SOLUTIONS for Basic Marketing Research:Customer Insights & Managerial 10th Edition by Brown; Suter &

SOLUTION MANUAL

SOLUTION MANUAL

SOLUTIONS MANUAL for Basic Marketing Research 10th Edition by Tom Brown, Tracy Suter, Gilbert Churchill Chapter 1-20

Solution Manual For Basic Marketing Research 10th Edition Tom J. Brown, Tracy A. Suter, Gilbert A. Churchill Chapter 1-20

Chapter 1: The Role of Marketing Research

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................... 2 Chapter Objectives ..................................................................................................................................... 2 Complete List of Chapter Activities and Assessments............................................................................. 2 Key Terms.................................................................................................................................................... 2 What's New in This Chapter ..................................................................................................................... 3 Chapter Outline .......................................................................................................................................... 3 Review Questions ........................................................................................................................................ 6 Additional Insights and Activities ............................................................................................................ 7

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Instructor Manual: Chapter 1: The Role of Marketing Research

PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to introduce marketing research as a much broader and more common activity than many people realize. In this chapter, we begin with the definition of marketing research before moving on to discuss the different types of firms that conduct marketing research. The variety of firms conducting research leads naturally into a brief introduction of the jobs in marketing research and the skills needed. The chapter concludes with the reasons anyone can benefit from a better understanding of marketing research.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 1-1

Define marketing research.

1-2

Discuss different kinds of firms that conduct marketing research.

1-3

List at least three different types of jobs in marketing research.

1-4

List three reasons for studying marketing research.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 1-1 1-2 1-3 1-4 1-1–1-4

PPT slide

Activity/Assessment

Duration

PPT slide 9 PPT slides 13–14 PPT slide 18 PPT slide 21 PPT slide 22

Knowledge Check 1.1 Discussion Activity Group Activity Polling Activity Self-Assessment

< 5 min 10–20 min 15–30 min 5–10 min 10–20 min

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KEY TERMS Marketing research The process of gathering and interpreting data for use in developing, implementing, and monitoring the firm’s marketing plans. [return to top]

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Instructor Manual: Chapter 1: The Role of Marketing Research

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

Several new examples to illustrate the role of marketing research include Rovio Entertainment, Starbucks, and the LEGO Group.

Data in Exhibit 1.3 reflects recent revenue figures for largest U.S. marketing research firms.

Research Window 1.2 has been updated with more recent compensation figures.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

1-1.

The Problem: Marketers Need Information (1-1, PPT Slides 4–9) a. Different companies need different kinds of information.  Information can be gathered in diverse ways.  The goal of marketing is to create exchanges with customers that satisfy the needs of both customers and marketers. b. Key elements of marketing managers’ focus:  The product or service  Price  Placement or channels of distribution  Promotion  Tangible elements at point of contact  Processes and people involved c. Not all factors in the marketing environment are under a marketer’s control.  Exhibit 1.1: The Environments Affecting Marketing d. Marketing research is the process of gathering and interpreting data for use in developing, implementing, and monitoring the firm’s marketing plans. e. Phases of the information management process:  Specifying what information is needed  Gathering relevant data from internal and external sources  Analyzing and interpreting data  Communicating results to decision makers f. Another way to look at marketing is to consider how management uses the information:  For planning  For problem solving  For control  Exhibit 1.2: Examples of Questions Marketing Research Can Help Answer g. Knowledge Check 1.1: < 5 minutes total. (PPT Slide 9)  Of the following, which provides the best definition of marketing research?

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Instructor Manual: Chapter 1: The Role of Marketing Research



1-2.

(a) Creating exchanges with customers that satisfy the needs of both customers and marketers (b) Specifying what information is needed, gathering relevant data, and communicating results to decision makers (c) Gathering and interpreting data for use in developing and implementing a firm’s marketing plans (d) Working behind the scenes to determine market segments and then taking marketing action Answer: c—Marketing managers have an urgent need for information—and marketing research is responsible for providing it. Marketing research is the firm’s formal communication link with the environment, which often contains factors not under an organization’s control.

Who Does Marketing Research? (1-2, PPT Slides 10–14) a. In 1879, advertising agency N. W. Ayers & Sons formalized the practice of marketing research by constructing a crude market survey of states’ and counties’ expected grain production. b. Three major categories of firms conduct marketing research:  Producers of products and services  Advertising agencies  Marketing research companies 1-2a. Companies That Produce or Sell Products and Services a. When firms could no longer sell all they could produce, they used marketing research to better gauge market needs and produce accordingly. b. Marketing research departments are common among industrial and consumer manufacturing companies. c. Publishers and broadcasters construct demographic profile data in order to sell advertising space/time. d. Financial institutions’ research includes forecasting, measuring market potential, market and sales analyses, and so on.

1-2b. Advertising Agencies a. Much agency research gauges consumer brand awareness and related advertising campaigns. b. They attempt to better understand consumer interest and behavior.

1-2c. Marketing Research Companies a. U.S. marketing research is a $47.1 billion industry. b. Some firms are large, with global reach (see Exhibit 1.3: The 10 Largest Marketing Research Firms in the United States), and may provide:  Standardized or syndicated research.  Information regularly collected and then sold to clients.  Custom-designed research. c. Other organizations that provide or conduct marketing research include government agencies, trade associations, and universities. d. Discussion Activity: 10–20 minutes total. (PPT Slides 13–14)

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Instructor Manual: Chapter 1: The Role of Marketing Research





1-3.

Discuss the distinction between the following terms often used in marketing research: (a) Consumer vs. customer (b) Information vs. data (c) Opportunities vs. problems Debrief: The terms consumer and customer are often used interchangeably in marketing; however, it is not always appropriate to do so. Consumers could be best characterized as the product users, while customers are most often the product buyers. This distinction could become important at the data collection stage of the marketing research process. If consumers and customers are not the same individuals, the insight from one may be more important for the researcher. Another important distinction is information versus data. Again, these terms are often used interchangeably, but it is important to recognize that information is defined as organized data. Said differently, having data does not necessarily mean one has information. Similar distinctions could be made between opportunities (positive connotation) and problems (negative connotation).

Job Opportunities in Marketing Research (1-3, PPT Slides 15–18) a. The U.S. Bureau of Labor Statistics projects a 22% growth rate for marketing research analysts through the year 2030.

1-3a. Types of Jobs in Marketing Research a. A marketing researcher’s tasks depend on the type of organization they work for. b. The type and scope of jobs vary greatly, ranging from simple analyses to management of a large marketing research department. c. Research Window 1.2: Marketing Research Company Job Titles and Mean Compensation d. A typical entry-level position might be a trainee position, becoming familiar with various tasks. e. Marketers need to be able to:  Interact effectively with others.  Understand business in general and marketing in particular.  Conduct basic numerical and statistical processes.  Work with the related technologies. f. Successful marketing researchers tend to be proactive rather than reactive and to fulfill the primary purpose of helping managers make better marketing decisions. g. Group Activity: 15–30 minutes total. (PPT Slide 18)  Form groups of three to five participants. As a group, do a brief online search to discover some of the possible job opportunities in marketing research.  As time allows, share with the larger class some of the jobs discovered and what, if anything, surprised you most about what you found.

1-4.

Why Study Marketing Research? (1-4, PPT Slides 19–21) a. Some students eventually become marketing researchers who:  Become ―information detectives.‖  Take data and convert them into information used to make decisions.

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Instructor Manual: Chapter 1: The Role of Marketing Research

b. Everyone needs to learn to be a smarter consumer of marketing research.  Suppliers use research to promote products/services.  Managers need to know how to evaluate the validity of marketing research. c. Every manager needs to gain an appreciation for the process, to understand what marketing research CAN and CANNOT do.  Does the process provide value to the organization? d. Polling Activity: 5–10 minutes total. (PPT Slide 21)  Of the reasons for learning about marketing research, which one is most applicable to your future plans? (a) Making marketing research a career (b) Being able to effectively evaluate marketing research information for business decision making (c) Interpreting and/or using market research in everyday life  Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response.

Self-Assessment (PPT Slide 22) 

Now that you’ve learned the definition of marketing research as well as some of the firms involved in it and what jobs might be available, do you see yourself with a career in marketing research? If yes, describe a bit more what you hope that career will look like. If no, describe what value a better understanding of marketing research can give you.

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REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

1. How is marketing research defined? What are the key elements of this definition?

Marketing research is the process of gathering and interpreting data for use in developing, implementing, and monitoring the firm’s marketing plans. Its key elements are the product or service, its price, its placement or the channels in which it is distributed, its promotion, the tangible elements at the point of contact, and the processes and people involved in making the exchange or delivering the service.

2. Who does marketing research? What are the primary kinds of research done by each enterprise?

Marketing research is performed by individuals and organizations, which includes companies that produce or sell products and services (marketing research gauges market needs), advertising agencies (to connect with target audiences and sell products), and marketing research companies (to make money as a business).

3. Why did marketing research begin to experience real growth after World War II? Marketing research grew after WWII because it was during this period that competition for customers greatly increased.

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Instructor Manual: Chapter 1: The Role of Marketing Research

4. In a large research department, who would be responsible for specifying the objective of a research project? For deciding on specific procedures to be followed? For designing the questionnaire? For analyzing the results? For reporting the results to top management?

A marketing researcher’s job varies, and it could range from simple analysis of questionnaire responses to the management of an entire research department.

5. What are the necessary skills for employment in a junior or entry-level marketing research position? Do the skills change as one changes job levels?

It is necessary to have good analytical, conceptual, communication, and human-relations skills to gain a job in marketing research. An applicant should also be comfortable working with numbers and with statistical techniques; it is necessary to maintain these skills as one changes job levels.

6. If so, what new skills are necessary at the higher levels?

Good management skills are necessary in the higher positions of marketing research. To be successful, one must be proactive rather than reactive, with the goal of helping managers make better marketing decisions.

7. Why is it important to study marketing research?

The text lists three reasons to study marketing research: (1) Some students pursue it as a career; (2) Almost everyone is a consumer of marketing research in one way or another; and (3) Managers must understand the process and potential outcomes of conducting research. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

1. Many students begin their marketing research courses not planning to be a researcher when they graduate. Accordingly, many of these students simply want to get through the course with a passing grade and get on to other classes that they perceive will be more interesting. Another set of students will have been putting off the course as long as possible, because they believe that they aren’t ―numbers people‖ and are afraid of the statistical and computational aspects of marketing research. Most students don’t realize just how much marketing research actually applies to them—whether or not they pursue a career in research. After all, we are in the Information Age. Further, most students will learn that the math isn’t nearly as difficult as they thought it might be. These points should be communicated very clearly during the first class session.

2. A useful starting point for the course is a quick review of the marketing concept (i.e., companywide consumer orientation with the objective of achieving long-run success by satisfying the wants, needs, and perceived needs of customers), particularly with respect to what it implies in regard to the task of marketing management.

3. It is possible that students place an overemphasis on analytical skills while either underestimating or not considering the other three common skill sets of marketing researchers. This presents a good opportunity to start at the end of the process and work backwards by asking questions such as: What

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Instructor Manual: Chapter 1: The Role of Marketing Research

skills are necessary to present the research findings (communication skills)? What skills are necessary to discover the research findings (analytical skills)? What skills are necessary to conduct the research (human-relations skills)? What skills are necessary to determine the research to be conducted (problem-solving/ conceptual skills)? Whether viewing the process backward or forward, students should see quickly that research is more than just numbers.

4. Ask the students to name (1) a company or industry and (2) the type of information that company or industry might need to make better marketing (i.e., product, placement, promotion, or pricing) decisions. Whether a manufacturer of goods or a provider of services, it would be difficult to find a company that cannot benefit from marketing research.

5. The fact that marketing research is used for many studies other than asking potential consumers what they want deserves emphasis. It is particularly productive when instructors are able to detail some of their own research and consulting experiences. Students are likely to have seen summaries of research studies (e.g., medical or political research is often newsworthy) in news reports, so this can serve to emphasize the research consumption reason for studying marketing research. Finally, even the best research conducted by the best researchers can be flawed, so it is critical for present and future managers to know that research is not perfect.

6. Marketing research can play numerous roles to assist marketing management in fulfilling its assigned tasks. The fact that marketing research is used for many studies other than asking potential consumers what they want deserves emphasis. It is particularly productive when instructors are able to detail some of their own research and consulting experiences. An alternative is to simply ask students what might be involved in a particular type of study. [return to top]

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 10 Chapter Objectives ................................................................................................................................... 10 Complete List of Chapter Activities and Assessments........................................................................... 10 Key Terms.................................................................................................................................................. 10 What's New in This Chapter .................................................................................................................... 11 Chapter Outline ........................................................................................................................................ 11 Review Questions ...................................................................................................................................... 16 Additional Insights and Activities ........................................................................................................... 16

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics

PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to take a look at the general marketing research process and its use of both existing and primary data as well as the internal and external sources from which these data come. In the marketing research process, the need for ethical practices is discussed, along with the utility, justice, and rights approaches to ethical reasoning. An important point to keep in mind is that not all research is valuable and that some should actually be avoided.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 2-1

Outline the marketing research process.

2-2

Describe the role of marketing ethics in research.

2-3

Discuss the main differences between the utility, justice, and rights approaches to ethical reasoning.

2-4

Identify types of research that should be avoided.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 2-1 2-2 2-3 2-4 2-1—2-4

PPT slide

Activity/Assessment

Duration

PPT slide 16 PPT slide 19 PPT slides 24–25 PPT slide 29 PPT slide 30

Knowledge Check 2.1 Polling Activity Discussion Activity Group Activity Self-Assessment

< 5 min 5–10 min 10–20 min 10–20 min 10–20 min

[return to top]

KEY TERMS Advocacy research Research conducted to support a position rather than to find the truth about an issue.

Ethics Moral principles and values that govern the way an individual or a group conducts its activities.

Justice approach A method of ethical or moral reasoning that focuses on the degree to which benefits and costs are fairly distributed across individuals and groups. If the benefits and costs of a proposed action are fairly distributed, an action is considered to be ethical.

Marketing ethics The principles, values, and standards of conduct followed by marketers. Marketing

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics

researchers must make many decisions over the course of a single research project. Throughout the process, researchers must consider the ethics involved in the choices they make.

Rights approach A method of ethical or moral reasoning that focuses on the welfare of the individual and that uses means, intentions, and features of an act itself in judging its ethicality. If any individual’s rights are violated, the act is considered unethical.

Sugging Attempting to sell products or services or ideas under the guise of marketing research.

Utility approach A method of ethical or moral reasoning that focuses on society and the net consequences that an action may have. If the net result of benefits minus costs is positive, the act is considered ethical; if the net result is negative, the act is considered unethical. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

Chapter 2 has been retitled to recast the emphasis on ethics within marketing research.

Exhibit 2.3 contains a new example.

Exhibit 2.4 has been updated to reflect the most recent version of the Code of Conduct for the Market Research Society.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

2-1.

The Marketing Research Process (2-1, PPT Slides 3–16) h. The marketing research process is a sequence of activities toward a goal of providing information necessary for decision making. i. Exhibit 2.2: Questions Typically Addressed at the Various Stages of the Research Process 2-1a. Problem Definition (Chapters 3 and 4) a. Process of problem definition:  Specifying the manager’s decision problem  Specifying research problem(s) to be addressed  Preparing a research request agreement b. Exploratory research can be used to clarify the issues.  Requires flexibility.  Outcome is a clear understanding of what information managers need to make a decision.

2-1b. Data Capture: Existing Data (Chapters 5 Through 7)

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics

a. Two ways marketing research gathers marketing intelligence:  Collecting data to address specific problems.  Putting systems in place that provide marketing intelligence on an ongoing basis. b. Needed data may already exist inside a company’s databases.  Using decision support systems (DSS).  Finding value within a growing variety of ―big data.‖ c. The scope of the problem may require existing data from external sources.  Including government, trade associations, and published sources.  Commercial sources—organizations that specialize in collecting and selling third-party data.

2-1c. Data Capture: Primary Data (Chapters 8 Through 15) a. When needed data are not available from existing sources, it means collecting data.  New data are referred to as primary data.  Usable information may also be available from unprocessed internal data. b. Generating new data is generally a time-consuming, expensive process.  Causal research uses experiments to identify cause-and-effect relationships between variables.  Descriptive research focuses on describing a population, often emphasizing the frequency or the extent to which two variables are related to one another. c. The group to be observed or questioned is known as the population, and the particular subset of the population to be studied is the sample. d. Design of a sample includes the specification of:  the sampling frame, which is the list of population elements from which the sample will be drawn.  the type of sampling plan to be used.  the sample size. e. Two basic types of sampling plans:  Probability sample: Each population member has a known, nonzero chance of being selected.  Nonprobability sample: Researchers choose individuals who will be part of the study, and results can’t safely be projected to the population.

2-1d. Data Analysis (Chapters 16 Through 18) a. Data analysis generally involves several steps:  Editing: making sure data gathered are complete and consistent.  Coding: assigning numbers to answers for computer analysis.  Aggregation: assigning appropriate time units to gathered data.  Merging: assembling data from different sources. b. Analysis involves explaining data relationships.

2-1e. Information Reporting (Chapters 19 and 20) a. Research results and conclusions are submitted to management in a written research report. b. Because it is the only aspect of the research effort management will see, it must be clear and accurate.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics

c. It may also include a research presentation.

2-1f. Data-Informed and Data-Driven Decisions a. A data-informed decision is one where:  Managers gain valuable insights from the research project.  Managers couple those insights with their own experiences to determine marketing actions. b. A data-driven decision is one where managers seek to implement the research findings as presented and reported.

2-1g. The Goal: Minimize Total Error a. Even the best projects contain errors of one kind or another. b. The goal is to minimize total error in the marketing research process, not any particular type of error. c. Thinking carefully about the key questions helps to minimize the different kinds of errors. d. Knowledge Check 2.1: < 5 minutes total. (PPT Slide 16) At what stage of the marketing research process would you be asking the question ―What analysis techniques will be used?‖ (a) Problem definition (b) Data collection (c) Data analysis (d) Information reporting  Answer: c. Data analysis  Most analysis is quite straightforward, involving frequency counts or simple descriptive statistics, but sometimes, the research calls for a deeper look at the data through analyses of differences or relationships across groups.

2-2.

Marketing Ethics in Research (2-2, PPT Slides 17–19) a. During any research project, researchers must consider the ethics of the choices they make.  Ethics – The moral principles and values that govern the way an individual or a group conducts its activities.  Marketing ethics – The principles, values, and standards of conduct followed by marketers. b. Exhibit 2.3: Questionable Ethical Decision Making in Marketing Research provides three examples of companies demonstrating questionable ethical decision making. c. Ethics apply to all situations in which there can be actual or potential harm of any kind. d. It’s important to remember that just because an action is legal does not necessarily mean it is ethical. e. Good ethics is good business. f. Exhibit 2.4: The Principles of the Market Research Society (MRS) Code of Conduct g. Polling Activity: 5–10 minutes total. (PPT Slide 19)  Although all the principles that make up the Market Research Society Code of Conduct are valuable, to which one would you give the highest priority?

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics

(a) MRS members shall ensure that their professional activities are not used to unfairly influence views and opinions of participants. (b) MRS members shall exercise independent professional judgement in the design, conduct and reporting of their professional activities. (c) MRS members shall respect the confidentiality of information collected in their professional activities. (d) MRS members shall protect the reputation and integrity of the profession.  Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response. In this particular activity, their answer can be related to whether their behavior, the protection of others, or the protection of the profession takes priority.

2-3.

