Numerical versus letter grades can be used as an example of how higher-level scaling assumptions (metric) provide more information than lower-level ones (categories).

For instance, take numerical grades. Suppose one student has an 89 average, and another student has an 81 average. Assuming that 80to 89 defines the “B” letter grade range, both are B students, but the 89 student is closer to an A, although the 81 student is closer to a C. This closeness is not contained in the categorical letter grade system, but it is in the metric grading system.

Class discussion can focus on whether it is better to have a ratio scale (1–100 scale) or an ordinal (A–F scale) as a way to measure the goodness of students. How would students feel about a categorical pass-or-fail system? Hopefully, students will desire a more sensitive scale rather than a less sensitive one. The point is that with a low level scale, there is little information about the performance of the individual student.

6. It is useful to remind students of the inability of observation and most other qualitative methods to look into the minds of those people being studied. The reason for scaling stems from a need to standardize respondents’ answers into a consistent format that can be used to summarize and compare their answers.

An in-class exercise can be used to illustrate the need for and benefits of using standardized scales. Select some subject with some controversial or emotional aspect to it. Possible college examples include: dropping football from the athletic program to save money, requiring freshmen and sophomores to live on campus, creating a walking campus with no student or faculty vehicles allowed, or not awarding a diploma unless the graduating student signs an agreement to contribute 1 percent of their income to the alumni fund. Have students write down how they “feel” about the issue. Gather up the written comments and assign the task of compiling the reactions to a team of three or four students. Once done, have other students observe how the team goes about the compilation task.

Class discussion should bring out the fact that the qualitative information required judgments along the way by the compilation team. Next, have students rate their feelings on the controversial topic using a 7-point negative-to-positive scale. Have a student team compile these responses with the rest of the class as observers. It should be apparent that the use of a rating scale greatly simplifies the judgments and compilation activities.

7. Lifestyles and their measurement is a good research topic. Assign the topic to a student or a student team to perform library research on it and report findings to the class. You may want to expand the assignment to include VALS. Sending students to the SRI VALS home page at www.strategicbusinessinsights.com/vals is always a fun experience for students. With a multimedia teaching room, instructors can visit and demonstrate VALS in class.

8. It is sometimes possible to demonstrate the halo effect to students by having them inspect the questionnaires from a survey that used a semantic differential or similar scale. Sometimes a respondent will place check marks all on one side, the other, or down the middle of the scale. Alternatively, students can see that a respondent has used the scale correctly when the check marks are consistent with the reverse positions of the scale items.

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9. Some sources use a “target shooting” analogy to teach students about reliability and validity. Picture a bull’s eye target with several bullet holes clustered very close together, but off the center of the target. This is reliability: the rifle was pointed at the center, but bullets were consistently off-center and in one spot. When the rifle sights are aligned so that the bullet hits dead center when the rifle is aimed there, it is valid. If the holes are consistently in the center area, the sight is valid and reliable.

10. If students need help or are confused about measurement of constructs and properties, consider using a matrix approach with the constructs listed in the first column. Have students volunteer what they think characterizes each consumer type. The cell descriptions that eventuate are the properties that distinguish the various types of consumers. (Feel free to use you own consumer types.)

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Construct BMW Owner

Consumer Type Saturn Owner Used Car Buyer

Age Education level Income level Family life cycle stage Favorite leisure activity Price sensitivity 11. If an instructor has foreign students who are willing to participate, it might be enlightening for other students to hear about difficulties the foreign students have encountered in trying to understand English idioms or word combinations. Another tact is to have a foreign graduate student show the difficulties he or she encounters when attempting to translate questions into his or her language. With the latter suggestion, it is a good idea to “prep” or otherwise clue the student in on the learning objective of this exercise in order to maximize its chances of succeeding.

ANSWERS TO REVIEW QUESTIONS 1. List each of the three basic question-response formats. Indicate the two variations for each one, and provide an example for each.

The answers are contained in the following table.

Format type Open-ended response format Categorical response format

Metric response format

Variation(s) Unaided

Example What Ford Explorer ads do you recall seeing or hearing in the past week?

Aided Do you recall any other television ads for Ford Explorer?

Dual choice Which one do you prefer? ___ Lease your car ___ Purchase your car Multiple choice What other automobile manufacturers did you consider when you were shopping for your new automobile? (Check all that apply.) ____ Chevrolet ____ Dodge ____ Toyota ____ Honda (other makes can be listed) Natural metric For how many weeks did you shop for your new automobile? ____ weeks Synthetic How satisfied are you with the service department metric at your local Ford dealership? ___ Very dissatisfied ___ Dissatisfied ___ Satisfied ___ Very satisfied

2. What is measurement? In your answer, differentiate an object from its properties, .

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both objective and subjective.

Measurement is defined as determining the description or amount of some element of interest to the researcher. One measures the “properties,” or specific features or characteristics of an object that can be used to distinguish it from another object.

Objective properties are physically verifiable characteristics such as age, gender, number of bottles purchased, store last visited, and so on, while subjective properties cannot be directly observed because they are mental constructs such as a person’s attitudes, opinions, or intentions.

3. Discuss the meaning of an operational definition. How does a researcher use an operational definition? Provide an example of the operational definition for each of the following: When a researcher specifies the procedure to measure a property of an object, the procedure is referred to as an operational definition. a. Likelihood of buying car X Likelihood can be a probability or the changes of doing something in the future. “How likely are you to buy Car X in the next year?

0%

10%

20%

30%

40%

50%

60% 70%

80%

90% 100%

b. Media habits such as reading the newspaper Depending on the objectives of the research, the operational definition might be what section of the paper is read.

“Which of the following sections of the local newspaper do you typically read? (Check all that apply.) ___ Local news ___ National news ___ World news ___ Sports ___ Entertainment (and so forth) c. Attitude toward global warming Attitudes are feelings or opinions that vary in intensity or by the degree to which a person agrees or subscribes to a belief.

Please indicate the degree to which you agree with each of the following statements:

Statement I am worried about global warming.

We should use alternative fuels to lessen global warming. . 114

Strongly Disagree

Disagree

Neither Disagree nor Agree

Agree

Strongly Agree

4. How does reliability differ from validity? In your answer, define each term.

A reliable scale is one in which a respondent responds in the same or in a very similar manner to an identical or nearly identical question. A valid scale, on the other hand, is one that truly measures the construct under study.

5. What is meant by the “level of measurement”? Identify and define the two levels of measurement described in this chapter.

When a researcher specifies an operational definition for the measurement of a property of a construct, he/she explicitly identifies the response scale’s level of measurement, meaning that the researcher has decided whether the scale is to be open-ended, categorical, or metric.

6. Distinguish a synthetic number scale from a synthetic label scale. What do these two types of scales have in common, and how do they differ?

A synthetic number metric scale is one where the researcher uses a number range such as 1–5, 1–7, 1–10, and so forth. A synthetic number only has meaning in the context of the scale from which it originates. A synthetic label metric scale such as a rating scale of “poor,” “fair,” “good,” “very good,” or “excellent” uses words to indicate different gradations or levels of the respondent’s opinion 7. Answer the following question: “Why is it important for a researcher to know the level of a scale that he or she uses to measure a property of a construct of interest?”

The choice of the level of measurement for a scale affects which analyses should or should not be performed. Refer to Table 7.3 for details.

8. Distinguish a symmetric synthetic scale from a nonsymmetric synthetic scale. Provide an example of each of these types of scales.

A symmetric scale has equal amounts of positive and negative positions. However, a nonsymmetric scale typically begins at the lowest positive position and extends to the highest positive position, plus it does not include the neutral, or no opinion, position on the scale.

9. Explain what is meant by a continuum along which a subjective property of an object can be measured.

Many scales are designed to measure psychological properties that exist on a continuum ranging from one extreme to another in the mind of the respondent. Refer to Table 7.3 for a visual aid on the continuum concept.

10. What are the arguments for and against the inclusion of a neutral response position in a symmetric scale?

A neutral point is not considered zero or an origin; instead, it is considered a midpoint along the continuum with numbers or labels that are artificially assigned at various levels on the continuum. If respondents are truly ambivalent, include the . 115

neutral position, but if they are prone to not giving their opinions by using the neutral option, you should not use it.

11. Distinguish among a modified Likert scale, a semantic differential scale, and a Stapel scale.

The modified Likert scale uses degrees of agreement and disagreement (strongly disagree—strongly agree). The semantic differential scale uses bipolar adjectives the anchor the scale, while a Stapel scale uses a plus-and-minus number system for the ratings.

12. What is the halo effect, and when and how does a researcher control for it?

The halo effect is a general feeling about a store or brand that can bias a respondent’s impression on its specific properties because the respondent does not read the specific rating characteristics carefully and lets the general feeling dominate all of the specific characteristic ratings. Randomly flipping favorable and negative ends of the descriptors in minimizes the halo effect.

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13. What consideration should be foremost in a researcher’s decision to use a symmetric synthetic rating scale versus a nonsymmetric one?

Ideally, when a synthetic scale is used in a survey, the researcher wants respondents to use all of the scale positions, meaning that for any one question, the responses should be spread across all of the scale positions. If the researcher believes there will be very few respondents who will make use of the negative side of a symmetric scale, the researcher should opt for a nonsymmetric scale.

14. Distinguish among a one-way labeled scale and an n-point scale.

A one-way labeled scale is one where the researcher is measuring some construct attribute with the use of labels that restrict the measure to the “positive” side. The n-point scale uses numbers such as a 5-point, 7-point, or 10-point scale instead of labels.

15. What is an “anchor” and how does an anchor give a context to an n-point scale?

The anchored n-point scale is one where a word is associated with the “1” and the “n” points on the scale. The anchors are important as they tell the respondent the context of the scale; that is, they indicate how to translate the range of the scale into a frame of reference to which the respondent can relate.

ANSWERS TO APPLICATIONS QUESTIONS 16. Mike, the owner of Mike's Market, which is a convenience store, is concerned about low sales. He reads in a marketing textbook that the image of a store often has an impact on its ability to attract its target market. He contacts the All-Right Research Company and commissions you, a marketing researcher, to conduct a study that will shape his store’s image. You are charged with the responsibility of developing the store-image part of the questionnaire.

Design a semantic differential scale that will measure the relevant aspects of the market’s image. In your work on this scale, you must do the following: (a) brainstorm ten convenience store properties to be measured, (b) determine the appropriate bipolar adjectives, (c) decide on the number of scale points, and (d) indicate how the scale controls for the halo effect. a. Possible store properties— location, checkout speed, value for the price, parking, friendliness, specials, etc. (Students should brainstorm at least ten properties) b. The bipolar adjectives should appear thusly—inconvenient location— convenient location, slow checkout—fast checkout, etc. c. Usually, 5- or 7-scale points are customary. If the respondents are unfamiliar with Mike’s Market, there definitely should be a neutral midpoint. d. Reversing some of the bipolar adjectives controls for the halo effect. .

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17. Each of the following examples involves a market researcher’s need to measure some construct. Devise an appropriate scale for each one. Defend the scale in terms of its level of measurement and use or nonuse of a “no opinion” or neutral response category. a. Mattel wants to know how preschool children react to a sing-along video game in which the child must sing along with an animated character and guess the next word in the song at various points in the video.

Preschool children cannot read, so a graphic rating scale is required.

Typically, no neutral position is provided on a graphic rating scale—the children will like the video game to some degree. b. TCBY is testing five new flavors of yogurt and wants to know how its customers rate each one on sweetness, flavor strength, and richness of taste.

TCBY needs evaluations on three subjective properties based on taste tests of each yogurt flavor. Considerations include the following: There is no need for a symmetric scale as it is very unlikely that the flavors would be sour, weak, or not rich. It is also doubtful that tasters will have a “no opinion” reaction to the flavors. Also, how much can a person discriminate in reporting a taste?

Not much, so the scale should be no more than 5 gradations with labels because people do not judge flavors with numbers.

Here is an example of the sweetness scale: Not Sweet

Somewhat Sweet

Sweet

Very Sweet

c. A pharmaceutical company wants to find out how much a new federal law eliminating dispensing of free sample prescription drugs by doctors will affect their intentions to prescribe generic versus brand-name drugs for their patients.

This is an instance of measuring intentions so a 0 percent to 100 percent intentions scale will fit. Eleven categories (0%, 10%, etc.) can be used as doctors can be expected to understand likelihoods of acting.

The modified Likert scale is also acceptable, for example: Statement I will prescribe generic drugs.

(Insert Disagree-Agree labels here) 1 2 3 4 5

A no opinion is also appropriate because doctors may not have decided what they are going to do yet. Face validity of the statement is apparent.

18. Harley-Davidson is the largest American motorcycle manufacturer, and it has been in business for several decades. Harley-Davidson has expanded into “signature” products such as shirts that prominently display the Harley-Davidson logo. Some people have a negative image of Harley-Davidson because it was the motorcycle favored by the Hell’s Angels and other motorcycle gangs. There are two research questions here. First, do consumers have a negative feeling toward Harley-Davidson, . 118

and, second, are they disinclined toward the purchase of Harley-Davidson signature products such as shirts, belts, boots, jackets, sweatshirts, lighters, and key chains?

Design a Likert measurement scale that can he used in a nationwide telephone study to address these two issues.

The task is for students to design a modified Likert scale amenable to telephone interview administration. The exercise is intended for students to realize the requirements of “describing” the scale to respondents over the telephone. Here is one approach.

(INTERVIEWER: READ) “I have a list of statements that I will read to you. As I read each one, please indicate whether you agree or disagree with it.”

Are the instructions clear? (IF NOT, REPEAT) INTERVIEWER: READ EACH STATEMENT. WITH EACH RESPONSE, ASK, “Would you say that you STRONGLY (dis)agree or SOMEWHAT (dis)agree?”

RESPONSE (CIRCLE) STATEMENT

SOMESTRONGLY WHAT AGREE AGREE

SOMEWHAT NEUTRAL DISAGREE

STRONGLY DISAGREE

_________________________________________________________________ a. (Statement goes here) 1 2 3 4 5 A different approach is to read off all the response alternatives to the respondent. It would look something like this: (INTERVIEWER: READ) “I have some statements that I will read to you. With each one, please tell me if you strongly agree, somewhat agree, somewhat disagree, strongly disagree, or you may neither agree nor disagree. Are these instructions clear?” (IF NOT, REPEAT)

19. Family Dollar store believes it has a niche market, and that its image in the minds of its customers is the following: a self-service store with bargain prices and no sales hassles but with reasonably fast checkout and for household and clothing items that are functional. Construct a Stapel scale approach that would measure Family Dollar’s image to determine the extent to which customers have an opinion consistent with Family Dollar’s belief.

The Stapel scale is has a +/- number format. Here is an example of how this scale can be used to measure Family Dollar’s image.

Indicate your opinion of Family Dollar store by circling the appropriate number. A high positive number means that Family Dollar performs well on the feature, while a high negative number means that Family Dollar performs poorly on the feature.

Bargains Checkout speed +3 +3 +2 +2 +1 +1 . 119

Sales hassles +3 +2 +1

Functional merchandise +3 +2 +1

0 -1 -2 -3

0 -1 -2 -3

0 -1 -2 -3

0 -1 -2 -3

20. Pick any fast food chain (McDonald’s, KFC, etc.) and construct a scale that measures the performance of that chain’s units. Use a one-way labeled scale that measures the chain on at least five performance attributes that you think are relevant.

Be sure to include any instructions for the respondents.

A handy one-way labeled scale for performance on attributes is poor, fair, good, very good, and excellent. The scale would appear as follows: Please rate (insert fast-food chain name here) by circling the appropriate word for each characteristic.

Characteristic A

Poor

Fair

Your Rating Good Very Good

B

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Excellent

(Repeat labels for each characteristic, or use labels as headings and have respondents circle the precode numbers [1, 2, 3, 4, 5] underneath the labels.)

21. Check in your library’s catalog for one of the handbooks mentioned in this chapter on marketing scales. Find a scale, and describe the construct the scale measures and show the scale items used to measure the construct. Also describe any measures of the reliability and validity of the scale as reported in the handbook.

The handbooks are listed in Marketing Research Application 7.1. Instructors should check with their librarians to see if these resources are available. A back-up is to use Gordon Bruner’s website (http://scaleresearch.siuc.edu/handbook.html) where some scales are identified. It may be necessary to provide students with more background on reliability and validity of scales as these handbooks report these issues with advanced statistical techniques and terminology.

CASE SOLUTIONS Case 7.1 Extreme Exposure Rock Climbing Center Faces the Krag Case Objective: This case requires students to identify constructs in research objectives and to create measurement scales for these constructs.

Answers to Case Questions Objective #1: What is the demographic and rock climbing profile of Extreme Exposure’s members?

Construct(s): Demographic characteristics of consumers and rock-climbing behavior.

For the demographics, students should apply the commonly used ones such as: age, education, marital status, income before taxes, dwelling type, etc.

For rock-climbing behavior, students will need to refer to the four rock-climbing types: top-roping, lead-climbing, bouldering, and rappelling. This construct can be measured simply by a nominal scale (What is your favorite type of rock-climbing?) or more sensitively with a ratio scale (Indicate for each of the four types of rock climbing what percent of the time you engage in that type.)

Objective #2: How satisfied are the members with Extreme Exposure’s climbing facilities?

Construct: Satisfaction.

Satisfaction is one of the constructs described in Table 7.5.

Because Extreme Exposure has several aspects, satisfaction must be measured for each aspect. Here is an example scale based on the recommendation in Table 7.5.

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Indicate for each feature of Extreme Exposure, how satisfied you are with its performance by circling the appropriate number.

Feature

Extremely Unsatisfied

Somewhat Unsatisfied

Neither Unsatisfied or Satisfied

Somewhat Satisfied

Extremely Satisfied

Location Hours of operation Prices Vertical climbs Number of climbing routes Difficulty of climbing routes Etc.

1 1 1 1 1

2 2 2 2 2

3 3 3 3 3

4 4 4 4 4

5 5 5 5 5

1

2

3

4

5

1

2

3

4

5

Objective #3: How interested are its members in (a) day trips to outdoor rock climbing areas, (b) group overnight and/or extended-stay rock climbing trips to the Canadian Rockies, and (c) a rock climber certification program?

Construct: Interest level Interest can be measured on an interest continuum. The following example is a labeled scale, but students can use a synthetic number scale such as 1 = Uninterested, 5 = Very interested.

How interested are you in each of the following rock climbing activities. Circle the number corresponding to your interest level for each one.

Feature Day trips to outdoor rock climbing areas

No interest 1

Little interest 2

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Some interest 3

Moderate Much interest interest 4 5

Group overnight and/or extended-stay rock climbing trips to the Canadian Rockies A rock climber certification program

1

2

3

4

5

1

2

3

4

5

Objective #4: What are members’ opinions of the annual membership fee charged by Extreme Exposure?

Construct: Attitude toward price; opinion of the price A modified Likert scale would work well here.

Statement The annual membership fee for Extreme Exposure is reasonable.

Strongly disagree

Disagree

1

2

Neither disagree or agree 3

Agree

Strongly agree

4

5

Objective #5: Will members consider leaving Extreme Exposure to join a new rock climbing center with climbs that are 10 feet higher than the maximum climb at Extreme Exposure?

Construct: Intentions to purchase Intentions to purchase is one of the constructs in Table 7.5, so students can use this recommendation.

If another rock climbing center opens in Sacramento with climbs that are 10 feet higher than the highest one at Extreme Exposure, how likely are you to consider joining the new center? ___ Very Unlikely ___ Somewhat Unlikely ___ Neither Unlikely nor Likely ___ Somewhat Likely ___ Very Likely Objective #6: Will members consider leaving Extreme Exposure to join a new rock climbing center with climbs that are 10 feet higher than the maximum climb at Extreme Exposure and whose annual membership fee is 20% lower than Extreme Exposure's?

Construct: Intentions to purchase The question could immediately follow the one about 10 feet higher walls.

If you found that there was a new center with climbs 10 feet higher than the highest one at Extreme Exposure charged an annual fee 20 percent lower than Extreme . 123

Exposure’s annual fee, how likely are you to consider joining the new center? ___ Very Unlikely ___ Somewhat Unlikely ___ Neither Unlikely nor Likely ___ Somewhat Likely ___ Very Likely Case 7.2 Integrated Case: Advanced Automobile Concepts Case Objective: This case requires students to develop/recommend scales to measure important constructs identified in the integrated case. A reasonable scale is identified for each of the constructs.

Answers to Case Questions 1. Current vehicle ownership (size and type of vehicle) What is your primary vehicle size?

No vehicle, economy, standard, luxury What is your primary vehicle type?

No vehicle, car, pick-up truck, SUV or van 2. Beliefs about global warming and the effects of the use of gasoline on global warming I am worried about global warming.

Global warming is a real threat.

We need to do something to slow global warming.

Gasoline emissions contribute to global warming.

Response scale: Very strongly disagree, Strongly disagree, Disagree, Neither disagree nor agree, Agree, Strongly agree, Very strongly agree 3. Beliefs about gasoline price levels and trends Gasoline prices will remain high in the future.

Gasoline prices are too high now.

High gasoline prices will impact what type of autos are purchased.

Response scale: Very strongly disagree, Strongly disagree, Disagree, Neither disagree nor agree, Agree, Strongly Agree, Very strongly agree 4. Opinions as to the impact of alternative fuel automobiles (hybrids, synthetic fuels, electric, etc.) on global warming Very small autos with very high mpg’s will reduce fuel emissions.

Very small autos with very high mpg’s will keep gas prices stable.

Very small autos with very high mpg’s will slow down global warming. .

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Small autos with high mpg’s will reduce fuel emissions.

Small autos with high mpg’s will keep gas prices stable.

Small autos with high mpg’s will slow down global warming.

Hybrid autos that use alternative fuels will reduce fuel emissions.

Hybrid autos that use alternative fuels will keep gas prices down.

Hybrid autos that use alternative fuels will slow down global warming.

Response scale: Very strongly disagree, Strongly disagree, Disagree, Neither disagree nor agree, Agree, Strongly agree, Very strongly agree 5. Intentions to buy an alternative fuel automobile How likely are you to buy a very small (one seat) hybrid auto within 3 years How likely are you to buy a small (two seat) hybrid auto within 3 years How likely are you to buy a standard size hybrid auto within 3 years How likely are you to buy a standard size synthetic fuel auto within 3 years How likely are you to buy a standard size electric auto within 3 years Response scale: Extremely unlikely, Very unlikely, Unlikely, Neutral, Likely, Very likely, Extremely likely 6. Preferences for various sizes of alternative fuel automobiles (mini, economy twodoor, economy four-door, standard) How desirable is a Super Cycle one seat hybrid?

How desirable is a Runabout Sport two seat hybrid?

How desirable is a Runabout with Luggage two seat hybrid?

How desirable is an economy four seat hybrid?

How desirable is a standard four seat hybrid?

Response scale: Very undesirable, Undesirable, Somewhat desirable, Neutral, Somewhat desirable, Desirable, Very desirable 7. Preferred television show type (e.g., drama, mini-series, sports), magazine type (e.g., business and finance, family living, travel), radio music genre (e.g., rock, jazz, easy listening), and newspaper section (e.g., local news, sports, editorial) What is your favorite television show type?

Comedy, Drama, Movies/Mini-Series, News/Documentary, Reality, Science-Fiction, Sports What is your favorite radio genre?

Classic Pop and Rock, Country, Easy Listening, Jazz and Blues, Pop and Chart, Talk What is your favorite magazine type?

Business and Money, Music and Entertainment, Family and Parenting, Sports and Outdoors, Home and Garden, Cooking, Food, and Wine, Trucks, Cars and Motorcycles, News, Politics, and Current Events . 125

What is your favorite local newspaper section?

Editorial, Business, Local news, National news, Sports, Entertainment

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CHAPTER 8 Designing Data Collection Forms LEARNING OBJECTIVES •

To gain an overview of the questionnaire design process

•

To learn the four “do’s” of question wording

•

To learn the four “don’ts” of question wording

•

To understand the basics of questionnaire organization

•

To understand the advantages of computer-assisted questionnaire design software

•

To learn how to precode questionnaires

•

To know what to consider to properly pretest a questionnaire

CHAPTER OUTLINE Types of Data Collection Forms Survey Questionnaires and Observation Forms The Functions of a Questionnaire The Questionnaire Design Process Developing Questions Four “Do’s” of Question Wording The Question Should Focus on One Topic The Question Should Be Brief The Question Should Be a Grammatically Simple Sentence If Possible The Question Should Be Crystal Clear .

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Four “Don’ts” of Question Wording Do Not “Lead” the Respondent to a Particular Answer Do Not Have “Loaded” Wording or Phrasing Do Not Use a “Double-Barreled” Question Do Not Use Words to Overstate the Condition Questionnaire Organization The Introduction Question Flow Computer-Assisted Questionnaire Design Questionnaire Creation Data Collection and Creation of Data Files Data Analysis and Graphs Coding the Questionnaire Performing the Pretest of the Questionnaire

KEY TERMS Anonymity

Pretest

Classification questions

Question bias

Computer-assisted questionnaire design

Question development

Confidentiality

Question evaluation

Cover letter

Question flow

Disguised survey

Questionnaire

Double-barreled question

Questionnaire design

Face validity

Questionnaire organization

Incentives

Screening questions

Leading question

Skip question .

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Loaded question

Transitions

Overstated question

Undisguised survey Warm-up questions

TEACHING SUGGESTIONS 1. Examples of good (and bad) questionnaires are helpful teaching aids because students are generally unfamiliar with questionnaires. So, examining actual questionnaires or parts of questionnaires is a useful way for students to become familiar with this critical marketing research tool. Create multimedia presentation files, overhead transparencies, or make copies of questionnaires that you have developed, or ones developed by students in previous classes and go over the opening comments, format, question flow, response scales, and logic.

2. Contrasting a telephone questionnaire to one that is self-administered will emphasize how telephone administration constrains the choice of scales and further requires the interviewer to describe the scale to the respondent. With self-administered questionnaires, the format and instructions are facilitated by the visual context.

Divide the class into two teams and have one team design a telephone questionnaire, while the other designs a mail questionnaire. The topic might be to determine the satisfaction level of car buyers with the automobile’s style, handling, fuel efficiency, and passenger compartment roominess. The need to tailor the questions to the form of data collection can be emphasized by comparing the two questionnaires.

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3. Invariably, some students will misspell “questionnaire” by omitting one of the n’s.

Write “questionnnnnnnnaire” on the chalk board, or on a presentation slide to drive the point home.

4. The four do’s and four do not’s described in the chapter may not be easy for students to remember. A divide-and-conquer strategy is to assign each student one “do” or a “do not” with instructions to formulate a question that illustrates each the point quite well. Students can share the questions they formulate in class. You might have a contest by class vote as to which one is the best (easiest to relate to, or remember) for each of the eight items. Prepare a list of the winners for distribution the next time you teach the class.