Three Methods of Ethical Reasoning (2-3, PPT Slides 20–25) a. Three common frameworks used to evaluate the ethical nature of a proposed action include the utility, justice, and rights approaches. b. Utility approach – A method of ethical or moral reasoning that focuses on society and the net consequences that an action may have.  If the net result of benefits minus costs is positive, the act is considered ethical.  If the net result is negative, the act is considered unethical. c. Justice approach – A method of ethical or moral reasoning that focuses on the degree to which benefits and costs are fairly distributed across individuals and groups.  If the benefits and costs of a proposed action are fairly distributed, an action is considered to be ethical.  Note that a ―fair‖ distribution is not necessarily an ―equal‖ distribution. d. Rights approach – A method of ethical or moral reasoning that focuses on the welfare of the individual and uses means, intentions, and features of an act itself in judging its ethicality.  If any individual’s rights are violated, the act is considered unethical.  Under the rights approach, a proposed action is right or wrong in and of itself—there is less concern about an action’s consequences.  It is nearly impossible to ensure that every right of every relevant individual or group has not been violated. e. Because the frameworks will not always lead to the same conclusion, some practical guidelines for what to do when the answer isn’t obvious are helpful. f. Exhibit 2.5: Applying the Ethical Frameworks in Practice  You have been hired to help a large producer of vegetables understand how consumers shop for produce in grocery stores. The company is considering different methods of displaying nutritional content because managers believe that if more people understood the nutritional value of their products, they would make better decisions about what they eat. You have decided to use observational research—you will observe shoppers without their knowledge so that they won’t change their shopping behaviors.  Was the aspect of disguise ethical using the: o utility approach? o justice approach? o rights approach? g. Discussion Activity: 10–20 minutes total. (PPT Slides 24–25)

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics



What are some of the pros and cons for each of the three ethical frameworks: utility approach, justice approach, and rights approach?

Approach Utility

Justice

Rights

2-4.

Pros • Greatest good for greatest number • Practical to apply • Impartial • Creates system of fairness • Distributes benefits and costs based on greatest moral acceptability • Respects individual rights • Emphasis of individual liberties especially attractive to Western cultures

Cons • Impersonal • Requires self-sacrifice • Allows infringement of individual rights • Becomes complicated with extenuating circumstances • Not everyone’s sense of justice is in agreement • Based on strict right/wrong without explaining reasoning behind it • Rather simplistic approach in a complex system

Research to Avoid (2-4, PPT Slides 26–29) a. The process of marketing research has many benefits, but it is not a perfect process, even when used appropriately. b. Examples of actions that are inappropriate or even unethical:  Stealing competitors’ documents  Falsifying data  Advocacy research – Research conducted to support a position rather than to find the truth about an issue  Sugging – Attempting to sell products, services or ideas under the guise of marketing research c. Some ethical tests are provided in Exhibit 2.6: Practical Guidelines for Ethical Analysis. d. Research should also be avoided when resources, such as time and budget, to do the research appropriately are lacking. e. Benefits of marketing research must always be weighed against the risks of tipping off a competitor. f. Group Activity: 10–20 minutes total. (PPT Slide 29)  Form groups of three to five participants. Each group is assigned one of the ethical tests listed in Exhibit 2.6 (common sense, one’s best self, making something public, ventilation, purified idea, big four, and gag test) and creates an example scenario to illustrate the use of that ethical test.  As time allows, groups may share their scenario with the class and describe how easy/difficult it was to construct the scenario.  Note: This activity could also be applied to the grocery store example in the text.

Ask students what each of the tests would look like for the decision made in that example.

Self-Assessment (PPT Slide 30)

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 2: The Research Process and the Importance of Ethics



How important are ethics in the various aspects of an individual’s life (personal, home, school, work, social groups, etc.)? How conscious are you of the ethics involved in everyday situations? In what ways might you increase your awareness of ethical considerations in your daily life?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

8.

What is the research process?

The research process provides information needed for decision making. It includes four stages: problem definition, data capture, data analysis, and information reporting.

9. What are the various forms of data capture?

Data may be captured by retrieving processed data from internal databases or from external sources, or it may be collected as primary data (i.e., retrieving unprocessed data from internal sources).

10. What is the most important error in research? Explain.

The most important error in research is total error; any research process will have some kind or error or another. The goal is to minimize total error in the research process, not just any particular type.

11. What are the main differences between the utility, justice, and rights approaches to ethical reasoning?

The utility approach focuses on society as the unit of analysis, and it stresses the consequences of an act on all of those directly or indirectly affected by it. If the benefits of the act to society exceed its costs, the act is considered ethical; if the net benefits are negative, the act is unethical.

The justice approach considers the degree to which costs and benefits are fairly distributed based on societal consensus. The rights approach focuses on the individual as the unit of analysis and specifically on the rights to which every individual is entitled.

12. Why is it important to consider marketing research ethics?

It is important to consider ethics in order to avoid causing harm of any kind (e.g., economic, physical, or mental) to an individual or a group. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

7. Briefly reviewing the research process might take this format:

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

 



Illustrate the structure of the course. Each instructor can point out the relative emphasis to be placed on each section.

Highlight the areas of additional study that those students seeking to become marketing research specialists might pursue. This discussion can be tied directly to related courses in the curriculum.

Emphasize the key error in research—total error. It is helpful here to point out that sampling error is one part of total error, and that increasing sample size to reduce sampling error might actually increase total error. This helps to allay somewhat the tendency demonstrated by beginning researchers to argue that the key to most research problems is to increase sample size.

8. It is particularly helpful here to demonstrate how slight changes in research questions can lead to substantial changes in the research process. This can be accomplished by alternate phrasings of a research question, such as "Who buys condominiums?" versus "Why do people buy condominiums?", and tracing through the research that would be needed to answer each question.

The differences in data collection, sampling, and field force procedures soon become obvious with class discussion.

9. Although the issue of ethics in marketing research is best addressed over the course of the term rather than in a single period, it is useful to sensitize students early to the fact that most marketing research techniques involve ethical issues and that the decision as to what is morally right in a given situation is not always clear. One useful way to begin is to review the essential differences between the utility, justice, and rights perspectives.

10. The frameworks may emphasize different perspectives by which the ethicality of some contemplated act can be evaluated, but no single approach provides precise answers to ethical decisions. In a utilitarian analysis, for example, one still needs to quantify costs and benefits; in deontological reasoning, one needs to evaluate the seriousness of a right’s infringement.

11. Once students have a better understanding of the nature of the arguments under each framework, it is useful to challenge them further with a few moral research dilemmas they might encounter.

Because students will only have limited understanding of the techniques of marketing research and their advantages and disadvantages early in the term, it is useful to pick from the large set the ethical dilemmas students can most easily understand. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 19 Chapter Objectives ................................................................................................................................... 19

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

Complete List of Chapter Activities and Assessments........................................................................... 19 Key Terms.................................................................................................................................................. 19 What's New in This Chapter .................................................................................................................... 20 Chapter Outline ........................................................................................................................................ 20 Review Questions ...................................................................................................................................... 25 Additional Insights and Activities ........................................................................................................... 26

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to familiarize students with the problem formulation process. Asking the wrong questions can be a costly endeavor that produces results that may be valid but not useful. The chapter begins with a look at the relationship between problems and opportunities before outlining the steps involved in problem formulation. Once that process is complete, the research proposal is the next task, which is briefly outlined in this chapter before being described in greater detail in future chapters.

The chapter concludes with a discussion of choosing a research supplier.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 3-1

Discuss the relationship between problems and opportunities in marketing research.

3-2

Specify the key steps in problem formulation.

3-3

Outline the various elements of the research proposal.

3-4

Describe the purpose of a request-for-proposal (RFP) when choosing a research supplier.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 3-1 3-2 3-3 3-4 3-1–3-4

PPT slide

Activity/Assessment

Duration

PPT slides 5–6 PPT slide 17 PPT slide 21 PPT slides 24–25 PPT slide 26

Discussion Activity 1 Knowledge Check 3.1 Knowledge Check 3.2 Discussion Activity 2 Self-Assessment

< 5 min 5–10 min 5–10 min < 5 min 10–20 min

[return to top]

KEY TERMS Decision problem The problem facing the decision maker for which the research is intended to provide answers.

Discovery-oriented decision problem A decision problem that typically seeks to answer ―what‖ or ―why‖ questions about a problem/opportunity. The focus is generally on generating useful information.

Request-for-proposal (RFP) A document that describes, as specifically as possible, the nature of the problem for which research is sought and that asks providers to offer proposals, including cost estimates, about how they would perform the job.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

Research problem A restatement of the decision problem in research terms.

Research proposal A written statement that describes the marketing problem, the purpose of the study, and a detailed outline of the research methodology.

Research request agreement A document prepared by the researcher after meeting with the decision maker that summarizes the problem and the information that is needed to address it.

Strategy-oriented decision problem A decision problem that typically seeks to answer ―how‖ questions about a problem/opportunity. The focus is generally on selecting alternative courses of action. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:  

The chapter’s introductory material provides an updated comparison and contrast of the relationship between consumer insights and managerial action (or inaction).

Exhibit 3.2 contains a new example of ―normal thinking.‖

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

3-1.

Problems versus Opportunities (3-1, PPT Slides 3–6) j. In a general interpretation of the terms:  ―Problem‖ = a negative situation  ―Opportunity‖ = a positive situation k. Here, problems and opportunities can be seen as two sides of the same coin. l. Regardless of perspective, both situations require good information about the marketing environment before managers make important decisions. m. Discussion Activity 1: 5–10 minutes total. (PPT Slides 5–6) Situation: Your company sells a variety of work boots at a range of price points. Your company and two other companies have a majority of the market, but you all are facing increasing competition. A needs analysis has pointed out that your website could be both more informative and more user-friendly. Discuss whether this is a problem or an opportunity. How is one different from the other?  Whether you view it as a problem (our website is deficient) or an opportunity (updating our website could attract greater numbers of customers), the situation requires information about what needs to be improved and how improvement can better serve customers and increase sales/revenue.

3-2.

The Problem Formulation Process (3-2, PPT Slides 7–17)

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

a. ―Defining the problem‖ or ―problem formulation‖ means a process of trying to identify specific areas where additional information is needed about the marketing environment. b. The steps of this process are summarized in Exhibit 3.1: Key Steps in Problem Formulation.

3-2a. Step One: Meet With the Client a. Meet with the client to establish trust and communication and to obtain information.  What is the problem or opportunity you’re facing?  What caused you to notice the problem, and why do you think this situation has occurred?  What is likely to happen if nothing changes in the next 12 months?  Is this likely to be an ongoing problem that requires gathering information on a continuous basis?  What do you hope to accomplish by using marketing research?  What actions will you take depending upon the information from the marketing research? b. Planned change versus unplanned change  With unplanned change in the environment, serendipity (reactive research), the research typically focuses on determining WHAT exactly is happening and WHY. o What has happened? (discovery) o Why has it happened? (discovery) o What should we do about the problem or opportunity? (strategy)  With planned changes (proactive research), the research typically focuses on HOW to bring about the desired change. o What can happen? (discovery) o Why could it happen? (discovery) o How should we implement the change? (strategy) 3-2b. Step Two: Clarify the Problem/Opportunity a. The job of a researcher is to serve as a consultant to help determine root causes and clear paths of action that get at the heart of the problem.  May need to challenge managers’ preexisting assumptions.  One of the most important things that a researcher provides a client is a set of ―new eyes.‖  Formulating the true problem or opportunity is often difficult unless you can break away from ―normal‖ thinking and question assumptions. b. Exhibit 3.2: The Problem With ―Normal Thinking‖ and the Value of Considering Other Perspectives c. Exploratory research may be helpful to determine underlying causes.

3-2c. Step Three: State the Manager’s Decision Problem a. Decision problem – The problem facing the decision maker for which the research is intended to provide answers.  A well-stated decision problem: o takes the manager’s perspective. o is as simple as possible. o is stated in the form of a question.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation



Discovery-oriented decision problem – Typically seeks to answer ―what‖ or ―why‖ questions about a problem/opportunity, with a focus on generating useful information.  Strategy-oriented decision problem – Seeks to answer ―how‖ questions about a problem/opportunity, with the focus on selecting alternative courses of action. b. The researcher, working with managers, must also decide whether this is a one-time information need or whether the information will be needed at regular intervals in the future.

3-2d. Step Four: Develop Possible Research Problems a. Research problem – A restatement of the decision problem in research terms. b. Actions to take to provide insight into the decision problem include:  Investigate current customer satisfaction.  Assess target market perceptions.  Determine target market awareness. c. At this stage, a researcher’s primary task is to develop the full range of research problems for a given decision problem—for examples, see Exhibit 3.3: Examples of the Relationship Between Decision Problems and Research Problems.

3-2e. Step Five: Select Research Problems(s) to Be Addressed a. There will often be MANY research problems associated with a single manager’s decision problem. b. Researchers normally can’t do everything. c. It is better to address one or two research problems fully than to try to do too many things at once. d. Examine the research problem(s) in terms of the trade-off between the value of the information to be obtained versus the costs of obtaining it.

3-2f. Step Six: Prepare the Research Request Agreement a. Research request agreement – A document prepared by the researcher after meeting with the decision maker that summarizes the problem and the information that is needed to address it. b. Its purpose is to make certain that everyone understands the problem to be addressed and what the research is to accomplish and includes:  Background: the events that led to the manager’s decision problem.  Decision problem: the underlying question confronting the manager.  Research problem(s): the range of research of problems that would provide input to the decision problem.  Use: the way each piece of information will be used.  Population and subgroups: groups from whom the information must be gathered.  Logistics: estimates of time and money available to conduct the research. c. Research Window 3.1 presents the research request agreement between a research group and a nonprofit organization seeking a one-time marketing research project on the topic of domestic violence. d. Knowledge Check 3.1: < 5 minutes total. PPT Slide 17.

A strategy-oriented decision problem seeks to answer ―how‖ questions about a problem/opportunity, and its focus is on:

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

(a) selecting alternative courses of action. (b) generating useful information. (c) finding the serendipity in unplanned change. (d) ensuring the value of the information to be obtained exceeds its costs.  Answer: a—selecting alternative courses of action.  The strategy-oriented decision problem is commonly used with planned change, with an emphasis on how the planned change should be implemented.

3-3.

The Research Proposal (3-3, PPT Slides 18–21) a. Research proposal – A written statement that describes the marketing problem, the purpose of the study, and a detailed outline of the research methodology. b. The research proposal differs from the research request agreement because it includes the proposed research method and is much more detailed.  The research proposal is NOT a formal agreement to begin the research but rather an agreement about the basic nature of the problem or opportunity.  The research proposal must be approved before the actual process of data collection begins. c. Note: Some instructors may find it useful to discuss the research proposal at this point, while others will want to postpone such a discussion until later on in the course when various methodological issues have been addressed.

3-3a. Problem Definition and Background a. This section summarizes information in the research request agreement. b. It is usually good to include a brief justification for the research problem being studied.

3-3b. Research Design and Data Sources a. This section includes the type of research involved: exploratory, descriptive, causal, etc. b. The type of data and their sources are also given.

3-3c. Sampling Plan a. A detailed description of the population to be studied is needed. b. In addition, information about sample size, sampling method, and the handling of data is provided.

3-3d. Data Collection Forms a. Information about forms to be used to gather primary data is stated. b. Explanations of data reliability and validity are also included. c. Actual forms are typically found in the appendix.

3-3e. Analysis a. The analysis discusses editing and proofreading of questionnaires, coding instructions, and the type of data analysis, including any specialized statistical techniques. b. It also includes an outline of the tables and figures that will appear in the report, typically in the appendix.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

3-3f. Time Schedule a. This detailed outline of the study plan should be divided into workable pieces. b. It is helpful to consider the participants, the qualifications and expertise, and overall estimate of the time needed to complete the study.

3-3g. Personnel Requirements and Cost Estimate a. A list of personnel and details of each of their assignments, including responsibilities and authority, are found here. b. Cost estimates—personnel pay, travel, materials and supplies, any calculated overhead charges—are included here.

3-3h. Appendices a. This section will include:  data collection forms (including script for telephone interviewers, recruiting messages for online surveys, and cover letters for written formats).  any technical information or statistical information that would have interrupted the flow of the text.  dummy tables or figures included in the analysis plan. b. Knowledge Check 3.2: < 5 minutes total. (PPT Slide 21) Which of these items is most likely to be found in the appendices of a research proposal? (a) Problem definition (b) Data collection forms (c) Time schedule (d) Personnel requirements and cost estimate  Answer: b—Data collection forms  This section will include data collection forms (including scripts for telephone interviewers, recruiting messages for online surveys, and cover letters for written formats), any technical information or statistical information that would have interrupted the flow of the text, and dummy tables or figures included in the analysis plan.

3-4.

Choosing a Research Supplier (3-4, PPT Slides 22–25) a. Request-for-proposal (RFP) – A document that describes, as specifically as possible, the nature of the problem for which research is sought and that asks providers to offer proposals, including cost estimates, about how they would perform the job. b. Benefits of outsourcing research:  Less expensive if workload tends to vary over the year.  Can find expertise to match the problem more readily.  Outside suppliers can bring a greater degree of objectivity. c. Discussion Activity 2: < 5 minutes total. (PPT Slides 24–25)  What are the potential benefits and drawbacks of establishing a long-term relationship with a marketing research supplier?

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

o

o

o

Benefits might include a consistency over ongoing projects, a level of trust not always available with project-by-project suppliers, a proven track record of results, and a greater awareness of the vendor’s capabilities.

Drawbacks might include a complacency on either side, either in how well management assesses quality of the research or in how carefully vendors carry out the research.

It is in your own best interest to learn how to determine the quality of a proposed or completed research project rather than to rely on a general (and possibly unwarranted) belief about the trustworthiness of the researcher who provided it.

Self-Assessment (PPT Slide 26) 

Do you tend to see a situation that lacks sufficient income as a ―problem‖ or an ―opportunity‖?

When faced with a problem, how do you go about defining the problem?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

13. What does it mean when we say that problems and opportunities are two sides of the same coin?

This saying means that both situations require good information about the marketing environment before managers can make important decisions. Today’s opportunity is tomorrow’s problem if a company fails to take advantage of the opportunity while its competitors do. Similarly, a company that successfully deals with a problem before its competitors has created an opportunity to move ahead in the industry.

14. What are the sources of marketing problems or opportunities? Are different sources typically associated with different research objectives? Explain.

The sources of marketing problems/opportunities are unplanned changes in the marketing environment and planned changes in the marketing environment.

15. What is ―normal thinking‖? Why is it a problem when defining the marketing problem/opportunity? ―Normal thinking‖ is when a problem/opportunity is looked at in a routine way. It can be detrimental because it can get in the way of understanding the true nature of the problem.

16. What is the basic nature of a decision problem?

The basic nature of a decision problem is to be able to state the problem facing the decision maker for which the research is intended to provide answers.

17. What are the fundamental characteristics of the two types of decision problems?

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 3: Problem Formulation

The fundamental characteristics of the two types of decision problems are as follows: Discoveryoriented decision problems ask ―what‖ or ―why‖ and generate information; strategy-oriented decision problems are usually directed at ―how‖ planned change should be implemented.

18. What is a research problem? Why is it important to develop the full range of possible research problems?

A research problem is a restatement of the decision problem in research terms, from the researcher’s perspective. It is important to fully understand the problem so that a full range of potential research problems can be identified and addressed at the beginning of the research process.

19. What is involved in a research request agreement? What is included in the written statement?

A research request agreement summarizes the problem formulation process in written form and is submitted to managers for approval. It includes the following sections: origin, decision problem, research problem(s), use, targets and their subgroups, and logistics.

20. How does the research proposal differ from the research request agreement?

A research proposal is a written statement that describes the marketing problem, the purpose of the study, and a detailed outline of the research methodology, while a research request agreement is a document (prepared by the researcher after meeting with the decision maker) that summarizes the problem and the information that is needed to address it.

21. What factors should be considered when choosing a research supplier?

When choosing a research supplier, one should consider the degree of objectivity a supplier can bring to the project, what the scope of the research is, the most critical areas of uncertainty, and the issues that would benefit most from research.

22. What are the benefits of using a request-for-proposal?

The benefits of using an RFP include allowing you to compare the proposals, cost estimates, and the potential performance of the suppliers you are considering for the job. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

12. Problem definition is tricky business and involves a great degree of thought and analysis. For example, consider the shift to digital music facilitated by iPods and similar portable music players as an example. A situation becomes much more complex when there are multiple decision makers, perhaps with conflicting objectives, multiple alternatives, multiple outcomes, and multiple environmental influences.

13. Students will enjoy a discussion of the problems with ―normal thinking,‖ especially if the instructor can bring in to class some of the many creative thinking exercises available. This will be especially useful for courses with a large project component. Students need to learn to think clearly about the

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 4: Exploratory Research

nature of the problem/opportunity confronting project sponsors. They must also learn the importance of quickly developing rapport with the client so that candid answers are given to their probing questions.

14. Students often seem to struggle with the ambiguity involved in problem formulation. The development of a step-by-step process for defining the problem is helpful, but there are still many subjective aspects of problem definition. Most students will benefit from a discussion of the different goals for discovery- versus strategy-oriented decision problems. If a manager seems to be asking for information only, the problem is usually discovery-oriented. If an actual decision is to be made, then the problem is strategy-oriented. The instructor should point out to students that while discoveryoriented research is probably the most straightforward and easy to conduct, it is with strategyoriented research that marketing research can be of greatest managerial use. It is clear, however, that in the earliest stages of most problems/opportunities, there is critical need for discovery-oriented research.

15. The use of outside suppliers by most companies is normal business practice. However, in making the decision to outsource and in order to benefit from the choice, three questions need to be asked: 

Is the research really necessary?



What are the most critical areas of uncertainty and the issues that would benefit the most from research?



Do the capabilities of the research supplier meet the needs of the company?

A formal comparative analysis of outside marketing research suppliers can assist the firm in making the proper choice of suppliers. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 4: Exploratory Research

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 29 Chapter Objectives ................................................................................................................................... 29 Complete List of Chapter Activities and Assessments........................................................................... 29 Key Terms.................................................................................................................................................. 29 What's New in This Chapter .................................................................................................................... 30 Chapter Outline ........................................................................................................................................ 30 Review Questions ...................................................................................................................................... 35

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 4: Exploratory Research

Additional Insights and Activities ........................................................................................................... 37

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 4: Exploratory Research

PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to examine the role of exploratory research in formulating a manager’s decision problem. A multitude of exploratory techniques are available, many of which are precursors to other forms of research as opposed to stand-alone approaches. These techniques come with the caveat that putting too much emphasis on exploratory findings, often because of a lack of time or other resources, could leave decision makers without the necessary information to make quality decisions. Doing exploratory research right makes subsequent forms of research more efficient and effective.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 4-1

Detail the basic uses of exploratory research.

4-2

Describe the various types of exploratory research.

4-3

Explain two major pitfalls to avoid with exploratory research.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 4-1 4-2 4-2 4-3 4-1–4-3

PPT slide

Activity/Assessment

Duration

PPT slide 6 PPT slide 15 PPT slide 21 PPT slide 24 PPT slide 25

Knowledge Check 4.1 Group Activity Knowledge Check 4.2 Knowledge Check 4.3 Self-Assessment

< 5 min 15–30 min < 5 min < 5 min 10–20 min

[return to top]

KEY TERMS Case analysis Intensive study of selected examples of the phenomenon of interest.

Data mining The process of uncovering patterns and other valuable information from large data sets.

Depth interview Interviews with people knowledgeable about the general subject being investigated.

Ethnography The detailed observation of consumers during their ordinary daily lives using direct observations, interviews, and video and audio recordings.

Exploratory research Research conducted to gain ideas and insights to better define the problem or opportunity confronting a manager.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 4: Exploratory Research

Focus group An interview conducted among a small number of individuals simultaneously; the interview relies more on group discussion than on directed questions to generate data.

Hypothesis A statement that specifies how two or more measurable variables are related.

Literature search A search of statistics, trade journal articles, other articles, magazines, newspapers, books, and/or online sources for data or insight into the problem at hand.

Moderator The individual that meets with focus group participants and guides the session.

Moderator’s guidebook An ordered list of the general (and specific) issues to be addressed during a focus group; the issues normally should move from general to specific.

Nominal groups A group interview technique that initially limits respondent interaction while attempting to maximize input from individual group members.

Projective methods Methods that encourage respondents to reveal their own feelings, thoughts, and behaviors by shifting the focus away from the individual using indirect tasks.

Role playing A projective method in which a researcher will introduce a scenario or context and ask respondents to play the role of a person in the scenario.

Sentence completion A projective method in which respondents are directed to complete several sentences with the first words that come to mind.

Storytelling A projective method of data collection relying on a picture stimulus such as a cartoon, photograph, or drawing about which the subject is asked to tell a story.

Word association A projective method in which participants are asked to respond to a list of words with the first word that comes to mind. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

A realignment of the learning objectives for this chapter better clarifies the material within the major sections.

Material on the exploratory research’s dark side has been reorganized for better chapter flow.

Research Window 4.1 has been updated.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

4-1.

Exploratory Research Basics (4-1, PPT Slides 3–6) n. The main purpose of exploratory research is to gain insights and ideas so that problems and opportunities can be more clearly defined.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 4: Exploratory Research

o. When conducted correctly, exploratory research should:  provide a better understanding of the situation.  possibly yield hypotheses – statements that specify how two or more measurable variables are related. p. This kind of research is not designed to come up with final answers and decisions. q. Uses of exploratory research:  Develop hypotheses.  Better formulate the manager’s decision problem.  Increase researcher’s familiarity with the problem.  Clarify concepts. r. Exploratory studies:  should almost always be relatively small in size.  provide flexibility as to methods for gaining insights and developing hypotheses. s. Knowledge Check 4.1: < 5 minutes total. (PPT Slide 6) Exploratory research is best used to: (e) confirm a manager’s prior assessment of a problem. (f) constitute the biggest share of a market research budget. (g) gain insights about problems. (h) find evidence that supports a manager’s position.  Answer: c—gain insights about problems.  Use exploratory research to obtain the ideas, insights, and hypotheses that these techniques were designed to deliver.

4-2.

Exploratory Research Types and Techniques (4-2, PPT Slides 7–21) a. Among the wide variety of exploratory research types and techniques, the most common are addressed in this section of the chapter. b. Exhibit 4.1: Types of Exploratory Studies 4-2a. Literature Search a. Almost all marketing research projects should start with a literature search. b. Literature search – A search of statistics, trade journal articles, other articles, magazines, newspapers, books, and/or online sources for data or insight into the problem at hand.  A typically quick and inexpensive way to conduct exploratory research.  Conceptual literature versus trade literature; also includes published statistics. c. Its major emphasis is on discovery of ideas and tentative explanations of the phenomenon and not on drawing conclusions.

4-2b. Depth Interviews a. Depth interviews – Interviews with people knowledgeable about the general subject being investigated. b. Some possibilities for interview candidates include:  those who work with it (e.g., employees, consultants).  those who study it (e.g., researchers, analysts).  those who live it (e.g., consumers).

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c. Depth interviews are used to collect exploratory information, don’t require a random sample, and are usually informal.

4-2c. Focus Groups a. Focus group – An interview conducted among a small number of individuals simultaneously; the interview relies more on group discussion than on directed questions to generate data. b. Characteristics of focus groups include:  Typically 8–12 people  1.5 to 2 hours in length  Homogeneous within group; heterogeneity introduced across groups  Participants carefully screened  Sessions recorded and transcribed c. A key advantage of focus groups over other methods is the synergy obtained from having multiple participants.  Traditional focus groups meet face-to-face, often at facilities designed for focus groups.  Online focus groups use web-based technology to meet virtually.  Research Window 4.1: Online Focus Groups and Webcam Interviews for Better Understanding Traveler Decision Making  Exhibit 4.2: Tips for Running an Online Focus Group d. The Role of the Moderator  Moderator – The individual who meets with focus group participants and guides the session.  The moderator needs to: o understand the background of the problem. o be aware of what the client hopes to obtain from the research process. o identify the overall plan for the use of focus groups. o lead the group discussion in a way that meets the objectives of the study. o stimulate interaction among group members.  Moderator’s guidebook – An ordered list of the general (and specific) issues (from general to specific) to be addressed during a focus group.  Exhibit 4.3 Seven Characteristics of Good Focus Group Moderators e. Nominal Groups  Nominal groups – A group interview technique that initially limits respondent interaction while attempting to maximize input from individual group members. o Typically produce more, and more varied, ideas than focus groups due to their concentration on individual participation. o Minimize potential concerns of ―group think,‖ domination by a few individuals, or the lack of involvement by respondents who are generally more quiet or shy.  Nominal group process:

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o

f.

Moderator proposes the question or topic for discussion and invites participants to think about and then record their thoughts on paper. o Respondents reveal their written responses one at a time. o Often, the moderator will write the individual responses for all group members to see. o Individuals are encouraged to record new ideas stimulated by the sharing from others. o Verbal discussion between group members is discouraged until after all participants have had a chance to reveal their ideas. o The complete set of individual responses is reviewed by the group and the moderator, with discussion centered on clarification of existing thoughts and elimination of duplication. o Group members prioritize the group’s ideas. o The ideas with the highest priority, as agreed upon by the group, are now the focus of the group discussion.

Group Activity: 15–30 minutes total. (PPT Slide 15) Form groups of three to five participants. Each group selects a consumer item, either familiar or made up, and then prepares an outline for a focus group session to assess the item’s popularity.

As time allows, groups can share their product, what general information they were seeking from the focus group activity, and what questions they planned to ask the focus group.  Note: This activity is mainly to get students to think about preparing for a research activity and is general in nature. You might encourage them to think about a marketing issue to go along with the consumer item that they select as a way of directing the questions for the focus group. This activity could also be an individual assignment.

4-2d. Data Mining a. Data mining – The process of uncovering patterns and other valuable information from large data sets. b. The use of powerful analytic technologies can quickly and thoroughly explore mountains of data to obtain useful information. c. Although most forms of exploratory research are qualitative in nature, data mining involves sophisticated quantitative analysis of data held in a company’s databases.

4-2e. Case Analyses a. Case analysis – Intensive study of selected examples of the phenomenon of interest. b. Case analysis is especially effective with cases reflecting:  recent change.  extremes of behavior.  the ―best‖ and ―worst‖ situations. c. Ethnography  Ethnography – The detailed observation of consumers during their ordinary daily lives using direct observations, interviews, and video and audio recordings.

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  

Like other methods of case analysis, ethnography is useful because it allows insights based on real behavior rather than on what people say.

Ethnography’s usefulness as a technique for generating insights depends on the quality and objectivity of the analysis.

Research Window 4.2: What Is Ethnographic Marketing Research?

4-2f. Projective Methods a. Projective methods – Methods that encourage respondents to reveal their own feelings, thoughts, and behaviors by shifting the focus away from the individual through the use of indirect tasks.  The usual concerns about the difficulty of data analysis and interpretation apply to projective methods.  Researchers must keep in mind that projective methods are not designed to get final answers or make decisions. b. Word Association  Word association – Method in which participants are asked to respond to a list of words with the first word that comes to mind.  Responses, recorded and later analyzed for meaning, are usually judged in three ways: o by the frequency with which any word is given as a response. o by the average amount of time that elapses before a response is given. o by the number of respondents who do not respond at all to a test word after a reasonable period. c. Sentence Completion  Sentence completion – Respondents are directed to complete several sentences with the first words that come to mind.  Offers a more directed stimulus than word association. d. Storytelling  Storytelling – Method that relies on a picture stimulus such as a cartoon, photograph, or drawing about which the subject is asked to tell a story.  The way an individual responds helps researchers interpret that individual’s values, beliefs, attitudes, and personality. e. Role Playing  Role playing – Method in which a researcher introduces a scenario or context and asks respondents to play the role of a person in the scenario. f. Knowledge Check 4.2: < 5 minutes total. (PPT Slide 21)  Which of the following is a projective method in which the researcher introduces a scenario? (a) Ethnography (b) Role playing (c) Data mining (d) Depth interviews  Answer: b—Role playing

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 4: Exploratory Research



4-3.

Role playing is a projective method in which a researcher introduces a scenario or context and asks respondents to play the role of a person in the scenario.

The Possible Dark Side of Exploratory Research (4-3, PPT Slides 22–24) a. The first pitfall occurs when researchers and managers fail to remain as objective as possible when reviewing and interpreting exploratory data.  It is easy to see what you expect or want to see in qualitative data. b. The second pitfall to avoid is to use exploratory research to obtain answers and decisions rather than the ideas, insights, and hypotheses that these techniques were designed to deliver. c. Knowledge Check 4.3: < 5 minutes total. (PPT Slide 24)  Consider the following scenarios. Which of them best represents an attempt to avoid the pitfalls that sometimes accompany exploratory research? (a) Your exploratory research has yielded some unexpected results. You see this as suggestive of new environmental opportunities. (b) The unexpected results of your exploratory research have disconfirmed what the marketing manager expected it to reveal. This research is obviously flawed. (c) Your exploratory research has yielded some results that you had not expected, but which the marketing manager sees as confirming what they knew would lead to their preferred decision. (d) When interpreting exploratory research results, two members of your team cannot agree on what they mean; both accuse the other of failing to be objective.  Answer: a—Your exploratory research has yielded some unexpected results.

You see this as suggestive of new environmental opportunities.  Researchers and managers must consciously work to remain as objective as possible when reviewing and interpreting exploratory data. It is very easy to see what you expect or want to see in qualitative data.

Self-Assessment (PPT Slide 25) 

When have you used exploratory research to learn more about an issue you faced? Which of the techniques listed in this chapter did you use in your research? How were they helpful?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

23. What are the basic uses for exploratory research?

The basic use of exploratory research is to help formulate the manager’s decision problem. It is also useful for developing hypotheses, gaining familiarity with a phenomenon, and/or clarifying concepts. In general, exploratory research is appropriate for any problem about which little is known. The output from this research is ideas and insights, not answers.

24. What are the key characteristics of exploratory research?

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Exploratory studies are typically small scale and are very flexible; anything goes. When conducted correctly, exploratory research should provide a better understanding of the situation and possibly yield hypotheses.

25. What is a literature search? What kinds of literature might be searched?

A literature search is a search of statistics, trade journal articles, magazines, newspapers, books, and/or online sources for data or insight into the problem at hand.

26. What are the characteristics of a depth interview? Who should be interviewed?

A depth interview attempts to tap the knowledge and experience of those familiar with a general subject being investigated. Anyone who knows anything about the issue at hand is a potential candidate for a depth interview.

27. How does a focus group with 8 to 12 people differ from a series of depth interviews with 8 to 12 people? How does a focus group differ from a nominal group?

A focus group interview with 8 to 12 people is conducted simultaneously and relies more on group discussion rather than on directed questions to generate data. A series of depth interviews with 8 to 12 people is more focused on collecting data and information on the subject being investigated. The primary difference in nominal groups is that they require written responses by participants before open-group discussions.

28. What characteristics should a good focus group moderator possess? Why is each important?

A good focus group should be small, last 1½ to 2 hours, and be relatively homogeneous. This helps to promote better discussion. A good moderator should help guide the discussion and work to include all participants.

29. How might focus groups be misused?

A focus group might be misused if the researcher uses their results to provide final answers to a problem. Focus groups should be used to generate ideas and insights to clarify problem definition.

30. In what types of situations would data mining be useful?

Data mining is useful when businesses are looking to boost sales and profits by better understanding their customers and/or improving the performance of the products and services they offer.

31. What are two common approaches to the use of case analyses?

Common approaches to the use of case analyses include observing a phenomenon as it occurs, conducting unstructured interviews, or using any variety of other approaches to analyze what is happening in a given situation.

32. What is the basic point of projective methods? What are some popular approaches?

Projective methods encourage respondents to reveal their own feelings, thoughts, and behaviors by shifting the focus away from the individual through the use of indirect tasks. Such methods include word association, storytelling, sentence completion, and role playing. [return to top]

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ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

16. For instructors who wish to give focus groups special emphasis, there are several ways to structure the class discussion. One of the best ways is by allowing students to see a focus group in operation. i.

Select 8–10 students from class to serve as the focus group panel. ii.

The instructor or one of the other students can then serve as the moderator. As moderator, the person could lead a class discussion aimed at determining the kinds of issues that need to be covered in exploring some topic, such as the music-buying habits of college students or how students feel about coffee. iii.

The moderator could then develop the discussion guide, which would be used to lead the focus group during the next class session. iv.

After the session, class discussion could be directed at discovering the insights students gathered from the interplay of the participants.

17. An alternative is to have a professional moderator lead the discussion. Alumni who are in the focus group business are sometimes willing to do so.

18. Another way of allowing students to see a focus group in operation is by showing them a video recording of an actual functioning group if the instructor has one available. Still another option, which is only slightly less desirable, is to have them analyze a focus group transcript for the insights it provides.

With these approaches, students soon learn to appreciate what they can and cannot hope to learn from focus groups. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 5: Decision Support Systems: Introduction

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 39 Chapter Objectives ................................................................................................................................... 39 Complete List of Chapter Activities and Assessments........................................................................... 39 Key Terms.................................................................................................................................................. 39 What's New in This Chapter .................................................................................................................... 40

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Chapter Outline ........................................................................................................................................ 40 Review Questions ...................................................................................................................................... 45 Additional Insights and Activities ........................................................................................................... 46

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to focus on information systems that provide a steady stream of existing data that managers can use to make decisions. The chapter begins with a discussion of the advantages and disadvantages of secondary data. A distinction between marketing information systems and decision support systems is made, followed by the identification of the major components of a decision support system. The chapter concludes with an explanation of knowledge management and its value to an organization.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 5-1

List the advantages and disadvantages of working with secondary data over primary data.

5-2

Discuss what is meant by marketing information system (MIS) and decision support system (DSS).

5-3

Identify the components of a decision support system.

5-4

Explain knowledge management.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 5-1 5-2 5-3 5-4 5-1–5-4

PPT slide

Activity/Assessment

Duration

PPT slides 10–11 PPT slide 17 PPT slide 23 PPT slide 28 PPT slide 29

Discussion Activity Knowledge Check 5.1 Knowledge Check 5.2 Group Activity Self-Assessment

5–15 min < 5 min < 5 min 10–20 min 10–20 min

[return to top]

KEY TERMS Customer relationship management (CRM) A system that gathers all relevant information about a company’s customers and makes it available to the employees that interact with the customers.

Data system The part of a decision support system that includes the processes used to capture and the methods used to store data coming from a number of external and internal sources. It is the creation of a database.

Decision support system (DSS) A system that combines data, models for guiding decisions, and a user

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interface that allows users to interact with the system to produce customized information.

Dialog system The part of a decision support system that permits users to explore the databases by employing the system models to produce reports that satisfy their particular information needs. It is the user interface of the decision support system, which is also called a language system.

Knowledge management (KM) The systematic collection of employee knowledge about customers, products and processes, and the marketplace.

Marketing dashboard A visual display of relevant marketing information designed to provide interactive access to a company’s key marketing metrics.

Marketing information system (MIS) A set of procedures and methods for the regular, planned collection, analysis, and presentation of information for use in making marketing decisions.

Model system The part of a decision support system that includes all the routines that allow the user to manipulate the data so as to conduct the kind of analysis the individual desires. It is the collection of analytical tools to interpret the database.

Primary data Information collected specifically for the investigation at hand.

Primary source The originating source of secondary data.

Secondary data Data that have already been collected, often for some other purpose or by some other organization.

Secondary source A source of secondary data that did not originate the data but rather secured them from another source. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:    

A realignment of the learning objectives for this chapter better clarifies the material within the major sections.

Material in Research Windows 5.1 has been updated and now contains reference to COVID-19 pandemic.

Research Windows 5.2, 5.3, and 5.4 have all been edited to reflect more current material and data.