5. Questionnaire design is a learned skill, and, unfortunately, the learning is gradual because each questionnaire design problem is unique. The more questionnaire design practice that instructors demand of their students, the better. Case 8.2 requires students to design a questionnaire.

6. Cover letters are becoming increasing personalized and sophisticated. Some instructors maintain a file folder of cover letters that are good examples and share them with students.

7. Computer-assisted questionnaire design programs are advancing rapidly, and there are a number of computer-assisted questionnaire design programs on the market.

Many of these companies have websites, and some allow downloading of evaluation copies of their products. One approach is to have students, or student teams review and demonstrate different computer-assisted questionnaire design software.

ANSWERS TO REVIEW QUESTIONS 1. What is a questionnaire and what are the functions of a questionnaire?

A questionnaire is the vehicle used to pose the questions that the researcher desires respondents to answer. The functions of a questionnaire are as follows: • • • • •

It translates the research objectives into specific questions that are asked of the respondents.

It standardizes those questions and the response categories so every participant responds to identical stimuli.

By its wording, question flow, and appearance, it fosters cooperation and keeps respondents motivated throughout the interview.

Questionnaires serve as permanent records of the research.

Finally, depending on the type of questionnaire used, a questionnaire can speed up the process of data analysis.

2. Distinguish between questionnaires and observation forms.

When we conduct surveys, the form is referred to as a questionnaire because the purpose is to ask questions of respondents. In observation studies, the form is referred to as an observation form. . 130

3. What is meant by the statement that questionnaire design is a systematic process?

Questionnaire design is a systematic process in which the researcher contemplates various question formats, considers a number of factors characterizing the survey at hand, ultimately words the various questions very carefully, and organizes the questionnaire’s layout. This process is outlined in Figure 8.1 4. What is meant by question bias? Write two biased questions using some of the words to avoid described in Table 8.1. Rewrite each question without using the problem word.

Question bias is defined as the ability of a question’s wording or format to influence respondents’ answers. There are 10 “bad” words described in Table 8.1 (All, Always, Any, Anybody, Best, Ever, Every, Most, Never, and Worst). These words are identified as “bad” because they are “absolute extremes” that require respondents to either completely agree or completed disagree with the statement.

Here are possible examples: Bad wording: Do you always read the newspaper in the morning?

Good wording: Do you typically read the newspaper in the morning?

Bad wording: Did you ever consider that your automobile insurance might be inadequate?

Good wording: Do you sometimes worry that your automobile insurance might not be enough coverage?

5. What are the four guidelines or “do’s” for question wording?

The question should be: (1) focused, (2) simple, (3) brief, and (4) crystal clear.

6. What are the four “don’ts” for question wording? Describe each.

Do not lead the respondent to a particular answer Do not have loaded wording or phrasing Do not use a double-barreled question.

Do not use words that overstate the condition.

A leading question is worded or structured in such a way as to give the respondent a strong cue or expectation as how to answer.

A loaded question has buried in its wording elements that allude to universal beliefs or rules of behavior.

A double-barreled question is really two different questions posed in one question.

An overstated question is one that places undue emphasizes on some aspect of the topic.

7. What is the purpose of a questionnaire introduction, and what things should it accomplish?

The functions of the questionnaire introduction are contained in Table 8.2. They are: • •

Identifies the surveyor/sponsor Indicates the purpose of the survey . 131

• • •

Explains how the respondent was selected Requests for/provides incentive for participation Determines if respondent is suitable

8. Distinguish anonymity from confidentiality.

Anonymity is where the respondent is assured that neither the respondent’s name nor any identifying designation will be associated with his or her responses.

Confidentiality means that the respondent’s name is known by the researcher, but it is not divulged to a third party, namely, the client.

9. Indicate the functions of (a) screening questions, (b) warm-ups, (c) transitions, (d) skip questions, and (e) classification questions.

These question types are identified and described with examples in Table 8.3. Their descriptions are listed below.

Type Screens Warm-ups Transitions (statements and questions)

Description First questions asked Immediately after any screens Prior to major sections of questions or changes in question format

Complicated and difficult-to-answer questions Classification and demographic questions

Middle of the questionnaire; close to the end Last section

10. List at least three features of computer-assisted questionnaire design programs that are more advantageous to a questionnaire designer than the use of a word processor program.

There are at least four distinct advantages of computer-assisted questionnaire design software packages—they are easier, faster, friendlier, and provide significant flexibility beyond that available with a traditional word processor.

11. What is coding and why is it used? Relate the special coding needed with “all that apply” questions.

Coding is the use of numbers associated with question response options to facilitate data analysis after the survey has been conducted.

A check all that apply question is just a special case of a “yes” or “no” question, so use a 1 = yes and 0 = no coding system. You will want to consider each concept being tested as a separate question.

12. What is the purpose of a pretest of the questionnaire and how does a researcher go about conducting a pretest?

A pretest involves conducting a dry run of the survey on a small, representative set of .

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respondents in order to reveal questionnaire errors before the survey is launched.

Before the questions are administered, participants are informed of the pretest, and their cooperation is requested in spotting words, phrases, instructions, question flow, or other aspects of the questionnaire that appear confusing, difficult to understand, or otherwise a problem. Normally, from 5 to 10 respondents are involved in a pretest, and the researcher looks for common problem themes across this group.

ANSWERS TO APPLICATION QUESTIONS 13. The Marketing Club at your university is thinking about undertaking a moneymaking project. All coeds will be invited to compete, and twelve will be selected to be in the “Girls of (insert your school) University” calendar. All photographs will be taken by a professional photographer and tastefully done. Some club members are concerned about the reactions of other students who might think that the calendar will degrade women. Taking each of the “don’ts” of question wording, write a question that would tend to bias answers such that the responses would tend to support the view that the calendar is degrading. Indicate how the question is in error, and provide a version that is in better form.

Do not lead the respondent to a particular answer Question: Should a conservative school such as ours allow a “Girls of ...” calendar project?

Error: Leads the respondent to give the conservative answer of “no.”

Do not have loaded wording or phrasing Question: Should our university allow the exploitation of women with a “Girls of…”calendar?

Error: The wording “exploitation of women” is loaded Do not use double-barreled questions Question: Would you be in favor if the Marketing Club donated the profits to charity and the University Administration sanctioned the project?

Error: Two questions are being asked: (1) donation to charity, and (2) Administration sanction Do not use words that overstate the condition Question: If only beautiful models were used, would you be in favor of the calendar?

Error: The terms beautiful and models are overstatements.

14. Using the Internet, find a downloadable trial version of a computer-assisted questionnaire design program and familiarize yourself with it. With each of the following possible features of computer-assisted questionnaire design programs, briefly relate the specifics on how the program you have chosen provides the feature. a. b. c. d. e. f.

Question type options Question library Font and appearance Web uploading (sometimes called “publishing”) Analysis, including graphics Download file format options . 133

Note: Some questionnaire design companies have moved to Internet-based systems that require no or minimal downloading of program files, so students may simply use “trial” or “examination” user privileges. The specific questionnaire design software found by the student will determine the answers to these features. Instructors should consider a show-and-tell format wherein a student or student team demonstrates the program in class using a multimedia platform and/or Internet access.

15. Panther Martin invents and markets various types of fishing lures. In an effort to survey the reactions of potential buyers, the company hires a research firm to intercept fishermen at boat launches, secure their cooperation to use a Panther Martin lure under development sometime during their fishing trip that day, meet them when they return, and verbally administer questions to them. As an incentive, each respondent will receive three lures to try that day, and five more will be given to each fisherman who answers the questions at the end of the fishing trip.

What opening comments should be verbalized when approaching fishermen who are launching their boats? Draft a script to be used when asking these fishermen to take part in the survey.

Students should apply the guidelines pertaining to introductions listed and described in Table 8.2. The five guidelines are: • • • • •

Identifies the surveyor/sponsor Indicates the purpose of the survey Explains how the respondent was selected Requests for/provides incentive for participation Determines if respondent is suitable

There is no need to use disguise; in fact, use of Panther Martin’s name may increase cooperation as it is well known and respected by fishermen. Anonymity and confidentiality are unnecessary as well.

Here is a sample script.

Hello, I’m ____________, and I’m doing a survey to test a new fishing lure developed by Panther Martin. We’re selecting fishermen at random to ask if they will give this lure a try. I would like to give you three lures to try today, and I’ll give you five more when you come in and answer some questions about your reactions.

There is no need to ask if the respondent is suitable as the interviewer can assess this by observation.

CASE SOLUTIONS Case 8.1 The Steakstop Restaurant: What Is Wrong with These Questions?

Case Objective: Students must identify and correct wording errors in questions on the questionnaire. . 134

Answers to Case Questions Carefully go over each question and identify what type of error was made. Then, correct the error using the “do’s” and “don’ts” of question wording.

Following are the errors and corrections provided by the case writer, Professor Tulay Girard.

1. “What meal did you choose? If steak, press 1. If seafood, press 2. If chicken, press 3.

If sandwich, press 4.”

Error: Vague word choice Correction: What meal did you order?

2. “Did you order one of our fantastic appetizers that everyone is raving about? If yes, press 1. If no, press 2.”

Error: Overstated question Correction: Did you order an appetizer?

2a. “How would you rate the quality of our appetizers? If good, press 1. If very good, press 2. If excellent, press 3. If exceptional, press 4.”

Error: Unsymmetrical and biased scale Correction: If very poor, press 1, if poor, press 2, if good, press 3, if very good, press 4.

3. “Were you pleased with the exceptional taste of your meal? If yes, press 1. If no, press 2.”

Error: Leading question Correction: Were you pleased with the taste of your meal?

4. “Please rate your hot and tasty meal based on your satisfaction. If good, press 1. If very good, press 2. If excellent, press 3. If exceptional, press 4.

Error: Double-barreled and leading question, and bad scale Correction: How satisfied were you with your meal? If very unsatisfied, press 1, if unsatisfied, press 2, if satisfied, press 3, if very satisfied, press 4.”

5. “All of our health-conscious customers consider the portion size of our meals to be ideal. Do you? If yes, press 1. If no, press 2.”

Error: Loaded question Correction: Was the portion size of your meal acceptable?

6. “Was your server attentive and responsive to your needs? If yes, press 1. If no, press 2.”

Error: double-barreled question; Correction: Split into two questions: Was your server attentive to your needs? Was your server responsive to your needs? . 135

7. “What does our competitor, Beef-O-Rama, charge for a complete meal? If under $15, press 1. If between $15 and $20, press 2. If over $20, press 3.”

Error: Vague question. It is hard for the respondent to answer this question because a complete meal can consist of a single or multiple menu items. It also assumes that the respondent has eaten at Beef-O-Rama.

Correction: Delete this question. The price information exists (secondary data) and can be obtained from viewing the menu of Beef-O-Rama (exploratory research).

Case 8.2 Integrated Case: Advanced Automobile Concepts Case Objective: Students must design a questionnaire that addresses the objectives of the integrated case.

Answers to Case Questions Go over the needed integrated case facts and information imparted to you in previous chapters and design an online survey questionnaire for Advanced Automotive Concepts.

There are a great many ways that the questionnaire can be designed; however, the important features are: • • • • • •

Qualifying question Warm-up questions that are related to research objectives Transitions Use of interval scales wherever possible and appropriate Use of sections approach to keep related topics together Demographics at the end

Following is a sample questionnaire developed for Advanced Automobile Concepts integrated case. This set of questions and scales is, in fact, the one that was used for the integrated case SPSS data set that will be referred to in the analysis chapters and that students will use for Advanced Automobile Concepts case questions at the end of the analysis chapters. Note: Alert students may catch the comment in an earlier integrated case that the online panel company being used has a database of demographic, possessions, media usage, and other variable on every panel member. So, these questions may not appear on their questionnaires.

This survey is being conducted for an automobile manufacturer seeking to develop new models of automobiles using synthetic or alternative fuel sources. It studies your opinions about global warming and gasoline usage as well as your reactions to some possible new automobile models.

To begin, tell us a little about yourself.

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1. What is your favorite television show type? (Select only one.)

Comedy Drama Movies/Mini-Series News/Documentary Reality Science-Fiction Sports 2. What is your favorite radio genre? (Select only one.)

Classic Pop & Rock Country Easy listening Jazz & Blues Pop & Chart Talk 3. What is your favorite magazine type? (Select only one.)

Business & Money Music & Entertainment Family & Parenting Sports & Outdoors Home & Garden Cooking/Food & Wine Trucks/Cars & Motorcycles News/Politics & Current Events 4. What is your favorite local newspaper section? (Select only one.)

Editorial Business Local news National news Sports Entertainment Next, how do you feel about global warming, gasoline usage, and gasoline prices?

Indicate your degree of agreement or disagreement to each of the following statements.

(Note: The scale for each question is: Very strongly disagree, Strongly disagree, Disagree, Neither disagree nor agree, Agree, Strongly Agree, Very strongly agree.)

1. I am worried about global warming.

2. Global warming is a real threat.

3. We need to do something to slow global warming.

4. Gasoline emissions contribute to global warming.

5. Americans use too much gasoline.

6. We should be looking for gasoline substitutes.

7. Gasoline prices will remain high in the future.

8. Gasoline prices are too high now.

9. High gasoline prices will impact what type of autos are purchased.

10. Very small autos with very high mpg’s will reduce fuel emissions.

11. Very small autos with very high mpg’s will keep gas prices stable.

12. Very small autos with very high mpg’s will slow down global warming.

13. Small autos with high mpg’s will reduce fuel emissions.

14. Small autos with high mpg’s will keep gas prices stable.

15. Small autos with high mpg’s will slow down global warming.

16. Hybrid autos that use alternative fuels will reduce fuel emissions.

17. Hybrid autos that use alternative fuels will keep gas prices down.

18. Hybrid autos that use alternative fuels will slow down global warming.

Now, indicate on a scale of 0 to 100 percent, the probability of you making each of the following automobile purchases in the next three years.

1.

2.

3.

4.

5.

Probability of buying a very small (one-seater) hybrid auto Probability of buying a small (two-seater) hybrid auto Probability of buying a standard size hybrid auto Probability of buying a standard size synthetic fuel auto Probability of buying a standard size electric auto .

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How do you feel about some possible new types of hybrid automobiles?

Indicate the how desirable each of the following hybrid automobile models is to you, personally. Use this scale: Very undesirable, Undesirable, Somewhat desirable, Neutral, Somewhat desirable, Desirable, Very desirable 1.

2.

3.

4.

5.

Super cycle one-seater; 120+ mpg city Runabout Sport two-seater; 90 mpg city, 80 mpg highway Runabout with Luggage two-seater; 80 mpg city, 70 mpg highway Economy four-seater; 70 mpg city; 60 mpg highway Standard four-seater; 60 mpg city; 50 mpg highway

Below are descriptions of various types of people. For each description, indicate how well it describes you on a scale where 1 = does not describe me at all and 7= describes me perfectly.

1. Novelist—very early adopter, risk taker, “way out,” “show off,” want to be unique and extraordinary 2. Innovator—early adopter, less risk taker than novelist, but into new technology; likes new products, but not “show offs” 3. Trendsetter—opinion leaders, well off financially and educationally, often the first adopters of new trends that are adopted by most of society.

4. Forerunner—early majority of population, respected and fairly well off; not opinion leaders, but adopt new products before the “average” person 5. Mainstreamer—late majority of population, “average people,” who are reserved and deliberate 6. Classic—laggards who cling to “old” ways Finally, the following questions will be used for classification purposes.

1. What is the size of your home town or city?

1 million, or more 500,000 to 1 million 10,000 to 100,000 Under 10,000

100,000 to 500,000

2. What is your gender?

Male Female 3. What is your marital status?

Unmarried Married 4. What is the number of people in household? _____________ 5. What is your age category?

Less than 18 18 to 24 50 to 64 65 and older

25 to 34 No response

35 to 49

6. What is your highest level of education?

Less than High School High School diploma College Degree Post graduate degree

Some college

7. Which of the following job categories best describes you?

Management Sales Service Agriculture Craft .

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General labor

Retired

8. What is your household income level?

Under $25,000 $25,000 to 49,000 $75,000 to 125,000 $125,000 and more 9. What is your primary dwelling type?

Single family Multiple family

$50,000 to 74,000

Condominium/Townhouse

10. What is your primary vehicle price type?

No vehicle Economy Standard 11. What is your primary vehicle type?

No vehicle Car Pick-up truck

Mobile home

Luxury SUV or van

12. Which of the following best describes your typical type of commute to work?

Single occupancy Multiple occupancy Public transportation Non-motorized Telecommute Thank you for participating in this survey.

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CHAPTER 9 Determining Sample Size and the Sample Plan LEARNING OBJECTIVES •

To become familiar with the basic concepts involved in sampling

•

To learn how to calculate the minimum sample size needed to achieve a predetermined level of accuracy

•

To understand the difference between “probability” and “nonprobability” sampling plans

•

To become acquainted with the specifics of four probability and four nonprobability sampling plans

CHAPTER OUTLINE Basic Concepts in Samples and Sampling Determining Size of a Sample The Accuracy of a Sample Formula to Determine Sample Accuracy How to Calculate Sample Size When Estimating a Percentage Variability: p times q Level of Confidence: z Desired Accuracy: e Explaining the Logic of Our Sample Size Formula How to Calculate Sample Size When Estimating a Mean The Effects of Incidence Rate and Nonresponse on Sample Size Using the XL Data Analyst to Calculate Sample Size How to Select a Representative Sample . 141

Probability Sampling Methods Simple Random Sampling Using the XL Data Analyst to Generate Random Numbers Systematic Sampling Cluster Sampling Stratified Sampling Nonprobability Sampling Methods Convenience Samples Judgment Samples Referral Samples Quota Samples Online Sampling Techniques KEY TERMS Accuracy of a sample

Proportionate sample size

Area sampling

Proportionate stratified sample

Census

Quota sample

Cluster sampling

Random digit dialing

Completed interviews

Random numbers technique

Completion rate

Random online intercept sampling

Confidence interval formula for sample size

Random sample Random starting point

Convenience sample Referral sample Demographic incidence Sample Desired accuracy Sample frame Disproportionate sample size Sample frame error .

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Geographic incidence

Sampling error

Incidence

Simple random sampling

Invitation online sampling

Skip interval

Judgment sample

Stratified sampling

Level of confidence

Surrogate measure

Nonprobability sampling method

Systematic sampling

One-step area sample

Table of random numbers

Online panel sampling

Two-step area sample

Population

Variability

Probability sample methods

Weighted average

Product incidence

Working phone rate

TEACHING SUGGESTIONS 1. National opinion polls tend to be around 1,000–1,200 in sample size, and this can be easily verified. Have students do background research on opinion polls (perhaps there is a political campaign underway) and bring their findings to class on the sample size and reported errors. The findings illustrate that these companies are using the sample size for a percentage formula with p and q equal to 50 percent and with a 95 percent level of confidence.

2. Figure 10.1 illustrates visually how large sample sizes fail to add to the accuracy of a survey. Because the graph is difficult to read with precision, consider using the following table to illustrate to students how little additional accuracy is gained with increases in the sample size, especially with large samples.

Sample Size 100 250 500 750 1,000 1,250 1,500 1,750 2,000

95% Accuracy ±9.8% ±6.2% ±4.4% ±3.6% ±3.1% ±2.8% ±2.5% ±2.3% ±2.2% .

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Accuracy Increase 3.6% 1.8% 0.8% 0.5% 0.3% 0.3% 0.2% 0.1%

3. The confidence interval approach to sample size involves several statistical concepts.

Although they are described in the chapter, it is necessary to review them thoroughly in class. Students may memorize the sample size determination formula, but it is important that they comprehend the factors in the formulas, namely, (1) the amount of variability believed to be in the population, (2) the desired accuracy, and (3) the level of confidence required in estimates of the population values.

4. Students do not easily grasp the concept of a sampling distribution. If you have a class of, say, 30 students, you can pair them up and simulate 15 different samples.

Have each pair give its average age (other variables might be: hours carried this term, hours worked per week, minutes to commute to campus), and plot all of the sample means in a distribution. If you select a variable where students are quite similar (e.g., age), the sampling distribution will be quite compact, but if you use a variable where students are dissimilar (e.g. distance of home town away from campus), the sampling distribution will be much less compact.

5. Use the 95 percent level of confidence in sample size determination examples. The 1.96 z value is about 2, and students have an easier time following the squaring computations than they do with the 2.58 z value for the 99 percent level of confidence.

6. The sample size determination function in the XL Data Analyst provides sensitivity analysis tables where the estimated “p” and the allowable error are varied slightly.

This feature is provided so users do not need to run the analysis several times. It shows how sensitive sample size is to variations in p of ±5% and ±10% and variations in the allowable error of ±.5% and ±1%. An extension of this feature is to add a cost factor (such as x dollars per respondent) and to show how the sensitivity estimates affect the cost of the sample.

7. The equiprobable aspects of a random sample can be demonstrated in a number of different ways. Here are two examples: Some Internet sites are devoted to lotteries and gambling. Although we do not have a specific one in mind, you might consider assigning a student or a team of students to search out one or more that lists the lottery numbers selected over time, perhaps for your state if you have a state lottery. Have students calculate the percent of times each number was drawn and comment on whether or not they have found the random selection process used to embody equal probability in all cases.

Put 3 x 5 index cards with students’ names in a hat or a box and have a series of actual blind draw samples. Replace the drawn names to the population pool after each sample. Maintain a record of how often each student’s name is selected.

8. The greater efficiency of systematic sampling over simple random sampling can be demonstrated with a class exercise. Identify two groups of students and give each a copy of the same page from the telephone book. Tell the first group to select 10 household names using a table of random numbers or random numbers generated via a spreadsheet program such as Microsoft Excel (simple random sampling), and tell the second group to select 10 names using systematic sampling. The second group should finish before the first group. . 144

9. A class exercise that illustrates basic differences between various sample methods is to use a data set with 10 or fewer variables listed in labeled columns and containing 200 or more respondents (an Excel or other spreadsheet printout will work). Break the class into several teams and give specific instructions for each team to draw 30 respondents and calculate the percentage distribution or mean of the variables. Each team should be assigned a different sampling method (convenience, simple random sampling, etc.). Have students time their teams or raise their hands when they have completed the work. Student teams using nonprobability sampling (except quota) will finish first, while teams using the more tedious probability methods such as stratified simple random sampling will finish last. However, nonprobability sampling method teams will have findings different from the true percentage distributions and means, although probability sampling method teams will have findings close to the actuals.

(Note: Instructors will have to provide a table or list of random numbers to teams needing them. Such tables can be generated by the random numbers feature of the XL Data Analyst.)

10. When learning about the skip interval used in systematic sampling, students sometimes ask how to determine the population size when a directory or phone book is used. In the absence of a precise number, the size is usually estimated by multiplying the approximate number of names on each page by the number of pages.

Some adjustments may need to be made for multiple listings such as children’s phone, discontinued phones, or areas of “white space” in the directory.

11. Some students may have difficulty understanding the weighted average calculations in stratified sampling. It may be necessary to illustrate how the mean changes with different stratum configurations. Here are some comparisons than can be used to demonstrate the effects of three different weighted average situations.

Stratum Mean A B 5 8

Estimated Population Mean 50%/50% 40%/60% 10%/90% 6.5 6.8 7.7

12. Students should come to realize that the success of quota sampling is greatly dependent on a prior knowledge of the population’s characteristics. One way to facilitate this understanding is to ask students what quota characteristics should be used in the following two cases.

Case One. Kellogg’s wants to know the reactions of parents to a new children’s cereal called, “Cheery-Os.” Case Two. Proctor & Gamble wants the reactions of potential buyers of its new hair rinse called, “Gentle Care.”

With case one, the quota characteristics would be: (1) parents (percent female versus male), (2) marital status (percent married versus separated), and (3) age of youngest child (percent 4, 5, 6, etc.). With case two, however, the target market is not identified well other than it is implicitly made up of women.

13. Instructors can use the random numbers function of the XL Data Analyst to illustrate the nature of random numbers. Use it to generate a large number of random numbers, . 145

then use Excel average and standard deviation functions to compute these values of each row and each column.

In theory, the average of any random number table row or any column should be approximately equal to the average of any other row or column. The standard deviations should be approximately equal as well.

14. When students work with a familiar population, they are better able to apply sample methods. Ask how the full-time students in your university would be sampled using each sample method described in the chapter. For example, where would they station interviewers for a convenience sample? How would they set up clusters or strata using student characteristics?

15. Here is a table the summarizes the differences between probability sampling methods and nonprobability sampling methods and may be useful as a means of helping students understand differences between these two types of sampling methods.

Probability Sampling Known chance of selection Takes more time Higher cost Can compute sample error

Nonprobability Sampling Unknown chance of selection Takes less time Lower cost Cannot compute sample error

ANSWERS TO REVIEW QUESTIONS 1. Define each of the following: a. Population The population is the entire group under study as specified by the research project. b. Sample A sample is a subset of the population that should represent that entire group. c. Census A census is defined as an accounting of everyone in the population. d. Sample Frame A sample frame is some master list of all the members of the population.

2. Indicate the sample frame error typically found in the households listing of a telephone book.

Sample frame error is the degree of misrepresentation of the population in the sample frame (directory or listing) being used. For the telephone directory, error is in the forms of: unlisted numbers, new numbers obtained after publication of the directory, numbers that are no longer valid (disconnections, moves, equipment . 146

failure).

3.

Explain why the Literary Digest survey of the Landon/Roosevelt election was so incorrect.

The Literary Digest had used an unusually large sample, but the sampling frame was not a list of the potential voters from both parties. Those who could afford a magazine subscription, telephone, or automobile were much more likely to be Republican than Democrat.

4. What is meant by accuracy of a sample and what is the formula to measure it?

The amount of sample error due to the size of the sample is referred to as accuracy of a sample.

The formula is:

5. What is the formula for estimating a percentage in the population? Explain what z, p, q, and e represent. n = Z2 (p q) e2 Where

n

= the calculated sample size

z

= standard error associated with the chosen level of confidence (typically, 1.96)

p

= estimated percentage in the population

q

= (100% − p)

e

= acceptable error (desired accuracy level)

6. What does p, q measure and what relationship does this have to the size of the sample, given all else is equal?

The percentage, p, will be the sample finding which is an estimate of the true population value. If our survey respondents have very little variability, it will be reflected in the sample size formula as 90% (p) times 10% (q), or 900. However, if there is great variability, meaning that no two respondents agree and we have a 50%/50% split, p times q becomes 50% times 50%, or 2,500, which is the largest possible p times q number possible. . 147

7. What do we mean by “level of confidence” and how is it expressed in the sample size formula?

We need to decide on a level of confidence, and it is customary among marketing researchers to use the 95 percent level of confidence, in which the z is 1.96. If a researcher prefers to use the 99 percent level of confidence, the corresponding z is 2.58. We use the phrase “level of confidence” because it refers to how confident we are that the sample finding will repeat itself if we conducted a different survey tomorrow or again the next day.

8. What is the formula for estimating a mean in the population? Explain how its components are different/like the components in the formula for estimating a percentage.

Where

n = the sample size z = standard error associated with the chosen level of confidence (typically, 1.96) s = variability indicated by an estimated standard deviation e = the amount of precision or allowable error in the sample estimate of the population

The formula and its application differ in two key ways from our percentages formula we have used thus far. First, instead of using p and q to express variability we use the standard deviation (s). Second, allowable error (e), is expressed in terms of the units being estimated instead of a percentage.

9. What are three methods for estimating the variance in the population using the formula to calculate the sample size for estimating a mean?

There are three ways we can estimate variability, s, in the population: a previous study; a pilot study; or estimate the range and divide by 6.

10. Explain how you would use XL Data Analyst to calculate the desired sample size to estimate a percentage in the population.

Use the menu sequence Calculate—Sample Size, which opens up the selection window where you can specify the allowable error (desired sample accuracy) and the estimated percent, p, value. The output includes tables that a researcher can use to inspect the sensitivity of the sample size to slight variations of e (with estimated p constant), ranging by .5% increments, or variations in the estimated p (with e constant), ranging by 5% increments.

11. How do probability samples differ from nonprobability samples and what is the .

148

significance of this difference?

Sample methods that embody random sampling are termed probability sampling methods because the chance of selection can be expressed as a probability. With probability sampling, all members of the population have a chance of being included in the sample and it is this characteristic that makes probability samples representative. With a nonprobability sampling method, all members of the population do not have a chance of being selected into the sample. Thus, nonprobability samples are not truly representative of the population.

12. What is simple random sampling and when would you use it?

With simple random sampling, the probability of being selected into the sample is “known” and equal for all members of the population. This sampling technique is expressed by the following formula: Probability of selection = sample size/population size. It is useful when you either have a small population or a large population contained in an electronic database such as a spreadsheet program.

13. What is systematic sampling and when would you use it?

Systematic sampling is a way to select a simple random sample from a directory or list that is much more efficient (uses less effort) than with simple random sampling, because with a physical list, the researcher must first enumerate (assign a unique identifier) each listing in order to select them using a random number. To ensure that every member of the population has a chance of being included in the sample drawn, it is important to begin at a random starting point in the list. Then, in order to create this “systematic skip” through the list, the researcher calculates a skip interval using the following formula: Skip interval = population list size/sample size.

14. What is cluster sampling and when would you use it?

In cluster sampling, the population is divided into subgroups, called “clusters,” each of which may represent the entire population. An application is area sampling where the researcher subdivides the population to be surveyed into geographic areas, such as census tracts, cities, neighborhoods, or any other convenient and identifiable geographic designation.

15. What is stratified sampling and when would you use it?

Stratified sampling separates the population into different subgroups, or strata, and then samples all of these subgroups using a random sampling plan. It is used when working with populations that contain unique subgroupings, each responding to the research question differently.

16. How would you use XL Data Analyst to generate random numbers?

The menu sequence is “Calculate—Random #’s,” and the selection window allows you to specify how many random integer numbers you want (up to 9,999), and you can also specify the largest possible value (up to 999,999,999).

17. Explain how you would use the skip interval in a systematic sample if you had to .

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draw a sample from your city telephone directory.

First, to begin at a random starting point, select a page of the directory using a table of random numbers. If there are X columns on each page, select a random number from 1 to X. And, after taking a sample of a few columns, you conclude there are about ## listings per column; so select a random number between 1 and ##. Now, make a ruler that, on average is the length of the listings making your skip interval, 100 listings. Beginning at the starting point, record the number of the listing and then rule off the next 100 listings and select the next number and continue using the ruler to “skip” systematically through the directory.

18. Would you use a proportionate or disproportionate stratified sample? Why? A proportionate sample size would occur if we allocated sample size based upon each stratum’s proportionate share of the total population. A disproportionate sample is any other allocation which would occur if we based our sample size per stratum not on its proportionate share of the population but on its variance as per our sample size formula. You should use a disproportionate sample size allocation in order to achieve the best statistical efficiency possible by using a stratified sample plan.

19. Describe the four types of nonprobability samples.

Refer to Table 9.1.

Convenience Sampling—the researcher selects respondents who are convenient. For example, the college professor surveys his/her class to learn about the attitudes of college students. The TV reporter interviews people at a busy intersection to learn how people in the city feel about an important upcoming event for the city. Sample selection error occurs in the form of the absence of members of the population who are not in the class or who are infrequent or nonusers of that location.

Judgment Sampling—the researcher uses his or her judgment or that of some other knowledgeable person to identify who will be in the sample. For example, the professor selects several different classes in different colleges to interview. The TV reporter interviews some people shopping downtown and some people who are shopping at a rural shopping mall. Subjectivity enters in here, and perhaps the judgment includes more members of the population than a convenience sample, still certain members of the population will not have a probability of being selected into the sample.

Referral Sampling—respondents are asked for the names, or identities of others like themselves who might qualify to take part in the survey. Members of the population who are less well known, disliked, or whose opinions conflict with the respondent have a low probability of being selected into a referral sample.

Quota Sampling—the researcher identifies quota characteristics such as demographic or product-use factors and uses these to set up quotas for each class of respondent. The sizes of the quotas are determined by the researcher’s belief about the relative size of each class of respondent in the population. Often quota sampling is used as a means of ensuring that convenience samples will have the desired . 150

proportions of different respondent classes, thereby reducing the sample selection error but not eliminating it. Again, there will be members of the population who have no probability of ending up in the sample.

20. What are the drawbacks of using a nonprobability sample and why are they used?

Nonprobability samples result in samples that are not representative of some larger population and when we use them, we cannot predetermine the accuracy of our findings because the sample size formulas are only used with representative samples.

However, they are used for the following reasons: • Quick • Easy to administer • Less costly than probability samples • Managers do not always require perfectly representative samples to make decisions 21. Briefly explain the three techniques presented in the chapter for online sampling.

The three techniques are: • Random online intercept sampling that selects website visitors on a random basis • Invitation online sampling where a sample is selected from a list of customers and an invitation to take part in the survey is emailed to those selected • Online panel sampling where a random sample of panel members is used

ANSWERS TO APPLICATION QUESTIONS 22. Here are four populations and a potential sample frame for each one. With each pair, identify (1) members of the population who are not in the sample frame, and (2) sample frame items that are not part of the population. Also, for each one, would you judge the amount of sample frame error to be acceptable or unacceptable?

Population a. Buyers of Scope mouthwash

Sample Frame Mailing list of Consumer Reports subscribers

Omitted are Scope buyers who do not subscribe to Consumer Reports, a very considerable number, no doubt. Included are all those who do subscribe but do not buy Scope, a very considerable percentage. The sample frame error is unacceptable as much of the population is not included, and those members included will have low incidence in the list. b. Listeners of a particular FM radio classical music station

Telephone directory in your city

Omitted are unlisted and new telephone listings, while included are those with listed phones that do not listen to FM music on the radio. The incidence rate will be low, but the sample frame is acceptable unless there is some peculiarity about listeners who have unlisted phones. . 151

c. Prospective buyers of a new day planner and prospective clients tracking kit

Members of Sales and Marketing Executives International (a national organization of sales managers)

Omitted are sales persons who do not belong to SMEI, probably a very considerable proportion of all salespeople in the area. Included are retired salespeople, sales managers who no longer do direct sales, and members who have moved away if the list has not been purged recently. The frame error is acceptable for a pilot study, but it does not include a large proportion of the target market. d. Users of weatherproof decking materials (to build outdoor decks)

Individuals’ names registered at a recent home and garden show

Omitted are all those who did not attend the show plus those who did attend but did not register. Included are people who have decks, people who do not want a deck, and people who cannot have a deck (e.g., may live in rented home). Assuming the show was well attended, the sample frame error is acceptable.

23. Here are some numbers that you can use to sharpen your computational skills for sample size determination. Crest toothpaste is reviewing plans for its annual survey of toothpaste purchasers. With each case that follows, calculate the sample size pertaining to the key variable under consideration. Where information is missing, provide reasonable assumptions. You can check your computations by using the sample size calculation feature of the XL Data Analyst.

Key Variable

Variability 23% share

Acceptable Error 4%

Confidence Level 95%

Market share of Crest toothpaste last year Percentage of people who brush their teeth per week How likely Crest buyers are to switch brands Percentage of people who want tartar-control features in their toothpaste Willingness of people to adopt the toothpaste

Unknown

5%

99%

30% switched last year 20% two years ago; 40% one year ago Unknown

5%

95%

3.5%

95%

6%

99%

The calculations and rationale for each follow: Case 1 2 3 4 5

p 77% 50% 30% 40% (most recent) 50% (worst case)

q 23% 50% 70% 60%

e 4% 5% 5% 3.5%

z 1.96 1.96 1.96 1.96

Sample size 425 384 323 753

50%

6%

2.58

462

.

152

24. A researcher has the task of estimating how many units of a new, revolutionary photocopy machine (it does not require ink cartridges and it is guaranteed not to jam) will be purchased among business firms in Cleveland for the coming annual sales forecast. She is going to ask the likelihood that they will purchase the new device and for those that are “very likely” to purchase, she wants the respondents at the companies to estimate how many machines their company will buy. She has data that will allow her to divide the companies into small, medium, and large firms based upon number of employees at the Cleveland office.

What sampling plan should be used? Why? This situation calls for stratified sampling with the company sizes (small, medium, and large) defining three strata because the characteristics of the strata differ. For example, some small firms may not be able to afford a single machine, but many large companies may purchase multiple units for multiple locations. Similarly, the resources, number of employees who will use the machines, and the nature of the copying will vary by stratum.

25. Honda USA is interested in learning what its 550 U.S. dealers think about a new service program Honda USA provided the dealers at the beginning of last year.

Honda USA wants to know if the dealers are using the program and, if so, their likes and dislikes about the program. Honda USA does not want to survey all 550 dealers but wants to ensure that the results are representative of all the dealers.

What sampling plan should be used? Why? Honda dealerships are relatively homogeneous or highly similar, so there is no need to stratify. Large geographic differences (weather, altitude, typology, etc.) exist, so area/cluster sampling is not appropriate. There are two reasonable options: simple random sample and systematic sample.

For SRS, number the dealers 1 through 550 and use a random number generator to select n unique random number between 1 and 550. Those dealers selected will represent all dealers as every dealer has the same probability of being selected.

For a systematic sample, arrange the dealerships alphabetically. Draw a random number between 1 and 550. Then use the skip interval (550/n) to select the dealers.

Those dealers selected will represent all dealers as every dealer has the same probability of being selected.

26. Applebee’s Restaurants has spent several thousand dollars advertising the restaurant during the last two years. They wish to have some input into what effect the advertising has made and they decide to measure TOMA (Top of Mind Awareness). A TOMA “score” for such a restaurant is the ranking a firm has as a result of asking a representative sample of consumers in the service area to “Name a non-fast-food restaurant.” The restaurant that is named by the most persons has the #1 TOMA score, and so on. It is important that Applebee’s management conduct their TOMA survey on a representative sample in the metropolitan area.

What sampling plan should be used? Why? . 153

Such surveys are commonly administered by telephone. The strong desire for a representative sample leads to the use of a probability sample method. A Random Digit Dialing (RDD) approach would ensure the selection of a random sample of households because unlisted numbers would be included in the sample frame.

27. We have a 7-point scale to measure the construct: likelihood of buying a new car. We wish to estimate the mean score on the scale and we want to be 95% confident that our sample statistic (mean) is within ±0.5 of a scale point from the true population parameter. a. Calculate the required sample size.

This use of the 7-point metric scale means that sample size formula for a mean should be used. There is no information on the variability, so to estimate “s,” it is advisable to use the “range/6” approach. Thus, the sample size (n) would be: = ((7/6)2 times 1.962)/.52 = (1.37 times 3.84)/.25 = 5.26/.25 = 21.0 Note: This may seem like a small sample size and it is. Why? With a 7-point scale you have limited variance. It does not require a large sample size to estimate a number from among only 7 choices! In addition, an allowable error of .5 means you have a fairly large allowable error for such a scale. b. After you calculate the sample size in 27a, you decide you want to halve the level of accuracy. What would the new sample size be?

This requires students to think about the concept of accuracy. HALVING accuracy means to make it LESS accurate so your new e should be set at 1.0 instead of .5.

When you do this the denominator is adjusted to 1 (squared) and the sample size would only be 5.26. Again, this is very small but it illustrates the need for small samples when the variance is low and your accuracy is low.

Alternatively, you may want to ask your students to DOUBLE accuracy. Then, the new accuracy level is now .25 rather than .5, so the denominator is adjusted to .252 (= .06), and the sample size is calculated to be 87.7 (rounded up to 88).

28. A plan is set before all state universities to require classes to meet either three or four days a week in order to make better use of classroom facilities, because currently classrooms sit empty Friday afternoons and evenings and on Saturday. However, some schools that have a large percentage of commuter schools are concerned that this proposal will put a hardship on their commuter students because it will require more driving to/from the campus. University officials want to estimate the average number of miles driven, one way, to campus by its commuter students. It is estimated that some students drive as little as 0 miles to campus while at least one student reported driving one way as far as 60 miles. University officials want to be 95% confident that the sample mean is no more than ±2 miles from the real average number of miles driven in the population. . 154

a. What is the required sample size?

This use of miles commuted means that sample size formula for a mean should be used. There is no information on the variability, so to estimate “s,” it is advisable to use the “range/6” approach. Thus, the sample size (n) would be… = ((60/6)2 times 1.962)/22 = (100 times 3.84)/4 = 384/4 = 96 b. After the study is conducted we learn that the standard deviation in the sample data, which is now our best estimate of s, is actually 12.3. What impact does this new measure of variation have on the mean number of miles driven that we also derived from our sample?

The key to understanding this question is to make students think about what they were doing when they calculated the sample size—they were estimating variance.

However, after the study, we have a representative sample from the population and we can easily calculate variance in this data. Data from the sample should now be our best estimate of the actual variance in the population. So, now students need to realize it is this latter estimate of variance that determines their e instead of what they estimated when they used the sample size formula. Here we estimated that the standard deviation was 10 (60/6) but now, after the study we learn from the sample data that we under-estimated variance as it is really 12.3. What impact does this have on our mean estimate of miles driven? It means we will be less accurate than the plus or minus 2 miles. Our e will be greater than the minimum desired level. Now, had we over-estimated variance, the impact would be that we have greater accuracy than the minimum we set as e in our sample size formula.

CASE SOLUTIONS Case 9.1 Target: Deciding on the Number of Telephone Numbers Case Objective: With this case students must use the formula for the number of telephone numbers needed in order to obtain a target final sample size.

Answers to Case Questions 1. With a desired final sample size of 250 for each region, what is the lowest total number of telephone numbers that should be purchased for each region?

2. With a desired final sample size of 250 for each region, what is the highest total number of telephone numbers that should be purchased for each region?

The low and high numbers are in the following table. Students must realize that a low estimated working, incidence, and completion rate will result in a higher total number, while a high set of these rates will result in a lower total number.

North

South

East .

155

West

High 1,429

Low 680

High 1,667

Low 1,068

High 601

Low 412

High 1,389

Low 850

3. What is the lowest and highest total number of telephone numbers to be purchased for the entire survey?

The lowest total is 3,166, and the highest total is 5,112.

Case 9.2 Integrated Case: Advanced Automobile Concepts Case Objective: This case requires students to compute sample size error and to calculate the associate costs of various sample sizes computed.

Answers to Case Questions For each of the following possible sample sizes, calculate the associated expected cost of the panel sample and the sample error.

1. 20,000 2. 10,000 3. 5,000 4. 2,500 5. 1,000 6. 500 Using p = q = 50%, the associated errors and costs are below.

Sample size 20,000 10,000 5,000 2,500 1,000 500

Error (95%) 0.7% 1.0% 1.4% 2.0% 3.1% 4.4%

Cost $200,000 $100,000 $50,000 $25,000 $10,000 $5,000

.

156

CHAPTER 10 Data Issues and Inputting Data into XL Data Analyst LEARNING OBJECTIVES •

To learn about the data matrix, data coding, and the data code book

•

To understand errors that occur during data collection

•

To become knowledgeable of various types of nonresponse errors

•

To become acquainted with data quality errors and how to handle them

•

To learn about the XL Data Analyst, including the Data and Define Variables worksheets

•

To read about special operations possible with the XL Data Analyst

CHAPTER OUTLINE Data Matrix, Coding Data, and the Data Code Book Errors Encountered During Data Collection Types of Nonresponse Errors Refusals to Participate in the Survey Break-Offs During the Interview Refusals to Answer Specific Questions (Item Omission) Preliminary Data Screening What to Look for in Raw Data Inspection Incomplete Response Nonresponses to Specific Questions (Item Omissions) Yea-Saying or Nay-Saying Patterns Middle-of-the-Road Patterns Other Data Quality Problems .

159

How to Handle Data Quality Issues What Is an “Acceptable Respondent”?

Introduction to Your XL Data Analyst The Data Set and Data Code Book Are in the XL Data Analyst Case Datasets and Building Your Own XL Data Analyst Data Set Special Operations and Procedures with XL Data Analyst Data Sets Selecting Subsets of the Data for Analysis Computing or Adding Variables KEY TERMS Acceptable respondent

Middle-of-the-road pattern

Break-off

Nay-saying

Data coding

Nonresponse

Data entry

Nonsampling errors

Data matrix

Refusal

Data worksheet

Respondent errors

Date code book

Unintentional fieldworker errors

Define variables worksheet

Unintentional respondent errors Value code

Fieldworkers Incomplete response

Value label

Intentional fieldworker errors

Variable description

Intentional respondent errors

Variable label

Item omission

Yea-saying .

160

TEACHING SUGGESTIONS 1. This is a new chapter not found in the 1st or 2nd editions of our textbook. We have addressed data quality and XL Data Analyst specifics for the following reasons. First, data quality issues are a constant concern, and they persist even with the use of panel data, which is becoming prevalent. Case 10.2, in fact, makes students realize that panelists can commit these errors. Second, the handling of suspicious responses and missing data is important because there is considerable potential for adverse impacts on data analysis. Last, we have found from our own teaching, that despite its Excel platform, the XL Data Analyst requires more introduction and description for students to understand its structure. Plus, we have included descriptions of data selection (via filtering) and the computation or addition of new variables in an XL Data Analyst dataset.

2. Implicit in this discussion of fieldworker errors is the message that professional data collection companies will control for field work errors. If you have a nearby data collection company, invite the manager to your class to talk about the company’s operations, particularly training, orientation sessions, and other quality control procedures. Alternatively, a field trip to a data collection company may be feasible.

3. Students may not appreciate the need for interviewer training. A way to combat this belief is to bring a (complicated) telephone or personal interview questionnaire to class and require two students to role play the interviewer and the respondent. It will become apparent that reading the instructions, following skip patterns, and making notations of the respondent’s answers on the questionnaire takes practice.

4. There is a danger when talking about respondent and interviewer errors that students will come away with a negative opinion about surveys. That is, class discussion sometimes dwells on the cheating, falsehoods, and other misrepresentations that occur in surveys and students may acquire a belief that these problems are rampant. The point to be made is that there may be significant problems if controls are not used, but most researchers will apply controls even if an ad hoc group is used. So the errors are kept to a minimum.

5. An experiential way to have students learn about the need for preliminary questionnaire screening is to bring a stack of completed questionnaires to class, pass them out to students, and have them inspect each for problems such as incomplete questionnaires, nonresponses to specific questions, yea-saying, nay-saying, or middle-of-the-road patterns. Outlier questionnaires can be spotted, and class discussion can be used to determine how to handle the outliers. Alternatively, because hard copy questionnaires are not common, the Instructor may have a dataset for students to examine. With a single worksheet, one can orient student on data labels, and the code book for variable descriptions, value codes, and value labels can be learned and used in this exercise as will.

6. It is strongly urged that students have hands-on experience with one or more of the XL Data Analyst datasets that accompany the textbook. The Advanced Automobile Concepts dataset is used in the Figures, so if possible, have students bring laptops (or use a PC lab) to inspect and closely examine the structure of the XL Data Analyst. . 161

Although students are not formally oriented on data analysis, an Instructor can use simple summarization with percents to show how data selection (filtering) can be used.

7. Instructors who want a different data set or who want to have students learn first-hand how to build an XL Data Analyst data set should strongly consider visiting the XL Data Analyst (www.xldataanalyst.com) website for updated versions, posted instructions, help, and FAQs.

ANSWERS TO REVIEW QUESTIONS 1. What is a data matrix and how does data coding explain the numbers found in a data matrix?

A data matrix is an arrangement of rows and columns identical to a spreadsheet in Microsoft Excel. Data coding is the identification of code values that are associated with the possible responses for each question on the questionnaire.

2. Distinguish intentional from unintentional fieldworker errors.

Intentional fieldworker errors are where the interviewer deliberately falsifies his or her work, such as cheating by submitting bogus completed questionnaires.

Unintentional fieldworker errors are where the interviewer makes mistakes such as those caused by fatigue or lack of understanding of how to administer the questions.

3. Distinguish intentional from unintentional respondent errors.

Intentional respondent errors are those committed when the respondent knowingly provides false answers.

Unintentional respondent errors occur when the respondent is confused, distracted, or otherwise inattentive.

4. Describe the three types of nonresponse possible in a survey.

Type Refusal Break-off Item omission

Description A prospective respondent declines to participate in the survey.

A respondent stops answering somewhere in the middle of the survey.

A respondent does not answer a particular answer, but continues to answer following questions.

5. How does a break-off differ from an item omission?

A break-off means that the respondent has stopped answering questions at some point in the survey, whereas an item omission means that the respondent has not answered a question or questions, but continues on with the survey and answers later questions. . 162

6. What, specifically, does a researcher look for during raw data inspection?

The researcher looks for problem respondents that fall into the following five categories: incomplete responses (break-offs), nonresponses to specific questions (item omissions), yea- or nay-saying patterns, and middle-of-the-road patterns.

7. Distinguish a middle-of-the-road pattern from a yea-saying pattern.

A middle-of-the-road pattern is seen as a preponderance of “no opinion” responses whereas a yea-saying pattern is evident in a respondent in the form of all “yes” or “strongly agree” answers.

8. List the three options open to a researcher with respect to how to handle data quality issues.

The options are: • • •

Throw out the respondent’s entire data row.

Leave the respondent’s entire row data in the dataset.

Set the bad data items to blanks or missing data and use only the good data items in subsequent analyses.

9. What are the two critical worksheets in the XL Data Analyst and how do they relate to each other?

The Data worksheet contains variable labels and raw numbers (data), whereas the Define Variables worksheet contains, linked variable labels, variable descriptions, value codes, and value labels. Thus, the Define Variables worksheet “defines” the aspects of each variable whose raw numbers are found on the Data worksheet.

10. Define each of the following: a.

Variable label A unique descriptive word in Row 1 of the Data worksheet for each variable column (e.g., Gender, or Q1, Q2, etc.)

b.

Variable description A long variable description for each variable in the cell beneath its variable label (e.g., Gender of the Respondent) on the Define Variables worksheet.

c.

Value code The related code values for each variable’s possible answer (value label) are placed in Row 3 with the codes separated by commas (e.g., 1, 2) on the Define Variables worksheet.

d.

Value label . 163

The value labels for each set of variable’s value codes are placed below them in Row 4 with the value labels separated by commas and in the same order as your value codes (e.g., Male, Female) on the Define Variables worksheet.

11. What two options exist for a researcher who wishes to build his or her own data set for use with the XL Data Analyst?

It can be done either manually or with the use of the XL Data Analysis Import Data feature.

12. What is the most important advice to remember when adding or computing a variable in an XL Data Analyst data set?

Always add or compute new variables at the end (far right-hand side) of the Data worksheet.

ANSWERS TO APPLICATION QUESTIONS 13. The following data matrix has ten respondents. There are seven questions on the survey (Q1–Q7). The possible answers for Q1–Q2 are “no” or “yes,” coded 0 and 1 respectively. Questions Q3–Q4 are answered with “none,” “somewhat,” “a fair amount,” and “very much,” coded 1, 2, 3, and 4, respectively. Questions Q5–Q6 are satisfaction questions with possible answers of “very unsatisfied,” “unsatisfied,” “neither unsatisfied nor satisfied,” “satisfied,” and “very satisfied,” coded 1, 2, 3, 4, and 5, respectively. Question Q7 is an open-ended question where the respondent provides his or her age. For each respondent, scrutinize the answers and indicate whether or not data quality error(s) is possibly apparent and, if so, identify the nature of the error. Also, indicate your judgment as to whether or not each is an “acceptable respondent.”

RES. Q1 Q2 1 0 0 2 1 1 3 1 1 4 0 1 5 6 0 1

Q3

Q4

Q5 4 1 2

Q6 2 1 4

1 1 3

1 4 1

3

7

1

0

8 9

1 0

10

1

Q7 27 38 53 19

4

3

3

1

3

5

4

43

0 0

2 4

1 4

1 3

4 2

63 32

1

1

3

3

3

21

.

164

ERROR Item omission (Q3–Q4); Acceptable Nay-saying (Q1–Q7); Unacceptable No error; Acceptable Item omission (Q5–Q6): Acceptable Refusal; Unacceptable Middle-of-road (Q5–Q6); Item omission (Q7); Acceptable Possible Yea-saying (Q5–Q6); Acceptable No error; Acceptable Possible Nay-saying (Q5–Q6); Acceptable Middle-of-road (Q5–Q6); Unacceptable

14. For all variables the data matrix in question 13, indicate how each of the following aspects of the Define Variables worksheet should be set up in order for XL Data Analyst to perform analyses and create fully labeled tables. a. b. c. d.

Variable labels Descriptions (of variables) Value codes Value labels

Row Row 1 Row 2 Row 3 Row 4

A Respondent Respondent

B Q1 Q1

Description

C Q2

D Q3

Q2

Q3

E Q4 Q4

Description

F Q5 Q5

Description

Description

Description

0,1

0,1

1,2,3,4

1,2,3,4

1,2,3,4,5

No, Yes

No, Yes

None, Somewhat, A fair amount, Very much

None, Somewhat, A fair amount, Very much

Very unsatisfied, Unsatisfied, Neither unsatisfied nor satisfied, Satisfied, Very satisfied

G Q6

H Q7

Q6

Age

Description Very unsatisfied, Unsatisfied, Neither unsatisfied nor satisfied, Satisfied, Very satisfied

15. You work part time in a telemarketing company. Your compensation is based on the number of credit card applicants you sign up with the telemarketing approach. The company owner has noticed that the credit card solicitation business is slowing down, and so she decides to take on some marketing research telephone interview business.

When you start work on Monday, she says that you are to do telephone interviews and gives you a large stack of questionnaires to have completed. What fieldworker errors are possible under the circumstances described here?