A new Exhibit 5.4 provides a knowledge management example.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

Research Window 5.1: ―Big Data‖ Visualized and Amplified (PPT Slides 3–4) t.

Tableau and similar analytic software services were built on two simple premises: 1. Databases should be converted to charts, graphs, interactive maps, and the like.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 5: Decision Support Systems: Introduction

2. Special programming skills need not be necessary to generate interactive visualizations of data.

5-1.

Secondary Data (5-1, PPT Slides 5–11) a. Secondary data – Data that have already been collected, often for some other purpose or by some other organization. b. Primary data – Information collected specifically for the investigation at hand.  As a general rule, these data should only be collected if secondary data are unavailable. c. Successful research projects should begin with a careful search for existing secondary data.

5-1a. Advantages and Disadvantages of Secondary Data a. The most significant advantages of secondary data are the time and money they can save. b. Two problems that commonly arise with secondary data:  They do not completely fit the problem.  They are not totally accurate. c. Assessing the quality of secondary data:  Primacy of source i. Primary source – The originating source of secondary data. ii. Secondary source – A source of secondary data that did not originate the data but rather secured them from another source.  Sponsor of the research i. Pay attention to whether it is research conducted to support a position (advocacy research) rather than to find the truth about an issue.  Evidence of quality i. Look for evidence that the researchers did a competent job and provided enough details about how the research was conducted.

5-1b. Types of Internal Secondary Data a. Transaction data examples: B2B sales invoices, retail receipts, online transactions, shopping card data. b. Customer communication examples: inquiries, complaints via telephone, online, mail, inperson. c. Marketing research tracking studies examples: satisfaction, awareness, image studies. d. Other sources: call reports, expense receipts, customer records, financial records, credit memos, warranty cards. e. Exhibit 5.1: Some Useful Sources of Internal Secondary Data f. Discussion Activity: 5–15 minutes total. (PPT Slides 10–11) The company’s product, a potato fertilizer, comes in several forms. Some forms are more effective more quickly, and others are reportedly safer for the environment. The marketing department wants to determine where to put its advertising dollars in backing specific products. What types of secondary data might be helpful for making this decision?  Various types of secondary data are applicable here. Transaction data will reveal what products are most popular. Communication data will provide

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information on consumers’ and others’ concerns about the products’ effects on the environment. Marketing research data can add information to both of these concerns. Other data would include government regulations, both actual and proposed; environmental group activities; and so on.

5-2.

The Systems Approach (5-2, PPT Slides 12–17) a. Because much of the information that marketing managers need for decision making is predictable, it can be organized inside a company’s database systems.  Advantage: Current information needed for normal operations is available when managers need it.  Disadvantage: Managers are limited to the information available in the database.

5-2a. The Evolution and Design of Information Systems a. Marketing information system (MIS) – A set of procedures and methods for the regular, planned collection, analysis, and presentation of information for use in making marketing decisions.  Useful for most of the data managers need for ongoing operations and decisions. b. Decision support system (DSS) – A system that combines data, models for guiding decisions, and a user interface that allows users to interact with the system to produce customized information.  Useful in more dynamic environments.  Gives managers direct access to data. c. Key output of good information systems:  Standardized up-to-the-minute reports needed for day-to-day operations.  Custom reports that can be easily produced by managers when needed.  Features that make it easy to use in an interactive mode by users. o Marketing dashboard – A visual display of relevant marketing information designed to provide interactive access to a company’s key marketing metrics. o Exhibit 5.2: Example of a Marketing Dashboard d. Systems designers go through analysis and a number of design steps to understand types of decisions, information available, what input is necessary, how to access and store data, and making data retrieval efficient in creating a decision support system.

5-2b. Customer Relationship Management a. Customer relationship management (CRM) – A system that gathers all relevant information about a company’s customers and makes it available to the employees that interact with the customers.

1. CRM is a process designed to aid marketers in identifying and solving customer needs. b. Research Window 5.2: CRM Definition and Solutions c. Knowledge Check 5.1: < 5 minutes total. (PPT Slide 17)  What is the biggest disadvantage of the systems approach? (i) Data cannot be rearranged or broken down by variables in real time. (j) Managers are limited to the information available in the database. (k) Customized information is not as reliable as standardized information.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 5: Decision Support Systems: Introduction

(l) Marketing dashboards only provide a select few marketing measures.  Answer: b—Managers are limited to the information available in the database.  The big advantage of the systems approach over the project approach is that current information is available when managers need it, with the caveat that the system is limited to the information available in the current database.

Answers a, c, and d are all false statements, not actual disadvantages.

5-3.

Components of Decision Support Systems (5-3, PPT Slides 18–23) a.

Three components function together to yield managerial information as shown in Exhibit 5.3: Components of a Decision Support System.

5-3a. The Data System a. Data system – The part of a decision support system that includes the processes used to capture and the methods used to store data coming from a number of external and internal sources.  It is the creation of a database. b. Concerns about privacy with commercial databases:  To what degree is an individual’s right to privacy violated in the generation and sharing of these databases?  Are companies able to safeguard collected data from those who would use the information for illegal purposes?  Research Window 5.3: Data Security Breaches: Selected Points of Focus and Incident Classification Patterns c. The key criterion for adding a particular piece of data to a database is whether it is useful for making decisions. d. The basic task of a DSS is to capture relevant marketing data in reasonable detail and to put those data in a truly accessible form.

5-3b. The Model System a. Model system – The part of a decision support system that includes all the routines that allow the user to manipulate the data so as to conduct the kind of analysis the individual desires.  It is the collection of analytical tools to interpret the database. b. More sophisticated models are continually created for manipulating data for even more specific purposes.

5-3c. The Dialog System a. Dialog system – The part of a decision support system that permits users to explore the databases by employing the system models to produce reports that satisfy their particular information needs.  It is the user interface of the decision support system, which is also called a language system. b. Knowledge Check 5.2: < 5 minutes total. (PPT Slide 23)  The part of the decision support system that consists of a collection of analytical tools to interpret the database is known as the (a) data system.

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(b) model system. (c) dialog system. (d) knowledge management.  Answer: b—model system.  The model system of a decision support system includes all the routines that allow the user to manipulate the data and perform analyses. It is the collection of analytical tools to interpret the database.

5-4.

Knowledge Management (5-4, PPT Slides 24–28) a. An executive in charge of information (typically the chief information officer, or CIO) can ensure that it is used in support of strategic thinking. b. Chief information officer (CIO):  serves as the liaison between the firm’s top management and its information systems department.  is responsible for planning, coordinating, and controlling the use of the firm’s information resources. c. Many companies extend the idea of information systems management to include organizational knowledge, or the information that resides inside its employees’ heads. d. Knowledge management (KM) is an effort to systematically collect organizational knowledge—about customers, products, processes, and the marketplace—and make it accessible to others. e. Exhibit 5.4: Knowledge Management at Khan Academy 5-4a. Limitations of the Systems Approach a. b. c. d.

Managers won’t always share their decision-making processes.

Managers across an organization have differing data needs.

The costs and time required to build information systems are often underestimated.

Data availability—analysis is limited to the data that are in the system.

5-4b. Intelligence Gathering in the Organization of the Future a. The value of insights gained from databases depends directly on the quality of the underlying data. b. Despite the value of DSSs, they sometimes do not provide enough information about what to do in specific instances. c. Both project-based approaches and systems-based approaches to gathering information will always be important. d. Group Activity: 10–20 minutes total. (PPT Slide 28) Form groups of three to five participants and do a quick online search for recent data breaches. Record the name of the company, extent of the breach, and what is being done to safeguard those whose information has been compromised. Share your information with the class.

As time allows, discuss within the group any experiences of a data breach of your personal information, such as credit card hacking or identity theft, and what the targeted company did to protect your information or regain your trust.  Note: This activity can also be done as an individual assignment.

Self-Assessment (PPT Slide 29)

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

How do you feel about working with data? Are you comfortable with numbers, or do the more interactive aspects of marketing research appeal more to you? How would you feel about taking on the task of a chief information officer?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

33. How does a project emphasis in marketing research differ from a systems emphasis?

The emphasis of the project approach is on conducting projects to address specific problems, while the emphasis of the systems approach is to put systems in place that provide data on an ongoing basis.

34. What are the main advantages and disadvantages of secondary data? Do these apply equally to internal and external secondary data?

The most significant advantages offered by secondary data are time savings and money savings for the researcher. Two disadvantages that commonly arise when secondary data are used are that they do not completely fit the problem and that they are not completely accurate. This applies equally to internal and external secondary data.

35. What are the main differences between a marketing information system and a decision support system?

A marketing information system is a set of procedures and methods for the regular, planned collection, analysis, and presentation of information for use in making marketing decisions. A decision support system is a system that combines data and models for guiding decisions, and it has a user interface that allows users to interact with the system to produce customized information.

36. In a decision support system, what is a data system? A model system? A dialog system?

Which of these is most important? Why? In a decision support system, the data system includes the processes used to capture data and the methods used to store data, coming from a number of external and internal sources. It is the creation of the database. A model system includes all the routines that allow the user to manipulate data so as to conduct the kind of analysis the individual desires. It is a collection of analytical tools to interpret the database. The dialog system permits users to explore databases by employing the system models to produce reports that satisfy their particular information needs. It is the user interface of the decision support system. All of these parts of the decision support system are equally important and rely on each other to produce the best results.

37. How does knowledge management expand the concept of an information system? What additional kinds of marketing intelligence can it provide?

Knowledge management expands the concept of an information system by including the management of knowledge that resides in employees’ heads and making it accessible to others. It

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can be a great asset for learning more about a company’s customers, its products and processes, and its marketplace. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

19. One particularly good way of emphasizing the difference between secondary and primary data is to discuss how each might be applied to the same information need. For example, class discussion can profitably be directed at providing competitive intelligence. The discussion works best when students are forced to think both about (1) why information in each area would be useful to know and (2) how it might be gotten using each approach. Below are examples of information in each area that might be needed: Background

  

Finance

    

Products



Pricing

        

Markets

  

Facilities

  

Company identification? Location? Brief history?

Affiliates? How the company is organized? When did it last alter its structure?

Shares outstanding? Ownership (insides, institutions, major shareholders)?

Statistics and performance analysis? Sales by division? Profitability by business unit?

Banks/investment banking firms used?

Stock market data? Current market value?

Ratios and industry comparisons?

Cash flow analysis? Assets and return on assets? Capitalization?

Working capital?

Products and services offered? Market position by product? Product strengths?

New product introductions?

R&D expenditures and apparent interests of technical personnel?

Patents held and pending?

Product features and quality? Technical sophistication?

General pricing policies?

Special selling arrangements?

Licensing and joint venture agreements?

Product costs?

Market segments served? Markets targeted? Degree of penetration?

Segment growth?

How does company view the direction of the industry?

Markets and geographic areas targeted for expansion?

Advertising, marketing, and sales efforts including budgets and ad agencies used?

Foreign trade? Recent orders? Government contracts?

Location? Size? Domestic vs. foreign?

Capacity? Capacity utilization? Announced capacity expansions?

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  

Product mix by plant? Shipments and profitability data?

Capital investments? Equipment purchases?

Number of production lines and shifts?

Once students have begun to recognize the differences between the two approaches for gathering different kinds of information, ask the students to compare and contrast the two approaches to stimulate discussion of the advantages and disadvantages of the two approaches. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 6: Decision Support Systems: Working With ―Big Data‖

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 48 Chapter Objectives ................................................................................................................................... 48 Complete List of Chapter Activities and Assessments........................................................................... 48 Key Terms.................................................................................................................................................. 48 What's New in This Chapter .................................................................................................................... 49 Chapter Outline ........................................................................................................................................ 49 Review Questions ...................................................................................................................................... 53 Additional Insights and Activities ........................................................................................................... 54

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 6: Decision Support Systems: Working With ―Big Data‖

PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to understand the dimensions and types of big data. The chapter begins by discussing the key dimensions of big data: volume, velocity, variety, veracity, and value. Examples of structured and unstructured data are provided before examining the various approaches to big data analysis. The chapter concludes by looking at the key challenges presented by big data integration.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 6-1

Identify the four primary Vs of ―big data.‖

6-2

Support the inclusion of a fifth V of ―big data.‖

6-3

Contrast structured and unstructured data.

6-4

Compare descriptive, predictive, and prescriptive analytical approaches.

6-5

List and discuss the key challenges of ―big data‖ integration.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 6-1 6-2 6-3 6-4 6-5 6-1–6-5

PPT slide

Activity/Assessment

Duration

PPT slide 6 PPT slide 11 PPT slides 18–19 PPT slide 22 PPT slide 25 PPT slide 26

Knowledge Check 6.1 Group Activity Discussion Activity Knowledge Check 6.2 Polling Activity Self-Assessment

< 5 min 10–20 min 5–10 min < 5 min 5–10 min 10–20 min

[return to top]

KEY TERMS Big data The process of capturing, merging, and analyzing large and varied data sets for the purpose of understanding current business practices and seeking new opportunities to enhance future performance.

Descriptive analysis Designed to enhance understanding of available data to benefit firm performance.

Mobile data Both structured and unstructured data available from mobile telephones, including smartphones and other mobile devices like tablet computers.

Omni-channel transactional data A collective term for all the different purchasing options a consumer

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 6: Decision Support Systems: Working With ―Big Data‖

has available, including store-based retailers, e-commerce sites, mobile purchasing, in-store pickup, home delivery, and so on.

Predictive analysis Designed to aid both explanatory and forecasting abilities for the betterment of the firm.

Prescriptive analysis Designed to optimize the various courses of action available to enhance firm performance.

Social data Unstructured data available from social media and social networking web-based platforms.

Structured data Data that can be written into rows on a spreadsheet or database based on standard column headings.

Unstructured data Data that take the form of social media comments, blog posts, other text-based communication, photos, video, audio, metaverse mentions, or any other form that is not easily arranged in structured format.

Variety The combination of structured and unstructured data collected in ―big data‖ systems.

Velocity The pace of data flow, both into and out of a firm.

Veracity The accuracy and trustworthiness of data collected in ―big data‖ systems.

Volume The sheer amount of data being collected in ―big data‖ systems. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:    

Numerous updates throughout the chapter reflect the continuous evolution of the topic of ―big data.‖ New examples have been inserted, including Kaggle and Georgia State University.

Material in Research Window 6.2 and Research Window 6.3 has been updated.

More recent statistics have been incorporated into Exhibit 6.2.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

6-1.

The Four Vs: Volume, Velocity, Variety, and Veracity (6-1, PPT Slides 3–6) u. Volume – The sheer amount of data being collected in ―big data‖ systems.  Some say volume is the most important aspect of big data. v. Velocity – The pace of data flow, both into and out of a firm. w. Variety – The combination of structured and unstructured data collected in ―big data‖ systems.

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x. Veracity – The accuracy and trustworthiness of data collected in ―big data‖ systems. y. Taken together, to capture high volumes of data with great velocity, of high variety, and unquestionable veracity is the essence of the phrase ―big data.‖ z. More specifically, we define big data as the process of capturing, merging, and analyzing large and varied data sets for the purpose of understanding current business practices and seeking new opportunities to enhance future performance. aa. Knowledge Check 6.1: < 5 minutes total. (PPT Slide 6)  The difference between the veracity and the variety of data is that: (m) veracity refers to the sheer amount of data collected, whereas variety refers to the pace of data flow in to and out of a company. (n) veracity refers to the pace of data flow in to and out of a company, whereas variety refers to the combination of structured and unstructured data collected in ―big data‖ systems. (o) veracity refers to the accuracy and trustworthiness of data collected in ―big data‖ systems, whereas variety refers to the sheer amount of data collected. (p) veracity refers to the accuracy and trustworthiness of data collected in ―big data‖ systems, whereas variety refers to the combination of structured and unstructured data collected in ―big data‖ systems.  Answer: d—veracity refers to the accuracy and trustworthiness of data collected in ―big data‖ systems, whereas variety refers to the combination of structured and unstructured data collected in ―big data‖ systems.  The third V is variety for the wide array of data types—both structured and unstructured—available to a firm, and the fourth V is veracity in reference to the accuracy and trustworthiness of the data.

6-2.

V Is Also for Value (6-2, PPT Slides 7–11) a. Companies around the world are investing in big data analytics to improve services and increase revenues. b. Findings from a study of business executives and managers across 18 countries:  91% of companies were working with big data.  75% planned to make additional investments.  73% had increased revenues due to big data. c. Ways in which firms have realized value:  Crowd sourcing big data analytics  Dealing with negative word of mouth  Enhancing higher education  Reducing carbon footprints  Creating personalized entertainment recommendations d. Research Window 6.1: Target, Big Data, and You  Provides a look at how analysts identify patterns in the data that allow them to predict which customers will need particular kinds of products. e. Group Activity: 10–20 minutes total. (PPT Slide 11)  Form groups of three to five participants. Choose an app that one participant has on their cell phone. Examine the app and make a list of ways the app appears to be either collecting or using big data.

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 o

6-3.

As time allows, groups may share the apps and big data insights with the larger class.

Note: One example would be Google Maps, which incorporates the user’s GPS, the location the user has indicated they wish to go, and data about road and traffic conditions on the particular route. You can also ask the class what additional big data they see as helpful in apps in general or in particular apps.

Marketplace Sources of ―Big Data‖ (6-3, PPT Slides 12–19) a. Firms investing in the capture, storage, and analysis of large and varied data sets are forward-looking and cutting edge. b. Exhibit 6.1: Sources of Data by Era c. Research Window 6.2: Solving Complex Business Problems with Customer Insights: An Interview with Karen Mangia by Jenn Vogel of Voxpopme’s ReelTalk 6-3a. Structured Data a. Structured data – Data that can be written into rows on a spreadsheet or database based on standard column headings.  Examples: transactional data, customer profile information obtained from registration materials or other sources.

6-3b. Unstructured Data a. Unstructured data – Data that take the form of social media comments, blog posts, other text-based communication, photos, video, audio, metaverse mentions, or any other form that is not easily arranged in a structured format. b. Social Data  Social data – Unstructured data available from social media and social networking web-based platforms. o Voice of the customer (VoC) impact: unstructured posts on social media networks such as Facebook, Twitter, YouTube, Instagram, LinkedIn, Reddit, TikTok, Pinterest, etc. o Establishment of a customer’s social network: peer-to-peer (P2P) and consumer-to-consumer (C2C) network communities.  Social network analysis is a popular tool for studying the social connections between people. c. Mobile Data  Mobile data – Both structured and unstructured data available from mobile phones, including smartphones and other mobile devices like tablet computers.  Smartphone and tablet data: o Data from texting, photo sharing, in-store shopping.  Location-based services: o Geo-targeted text messages, mapping services, location-sharing, and location data from call records. d. Omni-Channel Transactional Data  Omni-channel transactional data – A collective term for all the different purchasing options a consumer has available, including store-based retailers, ecommerce sites, mobile purchasing, in-store pickup, home delivery, and so on.

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o

Data that are connected to a particular purchaser across multiple purchasing channels. o Data across different platforms in potentially different formats are collected and tied together. e. Discussion Activity: 5–10 minutes total. (PPT Slides 18–19)  What is a social media influencer? Give some examples of super-influencers you are aware of. How do influencers affect big data?  Influencers use their relationship with their audience along with authority or knowledge to influence opinions and purchasing decisions of audience members.

Influencers might include Huda Kattan (beauty); Chiara Ferragni, Gigi Hadid (fashion); Jamie Oliver (food); Joanna Gaines, Lilly Singh (lifestyle); Jack Morris (travel); and Kayla Itsines (fitness). By sharing what they like or believe has value (even just mentioning product names), these influencers ―create‖ demand.

6-4.