It does not appear there is any training, supervision or monitoring of work, nor is there any mention of validation procedures. Interviewers under these circumstances may make errors from lack of understanding, or fatigue. Also, they may turn in bogus completed questionnaires.

CASE SOLUTIONS Case 10.1 IHOP Improvement Survey

.

165

Case Objective: This case requires students to understand the variable label, variable description, value codes, and value labels aspects of the Define Variables worksheet in the XL Data Analyst. Additionally, it tests if they know how to add a new variable to an XL Data Analyst dataset as per the recommendations in the chapter.

Answers to Case Questions 1. Indicate how Mary should set up the Define Variables worksheet in an XL Data Analyst data set file.

The Variable Descriptions, Value Codes, and Value Labels are identified below. Two tables are used, but these would continue horizontally in the worksheet. Note that Media 2 and Media 7 are not listed due to space constraints.

Age

Family income

Gender

Marital status

1,2,3,4,5,6,7,8,9,10 Ranges in $10,000 increments from Below $10,000 (1) to Over $100,000 (10)

1,2 Male, Female

1,2,3 Single, Married, Other

Family size

How often they eat at IHOP

Number of adults; number of children under 18 living at home

Estimated number of times per month

.

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Time of day they are most likely to use IHOP

1,2,3,4,5,6,7,8,9,10,11 Early morning (1), midmorning (2), late morning (3), noontime (4), early afternoon (5), midafternoon (6), late afternoon (7), early evening (8), late evening (9), around midnight (10), in the wee small hours (11)

Aspect 1 Satisfaction

Aspect 2 Satisfaction

Aspect 3 Satisfaction

Aspect 4 Satisfaction

Aspect 5 Satisfaction

Overall satisfaction with IHOP

Media 1

Media 2

Etc. Media 8

1,2,3,4,5

1,2,3,4,5

1,2,3,4,5

1,2,3,4,5

1,2,3,4,5

1,2,3,4,5

0,1

0,1

0,1

Poor, Fair, Good, Very Good, Excellent

Poor, Fair, Good, Very Good, Excellent

Poor, Fair, Good, Very Good, Excellent

Poor, Fair, Good, Very Good, Excellent

Poor, Fair, Good, Very Good, Excellent

Extremely satisfied, Somewhat satisfied, Neither Satisfied nor Dissatisfied, Somewhat Dissatisfied, Extremely Dissatisfied

No,

No,

No,

Yes

Yes

Yes

2. Describe how Mary should create a new variable that is equal to the total number of different advertising media where respondents recall seeing IHOP advertising.

She can create a new variable, say with the label Adtotal, as a new column to the right of the Media 8 column on the Data worksheet. Then for each cell in this new column, Mary can use the Excel function “= SUM ( )” or a simple summing formula (= Media1 + Media2 … + Media8) to total the 8 Media cells for each respondent.

Because the coding for each Media type is 0 (No) and 1 (Yes), the Adtotal variable will be the total of the # yeses, or the total number of media types recalled for each respondent. Utilities-Clean-up will link the new variable to the Define Variable worksheet. The variable Description can be “Total number of IHOP advertising media recalled.” No value codes or value labels are to be used as the number is now a total number of times, rather than a yes/no response.

Case 10.2 Integrated Case: The Advanced Automobile Concepts Survey Data Quality Case Objective This case acquaints students with the practical problems of running a data collection company, and it requires them to assess and compare capabilities of competing online panel providers.

Answers to Case Questions . 167

Keeping the quality of the data as a foremost concern, compare the practices of these two competing panel companies, and recommend one that you think CMG research should use for the Advanced Automobile Concepts survey. Why have you selected your choice over the competitor?

The table is provided below with an “Analysis” row after each set of company responses.

Question 1. What experience does your company have with providing online samples for market research?

Company A: We have conducted market research since 1999. We are the only panel company to take advantage of computer technology and provide a truly nationally representative U.S. sample online.

Company B: We have supplied online U.S. samples since 1990, Europe samples since 2000, and our Asian Panel went “live” in 2005. We have supplied approximately 5,000 online samples to our clients in the past ten years.

Company B is older and it has global coverage whereas A is younger an only has a U.S. sample.

Question 2. What are the people told when they are recruited?

Company A: Individuals volunteer for our online panel via our website where they are informed that they will be compensated with redemption points based on the number of surveys in which they take part.

Company B: We recruit household members by asking them to join our panel, telling them they can have a say in the development of new products and services. They are rewarded with “credits” that they can use to claim products.

Company B is more selective in that it has direct recruitment. Company A seems to have an “anyone” recruitment system.

Question 3. If the sample comes from a panel, what is your annual panel turnover/attrition/retention rate?

Company A: Our voluntary drop-out rate is approximately 5% per month. If a panelist misses ten consecutive surveys, he/she loses his/her panel membership.

Company B: We do a 1-for-1 replacement for each panel member who drops out voluntary (about 3% per year) or removed due to nonparticipation (about 2% per year).

Company B’s drop-out is low and its replacement system is superior to that of Company A. Question 4. What profile data is kept on panel members? For how many members is this data collected and how often is this data updated?

Company A: We maintain extensive individuallevel data, in the form of about 1,000 variables including demographics, household characteristics, financials, shopping and ownership, life styles, and more. All are updated every other year.

Company B: For each panelist, we have about 2,500 data points on demographics, assortment of goods and services owned, segmentation/lifestyle factors, health-related matters, political opinions, travel, financials, Internet usage, leisure activities, memberships, etc. Our updating is done annually.

It is obvious that Company B has more “stored” variables in their panel dataset. Plus, the annual update is more current than Company A’s every other year update.

Question 5. Explain how people are invited to take part in a survey.

Company A: Typically, a survey invitation is sent via e-mail and posted on every selected panel member’s personal member page. In either case, we have a link to the online survey location: “Click here to start your survey.” The email invitation is sent daily to selected panelists

Company B: Based on the client’s sample requirements, we e-mail selected panelists with a link to the online survey. After 48 hours, if the panelist has not participated, we send a reminder, and again 48 hours after the reminder.

.

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until the survey quota is filled.

The systems are almost identical except Company A’s reminder is daily while Company B’s reminder is every other day.

Question 5. How does your company handle data quality issues?

Company A: Our panel members sign a statement that they will be conscientious in responding to all questions in all surveys in which they participate.

Company B: In all surveys with most questions, we provide an option of “Refusal” or “Not applicable,” where our panelists can indicate that they read the question but chose not to answer it for valid reasons. Furthermore, we advise our clients to not use “neutral” or “no opinion” responses options unless they believe them to be absolutely necessary.

Company A uses an “honor” system, and does not indicate any data quality checking system. So, there is the danger that its panel members will “forget” or otherwise not be honorable. Company B provides “missing data” response options for its panelists, so they are more likely to use these than to make data quality errors. Moreover, Company B cautions its clients to not use of neutral or no opinion response categories, which might invite middle-of-the-road responses. Thus, Company B’s panel data is likely to be of higher quality than that of Company A. Assuming there are no other factors to consider (such as price), select Company B as it is superior to Company A in five out of the six areas.

.

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CHAPTER 11 Summarizing Your Data LEARNING OBJECTIVES •

To see an overview of the four types of data analysis performed by market researchers

•

To understand summarization data analysis, including when to use percentage, average, mode, and standard deviation

•

To become acquainted with the summarize function of your XL Data Analyst

•

To learn how to create effective presentation tables for categorical and metric variable summarizations

CHAPTER OUTLINE Types of Data Analysis Used in Marketing Research Summarizing the Sample Generalizing the Findings Finding Meaningful Differences Identifying Relationships Summarizing Your Sample Findings Summarizing Categorical Variables How to Summarize Categorical Variables with XL Data Analyst Summarizing Metric Variables How to Summarize Metric Variables with XL Data Analyst Flow Chart of Summarization Analysis

KEY TERMS . 171

Average

Mode

Central tendency

Percentage distribution

Data analysis

Range

Dataset

Relationship analysis

Differences analysis

Standard Deviation

Frequency distribution

Summarization analysis

Generalization analysis

Variability

TEACHING SUGGESTIONS 1. Chapter 11 describes descriptive analysis in detail. The four other types of analysis— summarization, generalization, differences analysis, and relationships analysis—are described very briefly simply to provide an overview. Each type is described in detail in the following chapters. For Chapter 11, students only need to know the definitions and basic purpose of these analysis types. Table 11.1 is a concise summary of the various types of analysis. Instructors may want to refer to this table and Figure 11.1 which compares various types of data analysis with respect to complexity and value as a preview of what topics will be covered in coming classes.

2. Some instructors may consider the descriptions of the measures of central tendency and variability too elementary. Students with a good statistics course in their backgrounds, theoretically, should know these concepts. However, many students seem to forget or repress these concepts, and especially, standard deviation. The descriptions and the computational examples are intended to help students recover their knowledge of these concepts. Virtually, no students are required to compute descriptive statistics by hand, and the examples are useful in helping them to “see” how the computations take place.

3. Measures of skewness and kurtosis are mentioned but not described in detail as marketing researchers rarely use them, and managers do not understand them.

Instructors who want their students to understand these concepts can provide supplementary reading from statistics texts or other sources. The current version of the XL Data Analyst does not report these measures.

4. It is vital that students understand the relationship between a variable’s type of scale and the appropriate descriptive analysis measure. They are summarized in Table 11.2.It is recommended that instructors quiz students, or otherwise devote class time to ensuring that students retain this knowledge that categorical variables should be summarized with frequencies and percentage distributions while metric variables should be summarized with averages, standard deviations, and the range.

5. The use of descriptive statistics can be illustrated effectively by using a research report. Display the questionnaire used in the study, and have students identify the .

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scaling assumptions and appropriate descriptive statistics for various questions. Then show the tables in the report that communicate the findings.

6. Instructors who want a different data set (such as a team project dataset) or who want to have students learn first-hand how to build an XL Data Analyst data set should remind students that the procedure for inputting their own data into the XL Data Analyst is in the previous chapter in the section titled, “Data Analyst Case Datasets and Building your own XL Data Analyst Dataset.” It gives step-by-step instructions for importing a CSV dataset and setting up the variable descriptions, value codes, and value labels.

ANSWERS TO REVIEW QUESTIONS 1. What is data analysis?

Data analysis is defined as the process of describing a dataset by computing a small number of measures that characterize the dataset in ways that are meaningful to the client.

2. Describe summarization analysis and provide an example.

Summarization analysis is describing the data in the sample with the use of percentages or averages. Examples are the percents of males and females in a sample or the average number of years of age of individuals in the sample.

3. Describe generalization analysis and provide an example.

Generalization analysis means that the researcher conduct hypotheses tests and/or compute confidence intervals in order to make statements about the population that the sample represents. For example, one could test the hypothesis that the population is 60 percent males or that the average population age is 37 years. Alternatively, one could establish 95 percent confidence intervals for estimating these values.

4. Describe differences analysis and provide an example.

With differences analysis, the researcher identifies a categorical variable (such as gender) and compares the groups represented by that variable (males versus females) by analyzing their differences on a second variable such as average age, whether or not they prefer Brand A, etc.

5. Describe relationship analysis and provide an example.

Relationship analysis seeks to find useful connections or associations among two or more variables under study. Using the American Express example, one could investigate if there is a relationship between gender and ownership (crosstabulation), age and total credit card debt (correlation), or how demographics and/or lifestyle variables relate to amount of credit card usage.

6. With regard to complexity, how do the four types of data analysis compare? .

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From simple to complex, they are: summarization, generalization, differences, and relationships.

7. With regard to value of the findings, how do the four types of data analysis compare?

From some to a great deal, they are: summarization, generalization, differences, and relationships.

8. What is meant by the term central tendency? Why is the central tendency an incomplete summarization of a variable?

The typical response is referred to as the “central tendency,” and there are two measures of central tendency: mode and average. It is incomplete because it does not relate how similarity or dissimilarity of the respondents (variability).

9. When summarizing a categorical variable, what is the proper measure of central tendency, and why is it the proper measure? What is the proper measure of variability and why?

The proper measure is the mode because with categorical variables, one should only count the number of times (or percentage of times) that the category occurs. The proper variability measure is a frequency distribution, a summary of the number of times each and every category appears for the entire sample, is used, but a percentage distribution is much more intuitive because all of its categories will sum to 100 percent. It is proper because one should only count categories.

10. When summarizing a metric variable, what is the proper measure of central tendency, and why is it the proper measure?

The proper measure is the average because with metric variables, one is dealing with gradations or degrees rather than categories.

11. Describe how a percentage distribution is computed.

Frequencies themselves are raw counts, and these frequencies are easily converted into percentages for ease of comparison. The conversion is arrived at very simply through a quick division of the frequency for each value by the total number of observations for all of the values, resulting in a percentage distribution.

12. When summarizing a metric variable, what is the most informative measure of variability, and why is it the most informative measure?

The most informative measure is the standard deviation (as opposed to the range— minimum and maximum) because the standard deviation is computed by explicitly comparing every data point to the average and the result can be interpreted by use of a normal curve’s properties.

13. Describe how a standard deviation is computed.

The standard deviation indicates the degree variability in the metric values in such a .

174

way as to be translatable into a normal or bell-shaped curve distribution. Calculate the average, then compare each respondent’s value to the average by subtracting the average from it, and square that difference. Sum these, divide by n-1 and then take the square root.

14. With reference to the formula, show how the standard deviation takes into account the typicality of every respondent.

Every respondent’s value is compared (subtracted) to the mean, so every respondent’s closeness or distance from the average is reflected in the computations.

15. What is the relationship between a standard deviation and a normal curve?

The relationship is described below.

Number of Standard Deviations from the Mean ±1.00 ±1.96 ±2.58

Percent of Area Percent of Area Under Curve to Right (Or Left) 68% 95% 99%

16.0% 2.5% 0.5%

16. What output is generated by the XL Data Analyst with the following types? a. Summarize – Average:-Average, Standard Deviation, Minimum, Maximum, Sample Size b. Summarize–Percent: Frequency distribution, Percent distribution

ANSWERS TO APPLICATION QUESTIONS 17. In a survey on magazine subscriptions, respondents write in the names of magazines they subscribe to regularly. What measures of central tendency can be used? Which is the most appropriate and why?

This is a case of a categorical variable as the respondents will be writing down magazine titles such as National Geographic, People, TV Guide, etc. There are two measures of central tendency: mode and average. The mode is the appropriate one for a categorical variable.

18. A manager has commissioned research on a special marketing problem. He is scheduled to brief the board of directors on the problem’s resolution in video conference tomorrow morning. The research director works late that night in the downtown San Francisco headquarters and completes the summarization data analysis, which will be sufficient for the presentation. However, less than an hour before the manager calls him for an early-morning briefing on the survey’s basic findings.

The researcher looks around in his office and an idea flashes in his head. He immediately grabs a blank questionnaire. What is he about to do to facilitate the . 175

quick communication of the study’s basic findings to the manager?

The researcher realizes that the manager can probably understand simple central tendency concepts such as the mode and the average, plus a percentage distribution is readily comprehended by most mangers. So, the researcher will compute and write in “average” with the average for the metric variable questions, and he can write in the percentages for the various categories that define the response options for the categorical questions. It will not be pretty, but the researcher has a good chance of communicating the basic finding with this approach, and the manager should be able to communicate to the other managers in the early morning meeting as these analysis concepts are fairly well understood.

19. A professor asks his students how many hours they studied for the last exam. He finds that the class average is 10.5 hours and the standard deviation is 1.5 hours. The minimum is 2 hours, and the maximum is 20 hours. How would you describe the typical student’s study time for this professor’s last exam?

One could use the standard deviation interpretation based on a normal curve that is provided in Table 11.5 The average (10.5 hours) ±1 standard deviation (1.5 hours) pertains to 68 percent of the class, and this is a range of 9 to 12 hours. Ninety-five percent of the students are ±1.96 times the standard deviation from the average, for a range of 7.6 to 13.4 hours.

Ninety-nine percent yields a range of 6.7 to 14.3 hours. Using the range, at least one student studied only 2 hours and a least one other studied 20 hours for the exam.

20. In a survey, Valentine’s Day rose buyers are asked to indicate what color of roses they purchased for their special friends. The following table summarizes the findings.

What color roses did you purchase last Valentine’s Day? Category Yellow Roses White Roses Red Roses Mixed colors Total

Frequency 66 78 159 82 385

Percent 17.1% 20.3% 41.3% 21.3% 100.0%

Describe the central tendency and variability apparent in this analysis.

Red roses are the most ordered (mode of 41.3 percent), but they are not ordered by a majority (over 50 percent).The next preferred are mixed colors, white roses, and then yellow roses, but they are very close as to percentages, so it is pretty much of a tie for second place with mixed colors and white roses.

21. An entrepreneur is thinking about opening an upscale restaurant. To help assess the market size, a researcher conducts a survey of individuals in the geographic target market who patronize upscale restaurants. The findings of the summarization analysis for two questions on the survey follow. . 176

Variable Total amount spent in upscale restaurants per month Average price expected to pay for an entree in an upscale restaurant

Standard Average Deviation Minimum

Maximum Sample

$150.11

$32.72

$5

$250

400

$28.87

$5.80

$16

$60

340

Describe the central tendency and variability apparent in this analysis.

The 400 upscale restaurant patrons in the survey spend about $150 per month, on average, at these restaurants, but there is a good deal of variability as 95 percent of the respondents ranged from $87.17 to $213.05 (average ±1.96 times standard deviation).At least one respondent spends as little as $5, while at least one spends as much as $250 (range).

With respect to expected entrée price, the average is $28.87, and 95 percent of the sample is within $17.50 and $50.24.At least one respondent expects to pay $16, and at least one other expects to pay as much as $60.

CASE SOLUTIONS Case 11.1The Prospective New Restaurant Survey Case Objective: This case requires students to identify the correct type of summarization, or descriptive, analysis based on a determination of whether a variable is metric or categorical.

Answers to Case Questions 1. Assuming that the data set is comprised of only respondents who answered “Yes” to question 1, indicate those variables where missing data would be found even if every respondent answered every question he or she is supposed to answer.

The only missing data in this instance would be where respondents were directed to “skip” or not answer questions because they are not appropriate.

Question 6 is not answered if the respondent answers “no” to question 5.

Question 8 is not answered if the respondent answers “no” to question 7.

Question 10 is not answered if the respondent answers “no” to question 9.

3. Determine what variables are categorical and indicate the appropriate summarization analysis. . 177

Refer to the table under questions 3.

3. Determine what variables are metric scales and indicate appropriate summarization analysis.

The following table identifies the scale type. Categorical variables should be summarized with percents and the mode. Metric variables should be summarized with the average, standard deviation, and range (maximum and minimum).

The answer is provided below each variable in the following table: Question 1.Do you eat at an upscale restaurant at least once every two weeks?

2.How many total dollars do you spend per month in restaurants (for your meals only)?

3. How likely would it be for you to patronize this restaurant?

4.Thinking again of the restaurant just described and remembering that drinks, appetizers, entrées, and desserts are priced separately (a la carte), what would you expect an average evening meal entrée item alone to be priced?

5. Would you describe yourself as one who listens to the radio?

6. To which type of radio programming do you most often listen?

7. Would you describe yourself as a viewer of television local news?

8. Which newscast do you watch most frequently?

9.Do you read the newspaper?

10. Which section of the local newspaper would you say you read most frequently?

11. Do you subscribe to City Magazine?

Response scale for Questions 12 through 21 22. In which year were you born?

23. What is your highest level of education?

24. What is your marital status?

25.Including children under 18 years of age living with you, what is your family size?

27. Which of the following categories best describes your before-tax household income?

28. What is your gender?

Scale Type Categorical Metric Metric Metric

Categorical Categorical Categorical Categorical Categorical Categorical Categorical Metric Metric Categorical Categorical Metric Categorical Categorical

Case 11.2 Integrated Case: Advanced Automobile Concepts Summarization Analysis Case Objective: This is the first analysis case using the integrated case. Students will need to learn how to use and interpret the XL Data Analyst’s summarization analyses applied to the Advanced Automobile Concepts survey dataset provided with the textbook. Pie charts are not provided here to conserve space. . 178

Answers to Case Questions 1. What is the demographic composition of the sample?

All demographic variables, except for number of people in the household, are categorical, so use percents.

Size of home town or city Category Under 10K 10K to 99K 100K to 499K 500K to 1 million 1 million and more Total

Frequency 154 177 176 226 267 1000

Percent 15.4% 17.7% 17.6% 22.6% 26.7% 100.0%

Frequency 505 495 1000

Percent 50.5% 49.5% 100.0%

Gender Category Male Female Total

Marital status Category Unmarried Married Total

Frequency 487 513 1000

Percent 48.7% 51.3% 100.0%

Age category Category 18 to 24 25 to 34

Frequency 121 174 .

179

Percent 12.1% 17.4%

35 to 49 50 to 64 65 and older Total

256 239 210 1000

25.6% 23.9% 21.0% 100.0%

Frequency 194 298 214 222 72 1000

Percent 19.4% 29.8% 21.4% 22.2% 7.2% 100.0%

Level of education Category Less than High School High School diploma Some college College Degree Post graduate degree Total

Job category Category Management Sales Service Agriculture Craft General labor Retired Total

Frequency 246 285 120 29 75 102 143 1000

Percent 24.6% 28.5% 12.0% 2.9% 7.5% 10.2% 14.3% 100.0%

Income category Category Under $25K $25K to $49K $50K to $74K $75K to $125K $125K and more

Frequency 256 343 194 137 70 .

180

Percent 25.6% 34.3% 19.4% 13.7% 7.0%

Total

1000

100.0%

Frequency 452 296 185 67 1000

Percent 45.2% 29.6% 18.5% 6.7% 100.0%

Dwelling type Category Single family Multiple family Condominium/Townhouse Mobile home Total

The typical household has about two members.

Variable Number of people in household

Average

Standard Deviation

Minimum

Maximum

Sample

2.2

1.4

1

9

1000

2. What is the automobile ownership profile of respondents in the survey?

.

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The automobile ownership variables are categorical, so use percents. The modes are: economy price range and car.

Primary vehicle price type Category No vehicle Economy Standard Luxury Total

Frequency 100 455 271 174 1000

Percent 10.0% 45.5% 27.1% 17.4% 100.0%

Primary vehicle type Category No vehicle Car Pick-up truck SUV or van Total

Frequency 100 432 210 258 1000

Percent 10.0% 43.2% 21.0% 25.8% 100.0%

3. How do respondents feel about (1) global warming and (2) the use of gasoline?

The scales are metric, so use averages. Highest agreement is that gasoline prices are too high now and lowest agreement is that gasoline emissions contribute to global warming. (All averages are greater than “neither disagree nor agree” = 4.)

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Average

Standard Deviation

Minimum

Maximum

Sample

Gasoline prices are too high now.

6.3

1.3

1

7

1000

I am worried about global warming.

6.1

1.5

1

7

1000

Global warming is a real threat.

5.6

1.8

1

7

1000

We need to do something to slow global warming.

5.3

1.9

1

7

1000

High gasoline prices will impact what type of autos are purchased.

5.3

2.3

1

7

1000

We should be looking for gasoline substitutes.

5.1

2.2

1

7

1000

Americans use too much gasoline.

5.0

2.0

1

7

1000

Gasoline prices will remain high in the future.

4.9

2.0

1

7

1000

Gasoline emissions contribute to global warming.

4.8

2.3

1

7

1000

Variable

4. What are the respondents’ opinions about the effects of the use of various kinds of hybrid vehicles?

The scales are metric, so use averages. They are most positive about keeping gas prices down and least positive about effects on global warming. (All averages are greater than “neither disagree nor agree” = 4.) . 183

Average

Hybrid autos that use alternative fuels will keep gas prices down.

Standard Deviation

5.8

2.2

1

7

1000

Hybrid autos that use alternative fuels will reduce fuel emissions.

5.1

2.1

1

7

1000

Hybrid autos that use alternative fuels will slow down global warming.

4.1

2.1

1

7

1000

Variable

Minimum

Maximum

Sample

5. What size of “new” automobile (very small with very high mpg, small with high mpg, and hybrid using alternative fuels) do people in the sample believe are likely to have the most positive effects?

The scales are metric, so use averages. They are most positive about small autos keeping gas prices stable and least positive (actually neutral) about reducing fuel emissions.

Average

Standard Deviation

Minimum

Maximum

Sample

Small autos with high mpg’s will keep gas prices stable.

4.9

2.5

1

7

1000

Small autos with high mpg’s will slow down global warming.

4.5

2.3

1

7

1000

Small autos with high mpg’s will reduce fuel emissions.

4.0

2.2

1

7

1000

Variable

6. What type of hybrid automobile is the most attractive to people in the sample in terms .

184

of likelihood of purchase in the next three years? What type is the least attractive?

Probabilities are metric, so use averages. The most probable/attractive model is the standard sized synthetic fuel one, and the least probable/attractive model is the very small (one-seat) hybrid one.

.

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Average

Standard Deviation

Minimum

40.2

21.5

0

100

1000

Probability of buying a standard size electric auto within 3 years

34.6

22.1

0

100

1000

Probability of buying a standard size hybrid auto within 3 years

30.1

21.2

0

100

1000

Probability of buying a small (twoseat) hybrid auto within 3 years

20.6

19.3

0

90

1000

Probability of buying a very small (one-seat) hybrid auto within 3 years

13.8

23.1

0

100

1000

Variable Probability of buying a standard size synthetic fuel auto within 3 years

.

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Maximum Sample

CHAPTER 12 Generalizing Your Findings LEARNING OBJECTIVES • To find out what it means to generalize the findings of a survey • To understand that a sample finding is used to estimate a population fact • To discover how to estimate a confidence interval for a percentage or an average • To learn how to test a hypothesis about a population percentage or an average • To become familiar with the “Generalize” functions of the XL Data Analyst

CHAPTER OUTLINE Generalizing a Sample’s Findings Estimating the Population Value How to Estimate a Population Percentage (Categorical Data) Calculating a Conference Interval for a Percentage Interpreting a 95% Confidence Interval for a Percentage How to Obtain a 95% Confidence Interval for a Percentage with XL Data Analyst How to Estimate a Population Average (Metric Data) Calculating a Confidence Interval for an Average Interpreting a Confidence Interval for an Average How to Obtain a 95% Confidence Interval for an Average with XL Data Analyst Flow Chart of Generalization Analysis for Confidence Intervals Testing Hypotheses About Percents of Averages Testing a Hypothesis About a Percentage .

188

Why Use the 95% Significance Level?

How Do We Know That We Have Made the Correct Decision?

Testing a Directional Hypothesis How to Test a Hypothesis About a Percentage with XL Data Analyst Is It t or z? And Why You Do Not Need to Worry About It Testing a Hypothesis About an Average How to Test a Hypothesis About an Average with XL Data Analyst Interpreting Your Hypothesis Test Flow Chart of Generalization Analysis for Hypothesis Tests How to Present Generalization Analyses Guidelines for Confidence Intervals The General Case The Findings-Specific Case Guidelines for Hypothesis Tests

KEY TERMS Confidence interval

Parameter

Directional hypothesis

“Parameter estimation”

Generalization

Population fact

Hypothesis

Sample finding

Hypothesis testing

Sampling distribution

Intuitive hypothesis testing

Standard error

Most commonly used level of confidence

Standard error of a percentage

Null hypothesis

Standard error of the average

TEACHING SUGGESTIONS 1. The effect of sample size on a confidence interval can be demonstrated with a simple .

189

spreadsheet program such as Excel. Let’s assume that p has been found to be 40 percent, what would be the confidence intervals under successively larger sample sizes? The following table is a spreadsheet-like comparison for 95 percent confidence intervals.

Sample Size

Lower Limit

Upper Limit

Range

100

30.4%

49.6%

19.2%

250

33.9%

46.1%

12.1%

500

35.7%

44.3%

8.6%

1000

37.0%

43.0%

6.1%

1500

37.5%

42.5%

5.0%

2000

37.9%

42.1%

4.3%

2. The effect of variability in the sample statistic can be illustrated in the same way.Let’s hold the sample size at 250, and compare different sizes of p.

.

190

Statistic (p)

Lower Limit

Upper Limit

Range

50%

43.8%

56.2%

12.4%

40%

33.9%

46.1%

12.1%

30%

24.3%

35.7%

11.4%

20%

15.0%

25.0%

9.9%

10%

6.3%

13.7%

7.4%

5%

2.3%

7.7%

5.4%

3. Some textbooks, particularly statistics textbooks, explicitly state the alternative hypothesis.We do not do so in our textbook, however Instructors who believe students should understand the concept of an alternative hypothesis may wish to add material of their own on the concept of an alternative hypothesis.

4. With the XL Data Analyst available to them, students may not appreciate doing hand calculations of confidence intervals or hypothesis tests. These calculations are more than tedious exercises. The point is to have students see what is in the numerator and what is in the denominator of each formula, so they can understand what is driving the computed value. Typically, the sample size is always in the denominator, while the hypothesized value and sample statistic are in the numerator. Larger sample sizes drive the computed value (e.g., a t-value) down; whereas, larger differences between the sample statistic and the hypothesized value will drive it up.

5. This chapter is the first encounter for students with XL Data Analyst statistical test output (namely, hypothesis tests).The text describes each interpreted output example in some detail; however, the statistical values are reported in the output as well.Instructors who prefer to have their students work with statistical values will find it useful to spend class time on how to find and interpret the findings.

6. Strictly speaking, the use on any generalization analysis (confidence interval, or hypothesis test) requires the use of random sampling. However, a true random sample is rare due to difficulties in acquiring complete population listings and high refusal rates. Instructors who use student projects or other datasets that are created with the use of convenience sampling or otherwise nonrandom sampling methods may opt to include a footnote to the effect that the sample is assumed to be random for educational purposes.

7. Instructors will find Marketing Research Application 12.1, “How to Estimate Market Potential Using a Survey’s Findings,” a useful demonstration of the economic impact of their university on specific, local businesses by doing the following: a. Have students or student teams identify some local establishment .

191

b.

c.

d. e.

patronized by university students. Examples are: pizza place, coffee shop, restaurant, bar, etc.

Using a convenience sample determine the following: i. Percent of the sample that patronized the establishment in the past month ii. Average number of times in the past month the establishment was patronized by those who used it. iii. Average amount spent per visit at the establishment The “Best Estimate” of the university student population annual sales is computed as follows: i. University student population size times ii. Percent of sample that patronized the establishment in the past month times iii. Average number of times in past month the establishment was patronized by those who used it times iv. Average amount spent per visit at the establishment times v. 12 months “Pessimistic” and “optimistic” sales estimates can be made using the confidence intervals approach described in the Marketing Research Application.

Because a nonrandom sample is used, be sure to use the footnote described in the above teaching suggestion (#6.).

ANSWERS TO REVIEW QUESTIONS 1. Distinguish between sample findings and population facts. How are they similar, and how may they differ?

A sample finding is whenever a percentage, or an average, or some other analysis value is computed with a sample’s data. However, because of the sample error involved, the sample finding must be considered an approximation of the population fact defined as the true value when a census of the population is taken, and the value had been determined using all members of the population.

2. Define generalization, and provide an example of what you might generalize if you moved to a new city and noticed that you were driving faster than most other drivers.

Generalization is the act of estimating a population fact from a sample finding.

Generalization is a form of logic where you make an inference about an entire group based on some evidence about that group.

With the driving example, your generalization would be based on observing that other drivers were driving slower than you (sample fact), and you would generalize that drivers in this city obey the speed limits.

3. What is a parameter, and what is parameter estimation?

The parameter is the actual population value being estimated (an average, or a percent).The generalization process is referred to as “parameter estimation” because sample facts and information is used to make the estimation. . 192

4. Describe how a confidence interval can be used by a researcher to estimate a population percentage.

A confidence interval is a range (lower and upper boundary) into which the researcher believes the population parameter falls with an associated degree of confidence (typically 95% or 99%).

The general formula for the estimation of a population percentage is written in notation form as follows.

Formula for a population percentage estimation Where: p = sample percentage = z value for 95% or 99% level of confidence (α [alpha] equals either 95% or 99% level of confidence) =

= standard error of the percentage

5. What two levels of confidence are used most often, and which one is most commonly used?

The two levels are 99% and 95%, and the 95% level of confidence is the one most commonly used.

6. Using the formula for a confidence interval for a percentage, indicate the role of: a. Sample finding (percentage) b. Variability c. Level of confidence Formula for a 95% confidence interval for a percentage

p is the sample finding pxq is the variability n is the sample size

One begins the estimation process with the sample percent, p. The variability is computed by the product of p times q, so highly variability (e.g., 50x50) increases this product, while low variability (e.g., 90x10) reduces it. The sample size, n, is in the denominator, so a large n reduces the confidence interval, while a small n increases it.

7. Indicate how a researcher interprets a 95% confidence interval. Refer to the sampling distribution in your explanation.

The interpretation is based on the normal curve or bell-shaped distribution. The .

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standard error is a measure of the variability in a population based on the variability found in the sample. If you theoretically took many, many samples and plotted the sample percentage, p, for all these samples as a frequency distribution, it would approximate a bell-shaped curve called the sampling distribution. The standard error is a measure of the variability in the sampling distribution based on what is theoretically believed to occur were we to take a multitude of independent samples from the same population.

8. In the case of a standard error of the average, indicate how it is affected by the following: a. Standard deviation b. Sample size The standard error of the average is calculated by dividing the standard deviation by the square root of n. Thus,as the standard deviation is larger, consequently so is the standard error of the average. As the sample size increases (decreases), the standard error of the average decreases (increases).

9. What is a hypothesis and what is the purpose of a hypothesis test? With a hypothesis test what is the null hypothesis?

A hypothesis is when one makes a statement about the population parameter based on prior knowledge, assumptions, or intuition. This statement most commonly takes the form of an exact specification as to what the population value is. A hypothesis test is a statistical procedure used to “support”(accept) or “not support”(reject) the hypothesis based on sample information. The null hypothesis a formal statement that there is no (or null) difference between the hypothesized π (or population average) value and the p (or sample average) value found in our sample 10. How does statistical hypothesis testing differ from intuitive hypothesis testing? How are they similar?

Intuitive hypothesistesting (as opposed to statistical hypothesis testing) is when someone uses something he or she has observed to see if it agrees with or refutes his or her belief about that topic. Statistical hypothesis testing uses the formulas and concepts described in the chapter.

11.When performing a hypothesis test, what critical value of z is the most commonly used one, and to what level of significance does it pertain?

The most commonly used z value is 1.95, and it pertains to the 95% level of significance.

12.When the person who posited a hypothesis argues against the researcher who has performed the hypothesis test and not supported it, who should win the argument and why?

The researcher is always correct as long as a random sample has been used (and the calculations are done and interpreted properly). . 194

13.Using a bell-shaped curve, show the acceptance (supported) and rejection (not supported) regions for the following levels of confidence. a. 95% level of confidence b. 99% level of confidence For a., the acceptance level is 95% of the bell-shaped curve (centered over the apex of the curve), while the rejection region is the 2.5% to the left and to the right of the 95% boundaries. With b., acceptance is 99% of the bell-shaped curve, while rejection is .5% to the left and right.

14. How does a directional hypothesis differ from a nondirectional one, and what are the two critical items to take into account when testing a directional hypothesis?

A directional hypothesis is one that indicates the direction in which you believe the population parameter falls relative to some hypothesized average or percentage.

When testing a directional (greater than or less than) hypothesis, the critical z value is adjusted downward to 1.64 and 2.33 for the 95% and 99% levels of confidence respectively. This adjustment is because only one side of the bell-shaped curve is involved in what is known as a “one-tailed” test. Of course, the sample percent, or average must be in the right direction away from the hypothesized value, and the computed z-value must meet or exceed the critical one-tailed z value in order for the hypothesis to be supported.

ANSWERS TO APPLICATION QUESTIONS 15. Here are several computation practice exercises in which you must identify which formula should be used and to apply it. In each case, after you perform the necessary calculations, write your answers in the blank column. a. Determine confidence intervals for each of the following: Mean of 150, stdev. of 30, n of 200, level of 95%—use average confidence interval

Percent of 67%, n of 300, level of 99%—use percentage formula

.

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Mean of 5.4, stdev. of 0.5, n of 250, level of 99%—use average formula

.

196

Percent of 25.8%, n of 500, level of 99%—use percentage formula

b. Test the following hypothesis and interpret your findings.

Hypothesis: mean=7.5, mean of 8.5, s.d. of 1.2, n of 670, level of 95%

The hypothesis is not supported.

Hypothesis: percent=86%, p of 95%, n of 1,000, level of 99%

The hypothesis is not supported.

.

197

Hypothesis: mean greater than 125, mean of 135, s.d. of 15, n of 500, level of 95%

The hypothesis is supported.

Hypothesis: percent less than 33%, p of 31, n of 120,level of 99%

The hypothesis is not supported.

16. The manager of Washington State Environmental Services Division wants a survey that will tell her how many households in the city of Seattle will voluntarily identify environmentally hazardous household materials like old cans of paint, unused pesticides, and other such materials than cannot be recycled, but should be disposed of and then transport all of their environmental hazardous items to a central disposal center located in the downtown area and open only on Sunday mornings. A random survey of 500 households determines that 20% of households would do so, and that each participating household expects to dispose of about five items per year with a standard deviation of two items. What is the value of parameter estimation in this instance?

Parameter estimation will be useful in estimating the percent of households in the population who will take part in the recycling program, and it can be used to estimate the number of aluminum cans that each household will recycle monthly, on the average.

To estimate the percent of households, apply the confidence interval formula for a percent. The 95% level of confidence is used below.

.

198

To estimate the number of cans, apply the confidence interval formula for a mean.

Note, however that the sample size is only 100, because only 20% of the 500 respondents indicated they would recycle and gave an indication of the number of cans per month. The 95% level of confidence is used below.

Combining the two estimates with the population of the city will yield an estimate of the total number of aluminum cans that would be recycled monthly. For instance, in a city of 100,000, the lower limit would be 100,000 times 13.7% times 94.1 cans per

.

199

month, while the upper limit would be 100,000 times 26.3% times 105.9 cans per month.

17. It is reported in the newspaper that a survey sponsored by Forbes magazine with 200 Fortune 500 company top executives has found that 75% believe that the United States trails Japan and Germany in automobile engineering. What percent of all Fortune 500 company top executives believe that the United States trails Japan and Germany?

This question requires application of the formula for a confidence interval for a percent.

18.Alamo Rent-A-Car executives believe that Alamo accounts for about 50% of all Cadillacs that are rented. To test this belief, a researcher randomly identifies twenty major airports with on-site rental car lots. Observers are sent to each location and instructed to record the number of rental company Cadillacs observed in a four--hour period. About 500 are observed, and 30% are observed being returned to Alamo Rent-A-Car. What are the implications of this finding for the Alamo executives' belief?

The executives have stated a hypothesis of p=50%.The sample information can be used to test the hypothesis as follows (using 95% level of confidence):

.

200

A computed z of -9.76 does not support the hypothesis.

CASE SOLUTIONS Case 12.1The Pets, Pets, & Pets Survey Case Objective: Students must use the proper formulas and interpretations to perform the various generalization analyses necessary.

Answers to Case Questions 1. How often did Pets, Pets, & Pets customers visit it last year? ± = =4.4±1.96 4.98162 =4.4±1.96 =4.4±.76 3.6 to 5.2 times 2. How much did they typically spend on the last visit? ± = =18.2±1.96 8.50162 =18.2± =18.2±1.31 $16.89 to $19.51 3. How likely are they to buy a Pets, Pets, & Pets on their next visit? ± = =5.3±1.96 1.50162 =5.3±1.96 .12 =5.3±.23 5.1 to 5.5

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201

4. What percent are “regular” Pets, Pets, & Pets customers? ± =44.4±1.96 44.4 55.6 162 =44.4±1.96 3.9 =44.4 ±7.7

36.7% to 52.1%

5. What percent has recommended Pets, Pets, & Pets to friends? ± =82.1±1.96 82.1 17.9 162 =82.1±1.96 3.0 =82.1 ±5.9 76.2% to 88.0% 6. The manager at Pets, Pets, & Pets believes that, on average, store customers own two pets. Is this belief supported by the survey? = − =1.64−2.77162 = −.36.06 =−6.0 Because -6.0 is greater than -1.96, the hypothesis is not supported. The confidence interval is: ± = =1.64±1.96 .77162 =1.64±1.96 =1.64±.12 1.5 to 1.8 pets 7.

The manager also believes that Pets, Pets, & Pets monthly newspaper advertising is highly effective and reaches 80% of the store’s customers. Is this belief supported by the survey?

= − = 46.9−8046.9 53.1 162 =−33.13.9

= –8.49

Because -8.49 is greater than -1.96, the hypothesis is not supported. The .

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confidence interval is: ± =46.9±1.96 46.9 53.1 162 =46.9±1.96 3.9 =46.9 ±7.8

39.1% to 54.7%

Case 12.2 Integrated Case: The Advanced Automobile Concepts Survey Generalization Analysis Case Objective: Students must use the XL Data Analyst with the Advanced Automobile Concepts data set to perform and interpret various generalization analyses.

Answers to Case Questions 1. What percent of the American public owns the following type of vehicle? a. Standard vehicle b. Luxury vehicle c. SUV or van The “primary vehicle type” variable is categorical, so compute 95% confidence intervals to the two answer categories of “economy” and “standard.”

Primary Vehicle Price Type

Category Economy Standard Total of All Categories

Frequency 455 271 1000

Lower Upper Percent Boundary Boundary 45.5% 42.4% 48.6% 27.1% 24.3% 29.9%

“SUV or van” is a category in the Primary vehicle type variable, so compute a 95% confidence interval for it.

Primary Vehicle Type

Category

Lower Upper Percent Boundary Boundary

Frequency .

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SUV or van Total of All Categories

258 1000

25.8%

23.1%

28.5%

2. How does the American public feel about the following statements? a. Hybrid autos that use alternative fuels will reduce fuel emissions. b. Hybrid autos that use alternative fuels will keep gas prices down. c. Hybrid autos that use alternative fuels will slow global warming.

The same metric scale is used for all three statements, so compute 95% confidence intervals for the averages.

Variable Hybrid autos that use alternative fuels will keep gas prices down.

Hybrid autos that use alternative fuels will reduce fuel emissions.

Hybrid autos that use alternative fuels will slow down global warming.

Sample

Average

Standard Deviation

Lower Boundary

Upper Boundary

1000

5.8

2.2

5.6

5.9

1000

5.1

2.1

5.0

5.2

1000

4.1

2.1

3.9

4.2

3. Prtincipals at Advanced Automobile Concepts fully understand that there is resistance to change in Zen.There are some senior executives who grew up during America’s “romance with the automobile” era of the 1950s and 1960s and who believe that most Americans want a large and powerful automobile.These executives point out that SUVs are extremely popular despite global warming warnings that have been issued for the past decade and numerous gasoline price surges where filling up the gas tank of an SUV or a luxury automobile approached the $100 mark.These executives further believe that hybrid and alternative fuel automobiles are generally regarded by the American public as undesirable because they are perceived to be unattractive, boxy in appearance, and sluggish in acceleration.In fact, some of these senior executives believe that the probability of the American public buying the various hybrid vehicles in the next three years is something like the following: Hybrid Vehicle Type

Probability .

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Probability of buying a very small (one-seat) hybrid auto within 3 years?

Probability of buying a small (two-seat) hybrid auto within 3 years?

Probability of buying an economy size hybrid auto within 3 years?

Probability of buying a standard size synthetic fuel auto within 3 years?

Probability of buying a standard size electric auto within 3 years?

5% 5% 15% 15% 20%

Test these hypotheses with the findings from the survey.

Probability is measured with a metric scale, so use the average hypothesis test analysis. If the hypothesis is not supported, then compute a 95% confidence interval to estimate the true population average.

Hypothesis Test for an Average Variable Description Probability of buying a very small (one-seat) hybrid auto within 3 years

Sample

Sample Average

Hypoth.

Average

1000

13.8

5.0

Does the sample support the hypothesized average?

At 95% level of confidence, this hypothesis is: NOT SUPPORTED

Variable Probability of buying a very small (1 seat) hybrid auto within 3 years

Sample

Standard Lower Upper Average Deviation Boundary Boundary

1000

13.8

23.1

12.3

Sample

Sample Average

Hypoth.

Average

1000

20.6

5.0

Hypothesis Test for an Average Variable Description Probability of buying a small (two-seat) hybrid auto within 3 years

.

205

15.2

Does the sample support the hypothesized average?

At 95% level of confidence, this hypothesis is: NOT SUPPORTED

Variable Probability of buying a small (two-seat) hybrid auto within 3 years

Sample

Average

Standard Deviation

Lower Boundary

Upper Boundary

1000

20.6

19.3

19.4

21.8

Hypothesis Test for an Average Variable Description Probability of buying an economy size hybrid auto within 3 years

Sample

Sample Average

Hypoth.

Average

1000

30.1

15.0

Does the sample support the hypothesized average?

At 95% level of confidence, this hypothesis is: NOT SUPPORTED

Variable Probability of buying an economy size hybrid auto within 3 years

Sample

Average

Standard Deviation

Lower Boundary

Upper Boundary

1000

30.1

21.2

28.8

31.4

Hypothesis Test for an Average

.

206

Variable Description

Sample

Sample Average

Hypoth.

Average

Probability of buying a standard size hybrid auto within 3 years

1000

40.2

15.0

Does the sample support the hypothesized average?

At 95% level of confidence, this hypothesis is NOT SUPPORTED

Variable Probability of buying a standard size hybrid auto within 3 years

Sample

Average

Standard Deviation

Lower Boundary

Upper Boundary

1000

40.2

21.5

38.8

41.5

Hypothesis Test for an Average Variable Description

Sample

Sample Average

Hypoth.

Average

Probability of buying a large size hybrid auto within 3 years

1000

34.6

20.0

Does the sample support the hypothesized average?

At 95% level of confidence, this hypothesis is: NOT SUPPORTED

.

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Variable Probability of buying a large size hybrid auto within 3 years

Sample

Average

Standard Deviation

Lower Boundary

Upper Boundary

1000

34.6

22.1

33.3

36.0

3. Using the findings from the survey, estimate the number of vehicles of each of the following hybrid types that are expected to be purchased over the next three years. a. Very small (one-seat) hybrid auto b. Small (two-seat) hybrid auto c. Economy size hybrid auto d. Standard size hybrid auto e. Large size hybrid auto One must use the 95% confidence intervals for these averages to estimate the population averages. The lower boundary percent times “There is an estimated 111,617,402 American households (U.S. Census, 2006 estimate)” noted in the case. The boundaries are as follows:

Variable Probability of buying a large size hybrid auto within 3 years Probability of buying a standard size hybrid auto within 3 years Probability of buying an economy size hybrid auto within 3 years Probability of buying a small (two-seat) hybrid auto within 3 years Probability of buying a very small (one-seat) hybrid auto within 3 years

Sample

Average

Standard Deviation

Lower Boundary

Upper Boundary

1000

40.2

21.5

38.8

41.5

1000

34.6

22.1

33.3

36.0

1000

30.1

21.2

28.8

31.4

1000

20.6

19.3

19.4

21.8

1000

13.8

23.1

12.3

15.2

.

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The numbers of household estimates are as follows: Probability of buying a large size hybrid auto within 3 years Probability of buying a standard size hybrid auto within 3 years Probability of buying an economy size hybrid auto within 3 years Probability of buying a small (two-seat) hybrid auto within 3 years Probability of buying a very small (one-seat) hybrid auto within 3 years

.

209

43,351,759

46,321,661

37,136,071

40,192,465

32,152,202

35,086,121

21,647,841

24,316,205

13,783,639

16,978,117

CHAPTER 13 Finding Differences LEARNING OBJECTIVES •

To understand how market segmentation underlies differences analysis

•

To learn how to assess the significance of the difference between two groups’ percentages

•

To learn how to assess the significance of the difference between two groups’ averages

•

To understand when “analysis of variance” (ANOVA) is used and how to interpret ANOVA findings

•

To comprehend what analysis is used when the averages of two variables using the same metric scale are compared for differences

•

To gain knowledge of the “Differences” analyses available with XL Data Analyst

CHAPTER OUTLINE Why Are Differences Important?

Testing for Significant Differences Between Two Groups Differences Between Percentages for Two Groups Using the XL Data Analyst to Determine the Significance of the Difference between Two Group Percents Differences Between Averages for Two Groups Using the XL Data Analyst to Determine the Significance of the Difference Between Two Group Averages Testing for Significant Differences for More Than Two Group Averages Why Use Analysis of Variance?

Using the XL Data Analyst to Determine the Significance of the Difference Among More Than Two Group Averages

. 212

Flow Chart of Differences Analyses for Groups Testing for Significant Differences Between the Averages of Two Variables Using the XL Data Analyst to Determine the Significance of the Differences Between the Averages of Two Variables How to Present Difference Analysis Findings KEY TERMS Alternative hypothesis

Market segmentation

Analysis of variance (ANOVA)

Standard error of a difference between two averages

Group comparison table

Standard error of the difference between two percentages

Grouping variable

Target variable

TEACHING SUGGESTIONS 1. The chapter begins by claiming that market segmentation is very important and the basis for market researchers to investigate statistically significant differences among identifiable groups of consumers. Instructors may use their favorite or most familiar market segmentation example to augment or emphasize the segmentation differences central point.

2. To identify market segments is only the beginning point of the marketing research notion of significant differences. Once the segments are identified, marketing research requires that data be gathered about the consumption patterns of the market segments, and then, these patterns are assessed statistically for significant differences.

The statistical concepts used to compare segments are percentages and averages.

3. It is important to emphasize that marketing segmentation is a conceptual notion, whereas segments can be identified in a great many ways they are not managerially relevant until statistically significant differences between them are shown, that are useful from a marketing strategy standpoint.

As a simple example, take a florist that segments the local market by geographic area: North, East, West, and South. The average dollars spent per purchase is calculated for three flower-giving days in the year. Assume that differences of ±$5 are not significant.

Area North

Father’s Day Mother’s Day Valentine’s Day $20

$17

$50 .

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East

$21

$31

$14

West

$15

$17

$25

South

$55

$36

$10

What are the promotional implications of these findings?

Answer by day.

Father’s Day—promote heavily to the South Mother’s Day—promote heavily to the South and East Valentine’s Day—promote heavily to the North, moderately to the West, and lightly to the East and South 4. The differences between groups is taken with percentages first because the formulas are less complicated, and students can relate to them easier than they can relate to the averages differences formulas. The discussion moves quickly from formulas to XL Data Analyst interpreted output where the differences are identified for users.

Instructors who are more concerned with their students’ learning the formulas may wish to spend more time on formulas and have students do in-class or other exercises that require them to use these formulas correctly.

5. The assumption of equal variances in the two samples of a test for the significance of the difference between two group averages is not discussed in the text’s coverage of these computations. This is because the XL Data Analyst tests this condition and uses the correct formula based on this test. Instructors who desire their students to understand the equal vs. unequal variances notion in this statistical test will need to draw on material from sources other than this textbook.

.

214

6. The description of one-way analysis of variance is not in depth. Instructors who desire students to have more knowledge of ANOVA may use this description as a foundation and move to a more in-depth coverage with their own materials.

7. The Schaffer’s post hoc test was selected above other post hoc tests due to its popularity and easy of computation. It is planned that “pro” version of the XL Data Analyst will detect conditions such as variance differences and group sample size differences and select the most appropriate post hoc test.

8. Due to space constraints, the “How to Present Difference Analysis Finding” does not include any examples. Interested Instructors should consult our larger textbook, Marketing Research, 6th edition, by the same authors, for examples.

9. The integrated case, Advanced Automotive Concepts, in this chapter can be combined with the case in the next chapter. With a large class, Instructors can divide the class up (perhaps according to first letter of last name) and assign students the task of determining the target market for one of the new model concepts (super cycle, runabout sport, etc.). The case in Chapter 14 requires students to use the target market findings to determine promotional vehicle recommendations.

ANSWERS TO REVIEW QUESTIONS 1. What are differences and why should marketing researchers be concerned with them?

Differences are instances where two (or more) groups are not the same. Market segmentation holds that within a product market there are different types of consumers who have different requirements, and these differences can be the basis of marketing strategies, and this is why marketing managers are vigilant to differences.

2. What are the three ways a researcher can investigate for differences?

There are three ways a researcher can analyze for differences: (1) compare one group to another group, (2) compare three or more groups, or (3) compare on variable to another that uses the same scale.

3. Why does the nature of the scale (categorical or metric) being used matter when performing a differences test?

With a categorical scale, one must use percentage differences, while with a metric scale, one uses differences between averages. The formulas differ, but the underlying logic and interpretation of the differences tests are the same.

4. What is the null hypothesis and what is the alternative hypothesis for a differences test?

To test whether a true difference exists between two group percentages or averages, we test the null hypothesis that the difference in their population parameters is equal to zero. The alternative hypothesis is that there is a true difference between the two group percentages or averages that we are comparing. . 215

5. When the percentages or the averages of two groups are compared, what is the nature of the comparison operation?

It is a simple subtraction of one percentage (average) from the other percentage (average).

6. When a standard error of a difference (between percentages or averages) is computed, what two factors are taken into account, and how does each affect the size of the standard error?

In essence, the standard error of a difference is the sum of the standard errors of the two percentages or averages being compared. The standard error of the percent of the average is determined by variability (pxq or standard deviation) and sample size. As variability goes up, so does the size of the standard error of the difference, while as sample size goes up, the standard error of the difference goes down.