Big Data Analysis (6-4, PPT Slides 20–22) a. Descriptive Analysis  Designed to enhance understanding of available data to benefit firm performance.  Examples: data mining, data fusion, neural network analysis, visualization. b. Predictive Analysis  Designed to aid both explanatory and forecasting abilities for the betterment of the firm.  Examples: regression analysis, time series analysis, simulation.  Research Window 6.3: Google.org Health Trends  Exhibit 6.2: Big Data and Privacy: What Are the Concerns and Benefits? c. Prescriptive Analysis  Designed to optimize the various courses of action available to enhance firm performance.  A key element of prescriptive analysis is optimization.  Optimization tools allow managers to uncover potential interactions and courses of action. d. Knowledge Check 6.2: < 5 minutes total. PPT Slide 22.  Regression analysis and simulation are techniques used most often in: (a) descriptive analysis. (b) predictive analysis. (c) data fusion. (d) prescriptive analysis.  Answer: b—predictive analysis.  Predictive analysis focuses on future-oriented, potential behaviors as opposed to merely classifying past behaviors.

6-5.

Key Challenges of ―Big Data‖ Integration (6-5, PPT Slides 23–25) a. Exhibit 6.3: Big Data and the Individual Consumer b. Key challenges that are less customer-focused and more firm-focused:  Access to and retrieval of data (including data integration) o The ability to make use of structured data to enhance decision making.

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 6: Decision Support Systems: Working With ―Big Data‖

o The ability to merge unstructured and structured data.  Analytic skills o Projected 28% growth rate in the number of jobs requiring data analytic skills.  Firm integration of big data o Finding insights hidden in newly integrated data sets and turning mounds of data into meaningful information. c. Polling Activity: 5–10 minutes total. (PPT Slide 25)  Because companies are concerned with adding value and getting a return on their investments in big data, which of the challenges to integrating big data will be the most difficult for most organizations? (a) The ability to access and retrieve data to make decisions. (b) The availability of employees with the necessary analytic skills. (c) The coordination of data integration both within and between firms.  Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response. In this particular activity, their answer can be related to which is most important and/or valuable to solve, or whether the issues must be solved in a particular order.

Self-Assessment (PPT Slide 26) 

When you think about big data, in what ways do you notice that it affects your daily life?

What worries, if any, do you have about the ubiquity of big data and the ways companies use it?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

38. Which of the four Vs—volume, velocity, variety, or veracity—creates the biggest challenge to marketing managers intent on ―finding a needle in a haystack‖?

Between volume, velocity, variety, or veracity, variety is the most challenging for marketing managers who are intent on ―finding a needle in a haystack.‖ 39. How might a marketing manager obtain value from ―big data‖ that is different from value obtained from traditional data sources?

The possibility of increased revenue opportunities is one value of big data.

Furthermore, big data can improve customer retention rates, reduce carbon footprints, and create personalized promotions better than traditional data collection.

40. Compare and contrast structured versus unstructured data. Using Facebook as an example, would data available from Facebook to a marketer be structured, unstructured, or both?

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Structured data are data that can be written into rows on a spreadsheet or database based on standard column headings. Unstructured data are data that take the form of social media comments, blog posts, other text-based communication, photos, video, audio, or any form that is not easily arranged in a structured format. VoC data, such as that from Facebook, is largely unstructured data.

41. In terms of marketplace sources of ―big data,‖ what is social data? Mobile data? Omnichannel transactional data?

Social data are unstructured data available from social media and social networking Web-based platforms. Mobile data are both structured and unstructured data available from mobile telephones, including smartphones or other mobile devices like tablet computers. Omni-channel transactional data are a collective term for all the different purchasing options a consumer has available, including store-based retailers, e-commerce sites, mobile purchasing, in-store pickup, home delivery, etc.

42. What are the main differences between descriptive, predictive, and prescriptive analyses?

Descriptive analysis is designed to enhance understanding of available data to benefit firm performance. Predictive analysis is designed to aid both explanatory and forecasting abilities for the betterment of the firm. Prescriptive analysis is designed to optimize the various courses of action available to enhance firm performance.

43. What are the three main challenges marketing managers face when attempting to integrate ―big data‖ into the firm?

Three key challenges to big data integration include (1) access to and retrieval of quality data, (2) lack of sufficient analytical skills within the firm, and (3) issues related to firm integration both within and between firms. The first and third challenges bring the importance of the four Vs into focus. Volume, velocity, variety, and veracity each add to those challenges, while the second challenge is one that could require both employee and potential employee training to address the challenge for the industry in total. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

20. It might be interesting to discuss the pros and cons of the Karen Mangia interview, specifically the second question and answer. Discussion of the collection of behavioral data leads to a subsequent discussion of consumer privacy (at least, that has been our experience). It is interesting to hear views of conceptual comparisons between freely giving away consumer data in social media outlets versus having consumer data captured unknowingly via some of the tools noted here.

21. Suggest that students look at both the quantitative and qualitative Web site reviews at Amazon, iTunes, or another consumer e-commerce marketplace to demonstrate the challenges and benefits of structured and unstructured data sources.

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22. The IBM Building, a Smarter Planet blog, is an excellent source for discussions of the different analytical approaches and the most up-to-date practical examples.

23. Exhibit 6.3 offers a perspective of the pros and cons of the ―big data‖ approach. The opportunity exists to have students pick sides and debate the issue in class using this exhibit and other resources they might discover. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 7: Using External Secondary Data

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 56 Chapter Objectives ................................................................................................................................... 56 Complete List of Chapter Activities and Assessments........................................................................... 56 Key Terms.................................................................................................................................................. 56 What's New in This Chapter .................................................................................................................... 57 Chapter Outline ........................................................................................................................................ 57 Review Questions ...................................................................................................................................... 61 Additional Insights and Activities ........................................................................................................... 62

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to look at where managers can get the information needed to make decisions when it isn’t available internally. Data suppliers sell to multiple companies, which allows the costs of collecting and analyzing data to be shared. Sometimes, needed data are available from published sources, so the chapter looks at getting started with that search. The chapter concludes with the quest for single-source data, what it might mean, and why we aren’t there yet.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 7-1

Describe the process of searching for published external secondary data.

7-2

Discuss two customer profiling tools.

7-3

Name the most impactful method for assessing product sales.

7-4

Discuss the means of measuring advertising effectiveness.

7-5

Define single-source data.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 7-1 7-2 7-3 7-4 7-5 7-1–7-5

PPT slide

Activity/Assessment

Duration

PPT slide 7 PPT slide 12 PPT slides 15–16 PPT slide 22 PPT slides 26–27 PPT slide 27

Group Activity 1 Group Activity 2 Discussion Activity 1 Polling Activity Discussion Activity 2 Self-Assessment

10–20 min 10–20 min 5–10 min 5–10 min 5–10 min 10–20 min

[return to top]

KEY TERMS Geodemography The availability of demographic, consumer behavior, and lifestyle data by arbitrary geographic boundaries that are typically quite small.

People meter A device used to measure when a television device is on, to what channel it is tuned, and who in the household is watching it.

Portable people meter A small device carried with a person or worn on a person’s clothing, used to

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measure when a person is listening to a radio station or watching a television broadcast outside their home.

Scanner An electronic device that automatically reads the bar code imprinted on a product, looks up the price in an attached computer, and instantly prints the description and price of the item on the cash register receipt.

Single-source data Data that allow researchers to link together purchase behavior, household characteristics, and advertising exposure at the household level.

Standardized marketing information Secondary data collected by companies that sell the data to multiple companies, allowing the costs of collecting, editing, coding, and analyzing them to be shared.

The data are standardized so that multiple companies can use them rather than customized for a specific company. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition: 

Resources and web addresses in Exhibit 7.2 have been updated.



Exhibit 7.3 is new and provides data sources for marketing associations.



An expanded Exhibit 7.4 looks at NAICS codes.



Updated data are presented in Exhibit 7.5.



The section on the Internet and the accompanying Exhibit 7.7 have both been updated and expanded.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

7-1.

External Secondary Data From Searching Published Sources (7-1, PPT Slides 3–7) bb. Relevant external secondary data on almost any problem a marketer might confront is likely available. cc. Exhibit 7.1: How to Get Started When Searching Published Sources of Secondary Data  Identify what you want to know and what you already know about your topic.  Develop a list of key terms and names.  Conduct an online search of relevant databases and web sites. o Exhibit 7.2: Key Online Sources of Secondary Data for Business Purposes o Exhibit 7.3: Sources of Data on Marketing Associations

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Instructor Manual: Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 7: Using External Secondary Data



Compile the information you have found; rework the list of key words and authors if necessary. i.

By the end of this step, a clear idea of the information sought is the goal.  Consult a reference librarian inside your firm, at a public library, or at a university library.  Identify authorities in the subject matter and consult with them. dd. Group Activity 1: 10–20 minutes total. (PPT Slide 7) Form groups of three to five participants. Go to data.census.gov and explore the website.

Choose a topic (e.g., e-bikes, a charity such as Stand Up to Cancer, or any other topic to which marketing research would apply) and list some data to find on the census.gov site.

As time allows, share your findings with the larger class, noting any particularly interesting or useful data you found.

7-2.

Standardized Marketing Information—Profiling Customers (7-2, PPT Slides 8–12) a. Standardized marketing information  Secondary data collected by companies that sell the data to multiple companies, allowing the costs of collecting, editing, coding, and analyzing them to be shared.  The data are standardized so that they can be used by multiple companies rather than customized for a specific company. b. North American Industry Classification System (NAICS)  Segmentation base for organizing business information reporting.  Exhibit 7.4: NAICS Codes for the Construction Sector c. Geodemography – The availability of demographic, consumer behavior, and lifestyle data by arbitrary geographic boundaries that are typically quite small.  Regularly update census data through statistical extrapolation, allowing data to be used with greater confidence.  Cluster-analyses of the census-produced data produce descriptive ―homogeneous groups‖ that describe the population. d. Exhibit 7.5: Sample Geodemographic Map of Wheaton, IL e. Group Activity 2: 10–20 minutes total. (PPT Slide 12) Form groups of three to five participants. Go to naics.com and explore the website.

Choose a business with which you are familiar (e.g., Panera, Dick’s Sporting Goods, Walmart, etc.) and search to find the NAICS code for that business as well as the business’s profile page and note what data are available there.

As time allows, share your findings with the larger class, noting any particularly interesting or useful data you found.  Note: This activity may also be assigned individually, in class or as a short written assignment.

7-3.

Standardized Marketing Information—Measuring Product Sales and Market Share (7-3, PPT Slides 13–16) a. Firms need accurate assessment of their progress in order to compete successfully.

7-3a. Diary Panels a. A diary panel is a representative group of individuals or households that tracks purchases and consumption over a time period.

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

For example, NPD Group tracks purchases through mobile app engagement to capture purchase data from panel members.

7-3b. Scanners a. Scanner – An electronic device that automatically reads the bar code imprinted on a product, looks up the price in an attached computer, and instantly prints the description and price of the item on the cash register receipt.  Information can be combined with other data sources to reveal patterns. b. Exhibit 7.6: Conducting a Store Audit to Measure Product Sales c. Scanners are so pervasive that most of the retail sales information available today is based on scanner data. d. Discussion Activity 1: 5–10 minutes total. (PPT Slides 15–16)  The textbook makes this statement: ―The effect of scanners on standardized marketing research has been profound.‖ In your experience, what evidence have you seen that supports this statement?  Individual answers will vary, but consider how pervasive scanners are and that most retail sales information available today is based on scanner data. Combining sales data, pricing data, regional data, historical versus current data, or comparing with other companies’ data are all approaches to marketing research.

7-4.

Standardized Marketing Information—Measuring Advertising Exposure and Effectiveness (7-4, PPT Slides 17–22) a. All media companies who sell advertising have an interest in understanding who is consuming their content. b. A number of different services have evolved to measure exposure to various media.

7-4a. Internet a. Advertisers need information about consumers’ online activities.  Only 7% of Americans are not online.  Although it is easy to count how often content is accessed, the demographics of those doing the accessing is less easy to determine.  Various companies assess Internet usage and also track mobile media usage and audience composition.  Exhibit 7.7: Top 10 Multi-Platform Properties (Desktop and Mobile) for September 2022 (Total United States–Home and Work Locations) b. Large research providers offer tools to assess a brand’s reach and impact with consumers.

7-4b. Television and Radio a. A familiar form of media research is Nielsen TV ratings.  Provides estimates of size and nature of viewing audience. b. People meter – A device used to measure when a television is on, to what channel it is tuned, and who in the household is watching it.  Portable people meters are worn by consumers to sense codes embedded in television and radio programming to determine actual exposure.

7-4c. Print Media

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a. Starch AdMeasure is one of the oldest services to measure exposure to and readership of print titles.  Measures the effectiveness of magazine advertisements.  Gauges reader interest and reactions to both editorial content and advertising.

7-4d. Cross-Platform Services a. When a single advertising campaign appears across a wide spectrum of media, researchers attempt deliver an overall assessment of its effectiveness.  MRI-Simmons USA uses a national probability sample (25,000 consumers) to collect data on product usage and media exposure.  Allows companies to better segment, target, and communicate to the most promising groups.  comScore tracks mobile web site usage automatically among panel members. b. Cross-platform research plays an increasingly important role. c. Polling Activity: 5–10 minutes total. (PPT Slide 22)  In your opinion, what do suppliers of consumer goods and services most need to know? (a) Which media outlets appeal to which types of consumers (b) The demographics of those accessing their web sites (c) Actual purchase behaviors as a result of specific marketing efforts  Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response. In this particular activity, you might invite them to reflect on how the three options are interrelated.

7-5.

Striving Toward Nirvana: Single-Source Data (7-5, PPT Slides 23–27) a. Single-source data – Data that allow researchers to link together purchase behavior, household characteristics, and advertising exposure at the household level.  Companies want the advantages of single-source data but are not in a position to capture proprietary data.  No existing system captures all the data marketers would like.  Even if the relevant data could be identified, the costs of collecting it would be prohibitive.  Exhibit 7.8: Single-Source Data in a Perfect World b. Managers and researchers need to understand the potential value of data that are increasingly available and to work to combine those data sources in ways that provide insight. c. Discussion Activity 2: 5–10 minutes total. (PPT Slides 26–27)  Why is single-source data more of an ideal rather than reality?  There are a variety of reasons why single-source data is still more dream than reality. Comprehensive data are difficult and expensive to capture; providers of standardized marketing information don’t typically customize information for individual clients; and no system currently exists to ―do it all.‖  Users of the data also need to shift focus from looking at the ―how‖ of marketing actions to the ―why‖ of marketing behaviors. The data are there; they just need an approach to combining them to reveal their insights.

Self-Assessment (PPT Slide 27)

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

What were your thoughts when you learned about geodemography? Describe the group into which you would be placed by a geodemographer.

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

44. Why should researchers look for published sources of secondary data before searching for standardized marketing information?

Researchers should look for published sources of secondary data before searching for standardized marketing information because due to the latter’s standardized nature, it may not always be a perfect fit for your company. Also, it is often more expensive.

45. What is ―standardized‖ about standardized marketing information?

Standardized marketing information is secondary data collected by companies that sell that data to multiple companies, allowing the costs of collecting, editing, coding, and analyzing them to be shared. The data are standardized so that multiple companies can use them rather than being customized for a specific company.

46. What does it mean to ―profile‖ customers or prospects? Why would a company need this information?

Profiling customers involves gathering data that may help a company anticipate their needs and wants in order to increase sales.

47. What is the purpose of geodemography?

Geodemography refers to the availability of demographic, consumer behavior, and lifestyle data by arbitrary geographic boundaries that are typically small. Geodemographers combine census data with their own survey data or data collected from other sources to produce customized products for clients.

48. Given that companies know their revenues, why do they also need standardized information about product sales and market share?

Companies need to know standardized information about product sales and market share because it further helps them profile their customers and prospects.

49. How does a diary panel work?

A diary panel keeps track of purchases made or products consumed over a period of time. It can be recorded on paper or reported online.

50. Why can the effect of scanners on standardized marketing information be described as ―profound‖?

Scanners are so pervasive that most of the retail sales information available today is based on scanner data. The ability to combine scanner data with other sources of data provides infinite ways to assess the effectiveness of various marketing actions.

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51. How are people meters used to assess television viewership?

People meters attempt to measure which household members are watching which television channels at which time. Each member of the family has his/her own viewing number. Whoever turns on the set, sits down to watch, or changes the channel is supposed to enter his or her number into the people meter, which is an electronic device that stores and transmits this information to a central computer for processing.

52. How are portable people meters used to assess radio listenership?

Portable people meters are pager-like devices that household members carry with them (or wear on their clothing) to measure radio listenership.

53. How might a company assess the success of its online advertising?

A company may assess the success of its online advertising by using services such as Digital Voice by the Nielsen Company or determining syndicated research reporting on mobile media usage and audience composition (for example, comScore’s Mobile Metrix).

54. What are cross-platform services? Why might they be important?

Cross-platform services are used when an advertising campaign appears simultaneously across a wide spectrum of media. This increases viewership, exposure, and audience types.

55. What is single-source data?

Single-source data are data that allow researchers to link together purchase behavior, household characteristics, and advertising exposure at the household level. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

24. One useful method of framing a discussion on the use of marketing information services is to focus on the issues that these databases may be used to address, along with their strengths and weaknesses when used in such a manner. One productive strategy is to display samples of the types of reports they produce. Examples can often be found in the promotional materials offered by the various companies. Complimentary copies are often available upon request.

25. An alternative strategy is to present various scenarios and to use class discussion to develop the advantages and disadvantages of addressing the problem using particular commercial databases. The following three scenarios are offered as a point of departure in that regard. a. The research manager for a large consumer products company is interested in determining consumers' attitudes toward a certain product. More importantly, she wants to see if consumers' attitudes are associated with their purchase behavior toward the product. Discuss the pros and cons of using a mail panel (see Chapter 6) vs. a diary panel in this situation. Which is more useful? What trade-offs might be involved in selecting one of the methods over the other?

Discussion points:

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 

Diary panels are especially useful for measuring sales; attitudes are not normally measured with them, though.

Mail panels typically are customized to the specific use (in this case, measuring attitudes and associating them with sales); sales in this case, though, would need to be measured by intention to purchase.

b. A consumer products company wants to develop a general profile of purchasers of its products in a certain trade area. Would diary panel data or scanner data be more useful for this purpose?

Discussion points:  Traditional scanner data would provide a good record of purchase behavior; moreover, demographic data would be available for participating scanner households, although probably not psychographic data.  Both demographic and psychographic information could be available with the diary panel data, although purchase data by household would not be as accurate.  Scanner data would represent many more purchases; this would be important if statistics were required for a small geographic area. c. The same consumer products company was interested in identifying potential customers in another region of the country based on the results of the demographic study. The company assumed that purchasers of its products would be similar across the country.

Could the company use scanner data for this purpose? What about the geodemographic research companies?

Discussion points:  Scanner data is not appropriate because it is useful for generating aggregate sales data, not the identification of customers, unless the study is being done in an area where a single-source scanner-based system is operating.  Geodemographers can develop profiles of consumers within relatively narrow geographic boundaries. [return to top]

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TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 65 Chapter Objectives ................................................................................................................................... 65 Complete List of Chapter Activities and Assessments........................................................................... 65 Key Terms.................................................................................................................................................. 65

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What's New in This Chapter .................................................................................................................... 66 Chapter Outline ........................................................................................................................................ 66 Review Questions ...................................................................................................................................... 70 Additional Insights and Activities ........................................................................................................... 72

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to make the distinction between exploratory, descriptive, and causal research. Part of the distinction requires understanding the difference between laboratory and field experiments, as well as the difference between internal and external validity. The chapter concludes with a discussion of the types of test marketing and the considerations for choosing one over the others.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 8-1

Discuss the three general types of primary data research.

8-2

Clarify the difference between laboratory experiments and field experiments.

8-3

Describe the use of an A/B test.

8-4

List the three major considerations in test marketing.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 8-1 8-2 8-3 8-4 8-1–8-4

PPT slide

Activity/Assessment

Duration

PPT slide 6 PPT slide 13 PPT slide 17 PPT slides 22–23 PPT slide 24

Knowledge Check 8.1 Group Activity Polling Activity Discussion Activity Self-Assessment

< 5 min 10–20 min 5–10 min 5–10 min 10–20 min

[return to top]

KEY TERMS Descriptive research Research in which the major emphasis is on describing characteristics of a group or the extent to which variables are related.

Causal research Type of research in which the major emphasis is on determining cause-and-effect relationships.

Experiment Scientific investigation in which an investigator manipulates one or more independent variables and observes the degree to which the dependent variables change.

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control some variables and manipulate others.

Internal validity The degree to which an outcome can be attributed to an experimental variable and not to other factors.

Field experiment Research study in a realistic situation in which one or more independent variables are manipulated by the experimenter under as carefully controlled conditions as the situation will permit.

External validity The degree to which the results of an experiment can be generalized, or extended, to other situations.

A/B test A field experiment in which two versions (i.e., A and B) of some marketing element are randomly assigned to subjects and then compared on outcomes of interest.

Market testing (test marketing) A controlled experiment done in a limited but carefully selected sector of the marketplace.

Standard test market A test market in which the company sells the product through its normal distribution channels.

Controlled test market An entire test program conducted by an outside service in a market in which it can guarantee distribution.

Simulated test market (STM) A study in which consumer ratings are obtained along with likely or actual purchase data often obtained in a simulated store environment; the data are fed into computer models to produce sales and market share predictions.

Virtual test market A simulated test market in which subjects ―interact‖ with products and stores electronically, rather than physically. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

The chapter’s Learning Objectives have been realigned to achieve greater clarity between major chapter sections and their associated learning objective.

New examples are provided in the discussion of A/B testing.

Updated and new examples can be found in Exhibit 8.3: (Mis)adventures in Market Testing.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

8-1.

Three Approaches to Generating New Data (8-1, PPT Slides 3–6) ee. Exploratory research is conducted to gain ideas and insights to better define the problem or opportunity confronting a manager.

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 

Exploratory data meet the definition of primary data.

At this point in the process, exploratory research might be the exclusive means of gathering new data.  Much of the time, managers’ instincts and exploratory results lead to reasonable decisions. ff. Descriptive research – Research in which the major emphasis is on describing characteristics of a group or the extent to which variables are related.  This is a common type of research.  It can be carried out using a survey or gathering behavioral data.  Is typically what one thinks of when hearing the term marketing research. gg. Causal research – Type of research in which the major emphasis is on determining cause-and-effect relationships.  These studies typically take the form of experiments.  Best when a company needs precise information about the effects of various marketing actions. hh. Exhibit 8.1: Relationships Among Types of Primary Data Research ii. Knowledge Check 8.1: < 5 minutes total. (PPT Slide 6)  Three of the following scenarios describe descriptive research. Which scenario describes causal research? (q) Tracking the usage of a university’s online tutoring service (r) Tracking what styles of a brewery’s beer are purchased in grocery stores versus liquor stores (s) Conducting a survey to categorize age of shoppers who make convenience store purchases of more than three items versus shoppers at a grocery store purchasing more than three items (t) Using three outlets in a test market to determine purchase quantities of an item at three different price points  Answer: d—Using three outlets in a test market to determine purchase quantities of an item at three different price points  Determining the cause and effect of different price points is causal research.

8-2.

Causal Research (8-2, PPT Slides 7–13) a. Because we can never know for certain that we have eliminated all other possible causes of an effect, we can never state with certainty that X caused Y. 8-2a. Establishing Causality a. For one thing to have ―caused‖ another, three conditions must be met:  Consistent variation between cause (X) and effect (Y). o Evidence of the extent to which X and Y occur together or vary together in the way predicted by the hypothesis. o All we can conclude is that the correlation makes the hypothesis more likely, but it is not proof.  Time order of cause and effect must be correct. o Evidence that shows X occurs before Y.  Other explanations are eliminated.

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o

Evidence allows the elimination of factors other than X as the cause of Y.

8-2b. Experiments as Causal Research a. With causal research, we use experiments to work toward establishing possible causal relationships. b. Experiment – Scientific investigation in which an investigator manipulates one or more independent variables and observes the degree to which the dependent variables change.  The basic point of an experiment is to change the levels of one or more X variables and examine the resulting impact on Y while at the same time controlling (holding constant) other variables that might impact Y. c. Laboratory Experiments  Laboratory experiment – Research investigation in which investigators create a situation with exact conditions in order to control some variables and manipulate others.  Internal validity – The degree to which an outcome can be attributed to an experimental variable and not to other factors.  Lab experiments tend to have higher levels of internal validity. d. Field Experiments  Field experiment – Research study in a realistic situation in which one or more independent variables are manipulated by the experimenter under as carefully controlled conditions as the situation will permit.  External validity – The degree to which the results of an experiment can be generalized, or extended, to other situations.  Field experiments tend to have higher levels of external validity. e. Group Activity: 10–20 minutes total. (PPT Slide 13) Form groups of three to five participants. You are the marketing researchers for an ice cream company that wants to introduce a new flavor or line of flavors. Choose either a laboratory experiment or a field experiment to gather data to help the company make a decision on the flavor(s). Outline the basic approach you would take for this experiment.

As time permits, share your ideas with the larger class and discuss which type of experiment might be more appropriate in this situation.

8-3.

Field Experiments in Marketing: A/B Testing (8-3, PPT Slides 14–17) a. A/B test – A field experiment in which two versions (i.e., A and B) of some marketing element are randomly assigned to subjects and then compared on outcomes of interest.  Also called split testing.  Commonly used in online settings as a means of optimizing performance of company web sites.  Sometimes, the difference between outcomes of versions tested is quite small. b. Exhibit 8.2: A General Structure for A/B Tests c. Research Window 8.1: How Key Ingredient Used A/B Tests to Avoid a Costly Mistake d. Polling Activity: 5–10 minutes total. (PPT Slide 17)  Which of the following experiments is closest to an A/B test?

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

8-4.

(a) Experiment 1: In an actual retail outlet, the control is an end-of-aisle display using cardboard cutouts of a celebrity holding the product, and the experiment is a display of the actual product. (b) Experiment 2: The control is a request for employees to submit office improvement suggestions, and the experiment is a mandatory survey of employees rating their satisfaction with office conditions. (c) Experiment 3: In a focus group, participants choose between the control, which is the current skyline of their city, and the experiment, which is the skyline after two substantial building projects.

Note: This activity has no single correct answer, because although option (a) is closest to the definition of an A/B test, the other two can be tweaked to fit into the A/B format. Students can be prompted to volunteer the reasoning for their response.

In this particular activity, their answer can be related to how options (b) and (c) would better fit the A/B test definition.

Field Experiments in Marketing: Market Testing (8-4, PPT Slides 18–23) a. Market testing (test marketing) – A controlled experiment done in a limited but carefully selected sector of the marketplace. b. Market testing can be used to examine almost every element of the marketing mix.

8-4a. Key Issues in Market Testing a. Cost: Can include everything from hardware and software setup costs to costs associated with live testing. b. Time: Can be substantial and not always available in desired quantities. c. Control: The most accurate test markets are those with high levels of external validity at the cost of experimental control. d. Test marketing has been called the most dangerous game in all of marketing because so many things can misfire, as illustrated by examples in Exhibit 8.3: (Mis)adventures in Market Testing.

8-4b. Types of Test Markets a. Standard test market – A test market in which the company sells the product through its normal distribution channels.  Choosing cities to use for test markets is really important.  Popular test-market cities have their own media outlets and the right kind of retail outlets. b. Controlled test market – An entire test program conducted by an outside service in a market in which it can guarantee distribution.  Sometimes called a forced-distribution test market. c. Simulated test market (STM) – A study in which consumer ratings are obtained along with likely or actual purchase data often obtained in a simulated store environment; the data are fed into computer models to produce sales and market share predictions.  Successful simulations rely heavily on the equations built into the computer model.  Virtual test market – A simulated test market in which subjects ―interact‖ with products and stores electronically, rather than physically.  Exhibit 8.4: Shopping for Ketchup in a Virtual Store

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d. Comparing the Three Types of Test Markets  Exhibit 8.5: Relative Advantages and Disadvantages of Different Types of Test Markets  The standard test market may be a logical choice when: o it is important for the firm to test its ability to actually sell to the trade and get distribution for the product. o the capital investment is significant and the firm needs a prolonged test market to accurately assess its capital needs or its technical ability to manufacture the product. o the company is entering new territory and needs to build its experience base so that it can play for real, but it wants to learn how to do so on a limited scale.  The controlled test market is a good indicator when a new product fits in nicely with a company’s existing line for which it already has distribution.  Advantage of simulated test markets include: o the protection they provide from competitors. o adeptness at assessing trial- and repeat-purchasing behavior. o being faster than full-scale tests and particularly good for spotting weak products. o being the least expensive. e. Discussion Activity: 5–10 minutes total. (PPT Slides 22–23)  What key concerns are inherent in experiments? o Cost is an issue in every one of an organization’s activities. It is important to balance cost with benefits. Although it is expensive to set up test markets and experiments, it may save a great deal in development and production costs to have a product with the features consumers actually want and will pay for. o Another problem is time. Experiments take time, but the amount of time taken for testing may prevent a product from launching early enough to capture enough of a market or to effectively balance development and production costs. o Control and providing the best evidence of cause and effect often require a tradeoff between internal and external validity. The most accurate test markets are those with high levels of external validity at the cost of experimental control.

Self-Assessment (PPT Slide 24) 

If asked, how would you explain the difference between descriptive research and causal research?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

56. What are the three basic types of research used to collect primary data? What is the basic purpose of each?

The three general types of primary data collection are exploratory research, descriptive research, and causal research. The major emphasis in exploratory research is on the discovery of ideas and

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insights. Descriptive research is typically concerned with describing characteristics of a group or the extent to which variables are related. A causal research design is concerned with determining cause-and-effect relationships.

57. Why is exploratory research considered to be a basic type of primary data research?

Exploratory research is the basic type of primary data research because its aim is to gather information, not to analyze it.

58. What is the general sequence in which the three basic types of research are employed?

When employing the three types of primary research, you should follow the following sequence: exploratory research, descriptive research, causal research.

59. Is it possible to establish that one thing causes another? Why or why not?

It is not possible to establish that one thing causes another in a scientific sense. In order to say one thing ―caused‖ another, three conditions must be met: (1) There must be consistent variation between the cause and the effect, (2) the time order of the cause and effect must be correct, and (3) other explanations must be eliminated.

60. What is an experiment?

An experiment is a scientific investigation in which an investigator manipulates one or more independent variables and observes the degree to which the dependent variables change.

61. What is the difference between a lab study and a field study?

A lab study is a research investigation in which investigators create a situation with exact conditions in order to control some variables and manipulate others. A field study is a research study in a realistic situation in which one or more independent variables are manipulated by the experimenter under as carefully controlled conditions as the situation will permit.

62. What is the difference between internal validity and external validity? Which form of validity is more important?

Internal validity is the degree to which an outcome can be attributed to an experimental variable and not to other factors. External validity is the degree to which the results of an experiment can be generalized, or extended, to other situations. External validity is more important.

63. What is an A/B test? What is the general structure for performing such a test?

An A/B test is a field experiment in which two versions (i.e., A and B) of some marketing element are randomly assigned to subjects and then compared to outcomes of interest. Fifty percent of users are assigned Control (existing system), and the rest are assigned Treatment (existing system with feature X). The users’ interactions are instrumented, analyzed, and compared at the end of the experiment.

64. What is market testing? What are the three basic types of test markets?

Market testing involves the use of a controlled experiment done in a limited but carefully selected section of the marketplace in order to predict the sales or profit outcomes of one or more proposed marketing actions. The three types of market testing are a standard market test, a controlled market test, and a simulated market test.

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65. Under what conditions is a standard test market a better choice than either simulated or controlled test markets?

A standard test market should be used when speed, cost, security, and internal validity are factors.

It is more suited for evaluating product extensions than for examining the likely success of a radically different and new product. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

26. Begin by emphasizing the key requirements of descriptive or causal designs—a specific hypothesis to be tested. It is helpful to review exactly what a hypothesis is and the characteristics of a good hypothesis.

27. When addressing the notion of causality, discuss what is implied when we say "X causes Y." This discussion can focus on the types of evidence that can be employed to infer the existence of a causal relationship between X and Y. One example that works well is to have students specify a priori the factors (the Xs) that contribute to success in college (the Y to be explained). Using a few selected factors, one can then construct some arbitrary tables that illustrate concomitant variation and ask the students what the tables suggest. Instructors can vary both the strength of the relationships pictured in the tables and the time order of the variables (e.g., success in high school versus success as a freshman in college). After seeing several made-up examples of what the relationship could look like, students soon learn to appreciate the fact that causality is always an inference and the tenuous nature of such inferences as well as what is demonstrated by each of the types of evidence.

28. To emphasize the importance of control in causal designs and how experiments are a means of controlling the situation in which an event occurs, review the basic definition of an experiment and point out that the primary difference between laboratory and field experiments is the artificiality of the situation. Both are somewhat contrived, and thus the difference is more one of degree than of kind. [return to top]

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TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 74

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Chapter Objectives ................................................................................................................................... 74 Complete List of Chapter Activities and Assessments........................................................................... 74 Key Terms.................................................................................................................................................. 74 What's New in This Chapter .................................................................................................................... 75 Chapter Outline ........................................................................................................................................ 75 Review Questions ...................................................................................................................................... 78 Additional Insights and Activities ........................................................................................................... 80

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to more fully explain descriptive research and some different forms that it can take. This explanation is followed by a discussion of several different categories of primary data, noting that the usual goal is to understand and predict customers’ behaviors. This chapter is the first of several that present the different stages in the process of collecting primary data using descriptive research.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 9-1

Cite three major purposes of descriptive research.

9-2

Name the type of primary data prioritized by marketers and why it is prioritized.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 9-1 9-1 9-2 9-1–9-2

PPT slide PPT slide 9 PPT slide 18 PPT slide 22 PPT slide 23

Activity/Assessment Knowledge Check 9.1 Group Activity Polling Activity Self-Assessment

Duration < 5 min 10–20 min 5–10 min 10–20 min

[return to top]

KEY TERMS Attitude An individual’s overall evaluation of something.

Behavior

What subjects have done or are doing.

Continuous panel A fixed sample of respondents who are measured repeatedly over time with respect to the same variables.

Cross-sectional study Investigation involving a sample of elements selected from the population of interest that are measured at a single point in time.

A fixed sample of respondents who are measured repeatedly over time but on variables that change from measurement to measurement.

Discontinuous panel

Intentions

Anticipated or planned future behavior.

Knowledge Insight into, or understanding of facts about, some object or phenomenon.

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Lifestyle How individuals live, what interests them, their values, and what they like.

Longitudinal study Investigation involving a fixed sample of elements that is measured repeatedly through time.

Motive A need, a want, a drive, a wish, a desire, an impulse, or any inner state that energizes, activates, moves, directs, or channels behavior toward goals.

Normal patterns of behavior exhibited by an individual; the attributes, traits, and mannerisms that distinguish one individual from another.

Personality

Cross-sectional study in which the sample is selected to be representative of the target population and in which the emphasis is on the generation of summary statistics, such as averages and percentages.

Sample survey

[return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

Streamlined learning objectives to better reflect the topical discussion of the major sections of the chapter.

Exhibit 9.3 has been updated.

A new Manager’s Focus addresses social media and its impact on demographic data.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

9-1.

Descriptive Research Designs (9-1, PPT Slides 3–9) jj. The purpose behind descriptive research design is to describe some group of people or other entities.  To describe the characteristics of certain groups.  To determine the proportion of people who behave in a certain way.  To make specific predictions. kk. Questions that need to be answered before data collection can begin:  Who, what, when, where, why, and how.

9-1a. Two Types of Descriptive Studies a. Exhibit 9.1: Classification of Descriptive Studies b. Longitudinal Analysis: Consumer Panels  Longitudinal study – Investigation involving a fixed sample of elements that is measured repeatedly through time.

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Continuous panel – A fixed sample of respondents who are measured repeatedly over time with respect to the same variables. o Discontinuous panel – A fixed sample of respondents who are measured repeatedly over time but on variables that change from measurement to measurement. o Panels are probably best for collecting detailed demographic information. o Panels are believed to be more accurate when measuring purchasing behavior. o Panels’ main disadvantage is that they are nonrepresentative and/or nonrandom. c. Cross-Sectional Analysis: Sample Survey  Cross-sectional study – Investigation involving a sample of elements selected from the population of interest that are measured at a single point in time. o This type of design provides a snapshot of the variables. o Emphasis is placed on selecting sample members. o Sample survey – Cross-sectional study in which the sample is selected to be representative of the target population and in which the emphasis is on the generation of summary statistics such as averages and percentages. o Advantages include being able to target and recruit very specific populations. o Another advantage is the ability to use a probability sampling plan. o Disadvantages include costs of time and money and the technical skill needed at all its stages. ll. Knowledge Check 9.1: < 5 minutes total. (PPT Slide 9)  Which of the following statements about descriptive research is accurate? (a) Descriptive research is much more flexible than exploratory research. (b) Making specific predictions is not part of descriptive research. (c) In descriptive research, panel data are believed to be more accurate than cross-sectional data, especially when measuring purchasing behaviors. (d) In descriptive research, cross-sectional studies are best for collecting detailed demographic information.  Answer: c—In descriptive research, panel data are believed to be more accurate than cross-sectional data, especially when measuring purchasing behaviors.  With cross-sectional designs, respondents are asked to remember and report their past behaviors, a process that inevitably leads to error because people tend to forget. o

9-2.

Types of Primary Data (9-2, PPT Slides 10–22) a. For both longitudinal and cross-sectional analysis, marketing data typically fall into one of seven categories. b. Exhibit 9.2: Seven Types of Primary Data 9-2a. Behavior a. Many marketing researchers believe behavior to be the most important of the types of primary data. b. Behavior – What subjects have done or are doing. c. Behavioral data can be obtained by:  observing behaviors.  asking consumers to remember and report their behaviors.

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d. Limitations:  Sometimes, there are no behavioral data to solve a marketing problem.  Behavioral data offer no direct insights into why consumers behave as they do.

9-2b. Demographic Characteristics a. Some commonly measured variables include age, education, occupation, marital status, gender, and income. b. Often used to divide a population into groups (for example, for market segmentation purposes).

9-2c. Psychological/Lifestyle Characteristics a. Personality – Normal patterns of behavior exhibited by an individual; the attributes, traits, and mannerisms that distinguish one individual from another.  Many marketers believe personality affects a consumer’s choices. b. Lifestyle – How individuals live, what interests them, their values, and what they like.  Also called psychographic analysis.

9-2d. Attitudes a. Attitude – An individual’s overall evaluation of something.

1. Marketers often measure people’s attitudes toward companies, products, and services as well as many ―attitude-like‖ variables including value, quality, and satisfaction.

2. Marketers want to shape attitudes or target people with favorable attitudes. b. Exhibit 9.3: Attitude Toward Reese’s Peanut Butter Cup from YouGovAmerica c. Similar variables that marketers are interested in include customer perception and customer satisfaction.

9-2e. Knowledge a. Knowledge – Insight into, or understanding of facts about, some object or phenomenon.  Marketers often want to know what individuals know or believe about products, brands, companies, advertisements, and so on. b. Exhibit 9.4: Approaches Used to Measure Awareness c. Group Activity: 10–20 minutes total. (PPT Slide 18)  Form groups of five to seven participants. Have one person do an online search for two or three company logos. That person then proceeds through the three approaches to measuring awareness and records the others’ responses to each question. o What ads for brands do you remember seeing? o Do you remember recently seeing ads for [category of product]? o Do you remember seeing this ad for [chosen product]?  As time allows, share your product and the results of your research with the larger class.