7. Describe how a directional hypothesis about the difference between two percentages or two averages is tested.

The procedure is identical to directional hypotheses that are stipulated in hypothesis tests. That is, you must first look at the sign of the computed z value to check that it is consistent with your hypothesized direction. Then you would use a cutoff z value such as 1.64 standard errors for the 95 percent level of confidence (2.33 standard errors for 99 percent level of confidence) because only one tail of the sampling distribution is being used.

8. What is ANOVA, and when is it used? Why is it termed efficient?

Analysis of variance, sometimes called ANOVA, is used to accomplish such differences tests for the averages when three or more groups are involved. ANOVA is a very efficient and convenient analysis that does all of these tests simultaneously.

One does not need to do all possible pairs of two group averages tests.

9. What is the null hypothesis in ANOVA? The null hypothesis in ANOVA is that none of the all possible group-to-group averages is significantly different; there is not one single significant difference that exists between any possible pair of groups.

10. How is a test of the difference between the averages of two variables different from a test of the difference between the averages of two groups with the same variable?

How is it similar?

It is different because the entire sample is used for each variable rather than different groups for the same variable. It is similar in logic and interpretation.

ANSWERS TO APPLICATION QUESTIONS 11. Are the following two sample results significantly different? . 216

a. Sample 1 mean of 10.6, s.d. of 1.5, n of 150; sample 2 mean of 11.7, s.d. of 2.5, n of 300; level of 95%

No, two means are not statistically significant in their differences. b. Sample 1 percent of 45%, n of 350; sample 2 percent of 54%, n of 250; level of 99%

The difference is significant as the computed z value is greater than 2.58.

.

217

c. Sample 1 mean of 1,500, s.d. of 550, n of 1,200; sample 2 mean of 1,250, s.d. of 500, n of 500, level of 95%

There is a statistically significant difference.

12. Demonstrate your understanding of your work in question 11, by drawing the sampling distributions of each case—a, b, and c—in the format presented in Figure 13.1. a. The sampling distributions will overlap to a great degree, and the computed z of .56 will be inside the 1.96 boundary. b. They will appear somewhat like those in Figure 13.1, with 45% and 54% as the respective midpoints. The computed z is 2.81, close to 2.58, but greater than 2.58. c. The two sampling distributions will have minimal overlap with respective midpoints of 1,500 and 2,500. The computed z is 9.12 a considerable distance away from 1.96.

13. A researcher is investigating different types of customers for a sporting goods store.

In a survey, respondents have indicated how much they exercise in approximate minutes per week. These respondents have also rated the performance of the sporting goods store across twelve difference characteristics such as good value for the price, convenience of location, and helpfulness of the sales clerks. The researcher used a 7point rating scale for these twelve characteristics where 1 = poor performance and 7 = excellent performance. How can the researcher investigate differences in the ratings based on the amount of exercise reported by the respondents?

There are three groups based on their weekly exercise time range, for example there could be a 60 minutes or less group, a 61–90 minutes group, and a 90+ minutes group. The ratings are on a 7-point metric scale, so the appropriate analysis is ANOVA for each characteristic using the three exercise groups.

14. A shoe manufacturer suspects there are six market segments that it can use effectively in its target marketing: toddlers, middle-school children, high school students, young and active adults, professionals, and senior citizens. How many pairs of averages can be assessed for significant differences? Specify each separate pair.

There are 18 separate pairs. Here is a matrix that identifies all of the pairs. . 218

Middle School Children Middle School Children Middle School Children Young and Active Adults Professionals

Toddler

Middle School Children

High School Students

1

2

3

Young and Active Adults 4

7

8 12

Professionals Senior Citizens 5

6

9

10

11

13

14

15

16

17 18

15. A marketing manager of a Web-based catalog sales company uses a segmentation scheme based on the incomes of target customers. The segmentation system has four segments: (1) low income, (2) moderate income, (3) high income, and (4) wealthy.

The company database holds information on every customer's purchases over the past several years, and the total dollars spent is one of the prominent variables. The marketing manager finds that the average total dollar purchases for the four groups are as follows: Market Segment Low income Moderate income High income Wealthy

Average Total Dollar Purchases $101 $120 $231 $595

Construct a table that is based on how the XL Data Analyst presents its findings for ANOVA that illustrates that the low- and moderate-income groups are not different from each other, but the other groups are significantly different from one another.

.

219

The result would look like the following:

Moderate income Moderate income Moderate income

Low income $101

Moderate income $120

High income $231

Wealthy $595

Equal

Unequal

Unequal Unequal

CASE SOLUTIONS Case 13.1 The Daily Advocate Lost Subscribers Survey Case Objective: Students must interpret the findings of several differences analyses and come up with market segmentation strategies based on these findings.

Answers to Case Questions 1. Why has the Daily Advocate’s circulation fallen in the face of a population boom in Capital City?

The table below is reconstructed from the one in the case, so only the variables with significant differences are reported.

Variable Analyzed Length of residence in the city Length of time as a subscriber Obtain news from the Internet Satisfaction with:.

Coverage of national news Coverage of national sports Editorial stance of the newspaper

Current Subscribers

Lost Subscribers

Difference

Finding

20.1 years 27.2 years 13%

5.4 years 1.3 years 23%

14.7 years 25.9 years -10.0%

5.5 5.7 6.1

2.3 3.2 4.0

3.2 2.5 2.1

Unequal Unequal Unequal Unequal Unequal Unequal Unequal

The lost subscribers are relatively new to the city (5.4 years), and their subscription length is short due to being newcomers and dropping the subscription. Compared to the current subscribers, the lost subscribers use the Internet as a news source. They are relatively unsatisfied with national news and national sports coverage by the Advocate, and they do not appreciate its editorial stance. Considering the influx of high-tech companies and positions into Capital City, these new residents probably view the Daily Advocate as a low-tech company.

2. What marketing strategies should the Daily Advocate consider in order to sustain itself as the primary news vehicle in Capital City? . 220

The new residents of Capital City are a new market segment in high-tech/Internet delivery of news, and they are definitely oriented toward national news and sports.

(They are probably oriented toward global news as well, but the survey did not address this topic.) It seems reasonable to suggest to the Daily Advocate that they strongly consider a web-based version of the newspaper with more emphasis on national news and sports and probably less editorial pontification.

Case 13.2 Integrated Case: The Advanced Automobile Concepts Survey Differences Analysis Case Objective: Students must perform the appropriate differences analyses on the Advanced Automobile Concepts survey data to answer the questions posed by the principals. Market segmentation differences are to be found and their appropriate target market descriptions generated.

Answers to Case Questions Your task is to apply appropriate differences analysis to your AAConcepts XL Data Analyst data set to determine the target market descriptions for each of the five possible hybrid models.

The six demographic variables to be used as grouping variables are: Gender (2 groups), Marital status (2 groups), Age category (7 groups), Education category (5 groups), Income category (5 groups), and Hometown size category (5 groups).

Preferences for each possible hybrid model (Super cycle one-seat; 120+ mpg city, Runabout Sport two-seat; 90 mpg city, 80 mpg highway, Runabout with luggage twoseat; 80 mpg city, 70 mpg highway, Economy four-seat; 70 mpg city; 60 mpg highway, and Standard four-seat; 60 mpg city; 50 mpg highway) are measured on a 7point metric scale.

It is more efficient to perform the differences tests by demographic variable. A summary table of the target market profiles follows these analyses.

Gender: males prefer the super cycle more than females; females prefer the standard size four-seat model more than males.

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Gender

Male

Female

Preference: Super Cycle oneseat hybrid

3.5

3.1

Sample Size

505

495

Preference: Runabout Sport two-seat hybrid

4.2

4.3

Sample Size

505

495

Preference: Runabout with Luggage two-seat hybrid

3.8

3.7

Sample Size

505

495

Preference: Economy fourseat hybrid

3.5

3.5

Sample Size

505

495

Preference: Standard fourseat hybrid

4.8

5.1

Sample Size

505

495

Difference Equal?* 0.4

No

0.0

Yes

0.1

Yes

0.1

Yes

-0.3

No

*Yes = Equal No = Not Equal at 95% level of significance Marital status: Unmarrieds prefer the Super Cycle and Runabout Sport more than marrieds; marrieds prefer the Runabout with luggage area and Standard four-seat hybrid model more than unmarrieds.

Marital status

Unmarried Married Difference Equal?*

Preference: Super Cycle oneseat hybrid

4.1

2.5

Sample Size

487

513

Preference: Runabout Sport two-seat hybrid

4.7

3.8

Sample Size

487

513

Preference: Runabout with Luggage two-seat hybrid

3.5

4.0

Sample Size

487

513

Preference: Economy fourseat hybrid

3.4

3.6

Sample Size

487

513

.

222

1.5

No

0.9

No

-0.5

No

-0.1

Yes

Preference: Standard fourseat hybrid

4.6

5.3

Sample Size

487

513

-0.8

No

*Yes = Equal No = Not Equal at 95% level of significance

Age category: Super Cycle is most preferred by the youngest drivers.

Group

Preference: Super Cycle one-seat hybrid 65 and 50 to 64 35 to 49 older 25 to 34

18 to 24

Sample Size

239

256

210

174

121

Average 50 to 64 35 to 49 65 and older 25 to 34

2.5

3.2 Not equal

3.3 Not equal Equal

3.3 Not equal Equal

4.9 Not equal Not equal

Equal

Not equal Not equal

Age category: Runabout Sport is most preferred by the youngest drivers.

Group

Preference: Runabout Sport two-seat hybrid 65 and 50 to 64 25 to 34 35 to 49 older

18 to 24

Sample Size

239

174

256

210

121

Average 50 to 64 25 to 34 35 to 49 65 and older

3.4

4.2 Not equal

4.3 Not equal Equal

4.4 Not equal Equal Equal

5.7 Not equal Not equal Not equal Not equal

Age category: Runabout with luggage is most preferred by the 18–34s.

Group

Preference: Runabout with luggage two-seat hybrid 65 and 50 to 64 older 35 to 49 18 to 24

25 to 34

Sample Size

239

210

256

121

174

Average 50 to 64

3.4

3.5 Equal

3.5 Equal

4.2 Not equal

4.7 Not equal

.

223

65 and older 35 to 49 18 to 24

Equal

Not equal Not equal

Not equal Not equal Equal

Age category: Economy four-seat hybrid is most preferred by the 50–64s

Group

Preference: Economy four-seat hybrid 65 and 18 to 24 25 to 34 35 to 49 older

50 to 64

Sample Size

121

174

256

210

239

Average 18 to 24 25 to 34 35 to 49 65 and older

1.8

2.5 Not equal

3.6 Not equal Not equal

4.0 Not equal Not equal Equal

4.6 Not equal Not equal Not equal Not equal

Age category: standard four-seat hybrid is most preferred by the 35–64s

Group

Preference: Standard four-seat hybrid 65 and 18 to 24 25 to 34 older 50 to 64

35 to 49

Sample Size

121

174

210

239

256

Average 18 to 24 25 to 34 65 and older 50 to 64

4.2

4.3 Equal

4.8 Not equal Not equal

5.3 Not equal Not equal

5.6 Not equal Not equal

Not equal

Not equal Equal

Education category: Super Cycle is most preferred by the less than high school diploma, high school diploma, and some college drivers Preference: Super Cycle one-seat hybrid

Group

Post graduate degree

College degree

Some college

Less than High School

High School diploma

Sample Size

72

222

214

194

298

Average

1.9

2.7

3.6

3.6

3.6

.

224

Post graduate degree

Not equal

College degree

Not equal

Not equal

Not equal

Not equal

Not equal

Not equal

Equal

Equal

Some college Less than High School

Equal

Education category: Runabout Sport is most preferred by the less than high school diploma, high school diploma and some college drivers Preference: Runabout Sport two-seat hybrid

Group

Post graduate degree

College degree

Some college

High School diploma

Less than High School

Sample Size

72

222

214

298

194

Average

3.3

3.4

4.6

4.6

4.7

Equal

Not equal

Not equal

Not equal

Not equal

Not equal

Not equal

Equal

Equal

Post graduate degree College degree Some college High School diploma

Equal

Education category: Runabout with luggage is most preferred by the some college and college degree drivers Preference: Runabout with Luggage two-seat hybrid

Group

Post graduate degree

High School diploma

Less than High School .

225

College degree

Some college

Sample Size

72

298

194

222

214

Average

3.2

3.2

3.3

4.3

4.6

Equal

Equal

Not equal

Not equal

Equal

Not equal

Not equal

Not equal

Not equal

Post graduate degree High School diploma Less than High School College degree

Equal

Education category: Economy four-seat hybrid is most preferred by the college degree and post graduate degree drivers Preference: Economy four-seat hybrid High School diploma

Less than High School

Some college

Post graduate degree

College degree

Sample Size

298

194

214

72

222

Average

2.7

2.7

3.6

4.6

4.8

Equal

Not equal

Not equal

Not equal

Not equal

Not equal

Not equal

Not equal

Not equal

Group

High School diploma Less than High School Some college Post graduate degree

Equal

Education category: Standard four-seat hybrid is most preferred by the college degree and post graduate degree drivers Preference: Standard four-seat hybrid Less than High School

High School diploma

Some college

Sample Size

194

298

Average

4.1

4.9

Group

.

226

Post graduate degree

College degree

214

72

222

4.9

5.6

5.7

Less than High School

Not equal

Not equal

Not equal

Not equal

Equal

Not equal

Not equal

Not equal

Not equal

High School diploma Some college Post graduate degree

Equal

Income category: Super Cycle is most preferred by the lowest income drivers Preference: Super Cycle one-seat hybrid Group

$125K and more

$50K to $74K

$75K to $124K

$25K to $49K

Under $25K

Sample Size

70

194

137

343

256

Average

1.8

2.6

2.6

3.6

4.2

Not equal

Not equal

Not equal

Not equal

Equal

Not equal

Not equal

Not equal

Not equal

$125K and more $50K to $74K $75K to $124K $25K to $49K

Not equal

Income category: Runabout Sport is most preferred by the lowest income drivers

.

227

Preference: Runabout Sport two-seat hybrid $125K and more

$75K to $124K

$50K to $74K

$25K to $49K

Under $25K

Sample Size

70

137

194

343

256

Average

2.6

3.5

3.6

4.7

5.1

Not equal

Not equal

Not equal

Not equal

Equal

Not equal

Not equal

Not equal

Not equal

Group

$125K and more $75K to $124K $50K to $74K $25K to $49K

Not equal

Income category: Runabout Sport with luggage is most preferred by the $25K to $74K income drivers Preference: Runabout with luggage two-seat hybrid Group

$125K and more

$75K to $124K

Under $25K

$50K to $74K

$25K to $49K

Sample Size

70

137

256

194

343

Average

2.3

2.6

3.1

4.5

4.7

Equal

Not equal

Not equal

Not equal

Equal

Not equal

Not equal

Not equal

Not equal

$125K and more $75K to $124K Under $25K $50K to $74K

Equal

Income category: Economy four-seat hybrid is most preferred by the to $75K to $125K income drivers

.

228

Preference: Economy four-seat hybrid Group

Under $25K

$25K to $49K

$50K to $74K

Sample Size

256

343

Average Under $25K

2.7

2.9 Equal

$25K to $49K

$125K and more

$75K to $124K

194

70

137

3.6

5.2

5.5

Not equal

Not equal

Not equal

Not equal

Not equal

Not equal

Not equal

Not equal

$50K to $74K $125K and more

Equal

Income category: Standard four-seat hybrid is most preferred by the $50K to $125K and more income drivers.

Preference: Standard four-seat hybrid Group

Under $25K

$25K to $49K

$50K to $74K

$75K to $125K

$125K and more

Sample Size

256

343

194

137

70

Average Under $25K

4.5

4.9

5.2

5.3

5.7

Equal

Not equal

Not equal

Not equal

Equal

Equal

Not equal

Equal

Equal

$25K to $49K $50K to $74K $75K to $124K

Equal

Size of city category: Super Cycle is most preferred by those living in cities of 1 million and more

.

229

Preference: Super Cycle one-seat hybrid Group

Under 10K

10K to 100K

100K to 500K

500K to 1 million

1 million and more

Sample Size

154

177

176

226

267

Average Under 10K

2.4

2.6 Equal

3.2 Not equal

3.4 Not equal

4.2 Not equal

Not equal

Not equal

Not equal

Equal

Not equal

10K to 100K 100K to 500K 500K to 1 million

Not equal

Size of city category: Runabout Sport is most preferred by those living in cities of 1 million and more Preference: Runabout Sport two-seat hybrid Group

Under 10K

10K to 100K

100K to 500K

500K to 1 million

1 million and more

Sample Size

154

177

176

226

267

Average Under 10K

3.2

3.5 Equal

4.3 Not equal

4.4 Not equal

5.2 Not equal

Not equal

Not equal

Not equal

Equal

Not equal

10K to 100K 100K to 500K 500K to 1 million

Not equal

Size of city category: Runabout Sport with luggage is most preferred by those living in cities of 1 million and more

.

230

Preference: Runabout with luggage two-seat hybrid Group

100K to 500K

Under 10K

10K to 100K

500K to 1 million

1 million and more

Sample Size

176

154

177

226

267

Average

2.3

3.2

3.2

4.2

5.1

Not equal

Not equal Equal

Not equal Not equal

Not equal Not equal

Not equal

Not equal

100K to 500K Under 10K 10K to 100K 500K to 1 million

Not equal

Size of city category: Economy four-seat hybrid is most preferred by those living in cities of 100K to 1 million Preference: Economy four-seat hybrid Group

1 million and more

Under 10K

10K to 100K

100K to 500K

500K to 1 million

Sample Size

267

154

177

176

226

Average

2.5

3.4

3.5

4.1

4.2

Not equal

Not equal Equal

Not equal Not equal

Not equal Not equal

Equal

Not equal

1 million and more Under 10K 10K to 100K 100K to 500K

Equal

Size of city category: Standard four-seat hybrid is most preferred by those living in cities of less than 1 million

.

231

Preference: Standard four-seat hybrid Group

1 million and more

Under 10K

100K to 500K

10K to 100K

500K to 1 million

Sample Size

267

154

176

177

226

Average

3.6

5.4

5.4

5.4

5.5

Not equal

Not equal Equal

Not equal Equal

Not equal Equal

Equal

Equal

1 million and more Under 10K 100K to 500K 10K to 100K

Equal

Summary of target market profiles findings.

Marital status

Unmarrieds

Runabout with Runabout luggage Sport twotwo-seat seat hybrid hybrid Both Both genders genders Unmarrieds Marrieds

Age

18–24 years Less than h.s. to some college

18–24 years Less than h.s. to some college

Demographic variable Gender

Education

Income Home town size

Super Cycle oneseat hybrid Males

18–34 years Some college to college degree

Under $25K Under $25K $25K– $74K 1 million 1 million 1 million and more and more and more

.

232

Economy four-seat hybrid Both genders Either unmarrieds, or marrieds 50–64 years College degree and post graduate degree $75K to $124+K Under 100K to 1 million

Standard four-seat hybrid Females Marrieds 35–64 years College degree and post graduate degree $50K to $124+K Under 10K to 1 million

CHAPTER 14 Determining Relationships LEARNING OBJECTIVES •

To learn what is meant by a “relationship” between two variables

•

To understand when and how cross-tabulations with chi-square analysis are applied

•

To become knowledgeable about the use and interpretation of correlations

•

To learn about the application and interpretation of regression analysis

•

To become proficient in the use of the XL Data Analyst to execute various types of relationship analyses

CHAPTER OUTLINE What Is a Relationship Between Two Variables?

Categorical Variables Relationships Cross-Tabulation Analysis Types of Frequencies and Percentages in a Cross-Tabulation Table Chi-Square Analysis of a Cross-Tabulation Table How to Present a Significant Cross-Tabulation Finding How to Perform Cross-Tabulation Analysis with the XL Data Analyst Correlation: Assessing Metric Variables Relationships Correlation Coefficients and Covariation Statistical Significance of a Correlation

Rules of Thumb for Correlation Strength The Pearson Product Moment Correlation Coefficient How to Perform Correlation Analysis with the XL Data Analyst

How to Present Correlation Findings . 233

Regression Analysis Computing the Intercept and Slope for Bivariate Regression Testing for Statistical Significance of the Intercept and the Slope Making a Prediction with Bivariate Regression Analysis Multiple Regression Analysis Working with Multiple Regression Using “Dummy” Independent Variables Three Uses of Multiple Regression How to Use the XL Data Analyst to Perform Regression Analysis How to Present Regression Analysis Findings Final Comments on Multiple Regression Analysis Flow Chart on Relationship Analyses

KEY TERMS Additivity

Linear relationship

Bivariate regression analysis

Multiple R

Chi-square analysis

Multiple regression analysis

Coefficient of determination

Null hypothesis for a correlation

Column percentages table

“Observed frequencies”

Correlation coefficient

Pearson product moment correlation

Covariation

Regression analysis

Cross-tabulation analysis

Relationship

Cross-tabulation cell

Residuals

Cross-tabulation table

Row percentages table

Dependent variable

R-Square value

Dummy independent variable

Scatter diagram .

234

“Expected frequencies”

Screening device

Frequencies table

Slope

Independent variable

Standard error of the estimate

Intercept

Standardized beta coefficient

Least squares criterion

Straight line formula

TEACHING SUGGESTIONS 1. In this edition, we dropped the notion of the use of Boolean operators to describe how cross-tabulations are determined. Instructors may retain this approach in their class presentations as it utilizes a concept that students know and use (AND, OR, etc. in search engines) with a concept that is new to them (cross-tabulations of categorical variables). The description relies heavily on students’ understanding that the category or group labels are used. It is recommended that instructors strongly emphasize that group labels are being used throughout their coverage of cross-tabulations.

2. Cross-tabulation with Chi-square analysis is a nonparametric statistical concept.

Nonparametic procedures, of course, are very different from parametric statistical techniques. Instructors who are comfortable with them may wish to delve deeper into nonparametric statistical techniques (assumptions, distributions, scales, etc.) as a way of helping students keep cross-tabulation analysis separate from correlation or other analyses.

3. Experience has taught that students will become confused with initial encounters with the various types of frequencies and percentages possible in cross-tabulation tables.

The chapter takes students step-by-step through these slowly; however, Instructors should consider using class time to review the various steps so students will gain a conceptual understanding of what each type is and how it is used. At the very least, students should understand how row and column percentages are useful in identifying the underlying association once statistical significance is found. This fact is underlined by the XL Data Analyst, which provides row and column percentages only if the cross-tabulation relationship is significant at the 95 percent level of confidence.

4. The null hypothesis is omnipresent in associative analysis tests, and it is the foundation for practically all statistical tests. We recommend that Instructors continually remind students of the null hypothesis of no association as they review the various associative analysis tests. It may be worthwhile to remind students that the null hypothesis is present in statistical inference tests such as t-tests (no difference between the means of the two groups), or analysis of variance (no difference between any two group means). For Instructors’ information, the null hypothesis concept is emphasized in the next chapter, particularly with descriptions of bivariate and multiple regression analyses.

5. For Instructors who want their student to actually compute statistics, consider end-ofchapter questions numbers 14 and 15. Number 14 provides observed frequencies in a . 235

cross-tabulation along with the computed Chi-square value. It has only four cells. The answer to question 14 below has the XL Data Analyst cross-tabulation output with the row and cell percentages, so Instructors will be spared the requirement to compute them. Question 15 will require students to set up the cross-tabulation table and to determine the observed frequencies, plus do all other computations.

6. For Instructors who want to emphasize the scatter diagram interpretation of a correlation, consider using Application Question 16. The correlation matrix for all five variables is provided in the answer to question 16 below as are all the scatter diagrams. Students should be able to build the data set in Excel for Windows quickly, and they can have Excel create all possible scatter diagrams.

7. There are many nuances to regression analysis not treated in this chapter’s introduction to the topic. The intent is to describe the basic concepts and to have students identify their related values on a printout. Instructors should be aware that in no way will students become more than fundamentally knowledgeable about regression, running it and interpreting the findings.

.

236

8. A constraint in Excel is that it will allow no more than 16 independent variables when performing regression analysis. When students (or instructors) are confronted with a larger number of independent variables, we recommend the following approach. a. Identify groups of independent variables such as demographics, lifestyle characteristics, usage and/or behavioral variables, performance evaluations and/or satisfaction variables, etc. b. Use regression analysis to identify the statistically significant independent variables in the first group (such as usage and/or behavioral variables). c. Add the next group to these variables (such as lifestyle characteristics) and determine the statistically significant independent variables. (It is necessary to rerun the regression each time as the addition of new independent variables affects the outcome every time due to changes in sample size, non-inclusion of missing observations, etc.) d. Continue with this approach until the final set of statistically significant independent variables are determined.

9. When running multiple regression, the XL Data Analyst checks for multicolinearity among the independent variables. That is, a requirement of multiple regression is that there is not high correlation between any two independent variables. Using a “tolerance” test, the program will detect the level of multicolinearity and issue a warning if the level is too high. Processing stops. Users should systematically find the two independent variables with the highest correlation and eliminate one from the multiple regression analysis. This approach may take several iterations if several independent variables are highly correlated.

ANSWERS TO REVIEW QUESTIONS 1. What is a relationship between two variables, and how does a relationship help a marketing manager? Give an example using a demographic variable and a consumer behavior variable, such as satisfaction with a brand.

A relationship is a consistent and systematic linkage between the levels or labels for two variables. Relationships are invaluable tools for the marketing researcher, for a relationship can be used for prediction, and it fosters understanding of the phenomena under study.

.

237

An example would be income. If Lexus found that high income consumers were satisfied with their Lexus automobiles, it would know that it was successful in its marketing of the Lexus.

2. What is the basis for a cross-tabulation table? What types of variables are best analyzed with a cross-tabulation table and why?

With cross-tabulation, the two categorical variables are arranged in a crosstabulation table, defined as a table in which data are compared using a row-andcolumn format. Categorical variables are best analyzed with a cross-tabulation table.

3. Illustrate how a cross-tabulation cell occurs in a cross-tabulation table. Provide an example using the variables of gender (categories: male and female) and vehicle type driven (SUV, sedan, sports car).

The intersection of a row and a column in a cross-tabulation table is called a crosstabulation cell. In the example, the cells are males who drive SUVs, males who drive sedans, males who drive sports cars, females who drive SUVs, females who drive sedans, and females who drive sports cars.

The cells are empty, but students can fill in frequencies, row, or column percents in their examples.

Gender Vehicle type SUV Sedan Sports car

Male

Female

4. Describe chi-square analysis by explaining the following: a. Observed frequencies Observed frequencies are the counts observed by applying the Boolean operators to the data set. b. Expected frequencies Expected frequencies would perfectly embody these cell frequencies if there was no significant relationship present. c. Chi-square formula

.

238

The Chi-square formula compares observed to expected frequencies and determines, via that formula, how close these values are to each other.

5. When a researcher finds a statistically significant chi-square result for a crosstabulation analysis, what should the researcher do next?

When you determine that a significant relationship does exist (no support for the null hypothesis of no relationship), two additional cross-tabulation tables can be calculated that are very valuable in revealing underlying relationships. The column percentages table divides the raw frequencies by their associated column total raw frequency. The row percentages table presents the data with the row totals as the 100 percent base for each. Inspection of these tables will yield a pattern of relationship or association between the two variables being analyzed.

6. Use a scatter diagram and illustrate the covariation for the following correlations: a. –.99 The pattern would define a downward sloping ellipse that is very compact.

c. +.21 The pattern would be a fat ellipse barely discernable as sloping up. .

239

c. +.76 The pattern would be a discernable ellipse with an upward slope.

7. Explain why the statistical significance of a correlation is important. That is, what must be assumed when the correlation is found to not be statistically significant?

The null hypothesis for a correlation states that the population correlation coefficient is equal to zero. If this null hypothesis is rejected (statistically significant correlation), then you can be assured that a correlation other than zero will be found in the population. But if the sample correlation is found not to be significant, the population correlation will be zero.

.

240

8. Describe the connection between a correlation and a bivariate regression analysis. In your discussion, specifically note: (1) statistical significance, (2) sign, and (3) use or application.

A bivariate regression means there are only two variables involved. The correlation coefficient is based on a straight line relationship, meaning a linear regression of two variables. A statistically significant correlation indicates that there is a linear (bivariate regression) relationship, and the correlation’s sign will be the same as the sign of the regression beta coefficient. Correlation gives a sense of the general relationship (negative, zero, or positive), but bivariate regression yields a predictive relationship equation.

9. Relate how a bivariate regression analysis can be used to predict the dependent variable. In your answer, identify the independent and dependent variables, intercept, and slope. Also, give an example of how the prediction should be accomplished.

The dependent variable, y, is predicted by the equation y = a + bx where a is the intercept and b is the slope of the independent variable, x. Following is the formula for prediction using confidence intervals.

95% Confidence Interval for a predicted y value using a regression equation

Predicted y = a + bx Confidence Interval = Predicted y ± 1.96 times Standard Error of the Estimate

10. When a regression analysis is performed, what assures the researcher that the resulting regression equation is the best or optimal regression equation? Explain this concept.

When any statistical analysis program computes the intercept and the slope in a regression analysis, it does so on the basis of the “least squares criterion.” The least squares criterion is a way of guaranteeing that the straight line that runs through the points on the scatter diagram is positioned so as to minimize the vertical distances away from the line of the various points. In other words, if you draw a line where the regression line is calculated and measure the vertical distances of all the points away from that line, it would be impossible to draw any other line that would result in a lower total of all of those vertical distances.

11. How does multiple regression differ from bivariate regression? How is it similar?

Multiple regression analysis is an expansion of bivariate regression analysis such that more than one independent variable is used in the regression equation. The addition of independent variables makes the regression model more realistic because predictions normally depend on multiple factors, not just one.

Multiple regression and bivariate regression are similar as both rely on the y = a + bx linear relationship. There are just multiple x’s with multiple regression.

12. Define and note how each of the following is used in multiple regression: .

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a. Dummy independent variable A dummy independent variable is defined as one that is scaled with a nominal 0 vs. 1 coding scheme. The 0 vs. 1 code is traditional, but any two adjacent numbers could be used, such as 1 vs. 2. The scaling assumptions that underlie multiple regression analysis require that the independent and dependent variables both be at least metric. However, there are instances in which a marketing researcher may want to use an independent variable that is categorical and identifies only two groups. b. Standardized beta coefficients With the standardized beta coefficient direct comparisons may be made between the resulting betas. The larger the absolute value of a standardized beta coefficient, the more relative importance it assumes in predicting the dependent variable. With standardized beta’s the researcher can directly compare the importance of each independent variable with others. c. Multiple R Multiple R assesses how much of the dependent variable, y, is accounted for by the multiple regression result.

13. How should you regard your knowledge and command of multiple regression analysis that is based on its description in this chapter? Why? The coverage in this chapter introduces you to regression analysis, and it provides you with enough information about it to run uncomplicated regression analyses with your XL Data Analyst, identify the relevant aspects of the output, and to interpret the findings. However, it barely scratches the surface of this complex data analysis technique.

ANSWERS TO APPLICATION QUESTIONS 14. A researcher has conducted a survey for Michelob Light beer. There are two questions in the survey being investigated in the following cross-tabulation table.

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White collar Blue collar Totals

Michelob Light Buyer 152 14 166

Michelob Light Nonbuyer 8 26 34

Totals 160 40 200

The computed chi-square value of 81.6 is greater than the chi-square table critical value of 3.8. Interpret the researcher’s findings.

There is a significant Chi-square value (greater than the table value), so the researcher is justified in further manipulation of the cross-tabulation to find the pattern of relationship.

The row percentages table reveals that 95% of the white collar individuals are Michelob Light buyers, whereas only 35% of the blue collar individuals purchase Michelob light.

White collar Blue collar Totals

Michelob Light Buyer 95% 35% 83%

Michelob Light Nonbuyer 5% 65% 17%

Totals 100% 100% 100%

Similarly, the column percentages table shows that 92% of the Michelob buyers are white collar individuals while 76% of the nonbuyers of Michelob Light are blue collar workers.

White collar Blue collar Totals

Michelob Light Buyer 92% 8% 100%

Michelob Light Nonbuyer 24% 76% 100%

Totals 80% 20% 100%

15. Following is some information about ten respondents to a mail survey concerning candy purchasing. Construct the various different types of cross-tabulation tables that are possible. Label each table, and indicate what you find to be the general relationship apparent in the data.

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This small data set can be easily made into an XL Data Analyst Excel file. Here is the result of cross-tabulation analysis.

CROSSTABS VARIABLES ANALYZED Row Variable: Plain M&M’s

Column Variable: Peanut M&M’s Observed Frequencies Plain M&M’s

No

Yes

No

2 3 5

5 0 5

Yes

Grand Total

Grand Total 7 3 10

Statistical Values df

Chi Sq

4.29

1

Sig

0.04

There IS a significant association between these two variables.

(95% level of confidence) The column percents table shows that folks who eat peanut M&M’s do not eat plain ones.

Column Percents Plain M&M’s No Yes Grand Total

Peanut M&M’s No 40% 60% 100%

Yes 100% 0% 100%

Grand Total 70% 30% 100%

The row percents table shows that people who eat plain M&M’s do not eat peanut ones. . 244

Row Percents Plain M&M’s No Yes Grand Total

Peanut M&M’s No 29% 100% 50%

Yes 71% 0% 50%

Grand Total 100% 100% 100%

16. Morton O’Dell is the owner of Mort’s Diner, which is located in downtown Atlanta, Georgia. Mort’s opened up about twelve months ago, and it has experienced success, but Mort is always worried about what food items to order as inventory on a weekly basis. Mort’s daughter, Mary, is an engineering student at Georgia Tech, and she offers to help her father. She asks him to provide sales data for the past ten weeks in terms of pounds of food bought. With some difficulty, Mort comes up with the following list.

Mary uses these sales figures to construct scatter diagrams that illustrate the basic relationships among the various types of food items purchased at Mort’s Diner over the past ten weeks. She tells her father that the diagrams provide some help in his weekly inventory ordering problem. Construct Mary’s scatter diagrams with Excel to indicate what assistance they are to Mort. Perform the appropriate correlation analyses with the XL Data Analyst and interpret your findings.

Here is a correlation matrix built on the XL Data Analyst correlation operation, and the scatter diagrams follow. None of the correlations are significant (due to sample size of 10), but assume they are for pedagogical purposes.

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Fish Fowl Vegetables Desserts

Meat -0.14 -0.94 -0.67 -0.21

Fish

Fowl

Veggies

0.25 0.47 0.31

0.85 0.28

0.11

Implications are as follows.

If Mort has “Nebraska beef week,” cut back on fowl.

If Mort has “Maryland chicken week,” cut back on meat and increase veggies.

If Mort has “Florida fish week,” meat and fowl will not be affected, increase veggies.

Desserts are fairly independent of main entrees.

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17. A pizza delivery company like Domino’s Pizza wants to predict how many of its pizzas customers order per month. A multiple regression analysis finds the following statistically significant results: Variable Intercept Pizza is a large part of my diet.* I worry about calories in pizzas.* Gender (1 = female; 2 = male) Standard error of the estimate

Coefficient or Value +2.6 +.5 –.2 +1.1 +.2

* Based on a scale where 1= “strongly disagree,” 2 = “somewhat agree,,” 3 = “neither agree nor disagree,” 4 = “somewhat agree,” and 5=”strongly . 247

agree.”

Compute the predicted number of pizzas ordered per month by each of the following three pizza customers. a. A man who strongly agrees that pizza is a large part of his diet but strongly disagrees that he worries about pizza calories Y = 2.6 + .5 x 5 -.2 x 1 +1.1 x 2 = 2.6 + 2.5 -.2 + 2.2 = 7.1 ± 1.96 x .2 (6.7 to 7.5) b. A woman who is neutral about pizza being a large part of her diet and who somewhat agrees that she worries about calories in pizzas Y = 2.6 + .5 x 3 -.2 x 4 + 1.1 x 1 = 2.6 + 1.5 -.8 + 1.1 = 4.4 ± 1.96 x .2 (4.0 to 4.8) c. A man who somewhat disagrees that he worries about pizza calories and is neutral about pizza being a large part of his diet Y = 2.6 +.5 x 3 -.2 x 2 + 1.1 x 2 = 2.6 + 1.5 -.4 +2.2 = 6.1 ± 1.96 x .2 (5.7 to 6.5) 18. Segmentation Associates, a company that specializes in using multiple regression as a means of describing market segments, conducts a survey of various types of automobile purchasers.

The following table summarizes a recent study’s findings. The values are the standardized beta coefficients of those segmentation variables found to be statistically significant. Where no value appears, that regression coefficient was not statically significant.

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Interpret these findings for an automobile manufacturer that has a compact automobile, a sports car, and a luxury automobile in its product line.

Compact automobile buyers are younger, less educated, with larger families, and have lower income. They are strong on family values and bargain hunting. They have American pride, but they do not embrace change nor are they cosmopolitan. They are not financially secure.

Sports car buyers are younger, more educated, higher in income, and of small family size. They are cosmopolitan, active, and optimistic. They embrace change, although they are liberal (not conservative) and they do not hunt for bargains.

Luxury automobile buyers are older and have more income. They are financially secure and conservative. They are optimistic, embrace family values, and are conservative, but they are not active.

CASE SOLUTIONS Case 14.1 Friendly Market Versus Circle K

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Case Objective: This case is intended to give students practice on cross-tabulation analysis using an XL Data Analyst data set. The appropriate crosstabulation analyses follow each question along with interpretations.

1. Do customers patronize both Friendly Market and Circle K? CROSSTABS VARIABLES ANALYZED Row Variable ->> Do you use Friendly Market regularly?

Column Variable >> Do you use Circle K regularly?

Observed Frequencies

No Yes

Grand Total

No

Yes

13 26 39

77 46 123

Grand Total 90 72 162

There IS a significant association between these two variables.

(95% level of confidence) Column Percents

No Yes

Grand Total

No

Yes

33% 67% 100%

63% 37% 100%

No

Yes

14% 36% 24%

86% 64% 76%

Grand Total 56% 44% 100%

Row Percents

No Yes

Grand Total

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Grand Total 100% 100% 100%

There is a relationship, and the row percents table reveals that 64% of the Friendly Market Patrons also use Circle K. The column percent table shows that 37% of those who use Circle K also use Friendly Market.

2. What demographic characteristics profile Friendly Market’s customers? That is, what characteristics are related to patronage of Friendly Market?

CROSSTABS VARIABLES ANALYZED Row Variable ->> Where do you live?

Column Variable >> Do you use Friendly Market regularly?

Observed Frequencies

Own home Rent

Grand Total

No

Yes

24 66 90

5 67 72

Grand Total 29 133 162

There IS a significant association between these two variables.

(95% level of confidence) Column Percents

Own home Rent

Grand Total

No

Yes

27% 73% 100%

7% 93% 100%

No

Yes

83% 50% 56%

17% 50% 44%

Grand Total 18% 82% 100%

Row Percents

Own home Rent

Grand Total

Finding: Friendly Market users are mostly renters.

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Grand Total 100% 100% 100%

CROSSTABS VARIABLES ANALYZED Row Variable ->> What is your gender?

Column Variable >> Do you use Friendly Market regularly?

Observed Frequencies

Male Female

Grand Total

No

Yes

51 39 90

25 47 72

Grand Total 76 86 162

There IS a significant association between these two variables.

(95% level of confidence) Column Percents

Male Female

Grand Total

No

Yes

57% 43% 100%

35% 65% 100%

No

Yes

67% 45% 56%

33% 55% 44%

Grand Total 47% 53% 100%

Row Percents

Male Female

Grand Total

Grand Total 100% 100% 100%

Finding: Friendly Market users are more females than males.

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CROSSTABS VARIABLES ANALYZED Row Variable What is your work >> status?

Column Variable >> Do you use Friendly Market regularly?

Observed Frequencies

Full-time Part-time Retired/Do Not Work

Grand Total

No

Yes

46 28 16 90

42 17 13 72

Grand Total 88 45 29 162

There is NO significant association between these two variables.

(95% level of confidence) Finding: Work status is not related to patronage of Friendly Market.

3. What demographic characteristics profile Circle K’s customers? That is, what characteristics are related to patronage of Circle K? CROSSTABS VARIABLES ANALYZED Row Variable ->> Where do you live?

Column Variable ->> Do you use Circle K regularly?

Observed Frequencies

Own home Rent

Grand Total

No

Yes

8 31 39

21 102 123

Grand Total 29 133 162

There is NO significant association between these two variables.

(95% level of confidence)

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CROSSTABS VARIABLES ANALYZED Row Variable ->> What is your gender?

Column Variable ->> Do you use Circle K regularly?

Observed Frequencies

Male Female

Grand Total

No

Yes

15 24 39

61 62 123

Grand Total 76 86 162

There is NO significant association between these two variables.

(95% level of confidence)

CROSSTABS VARIABLES ANALYZED Row Variable ->> What is your work status?

Column Variable ->> Do you use Circle K regularly?

Observed Frequencies

Full-time Part-time Retired/Do Not Work

Grand Total

No

Yes

18 11 10 39

70 34 19 123

Grand Total 88 45 29 162

There is NO significant association between these two variables.

(95% level of confidence) Finding: None of the three demographic variables is related to use/nonuse of Circle K. This is typical for convenience stores as just about anyone uses them.

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4. What is the lifestyle profile related to Friendly Market’s customers?

In the output below, one finds the only related lifestyle dimension is “I shop where they know my name,” and 75% of the Friendly Market users agree with this statement. This is consistent with the approach of Friendly Market’s owner.

CROSSTABS VARIABLES ANALYZED Row Variable ->> I often shop for bargains.

Column Variable ->> Do you use Friendly Market regularly?

Observed Frequencies

Disagree Neutral Agree

Grand Total

No

Yes

1 17 72 90

3 18 51 72

Grand Total 4 35 123 162

There is NO significant association between these two variables.

(95% level of confidence)

CROSSTABS VARIABLES ANALYZED Row Variable ->> I always pay cash.

Column Variable ->> Do you use Friendly Market regularly?

Observed Frequencies

Disagree Neutral Agree

Grand Total

No

Yes

9 62 19 90

16 39 17 72

Grand Total 25 101 36 162

There is NO significant association between these two variables.

(95% level of confidence)

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CROSSTABS VARIABLES ANALYZED Row Variable ->> I like quick, easy shopping.

Column Variable ->> Do you use Friendly Market regularly?

Observed Frequencies

Disagree Neutral Agree

Grand Total

No

Yes

15 52 23 90

5 48 19 72

Grand Total 20 100 42 162

There is NO significant association between these two variables.

(95% level of confidence)

CROSSTABS VARIABLES ANALYZED Row Variable ->> I shop where they know my name.

Column Variable ->> Do you use Friendly Market regularly?

Observed Frequencies

Disagree Neutral Agree

Grand Total

No

Yes

43 29 18 90

2 15 55 72

There IS a significant association between these two variables.

(95% level of confidence)

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Grand Total 45 44 73 162

Column Percents

Disagree Neutral Agree

Grand Total

No

Yes

48% 32% 20% 100%

3% 21% 76% 100%

No

Yes

96% 66% 25% 56%

4% 34% 75% 44%

Grand Total 28% 27% 45% 100%

Row Percents

Disagree Neutral Agree

Grand Total

Grand Total 100% 100% 100% 100%

CROSSTABS VARIABLES ANALYZED Row Variable ->> I am always in a hurry.

Column Variable ->> Do you use Friendly Market regularly?

Observed Frequencies

Disagree Neutral Agree

Grand Total

No

Yes

41 44 5 90

27 36 9 72

Grand Total 68 80 14 162

There is NO significant association between these two variables.

(95% level of confidence) Case 14.2 Integrated Case: The Advanced Automobile Concepts Survey Relationships Analysis Case Objective: This case requires students to perform and interpret various relationship analyses using the XL Data Analyst. .

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Answers to Case Questions 1. Use each unique hybrid model demographic profile to determine whether or not statistically significant associations exist, and if they do, recommend the specific media vehicles for radio, newspaper, television, and magazines. Do not forget to use the Zen Motors advertising division’s preferred demographic.

In the case the preferred demographics are, “Nick says that for television and newspaper, they prefer to use education; for radio, they prefer to use age; and for magazines, they prefer to use income.” The media variables are all categorical variables, and the demographic variables are also catergorical, so cross-tabulation analysis is the proper analysis. The appropriate cross-tabulations follow, and then tables are provided with the correct interpretations of these findings for each target market.

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Television—Education

Newspaper— Education . 259

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260

Radio—Age

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261

Magazines—Income

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The best approach is to use the percents table that indicates the highest concentration (largest percents) within the demographic category for each relevant media type.

Because the demographic variable was selected as the column variable in the above analyses, the columns percentage tables are most informative. Here are the recommended media vehicles for each model target market.

Demographic variable Age Education Income

Demographic variable Age Education Income

Super Cycle one-seat hybrid 18–24 years Less than h.s. to some college Under $25K

Recommended Promotional Vehicles Radio: Pop & Chart TV: Reality, movies/mini-series, and comedy Newspaper: Entertainment and local news, perhaps sports Magazine: Music & entertainment

Runabout Sport twoseat hybrid 18–24 years Less than h.s. to some college Under $25K

Recommended Promotional Vehicles Radio: Pop & Chart TV: Reality, movies/mini-series, and comedy Newspaper: Entertainment and local news, perhaps sports Magazine: Music & entertainment

Runabout Demographic with variable Luggage two-seat hybrid Age 18–34 years Education Some college to college degree Income $25K–$74K

Recommended Promotional Vehicles Radio: Talk, and possibly Pop & chart and Country TV: Comedy and drama Newspaper: Local news and national news, perhaps sports Magazine: Music & Entertainment, Family & Parenting

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Demographic variable Age Education

Income

Demographic variable Age Education

Income

Economy four-seat hybrid 50–64 years College degree and post graduate degree $75K to $125+K Standard four-seat hybrid 35–64 years College degree and post graduate degree $50K to $125+K

Recommended Promotional Vehicles Radio: Easy listening TV: Drama and news/documentary Newspaper: Local news and national news, perhaps sports Magazine: Business & Money, perhaps Family & Parenting

Recommended Promotional Vehicles Radio: Country, jazz & blues, Easy listening TV: Drama and news/documentary Newspaper: Local news and national news Magazine: Family & Parenting, Business & Money

2. What is the lifestyle of each of the possible target markets, and what are the implications of this finding for the advertising message that would “speak” to this market segment when the hybrid model is introduced?

Lifestyle is often used to flesh out market segment profiles. By correlating the preference for each model type against the six lifestyle types, a single lifestyle will be found to have significant and at least moderately high strength.

The lifestyle for the Super Cycle is the “novelist.”

Preference: Super Cycle Sample one-seat hybrid Correlation Size Significant?* Strength With...

Lifestyle: Novelist 0.79 1000 Yes Moderate Lifestyle: Innovator 0.50 1000 Yes Weak Lifestyle: Trendsetter 0.20 1000 Yes None Lifestyle: Forerunner -0.31 1000 Yes Very weak Lifestyle: Mainstreamer -0.42 1000 Yes Weak Lifestyle: Classic -0.38 1000 Yes Very weak *Yes = significantly different from zero at 95% level of confidence. Error means no variance or some other data error. .

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The lifestyle for the Runabout Sport is the “innovator.”

Preference: Runabout Sample Sport two-seat hybrid Correlation Size Significant?* Strength With...

Lifestyle: Novelist 0.55 1000 Yes Weak Lifestyle: Innovator 0.73 1000 Yes Moderate Lifestyle: Trendsetter 0.22 1000 Yes Very weak Lifestyle: Forerunner -0.33 1000 Yes Very weak Lifestyle: Mainstreamer -0.40 1000 Yes Very weak Lifestyle: Classic -0.58 1000 Yes Weak *Yes = significantly different from zero at 95% level of confidence. Error means no variance or some other data error.

The lifestyle for the Runabout with luggage is the “trendsetter.”

Preference: Runabout with Sample Luggage two-seat hybrid Correlation Size Significant?* Strength With...

Lifestyle: Novelist 0.12 1000 Yes None Lifestyle: Innovator 0.14 1000 Yes None Lifestyle: Trendsetter 0.72 1000 Yes Moderate Lifestyle: Forerunner -0.23 1000 Yes Very weak Lifestyle: Mainstreamer -0.20 1000 Yes None Lifestyle: Classic -0.07 1000 Yes None *Yes = significantly different from zero at 95% level of confidence. Error means no variance or some other data error.

The lifestyle for the Economy, four-seat hybrid is the “forerunner.”

Preference: Economy fourSample seat hybrid Correlation Size Significant?* Strength With...

Lifestyle: Novelist -0.35 1000 Yes Very weak Lifestyle: Innovator -0.31 1000 Yes Very weak Lifestyle: Trendsetter -0.18 1000 Yes None Lifestyle: Forerunner 0.73 1000 Yes Moderate Lifestyle: Mainstreamer 0.37 1000 Yes Very weak Lifestyle: Classic 0.23 1000 Yes Very weak *Yes = significantly different from zero at 95% level of confidence. Error means no variance or some other data error.

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The life style for the Standard, 4-seat hybrid is the “mainstreamer.”

Preference: Standard fourSample seat hybrid Correlation Size Significant?* Strength With...

Lifestyle: Novelist -0.42 1000 Yes Weak Lifestyle: Innovator -0.37 1000 Yes Very weak Lifestyle: Trendsetter -0.17 1000 Yes None Lifestyle: Forerunner 0.35 1000 Yes Very weak Lifestyle: Mainstreamer 0.75 1000 Yes Moderate Lifestyle: Classic 0.28 1000 Yes Very weak *Yes = significantly different from zero at 95% level of confidence. Error means no variance or some other data error.

3. Perform the proper analyses to identify the salient demographic, belief, and/or attitude factors that are related to preferences for each of the five different hybrid models under consideration. With each hybrid automobile model, prepare a summary that: a. Lists the statistically significant independent variables (use 95% level of confidence). b. Interprets the direction of the relationship of each statistically significant independent variable with respect to the preference for the hybrid model concerned. c. Assesses the strength of the statistically significant independent variables as they join to predict the preferences for the hybrid model concerned.

The textbook notes that there are some variables that are recoded, and the new dataset is “AAConcepts.Recoded.xlsm. Specifically, the following variables are recoded with midpoints—size of hometown or city, age, education, and income.

This procedure converts categorical variables to metric ones, and allows them to be used as valid independent variables in a multiple regression analysis. Because gender and marital status have two categories (male—female, and unmarried— married), they can be used as dummy independent variables.

Because of the constraint in Excel with regards to the maximum number of independent variables, a recommended approach is to do the multiple regression analyses in blocks: (1) demographics, (2) beliefs toward global warming and gasoline; and (3) attitudes toward small and hybrid automobiles.

Following is a complete set of the multiple regression analysis tables for the preference for the Super Cycle one-seat hybrid.

Demographic variables—there is no trimmed table as all variables are found to be significant.

Results: Least Squares Method of Regression Dependent Variable Total Cases

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1000

Preference: Super Cycle oneseat hybrid Independent Variable(s) Income category Level of education Age category Number of people in household Marital status Gender Size of hometown or city Intercept *95% level of confidence

Coefficient -0.01 -0.14 -0.03 -0.43 -0.68 -0.37 0.00 8.27

Standardized -0.28 -0.22 -0.32 -0.34 -0.19 -0.11 0.34

Significant?* Yes Yes Yes Yes Yes Yes Yes Yes

Interpretation: Greater preference for the Super Cycle is found with individuals with relatively less: income, education, age, and family size, while greater preference is found with people living in larger cities. It is stronger with males and unmarried individuals. The standardized betas reveal that the most important variables are size of hometown or city and number of people in the household (tied) with age almost as important. The least important variable is gender. Note: This finding is consistent with the target market analyses performed in the previous chapter, and it provides more insight (unmarried males).

Belief variables Results: Least Squares Method of Regression Dependent Variable Total Cases

1000

Preference: Super Cycle oneseat hybrid Independent Variable(s)

Coefficient

Standardized

Significant?*

High gasoline prices will impact what type of autos is purchased.

Gasoline prices are too high now.

-0.01 0.01

-0.02 0.01

No No

Gasoline prices will remain high in the future.

0.01

0.01

No

We should be looking for gasoline substitutes.

Americans use too much gasoline.

0.07

0.09

Yes

0.01

0.01

No

Gasoline emissions contribute to global warming.

-0.14

-0.18

Yes

-0.07 0.06

-0.08 0.06

Yes No

0.00 3.57

0.00

No Yes

We need to do something to slow global warming.

Global warming is a real threat.

I am worried about global warming.

Intercept *95% level of confidence

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If found, variables with zero variance are not included

Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Super Cycle oneseat hybrid Independent Variable(s)

Coefficient

Standardized

Significant?*

We should be looking for gasoline substitutes.

0.07

0.09

Yes

Gasoline emissions contribute to global warming.

-0.11

-0.14

Yes

-0.07 3.82

-0.07

Yes Yes

We need to do something to slow global warming.

Intercept *95% level of confidence

Interpretation: The trimmed table reveals that more preference for the Super Cycle is found with individuals who believe stronger than average that we should be looking for gasoline substitutes and less preference is found with those who believe stronger that gasoline emissions contribute to global warming and that we need to do something to slow down global warming. The belief that gasoline emissions contribute to global warming is the most important one. So, to market to these individuals, claim that the Super Cycle is a good substitute gasoline vehicle and not that will slow down global warming.