9-2f. Intentions a. Intentions – Anticipated or planned future behavior. b. Marketers often need this type of information to assess demand for a product or service. c. Estimating demand for products and services accurately is one of the most difficult tasks a marketing researcher faces.

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d. Sample survey responses include:  definitely would buy  probably would buy  undecided  probably would not buy  definitely would not buy 9-2g. Motivation a. Motive – A need, a want, a drive, a wish, a desire, an impulse, or any inner state that energizes, activates, moves, directs, or channels behavior toward goals. b. Researchers’ interest in motives typically involves determining why people behave as they do. c. If we understand what drives consumer behavior, we are in better position to anticipate consumer needs and offer products and services that satisfy those needs. d. Polling Activity: 5–10 minutes total. (PPT Slide 22)  Of the seven types of primary data, which do you believe has the most value to researchers? a. Demographics b. Motivation c. Personality/Lifestyle d. Attitudes  Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response. In this particular activity, their answer can be related to how six of the types all contribute to behavior, which marketers believe is most important.

Self-Assessment (PPT Slide 23) 

Think about the types of primary data and how each helps reveal information about the needs of the customer. How does primary data about you reveal your needs that might be of interest to a marketer?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

66. What are the basic uses of descriptive research?

The basic uses of descriptive research are to describe the characteristics of certain groups, to determine the proportion of people who behave a certain way, and to make specific predictions.

67. What are the six specifications of a descriptive study?

The six specifications of a descriptive study are the answers to who, what, when, where, why, and how in the research.

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68. What are the main types of descriptive studies, and what do their differences mean?

The main types of descriptive studies are cross-sectional studies and longitudinal studies. A crosssectional study is an investigation involving a sample of elements selected from the population of interest that are measured at a single point in time. A longitudinal study is an investigation involving a fixed sample of elements that is measured repeatedly through time.

69. What are the two basic forms of panels? How do they differ?

A panel is a fixed set of elements. In a continuous panel, a fixed sample of subjects is measured repeatedly with respect to the same type of information. In a discontinuous panel, a sample of elements is still selected and maintained, but the information collected from the members varies with the project.

70. What is a sample survey? What are its advantages and disadvantages?

A sample survey involves the study of a number of cases at the same time. It attempts to be representative of some known population. With a sample survey, very specific populations can be targeted, and it can use a probability sampling plan that will allow the results of the sample to be projected to the overall population. However, sample surveys are expensive in terms of time and money.

71. What types of primary data interest marketing researchers most? What are the differences between the types of data?

Primary data can measure behavior, demographic/socioeconomic characteristics, psychological/lifestyle characteristics, attitudes, awareness/knowledge, intentions, and motivation.

Marketers are typically most concerned with consumers’ behaviors because they can use these data to make important decisions.

72. What is an attitude? Why do marketers care about attitudes?

Attitude is an individual’s overall evaluation of something. It is important in marketing because attitudes are generally thought to lead to behaviors.

73. What are three ways to assess awareness? What is the basic difference between measures of recall and measures of recognition?

The three ways to assess awareness are through unaided recall, aided recall, and recognition. With unaided recall, the consumer is asked to remember things without prompting clues; this method results in the highest level of awareness. Aided recall is when customers are prompted with a cue, and recognition recall is when the consumer is asked simply to recognize something (this represents the lowest level of awareness).

74. What is the basic problem in measuring consumers’ intentions about future behaviors?

The basic problem in measuring consumers’ intentions about future behaviors is that they are speculative and subject to change. [return to top]

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ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

29. Using some examples of your own choosing, have the class bring out what is involved in specifying the who, what, why, when, where, and how, the specification of which is so vital to successful descriptive research. It is particularly helpful if the class is asked to develop specific hypotheses and then is forced to develop the dummy tables that would be used to investigate them and to further specify the evidence that would lead to their support.

30. When reviewing the main types of descriptive designs, talk about the advantages and disadvantages of each.

31. Begin with a discussion of the main types of primary data of interest to marketers. Ask the students to define what is meant by demographic/socioeconomic characteristics, psychological/lifestyle characteristics, attitudes/opinions, awareness/knowledge, intentions, motivation, and behavior. It is also helpful here to review a behavior checklist as to the kinds of behavior information of interest. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 10: Collecting Data by Observation

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 81 Chapter Objectives ................................................................................................................................... 81 Complete List of Chapter Activities and Assessments........................................................................... 81 Key Terms.................................................................................................................................................. 81 What's New in This Chapter .................................................................................................................... 82 Chapter Outline ........................................................................................................................................ 82 Review Questions ...................................................................................................................................... 86 Additional Insights and Activities ........................................................................................................... 87

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to look at the two basic means of collecting primary data: communication and observation. The chapter then distinguishes observational research as going beyond casual observation in its systematic planning, observing, and recording of specific behaviors or activities. The chapter concludes with the important considerations researchers must be aware of as they gather observational data, including structure, setting, and means.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 10-1 Describe the two basic means of obtaining primary data.

10-2 Contrast causal observation from observational research.

10-3 List the important considerations in the use of observational methods for data collection.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 10-1 10-2 10-3 10-3 10-1–10-3

PPT slide PPT slide 6 PPT slides 10–11 PPT slide 21 PPT slide 22 PPT slide 23

Activity/Assessment Knowledge Check 10.1 Discussion Activity Knowledge Check 10.2 Group Activity Self-Assessment

Duration < 5 min 5–10 min < 5 min 10–20 min 10–20 min

[return to top]

KEY TERMS Communication A method of data collection involving questioning respondents to secure the desired information using a data collection instrument called a questionnaire.

Contrived setting Subjects are observed in an environment that has been specially designed for recording their

behavior.

Debriefing The process of providing appropriate information to respondents after data have been collected using disguise.

Disguise The amount of knowledge people have about a study in which they are participating.

Disguised observation The subjects are not aware that they are being observed.

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by Observation Eye camera A device used by researchers to study a subject’s eye movements while they are reading

advertising copy.

Facial coding A technique for measuring emotions that uses cameras to record small, involuntary movements

(microexpressions) in the muscles around a person’s mouth and eyes.

Galvanometer A device used to measure the emotion induced by exposure to a particular stimulus by

recording changes in the electrical resistance of the skin associated with the tiny degree of sweating that accompanies emotional arousal; in marketing research, the stimulus is often specific advertising copy.

Human observation Individuals are trained to systematically observe a phenomenon and to record on the observational form the specific events that take place.

Mechanical observation An electrical or mechanical device observes a phenomenon and records the events that

take place.

Natural setting Subjects are observed in the environment where the behavior normally takes place.

Observation A method of data collection in which the situation of interest is watched and the relevant facts, actions, or behaviors are recorded.

Response latency The amount of time a respondent deliberates before answering a question.

Structure The degree of standardization used with the data collection instrument.

Structured observation Method of observation in which the phenomena to be observed (typically behaviors) can be defined precisely along with the categories used to record the phenomena.

Undisguised observation The subjects are aware that they are being observed.

Unstructured observation Method of observation in which the researcher has a great deal of flexibility in terms of what to note and record.

Voice-pitch analysis Analysis that examines changes in the relative frequency of the human voice that

accompany emotional arousal. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition: 

Streamlined learning objectives better reflect the topical discussion of the major sections of the chapter.



An updated Introduction highlights new industry tools and techniques.



Updated Research Window 10.1 looks at observational research for King’s Hawaiian.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

10-1.

Means of Obtaining Primary Data (10-1, PPT Slides 3–6)

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mm. 

Two methods of collecting data: Communication – A method of data collection involving questioning respondents to secure the desired information using a data collection instrument called a questionnaire.  Observation – A method of data collection in which the situation of interest is watched and the relevant facts, actions, or behaviors are recorded. nn. Exhibit 10.1: Basic Choices Among Means for Collecting Primary Data 10-1a. Versatility a. Versatility refers to the ability of a technique to collect information on the different types of primary data. b. Communication techniques are more versatile than observational techniques.

10-1b. Speed and Cost a. Communication is often a faster means of data collection than observation. b. In some cases, such as in the use of scanners, observation is faster and costs less than communication.

10-1c. Objectivity and Accuracy a. The observation method has significant advantages in objectivity and accuracy. b. These techniques don’t depend on a respondent’s willingness to provide information. c. Recording of the behavior happens as the behavior occurs, making it unnecessary to depend on a respondent’s memory. d. Knowledge Check 10.1: < 5 minutes total. (PPT Slide 6)  The advantages that the observation method has over the communication method include: (a) versatility and objectivity. (b) objectivity and accuracy. (c) accuracy and cost efficiency. (d) cost efficiency and versatility.  Answer: b—The observation method’s advantages include objectivity and accuracy, whereas the communication method’s advantages are its versatility, cost efficiency, and speed.

10-2.

Observation Research (10-2, PPT Slides 7–11) a. Casual observation is a means of gathering information of the world around us. b. Observational research involves systematic planning, watching, and recording of a situation. c. It may involve direct or indirect observation, simple or sophisticated observation. d. Most marketing researchers agree that observational techniques are preferrable to communication methods. e. Discussion Activity: 5–10 minutes total. (PPT Slides 10–11)  Why is observation often the best method for generating valid data about individuals’ behavior?  Debrief: Observational research is an incredibly useful tool. Because observation allows the recording of behavior as it occurs, it doesn’t depend on the respondent’s

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memory in reporting what occurred and usually produces more objective data than do communication approaches. It is often more useful to examine people’s behavior than to gather their answers to survey questions and then work backward to determine what led to that behavior, especially when the goal is to predict future behavior.

10-3.

Important Considerations Specific to Observational Research (10-3, PPT Slides 12–22) 10-3a. Structured Versus Unstructured Observation a. Structure – The degree of standardization used with the data collection instrument. b. Structured observation – Method of observation in which the phenomena to be observed (typically behaviors) can be defined precisely along with the categories used to record the phenomena. c. Unstructured observation – Method of observation in which the researcher has a great deal of flexibility in terms of what to note and record. d. Advantages of using higher levels of structure:  Reduces the potential for bias.  Increases the reliability of observations.  Coding and analysis of data is simpler. e. Disadvantages of using higher levels of structure:  Additional information is no longer available.  More difficult to code phenomena that differ from person to person.

10-3b. Disguised Versus Undisguised Observation a. Disguise – The amount of knowledge people have about a study in which they are participating.  With disguised observation, subjects are not aware that they are being observed.  With undisguised observation, subjects are aware that they are being observed. b. Ethics of disguise in observation require the process of debriefing – providing appropriate information to respondents after data have been collected. c. There are fewer ethical concerns with mystery shopping than with observing and recording consumer behavior.

10-3c. Natural Versus Contrived Setting for Observation a. Natural setting – Subjects are observed in the environment where the behavior normally takes place. Examples:  Shopping in a store  Using or consuming a product at home b. The great benefit of natural observation is that the recorded behaviors occur naturally, without prompting. c. Contrived setting – Subjects are observed in an environment that has been specially designed for recording their behavior. Examples:  ―Fake‖ store  Computer simulation d. Advantages of the contrived setting:  Researchers can control outside influences that might affect the observed behaviors.  Researchers don’t have to wait for events to occur but instead can ask the participants to engage in whatever behavior they want to study.

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e. Research Window 10.1: Observational Research in a Natural Setting: King’s Hawaiian 10-3d. Human Versus Mechanical Observation a. With human observation, individuals are trained to systematically observe a phenomenon and to record on the observational form the specific events that take place. b. With mechanical observation, an electrical or mechanical device observes a phenomenon and records the events that take place. c. Research Window 10.2: ―Driving‖ Toward Golfer Insights at PING d. The two most common forms of mechanical observation:  Video recorders  Bar code scanners e. A group of mechanical observation techniques that might be generally categorized under the ―neuroscience‖ label are used to gauge attention, interest, attitude strength, and emotional response to various stimuli and include the following:  Response latency – The amount of time a respondent deliberates before answering a question.  Galvanometer – Device used to measure the emotion induced by exposure to a particular stimulus by recording changes in the electrical resistance of the skin.  Voice-pitch analysis – Analysis that examines changes in the relative frequency of the human voice that accompany emotional arousal.  Eye camera – Device used by researchers to study a subject’s eye movements while they are reading advertising copy.  Facial coding – Measuring emotions via camera to record small, involuntary movements (microexpressions) in the muscles around a person’s mouth and eyes. f. Knowledge Check 10.2: < 5 minutes total. (PPT Slide 21)  When the phenomena to be observed (typically behaviors) can be defined precisely along with the categories used to record the phenomena, the method used is: (a) human observation. (b) mechanical observation. (c) structured observation. (d) unstructured observation. b. Answer: c—Structuring the observation reduces the potential for bias and increases the reliability of observations. Coding and analysis of the data is far simpler (and less costly) when there are a few well-defined behaviors or observable demographic variables to be considered. g. Group Activity: 5–10 minutes total. (PPT Slide 22) c. Form groups of three to five participants. Suppose that your group is investigating the increase in purchase of a food product when free samples are given in a grocery store. Create a brief outline of how you would go about this research using observation. Include details about the four basic choices related to collecting data used in observation methods (refer to Exhibit 10.1).

As time allows, share your outline and other related thoughts with the larger class. d. Note: You may use other scenarios of your choosing for this activity. You may also specify that groups focus on one of the four choices specifically in their outline.

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Self-Assessment (PPT Slide 23) 

Have you ever been a secret shopper for some brand? What value do you feel that secret shoppers provide? What concerns, if any, do you have about secret shoppers?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

75. What are the general advantages and disadvantages of obtaining data by communication?

By observation?

Observation involves scrutinizing the situation of interest and recording relevant facts, actions, or behaviors. Communication involves questioning respondents to secure the desired information, using a data collection instrument called a questionnaire. Observation data are typically more objective and accurate. Communication data are more versatile, faster, and more cost efficient.

76. What does a high degree of structure look like in an observational study?

In an observational study, a high degree of structure looks like when the phenomena being observed (such as behaviors) can be defined precisely along with the categories used to record the phenomena.

77. What are mystery shoppers? What is their purpose?

Mystery shoppers are disguised observers who are paid to shop. While shopping, they carefully observe a phenomenon and then write a formal report to the client company.

78. What is the key ethical issue with the use of disguise in observation research? How is this issue typically remedied in disguised marketing research projects?

Most often, an observer’s presence is disguised in order to control the tendency of people to behave differently when they know their actions are being watched. The key ethical issue with disguise in observational research is that it can make some people uncomfortable, because any way you look at it, the use of a disguise amounts to a conscious effort to deceive the respondent or at least to withhold information. Respondents can be given appropriate information following the task in the form of a debriefing to help mitigate any perceived violations of privacy.

79. What are the primary advantages and disadvantages of working in a natural setting as contrasted with a contrived setting?

The advantage of a contrived setting is that researchers are better able to control outside influences that might affect observed behavior. The disadvantage of this approach is that the contrived setting itself may cause differences in behavior and thus threaten the external validity of the findings. A contrived setting, however, usually speeds the data collection process, results in lower-cost research, and allows the use of more objective measurements. In a natural setting, subjects are observed in the environment where their behavior normally takes place. With this approach, the behaviors occur more naturally and without prompting.

80. What are some of the available types of mechanical observation?

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The approaches used with mechanical observation range from straightforward things like video cameras, tape recorders, and bar-code scanners to more complex approaches such as response latency, galvanometers, voice-pitch analysis, eye cameras, and facial coding. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

32. The distinctions between structured-unstructured, disguised-undisguised, natural setting and laboratory setting observation studies can be made quickly because of the students' familiarity with these notions from discussing communication studies. The distinction between human and electrical/ mechanical observation requires somewhat more time because it is helpful here to elaborate in some detail the advantages that accrue with each method. There is also some benefit in reviewing some of the main types of electrical/ mechanical observation.

33. The discussion can then be directed to the development of an observational study to answer some specific research question. The following problem, for example, has proven helpful in distinguishing among the various types of observational studies and the advantages that arise with each. 

Assume you were interested in assessing the influence of children on a family's purchasing decision. How would you investigate this problem by observation?



The sequence of suggestions usually contains varying degrees of structure and disguise, and some even contain elements of mechanical observation. This can be pointed out at the time or at the end of the class period.

34. Students also seem to appreciate explicitly discussing unobtrusive observational techniques. The book by Eugene J. Webb, Donald T. Campbell, Richard D. Schwartz, and Lee Sechrest, Unobtrusive Measures (Thousand Oaks, CA: Sage Publications, 1999), is a particularly good source of examples.

Their discussion often evokes a good deal of class interaction, which brings into sharper focus the advantages and limitations of observation versus communication methods of data collection. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 11: Collecting Data by Communication

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 89 Chapter Objectives ................................................................................................................................... 89

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11-3

Differentiate among the main methods of administering questionnaires. ............................... 89

Complete List of Chapter Activities and Assessments........................................................................... 89 Key Terms.................................................................................................................................................. 89 What's New in This Chapter .................................................................................................................... 90 Chapter Outline ........................................................................................................................................ 90 Review Questions ...................................................................................................................................... 94 Additional Insights and Activities ........................................................................................................... 95

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to reiterate the difference between observational techniques and communication techniques in collecting data. In order to gain meaningful insights into why customers behave the way they do, understanding what questions to ask and the form that asking them takes is important. The chapter discusses three key decisions involved in using communication-based approaches: the degree of structure to use, whether to disguise the questionnaire, and which method to use. The chapter concludes with a comparison of primary communication techniques across three levels of research control.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 11-1

Explain the concept of structure as it relates to questionnaires.

11-2

Explain what is meant by disguise in a questionnaire context.

11-3

Differentiate among the main methods of administering questionnaires.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 11-1 11-2 11-3 11-1–11-3

PPT slide PPT slide 7 PPT slide 11 PPT slides 20–21 PPT slide 22

Activity/Assessment Knowledge Check 11.1 Group Activity Discussion Activity Self-Assessment

Duration < 5 min 15–20 min 5–10 min 10–20 min

[return to top]

KEY TERMS Consumer intercept A method of data collection in which interviewers in a heavily trafficked location stop or interrupt a sample of those passing by to ask them if they would be willing to participate in a research study.

Debriefing A process of informing respondents of the true purpose of a disguised study after respondents have participated in the study itself.

Disguise An activity where a researcher attempts to hide the purpose or the sponsor of the study in order to receive the most objective results possible.

Fixed-alternative questions Questions in which the responses are limited to stated alternatives.

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In-bound survey A method of data collection in which respondents access a survey by telephone or online to respond to survey items.

Online survey A method of administration that relies on the Web for completing the survey.

Open-ended question A question in which respondents are free to reply in their own words rather than being limited to choosing from among a set of alternatives.

Paper-based survey A survey usually administered by mail to designated respondents with an accompanying cover letter. The respondents return the questionnaire by mail to the research organization.

Personal interview Direct, often face-to-face conversation between a representative of the research organization, the interviewer, and a respondent or interviewee.

Telephone interview Telephone conversation between a representative of the research organization, the interviewer, and a respondent or interviewee. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

Material regarding the role of disguise has been expanded and distinguished from observation.

Numerous examples of topics within the chapter have been added or updated.

Much of the material regarding online surveys has been updated and/or expanded, including Exhibits 11.1, 11.3, and 11.4.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

11-1.

Structured versus Unstructured Communication (11-1, PPT Slides 3–7) oo. Structure is the degree of standardization used with the data collection instrument. pp. Fixed-alternative questions – Questions in which the responses are limited to stated alternatives. qq. Open-ended question – A question in which respondents are free to reply in their own words rather than being limited to choosing from among a set of alternatives.

11-1a. Advantages of High Structure a. Highly structured questions are relatively simple to administer. b. Data coding and analysis are greatly simplified. c. Standardized questions and responses provide an identical frame of reference that improves the consistency of responses across different people.

11-1b. Disadvantages of High Structure

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a. Fixed-alternative questions may encourage misleading answers, depending on their wording. b. Respondents may choose a response closest to their true feelings when it isn’t reflective of their feelings or knowledge. c. Omitted responses can also be problematic.