Attitude Variables Results: Least Squares Method of Regression Dependent Variable Total Cases

1000

Preference: Super Cycle one-seat hybrid Independent Variable(s)

Coefficient

Standardized

Significant?*

Hybrid autos that use alternative fuels will slow down global warming.

-0.02

-0.03

No

Hybrid autos that use alternative fuels will keep gas prices down.

-0.04

-0.05

No

Hybrid autos that use alternative fuels will reduce fuel emissions.

-0.03

-0.04

No

Small autos with high mpgʼs will slow down global warming.

-0.01

-0.02

No

Small autos with high mpgʼs will keep gas prices stable.

0.11

0.16

Yes

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Small autos with high mpgʼs will reduce fuel emissions.

-0.01

-0.01

No

Very small autos with very high mpgʼs will slow down global warming.

-0.02

-0.03

No

Very small autos with very high mpgʼs will keep gas prices stable.

0.09

0.12

Yes

-0.01

No Yes

Very small autos with very high mpgʼs will reduce fuel emissions.

-0.01 Intercept 3.07 *95% level of confidence If found, variables with zero variance are not included

Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Super Cycle one-seat hybrid Independent Variable(s)

Coefficient

Standardized

Significant?*

Hybrid autos that use alternative fuels will keep gas prices down.

-0.07

-0.08

Yes

Small autos with high mpgʼs will keep gas prices stable.

0.10

0.15

Yes

0.08 2.83

0.11

Yes Yes

Very small autos with very high mpgʼs will keep gas prices stable.

Intercept *95% level of confidence

Interpretation: Greater preference for the Super Cycle is found in individuals who are less of the opinion that hybrid autos that use alternative fuels will keep gas prices down and more with the opinions that small autos with high mpg’s will keep gas prices stable and that very small autos with high mpg’s will keep gas prices stable. The small autos opinion is the most important one with respect to the preference for the Super Cycle. To market this model, use the claim that it will stabilize gas prices because of its small size, not because of its hybrid fuel.

Preference for Roundabout Sport two-seat hybrid Demographic variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Runabout Sport twoseat hybrid Independent Variable(s)

Coefficient

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269

Standardized

Significant?*

Income category Level of education Age category Number of people in household Marital status Size of hometown or city Intercept *95% level of confidence

-0.01 -0.14 -0.03 -0.60 0.27 0.00 8.91

-0.32 -0.21 -0.32 -0.49 0.08 0.38

Yes Yes Yes Yes Yes Yes Yes

Interpretation: Greater preference for the runabout sport model is found with individuals with relatively less: income, education, age, and family size, whereas greater preference is found with people living in larger cities. It is stronger with married individuals. The standardized betas reveal that the most important variable is the number of people in the household. The least important variable is marital status. Note: This finding is consistent with the target market analyses performed in the previous chapter, and it provides more insight (married males or females).

Belief Variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Runabout Sport twoseat hybrid Independent Variable(s) Gasoline emissions contribute to global warming.

Intercept *95% level of confidence

Coefficient

Standardized

Significant?*

-0.15 4.98

-0.20

Yes

Interpretation: The trimmed table reveals that more preference for the Runabout Sport model is found with individuals who believe less than average that gasoline emissions contribute to global warming. So, to market to these individuals, claim that the Runabout Sport is a good substitute gasoline vehicle that will reduce gasoline emissions.

Attitude Variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Runabout Sport twoseat hybrid Independent Variable(s) Hybrid autos that use alternative fuels will keep gas prices down.

Coefficient

Standardized

Significant?*

-0.07

-0.08

Yes

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Small autos with high mpgʼs will keep gas prices stable.

Very small autos with very high mpgʼs will keep gas prices stable.

Intercept *95% level of confidence

0.15

0.22

Yes

0.08 3.58

0.10

Yes Yes

Interpretation: Greater preference for the Super Cycle is found in individuals who are less of the opinion that hybrid autos that use alternative fuels will keep gas prices down and more with the opinions that small autos with high mpg’s will keep gas prices stable and that very small autos with high mpg’s will keep gas prices stable. The small autos opinion is the most important one with respect to the preference for the Super Cycle. To market this model, use the claim that it will stabilize gas prices because of its small size, not because of its hybrid fuel.

Runabout sport with luggage two-seat hybrid Demographic variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Runabout with luggage two-seat hybrid Independent Variable(s) Income category Level of education Age category Number of people in household Marital status Size of hometown or city Intercept *95% level of confidence

Coefficient -0.01 0.17 -0.02 -0.20 1.07 0.00 2.20

Standardized -0.23 0.25 -0.21 -0.15 0.29 0.50

Significant?* Yes Yes Yes Yes Yes Yes Yes

Interpretation: Greater preference for the Runabout with luggage model is found with individuals with relatively less: income, age, and family size, whereas greater preference is found with people with more income and living in larger cities. It is stronger with married individuals. The standardized betas reveal that the most important variable is the size of hometown or city. The least important variable is household size. Note: this finding is consistent with the target market analyses performed in the previous chapter, and it provides more insight (married males or females).

Belief Variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Runabout Sport twoseat hybrid

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Independent Variable(s) Gasoline emissions contribute to global warming.

Intercept *95% level of confidence

Coefficient

Standardized

Significant?*

-0.15 4.98

-0.20

Yes

Interpretation: The trimmed table reveals that more preference for the Runabout with luggage model is found with individuals who believe less than average that gasoline emissions contribute to global warming. So, to market to these individuals, claim that the Runabout Sport is a good substitute gasoline vehicle that will reduce gasoline emissions.

Attitude Variables There is NO linear relationship (95% level of confidence) Interpretation: There is no linear relationship; therefore no significant independent attitude variable is identified. Within the context of multiple regression, no attitudes toward very small, small, or hybrid automobiles are related to preference for the Runabout Sport with luggage model.

Economy four-seat hybrid Demographic variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Economy four-seat hybrid Independent Variable(s) Income category Level of education Age category Size of hometown or city Intercept *95% level of confidence

Coefficient 0.02 0.25 0.04 0.00 -2.29

Standardized 0.35 0.37 0.38 -0.21

Significant?* Yes Yes Yes Yes Yes

Interpretation: Greater preference for the Economy four-seat hybrid model is found with individuals with relatively more: income, education, and age, whereas less preference is found with people living in larger cities. The standardized betas reveal that it is strongest with age, education, and income (practically tied). The least important variable is city size. Note: This finding is consistent with the target market analyses performed in the previous chapter, and it provides more insight (married or unmarried males or females).

Belief Variables . 272

Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Economy four-seat hybrid Independent Variable(s) High gasoline prices will impact which types of autos are purchased.

Gasoline emissions contribute to global warming.

Global warming is a real threat.

Intercept *95% level of confidence

Coefficient

Standardized

Significant?*

0.07

0.09

Yes

0.21 -0.11 2.72

0.26 -0.11

Yes Yes Yes

Interpretation: Preference for the Economy four-seat hybrid model is greater with the belief that high gasoline prices will impact which type of autos are purchased and that gasoline emissions contribute to global warming. Less preference occurs with the belief that global warming is a real threat. The most important variable is the “gasoline emissions contribute to global warming” belief. Therefore, to market this model, do not use global warming as a fear factor, but point out that the hybrid will contribute less to gasoline emissions and that high gasoline prices can be countered by buying a hybrid model.

Attitude Variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Economy four-seat hybrid Independent Variable(s)

Coefficient

Standardized

Significant?*

Hybrid autos that use alternative fuels will slow down global warming.

0.08

0.10

Yes

Hybrid autos that use alternative fuels will keep gas prices down.

0.18

0.21

Yes

-0.06 2.41

-0.08

Yes Yes

Small autos with high mpgʼs will keep gas prices stable.

Intercept *95% level of confidence

Interpretation: Preference for the Economy four-seat hybrid model is greater with opinions that hybrid autos will slow down global warming and that they will keep gasoline prices down. It is less with the opinion that small autos with high mpg’s will keep gas prices stable. The hybrid autos keeping gas prices down variable is the most important. To market this model, claim that even though it is economy . 273

(mid-) size, its hybrid nature will keep gas prices down and will slow down global warming.

Standard four-seat hybrid model Demographic Variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Standard four-seat hybrid Independent Variable(s) Income category Level of education Age category Number of people in household Gender Size of hometown or city Intercept *95% level of confidence

Coefficient 0.00 0.19 0.03 0.47 0.24 0.00 0.52

Standardized 0.08 0.31 0.29 0.40 0.07 -0.40

Significant?* Yes Yes Yes Yes Yes Yes Yes

Interpretation: Preference for the Standard four-seat hybrid model is great with more: income, education, age, and household size. It is less with individuals living in large cities. It is greater with females than with males. The most important variable is household size, while the least important one is gender. These findings are consistent with the target market difference findings and add insight about the gender factor.

Belief Variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Standard four-seat hybrid Independent Variable(s) Gasoline emissions contribute to global warming.

Global warming is a real threat.

Intercept *95% level of confidence

Coefficient

Standardized

Significant?*

0.16 -0.07 4.59

0.23 -0.08

Yes Yes Yes

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Interpretation: Preference for the Standard four-seat hybrid model is greater with the belief that gasoline emissions contribute to global warming and less that global warming is a threat. The former belief is the more important one. To market this model, claim that as a hybrid, it has fewer gasoline emissions and therefore contributes less to global warming but do not use global warming as a fear factor.

Attitude Variables Trimmed Regression Analysis Results (Backward Stepwise) Dependent Variable Total Cases 1000 Preference: Standard four-seat hybrid Independent Variable(s)

Coefficient

Standardized

Significant?*

0.08

0.11

Yes

-0.08 4.91

-0.13

Yes Yes

Hybrid autos that use alternative fuels will keep gas prices down.

Small autos with high mpgʼs will keep gas prices stable.

Intercept *95% level of confidence

Interpretation: Preference for the Standard four-seat hybrid model is greater with the opinion that hybrid autos using alternative fuels will keep gas prices down and less with the opinion that small autos with high mpg’s will keep gas prices stable.

To market this model, point out that even though it is large or mid-size, it is a hybrid and thus can keep gas prices down.

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CHAPTER 15 Preparing and Presenting Your Research Report EARNING OBJECTIVES •

To appreciate the importance of the marketing research report

•

To learn about an online report writing tool, the iReportWriter Assistant, that will help you write better reports

•

To know what material should be included in each part of the marketing research report

•

To learn the basic guidelines for writing effective marketing research reports

•

To learn how to organize the written report by making effective use of headings and subheadings

•

To know how to use visuals such as figures, tables, charts, and graphs

•

To learn how to make pie and bar charts using XL Data Analyst and Excel

•

To understand there are ethical considerations in preparting visuals

•

To learn the basic principles for presenting your report orally

CHAPTER OUTLINE The Importance of the Marketing Research Report Improving the Efficiency of Report Writing Organizing Your Written Report Front Matter Title Page Letter of Authorization Letter/Memo of Transmittal Table of Contents . 277

List of Illustrations Abstract/Executive Summary Report Body Introduction Research Objectives Method Findings Limitations Conclusions and Recommendations End Matter Following Guidelines and Principles for the Written Report Form and Format Headings and Subheadings Visuals Style Using Visuals: Tables and Figures Tables Pie Charts Bar Charts Ensuring Ethical Visuals Presenting Your Research Orally

KEY TERMS Abstract/executive summary

List of illustrations

Appendices

Marketing research report

Bar charts

Memo of transmittal .

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Body

Method

Conclusions

Online reporting software

End matter

Oral presentation

Ethical visual

Pie charts

Figures

Recommendations

Findings

Research objectives

Front matter

Subheadings

Headings

Table

Introduction

Table of contents

Letter of authorization

Tables

Letter of transmittal

Title page

Limitations

Visuals

TEACHING SUGGESTIONS 1. With the 3rd edition of our textbook, we introduce the iReportWriting Assistant. This is a web-based set of resources for report writing including PowerPoint show, templates, grammar help and quizzes, citations reference sites, entire marketing research report example, and discussion on plagiarism. Previously, the plagiarism material was included in Chapter 15; however, because it is covered in the iReportWriting Assistant, this material was removed to avoid redundancy. Instructors should consider using this resource according to the backgrounds and needs of their students.

2. Although the comments in the chapter about good form, proper format, and professional appearance of the research report are indisputable, a way to convince students of these necessities of report preparation is to show them a report that does not possess these characteristics. If an Instructor has had an experience where a student team or an individual student’s report was especially atrocious, it may be useful to make visuals of selected “bad” spots and show them to the class and thereby generate class discussion on the errors and how the flaws should have been handled.

3. As Instructors review the various sections of a research report, an effective approach is to show or give students examples of each one. Use student reports from previous classes, or use professional reports that have been acquired from consulting, business contacts, or colleagues.

4. A form of oral presentation that is not emphasized in the chapter, but one that is .

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nonetheless important is the use of PowerPoint or PowerPoint-like “slide shows.”

With the use of such graphics programs, effective and impressive bullet charts, tables, and visuals can be produced. Animation or other display options can be used very effectively. Most students are quite familiar with presentation software such as PowerPoint, and it works seamlessly with Microsoft Excel and Microsoft Word, so the expectation for any oral presentation is that it will be done with a slide show.

5. If students are to prepare team research project reports for sponsors, there is a concern about quality control. One way to gain uniformity in team reports is to provide students with copies of previous reports and to tell them to use these as “templates” for form, format, flow, and style. Some Instructors retain word processor files of finished reports and provide them to students as “templates” or electronic examples.

After all, this is the way practicing marketing researchers prepare their reports.

6. We have included step-by-step instructions on how to create Excel graphs. If students are familiar with word processing, other spreadsheet, graphics, or other software and can produce professional quality tables and graphs with these, they have the option of using these programs. Excel table output is transportable to other software as raw data tables.

7. With team projects, students sometimes fail to utilize specialization. That is, the team members should identify members’ special skills and allocate the work of the project accordingly. For instance, one team member may be the statistical analysis specialist, another may be the graphics person, and another may be the report writer. It is also prudent to have the team identify an “enforcer” or someone who makes sure that the specialists perform their work on time.

8. The Advanced Automobile Concepts integrated case can be used as the basis for a complete report because it has the problem statement, research objectives, research method, and dataset. To do so, would be an extension of the Advanced Automobile Concepts case at the end of Chapter 15.

ANSWERS TO REVIEW QUESTIONS 1. Briefly explain the features of the iReportWriting Assistant.

The iReportWriting Assistant that is described at the beginning of the chapter has the following features: • • • • • • • •

The Writing Process PowerPoints that describe pre-writing, writing, and revising.

Templates for title page, letter of authorization, letter of transmittal, table of contents, list of illustrations, and research objectives.

Headings and Subheadings descriptions.

Online Grammar Help in the form of the Purdue Online Writing Lab (OWL), which has grammar help and many writing resources including Flash movies.

Grammar Quizzes links to online tests.

Citations Sites links for general as well as electronic citations.

Marketing Research Report example.

Plagiarism definition with several links to useful sites. . 280

2. Discuss the relative importance of the marketing research report to the other stages in the marketing research process.

The marketing research report is the product that represents the efforts of the marketing research team, and it may be the only part of the project that the client will see. If the report is poorly written, riddled with grammatical errors, sloppy or inferior in any way, the quality of the research (including its analysis and information) becomes suspect and its credibility is reduced. If, on the other hand, all aspects of the report are done well, the report will not only communicate properly, but it will also serve to build credibility.

3. What are the components of the marketing research report?

These are identified in Table 15.1 and each one is described in the chapter.

• Front Matter o Title Page o Letter of Authorization o Letter/Memo of Transmittal o Table of Contents o List of Illustrations o Abstract/Executive Summary • Body o Introduction o Methodology o Results o Limitations o Conclusions and Recommendations • End Matter 4. When should you include or omit a letter of authorization?

The letter of authorization is the marketing research firm‘s certification to do the project. It includes the name and title of the persons authorizing the research to be performed, and it may also include a general description of the nature of the research project, completion date, terms of payment, and any special conditions of the research project requested by the client or research user. If you allude to the conditions of your authorization in the letter/memo of transmittal, the letter of authorization is not necessary in the report. However, if your reader may not know the conditions of authorization, inclusion of this document is helpful.

5. When should a letter of transmittal versus a memo of transmittal be used?

Use a letter of transmittal to release or deliver the document to an organization for which you are not a regular employee. Use a memo of transmittal to deliver the document within your own organization. The letter/memo of transmittal describes the general nature of the research in a sentence or two and identifies the individual who is releasing the report. The primary purpose of the letter/memo of transmittal is to orient the reader to the report and to build a positive image of the report.

6. What is the difference between a table and a figure? . 281

Tables are words or numbers that are arranged in rows and columns.

Figures are graphs, charts, maps, pictures, and so on.

7. Why is the abstract or executive summary important?

The abstract or executive summary is a “skeleton” of your report. It serves as a summary for the busy executive or a preview for the in-depth reader. It is important because it may be the only part of the report read by some managers.

8. What should be covered in the body of the report?

The body is the bulk of the report. It contains an introduction to the report, an explanation of your methodology, a discussion of your results, a statement of limitations, and a list of conclusions and recommendations.

9. What are your options regarding placement of the research objectives in the report?

Research objectives may be listed either as a separate section or within the introduction section.

10. Discuss what information should be included in the method section of the report.

The method describes, in as much detail as necessary, how you conducted the research including a description of the sample plan and sample size determination, the method of gathering data and how the data are analyzed.

11. What is found in the findings section and how is this information logically organized?

The findings section is the most important and most detailed portion of the report.

This section should be organized around the research objectives for the study.

Normally, the findings are presented by tables, charts, figures, and other appropriate visuals that are accompanied by short narrative descriptions.

12. Distinguish conclusions from recommendations.

Conclusions are the deductions and inferences that have come about based on the research findings.

Recommendations are suggestions for how to proceed based on the conclusions.

13. Why are headings and subheadings important to use?

In a long report, your reader needs signals and sign posts that serve as a road map.

Headings and subheadings perform this function. Headings indicate the topic of each section. All information under a specific heading should relate to that heading, and subheadings should divide that information into segments. A new heading should introduce a change of topic.

14. Illustrate the differences between levels of headings. . 282

Headings indicate the topic of each section. All information under a specific heading should relate to that heading, and subheadings should divide that information into segments.

15. What are guidelines to preparing tables, pie charts, and bar charts?

Tables allow the reader to compare numerical data.

Effective table guidelines are as follows: • Use no or only one decimal place (12% or 12.2%) unless more are called for by convention ($12.23).

• Place items you want the reader to compare in the same column, not the same row.

• If you have many rows, darken alternate entries or double-space after every five entries to assist the reader to accurately line up items.

• Total columns and rows when appropriate (100%).

Some guidelines for pie charts are: • Have a limited number of segments (four to eight, at most).

• Place labels outside the pie slices to enhance readability for small slices.

• Include an understandable legend.

Some guidelines for bar charts are: • Put bars in logical order or arrangement.

• Label both axes.

• Ensure that bars are visually distinct.

• Use major and minor gridlines sparingly.

• Place labels near or in bars subject to readability.

16. What are guidelines to preparing ethical visuals?

To ensure that you have objectively and ethically prepared your visuals you should do the following: • Double and triple check all labels, numbers, and visual shapes. A faulty or misleading visual discredits your report and work.

• Exercise caution if you use three-dimensional figures. They may distort the data by multiplying the value by the width and the height.

• Make sure all parts of the scales are presented. Truncated graphs (having breaks in the scaled values on either axis) are acceptable only if the audience is familiar with the data.

APPLICATION QUESTIONS 17. Assume you have conducted a marketing research project for your university and that the project basically consists of providing them with a descriptive study of the student population. You’ve collected demographic information and information on students’ attitudes toward various campus services and student life activities. Write a title page for the report. Would you use a memo or letter of transmittal? Write it. . 283

The title page: STATE U STUDENT PROFILE: DEMOGRAPHIC DESCRIPTION AND STUDENTS’ ATTITUDES TOWARD CAMPUS SERVICE AND CAMPUS LIFE Prepared for Dr. John Appleseed Dean, Office of Student Services Prepared by Sydney Student Marketing Major Date Because the researcher is “within” State U (i.e., is a student), he should prepare a memo of transmittal. It should be personal and slightly informal. Here is a sample: Dr. Appleseed: I have completed the survey of State U students’ demographic profile as well as their attitudes on various State U student services and campus life factors. With a representative sample of 250 State U students, I found that most are very satisfied with the University’s services and campus life in general. However, you may consider a follow-up survey on the registration experiences of State U students as this area received an overall “Fair” rating.

Thank you for the opportunity to use my research skills to execute a survey for State U. Respectfully, Sydney Student 18. Using your XL Data Analyst and Excel, prepare a pie chart and a bar chart using any data you have.

Students should select a categorical variable and create a chart. If a pie chart, it should have no more than eight segments, and the percentages should be outside the pie slices. Students can use the “create graph” feature in the “Summarize-Percents” operation, but they should realize the six segment rule from the chapter. With a bar chart, students must use Excel’s graphing features applied to a table or convert an XLDA pie chart using these features. Regardless, the figures should be clear and readable.

19. Why do you think we included a discussion of ethics in preparing visuals? If you wanted to make your pie chart or bar chart you prepared in the question above unethical, describe what you could do. . 284

An unethical figure misrepresents the findings. It intentionally distorts the figure so its interpretation is slanted in some way, and not consistent with the actual data. To render a figure unethical, do not do or do the opposite of what was recommended in order to have ethical charts. That is, • Do not double and triple check all labels, numbers, and visual shapes. In fact, mislabel, misnumber, or make the shapes incorrect.

• If you use three-dimensional figures, distort the data by multiplying the value by the width and the height.

• Truncate graphs (having breaks in the scaled values on either axis) so they are misleading.

20. Now look at some of the answers you have written above. Evaluate your answers in light of the information contained in this chapter in Marketing Research Application 15.2 on developing logical paragraphs.

This question should sensitize students to the fact that answering review or application questions is not reporting writing. The typical answer (unless specifically required to be in paragraph form by the Instructors) will be curt, cryptic, and most likely without a topic sentence, transitions, and contrary to the guidelines for writing paragraphs.

21. Visit your library and ask your reference librarian if he or she is aware of any marketing research reports (you can also ask for “reports written by faculty of the university”) that have been placed in the library. Chances are good that you will be able to find several reports of various kinds. Examine the reports. What commonalities do they have in terms of the sections that the authors have created?

Look at the sections carefully. What types of issues were addressed in the introduction section? The method section? How did the authors organize all of the information reported in the findings section? Are recommendations different from conclusions?

The point of this exercise is to make students realize that the report format and it sections are fairly universal to research reports in professional areas. The specific terms used in the section titles may differ somewhat, but in general the Introduction will be a statement of the problem, its background, and research objectives. The method section will describe the mechanics of the survey—questionnaire design, data collection method, sample plan, and other steps. The findings section will have tables and figures, organized in some logical fashion, most probably according to the research objectives. The conclusions should be identified separately from the recommendations either as subsections, or the appropriate conclusion(s) may be identified and then paired with the recommendation(s).

CASE SOLUTIONS Case 15.1 Integrated Case: Advanced Automotive Concepts: Using iReportWriter Assistant Case Objective: This case requires students to identify features of iReportWriter Assistant. . 285

Answers to Case Questions 1. What is it about the information in this case that Rogers should consider doing before he actually begins to write the report? Name some specific issues Rogers should address.

Rogers needs to revisit the consider the factors identified in the chapter under “Organizing the Written Report,” specifically: • • • • • • • • •

What message does he want to communicate?

What is his purpose?

Who is the audience?

Who is the primary audience??

What does the audience know?

What does the audience need to know?

What biases or preconceived notions of the audience might serve as barriers to his message?

What strategies can he use to overcome these negative attitudes?

What are his audience’s interests, values, and concerns?

2. Should Rogers include the standard “Conclusions and Recommendations” section of the report? Why or why not?

Yes, he should include this section. However, although his conclusions about the findings can be stated with considerable confidence, Rogers does not know all of the issues (such as funding, timing, internal politics, etc.) involved with Advanced Automobile Concepts, so his recommendations should be tentative. He should offer tentative recommendations, at best.

3. We are told that Rogers has made a list of issues to include in the “Method” section of the report. What, if anything, is included in the iReportWriter Assistant that could help him ensure that he has included everything?

There is a template for the Method Section of a marketing research report, and there is an example report that has a Method Section.

4. What section of the iReportWriter Assistant should Rogers seek out to help him with properly citing the secondary sources used in the marketing research report?

The IReportWriter has a feature the assists you in using the proper citations.

Case 15.2 Integrated Case: Advanced Automotive Concepts: Making a PowerPoint Presentation Case Objective: This case study requires students to think about and to use XL Data Analyst to construct presentation quality tables and graphs. The Advanced Automobile Concepts survey dataset is to be used.

Answers to Case Questions . 286

Students should follow the instructions in Chapter 15 in order to create professional quality tables and graphs.

1. Using a word processing program, write out several of the statements that you think would be appropriate to present to the client, Nick Thomas, for an oral presentation.

Nick will be interested in the key findings, so here is a quick summary.

• • • • • • • •

Americans are concerned about global warming.

Americans think that gasoline emissions contribute to global warming.

Americans thinks that gasoline prices are high and will rise in the future.

Some Americans believe that hybrid and/or synthetic fuel automobiles will slow down global warming.

Some Americans expect to purchase a hybrid automobile in the next three years.

All five of the hybrid model vehicles proposed by Advanced Automobile Concepts are desirable to some Americans.

Each hybrid model vehicle proposed by Advanced Automobile Concepts appeals to a distinct market segment as defined by demographics and lifestyle.

The hybrid model market segments have distinct media profiles that may be used for effective promotion campaigns.

2. Import the statements you prepared in question 1 into PowerPoint using copy and paste. Experiment with different color text and font sizes and styles.

Students should follow the guidelines in Chapter 15 for proper presentation format.

3. Using the XL Data Analyst, run several frequency distributions. Using Excel or Microsoft Word, select a table style that embodies the guidelines for good tables provided in this chapter. Make any modifications to the table style that result in an effective table format.

Students should use the guidelines and instructions in Chapter 15 to accomplish this task. There are many variations that are possible under the Excel Table style or Word Table tools. Generally, the “cleaner“ the table, the better; however, some students may come up with variations that are colorful or otherwise creative.

4. Using Excel, make a bar chart of the answers to the question regarding the variable “Americans Use Too Much Gasoline.” Experiment with the different options of bar charts available to you in Excel. Select a bar chart and import that chart into PowerPoint using copy and paste. Experiment with making edits on your slide.

Again, students should use the guidelines and instructions in Chapter 15 to accomplish this task. The possible variations are myriad. This exercise is intended to provide practice for students who are going to use Excel (or PowerPoint) graphics features to create professional graphs in their final reports.

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