11-1c. Determining the Level of Structure a. High structure is most useful when possible replies are well established, limited in number, and clear-cut. b. It is less effective at getting primary data on motivations. c. Knowledge Check 11.1: < 5 minutes total. (PPT Slide 7)  Which of the following represents the most structured format for collecting data by communication? (a) Open-ended questionnaire (b) Undisguised observation (c) Debriefing (d) Fixed-alternative questionnaire  Answer: d—In a highly structured questionnaire, everyone receives the same questions, and everyone responds by choosing from among the same set of possible answers.

11-2.

Disguised versus Undisguised Communication (11.02, PPT Slides 8–11) a. Disguise – An activity where a researcher attempts to hide the purpose or the sponsor of the study in order to receive the most objective results possible. b. There are two general situations in which disguise is often necessary:  When knowledge of the sponsor or topic is likely to cause respondents to change their answers.  When creating a more natural environment in which to collect data is preferred.

11-2a. The Ethics of Disguise in Communication a. Under the rights model of ethics, the use of disguise amounts to a violation of the respondent’s right to know. b. Debriefing – A process of informing respondents of the true purpose of a disguised study after the respondents have participated in the study itself. c. The use of disguise and the amount of debriefing necessary involve the trade-off between gaining true and useful information while avoiding potential harm to respondents.

11-2b. Determining the Appropriateness of Disguise a. There are many degrees of disguise. b. It is important not to reveal underlying purposes for a study if they have the potential of altering responses. c. Group Activity: 15–20 minutes total. (PPT Slide 11)  Form groups of three to five participants. Choose a current newsworthy issue at your university or local community or use an issue assigned by the instructor. Formulate the phrasing for related research questions using the four combinations of structure and disguise. When formulating these questions, consider the strengths and weaknesses of each type of combination.

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

11-3.

As time allows, share with the larger class which combination of structure and disguise your group felt best suited to the issue selected for your group and why you believe it to be so.

Note: To drive home the distinctions of the four combinations of structure and disguise, it is helpful to develop a hypothetical research question, e.g., people's attitudes toward mandatory recycling of newspapers, glass, and aluminum or some other controversial issue locally, and to query the students as to how the research questions would be phrased using each of the combinations. The task of generating questions seems to help the students distinguish the basic types and also seems to alert them to the strengths and weaknesses of each type.

Methods of Administering Questionnaires (11-3, PPT Slides 12–21) a. For each general method of collecting primary data via communication, three aspects need consideration.  Sampling control involves identifying and obtaining responses from a target population.  Information control concerns the number and type of questions used and the likelihood of introducing errors.  Administration control refers to resource issues: time and money. b. For an overview of relative usage of the methods, see Exhibit 11.1: Percentage of Market Research Industry Using Each Method.

11-3a. Online Surveys a. Virtually all market research industry professionals use online surveys to gather communication-based data. b. Online survey – A method of administration that relies on the Web for completing the survey.  Sampling control is often problematic. o In-bound survey – A method of data collection in which respondents access a survey by telephone or online to respond to survey items. o Exhibit 11.2: Using In-Bound Surveys to Capture Responses from Difficult-to-Find Populations o Exhibit 11.3: Momentive/SurveyMonkey Panel Sample Selection Attributes (partial list)  In terms of information control, online surveys provide good flexibility and allow for visuals and complex material.  Administratively, online surveys are inexpensive and offer quick turnaround.

11-3b. Telephone Interviews a. Telephone interview – Telephone conversation between a representative of the research organization, the interviewer, and a respondent or interviewee.  The shift to mobile phones eliminated easily accessible and highly accurate phone directories (a difficulty for sampling control).  A big disadvantage is the limited amount of data that can be gathered from any given individual (a difficulty for information control).

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

Cost of data collection from cell phone users continues to rise (a difficulty for administrative control).

11-3c. Personal Interviews a. Personal interview – Direct, face-to-face conversation between a representative of the research organization, the interviewer, and a respondent or interviewee. b. This type of interview can be conducted in lots of different locations.  Sampling control: generally strong sampling control (including higher response rates).  Information control: great flexibility, but higher levels of interviewer bias.  Administrative control: time- and cost-intensive. c. Consumer intercept – A method of data collection in which interviewers in a heavily trafficked location stop or interrupt a sample of those passing by to ask them if they would be willing to participate in a research study.

11-3d. Paper-Based Surveys a. Paper-based survey – A survey usually administered by mail to designated respondents with an accompanying cover letter; the respondents return the questionnaire by mail to the research organization.  Lower degree of sampling control (mailing lists often available, but no control over who completes survey, and often low response rates).  No interviewer bias and can offer anonymity, but less flexibility (no explanation or follow-up, no complex materials).  Lower cost than personal or telephone interviews.

11-3e. Comparing Methods of Administering Questionnaires a. Exhibit 11.4: Primary Communication Methods of Data Collection: Relative Advantages (+) and Disadvantages (–) b. Because each method possesses advantages and disadvantages, the specific situation of a project determines the approach used. c. No method is superior in all situations. d. Sometimes, combining methods offers the most effective approach. e. Discussion Activity: 5–10 minutes total. (PPT Slides 20–21)  Why are online surveys such an attractive method to marketing researchers? Despite this attractiveness, what are some of the disadvantages in relying on data obtained via online surveys?  Debrief: Compared with other options, online surveys provide a great deal of administrative control because they are quite inexpensive and provide rapid turnaround. A survey can be put into the field and data retrieved from respondents and analyzed much more quickly than with any other method. They are also better at collecting larger amounts of certain types of information.  The disadvantages come primarily in the form of being able to contact the appropriate sample group and then getting people to actually respond to the questionnaire. Even though online surveys can collect larger amounts of information, they are limited in their ability to effectively probe more deeply into answers. The

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level of privacy regarding identity of respondents and their answers when responding online is a concern for many potential respondents.

Self-Assessment (PPT Slide 22) 

Think about the types of surveys and interviews you have participated in. Which type is most common? Which type do you believe provided the most related information to the survey conductor/interviewer?

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REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

81. What are the advantages of higher degrees of structure? What do researchers gain through the use of lower structure?

The advantages of higher degrees of structure are that such questions are easier to administer, it greatly simplifies data coding and analysis, and such questions help you improve the consistency of responses across different people. However, fixed-alternative questions may force a subject to respond to a question on which he or she does not really have an opinion. They also may prove inaccurate if none of the response categories allows the accurate expression of the respondent’s opinion.

82. What is a disguised questionnaire? What are the ethical considerations in using disguise?

A disguised questionnaire attempts to hide the purpose or sponsor of the study. It is important to note that the use of disguise can make some people uncomfortable, and any way you look at it, the use of disguise amounts to a conscious effort to deceive the respondent, or at least to withhold information.

83. What are two situations in which the use of disguise would be advisable?

Disguise is useful when knowledge of the purpose of the study or its sponsor would cause respondents to change their answers. Disguise is also used to help create a more natural research environment for those participating in the research, especially experimental research.

84. Why are online surveys such a popular choice for collecting communication data?

Online surveys are a popular choice for collecting communication data because they have many administrative control advantages.

85. How do in-bound surveys work? When are they especially useful?

With in-bound surveys, for example, respondents access a survey online to respond to survey items. They are useful to capture responses of difficult-to-find populations.

86. How do online surveys, telephone interviews, personal interviews, and paper-based surveys differ with respect to the following: a. sampling control b. information control c. administrative control

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Online surveys rely on the Web for completing the survey. The sampling control ability has been steadily increasing in recent years, and they have very effective information control. Compared with other methods, online surveys are quite inexpensive and have a very fast turnaround.

Telephone interviews are conversations between a representative of the research organization, the interviewer, and a respondent or interviewee. The use of mobile phones has increased the challenge of this method’s sampling control; the information exchange is simple and straightforward, but the cost is rising. Personal interviews are direct face-to-face conversations between an interviewer and a respondent. The sampling control is more difficult than other methods, its administrative costs are typically more expensive, and it allows for thorough information control. Paper-based surveys are administered by mail to designated respondents with an accompanying cover letter and then are returned by mail. Mailing lists that you can purchase allow for good sampling control, and there are nice advantages of information and administrative control to this method. See Exhibit 11.4.

87. How might a researcher combine different methods of communication in the same project?

Give an example.

Researchers can combine different methods of communication for the same project. For example, a business manager could receive a letter, an email, or a phone call asking for her help in a study, and then a paper-based survey could be sent to her in the mail. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

35. A brief overview of the choices one needs to make when collecting data using communication methods is a helpful place to start.

36. Organization for a discussion of structure and disguise can consider the four combinations and their research characteristics. For example, a structured-disguised questionnaire takes the form of standardized questions and standardized responses and has the characteristics of being simple to administer, simple to analyze, difficult to interpret because of the impact of disguise, and finally, being the least used of the four methods.

37. In discussing the possible ways by which questionnaires can be administered, it is useful to have a specific focus with which to frame the discussion. It seems to work particularly well when the same issue is used here that was used to frame the degree of structure and degree of disguise discussion, e.g., mandatory recycling. Summarize the general advantages and disadvantages of the various ways of administering questionnaires via Exhibit 11.4. [return to top]

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TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 97 Chapter Objectives ................................................................................................................................... 97 Complete List of Chapter Activities and Assessments........................................................................... 97 Key Terms.................................................................................................................................................. 97 What's New in This Chapter .................................................................................................................... 98 Chapter Outline ........................................................................................................................................ 99 Review Questions .................................................................................................................................... 103 Additional Insights and Activities ......................................................................................................... 104

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to define measurement as it is used in marketing research. To do so, the chapter lists and describes four scales of measurement and proceeds to cover some widely used attitude scaling techniques. When designing scales, the researcher needs to make some key decisions, including whether to use a global or composite scale. Finally, the chapter covers the importance of understanding validity and reliability in measurement.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 12-1 Define the term measurement as it is used in marketing research.

12-2 Discuss widely used attitude scaling techniques in marketing research.

12-3 List some other key decisions to be made when designing scales.

12-4 Compare and contrast validity and reliability in the context of survey items.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 12-1 12-2 12-3 12-4 12-1–12-4

PPT slide PPT slide 10 PPT slide 18 PPT slides 21–22 PPT slide 27 PPT slide 28

Activity/Assessment Group Activity Knowledge Check 12.1 Discussion Activity Polling Activity Self-Assessment

Duration 10–20 min < 5 min 5–10 min 5–10 min 10–20 min

[return to top]

KEY TERMS Comparative-ratings scales A scale requiring subjects to make their ratings as a series of relative judgments or comparisons rather than as independent assessments.

A measure designed to provide a comprehensive assessment of an object or phenomenon with items to assess all relevant aspects or dimensions.

Composite measures

A comparative-ratings scale in which an individual divides some given sum among two or more attributes on a basis such as importance or favorability.

Constant-sum method

A measure designed to provide an overall assessment of an object or phenomenon, typically using one or two items.

Global measure

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Graphic-ratings scales A scale in which individuals indicate their ratings of an attribute along a continuous line or other graphic figure that runs from one extreme of the attribute to the other.

Interval scale Measurement in which the assigned numbers legitimately allow the comparison of the size of the differences among and between members.

A scale on which individuals must indicate their ratings of an attribute or object by selecting the response category that best describes their position on the attribute or object.

Itemized-ratings scales

Measurement The process of assigning numbers to represent properties of an object’s attributes.

Measurement in which numbers are assigned to objects or classes of objects solely for the purpose of identification.

Nominal scale

Measurement in which numbers are assigned to data on the basis of some order (for example: more than, greater than) of the objects.

Ordinal scale

Error in measurement due to temporary aspects of the person or measurement situation and affects the measurement in irregular ways.

Random error

Ratio scale Measurement that has a natural, or absolute, zero and therefore allows the comparison of absolute magnitudes of the numbers.

Ability of a measure to obtain similar scores for the same object, trait, or construct across time, across different evaluators, or across the items forming the measure.

Reliability

A problem that arises when respondents answer questionnaire items in a similar way without thinking about the items.

Response set bias

A method of assessing attitudes in which individuals are asked directly for their beliefs about or feelings toward an object or class of objects.

Self-report

A self-report technique for attitude measurement in which the subjects are asked to check which cell between a set of bipolar adjectives or phrases best describes their feelings toward the object.

Semantic-differential scale

Snake diagram A diagram that connects the average responses to a series of semantic-differential statements, thereby depicting the profile of the object or objects being evaluated.

Summated-ratings scale A self-report technique for attitude measurement in which respondents indicate their degree of agreement or disagreement with each of several statements.

Error in measurement that is also known as constant error since it affects the measurement in a constant way.

Systematic error

Validity The extent to which differences in scores on a measuring instrument reflect true differences among individuals, groups, or situations in the characteristic that it seeks to measure or true differences in the same individual, group, or situation from one occasion to another, rather than systematic or random errors. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition: 

Editorial updates throughout the chapter provide clearer discussion of the section’s topic.

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[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

12-1.

Scales of Measurement (12-1, PPT Slides 3–10) rr. We measure the attributes of objects and not the objects themselves. ss. Measurement – The process of assigning numbers to represent properties of an object’s attributes.  For some attributes, numbers are assigned to represent quantities of the attribute.  For other attributes, the numbers simply represent category membership. tt. The levels of measurement and associated data quality dictate the kinds of analyses that are possible. uu. Exhibit 12.1: Scales of Measurement vv. Exhibit 12.2: Assessing a Respondent’s Reaction to Soft Drinks with Nominal, Ordinal, Interval, and Ratio Scales contains examples of the four types of scales discussed next.

12-1a. Nominal Scale a. Nominal scale – Measurement in which numbers are assigned to objects or classes of objects solely for the purpose of identification. b. With nominal scales, the numbers don’t mean anything other than simple category identification.  Mode is the only legitimate summary measure of central tendency or average.

12-1b. Ordinal Scale a. Ordinal scale – Measurement in which numbers are assigned to data on the basis of some order (for example: more than, greater than) of the objects.  Allows for the calculation of median as a measure of central tendency in addition to mode.

12-1c. Interval Scale a. Interval scale – Measurement in which the assigned numbers legitimately allow the comparison of the size of the differences among and between members. b. These scales permit the indication of preference of one object over another.  Mean scores can be calculated in addition to median and mode as measures of central tendency.

12-1d. Ratio Scale a. Ratio scale – Measurement that has a natural, or absolute, zero and therefore allows the comparison of absolute magnitudes of the numbers.

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

Because the more powerful scales include the properties possessed by the less powerful ones, ratio scales allow comparison of intervals, the ranking of objects, and using numbers to identify. b. Group Activity: 10–20 minutes total. (PPT Slide 10)  Form groups of three to five participants. As a group, think of some of the academic attributes of students at your school. Then, create examples of how you might use the four scales (nominal, ordinal, interval, and ratio) to measure the properties of one or more of those attributes.  As time allows, share the attributes you selected and your scale examples with the larger class.  Note: Discussing the distinctions between nominal, ordinal, interval, and ratio scales provides the lead-in to this activity. Stress the properties of each scale, the possible transformations, and the measures of average permissible with each. It is helpful if an example that relates to the students' personal experiences can be employed. Some examples of each scale using something related to their academic role might include student number for nominal, class standing for ordinal, grade point average as interval, and number of credits completed as ratio.

12-2.

Measuring Attitudes and Other Unobservable Concepts (12-2, PPT Slides 11–18) a. Many of the qualities we measure can’t be seen or touched. b. Self-report – A method of assessing attitudes in which individuals are asked directly for their beliefs about or feelings toward an object or class of objects.  Most common approach to measuring attitudes. c. Exhibit 12.3: Using Rating Scales to Assess Unobservable Concepts 12-2a. Itemized-Ratings Scale a. Itemized-ratings scales – A scale on which individuals must indicate their ratings of an attribute or object by selecting the response category that best describes their position on the attribute or object. b. Two of the most used itemized-ratings scales include:  Summated-ratings scale – A self-report technique for attitude measurement in which respondents indicate their degree of agreement or disagreement with each of a number of statements. o Exhibit 12.4: Example of Likert Summated-Ratings Scale  Semantic-differential scale – A self-report technique for attitude measurement in which the subjects are asked to check which cell between a set of bipolar adjectives or phrases best describes their feelings toward the object. o Exhibit 12.5: Example of Semantic-Differential Scaling Form o Snake diagram – A diagram that connects the average responses to a series of semantic-differentiated statements, thereby depicting the profile of the object(s) being evaluated. o Exhibit 12.6: Snake Diagram Showing Contrasting Profiles of Banks A and B  Endless variations of itemized-ratings scales are possible. o Most are used to capture interval-level data.

12-2b. Graphic-Ratings Scale

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a. Graphic-ratings scales – A scale in which individuals indicate their ratings of an attribute along a continuous line or other graphic figure that runs from one extreme of the attribute to the other.  Include formats such as a slider scale and a thermometer scale.  These scales provide for an infinite number of possible response positions along the continuous scale. b. Exhibit 12.7: Graphic-Ratings Scale 12-2c. Comparative-Ratings Scale a. Comparative-ratings scales – A scale requiring subjects to make their ratings as a series of relative judgments or comparisons rather than as independent assessments.  Constant-sum method – A comparative-ratings scale in which an individual divides some given sum among two or more attributes on a basis such as importance or favorability.  Exhibit 12.8: Constant-Sum Comparative-Ratings Scale b. Comparative-ratings scales are effective for eliminating errors, such as the halo effect, that are common in scaling. c. Comparative scaling methods allow more insight into the relative ranking, if not the absolute importance, of the attributes. d. Knowledge Check 12.1: < 5 minutes total. (PPT Slide 18)  Which of the following statements most accurately reflects the purpose of a snake diagram? (a) It connects the average responses to a series of semantic-differential statements and provides a profile of the objects being evaluated. (b) It reveals any errors in measurement that are due to temporary aspects of the measurement situation. (c) It provides for consistency in being able to obtain similar scores for the same object across time. (d) It assesses attitudes of individuals when asked directly about their feelings toward an object.  Answer: a—Semantic-differential scales are flexible and easy to use. A snake diagram presents the results of such scales for attributes across two or more related objects that allows for their comparison.

12-3.

Other Considerations in Designing Scales (12-3, PPT Slides 19–22) 12-3a. Number of Items or Questions in a Scale a. Global measure – A measure designed to provide an overall assessment of an object or phenomenon, typically using one or two items. b. Composite measures – A measure designed to provide a comprehensive assessment of an object or phenomenon with items to assess all relevant aspects or dimensions. c. The number of questions to include in a composite measure is as many as it takes to fully capture the concept being measured.

12-3b. Number of Scale Positions or Response Options a. For most purposes, a minimum of five response categories/options should be included.

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b. When deciding whether to use an odd or even number of response categories, consider that an odd number allows respondents to choose the ―neutral‖ response in the center.

12-3c. Including a ―Don’t Know‖ or ―Not Applicable‖ Response Category a. If a meaningful percentage of respondents are likely not to have encountered the object or issue being addressed, this option may be a good idea. b. ―No opinion‖ options are most frequently chosen by those answering anonymously, for questions later in a survey, or by respondents who devoted little effort to completing the survey. c. Discussion Activity: 5–10 minutes total. (PPT Slides 21–22)  Do you believe in adding a ―don’t know‖ or ―not applicable‖ response category as an option in a measurement scale? Why or why not?  Answer: It is often valuable when a notable percentage of respondents may not have encountered the object addressed in the study. Otherwise, their responses may add to errors in the study results.  However, if most respondents will have an opinion, then including it can add to errors. This response is often selected for questions toward the end of a survey, by those answering anonymously, or by those less motivated to put forth effort in answering questions in the survey.

12-4.

Establishing the Validity and Reliability of Measures (12-4, PPT Slides 23–27) a. The components of an observed response include the truth plus two types of errors:  Systematic error – Error in measurement that is also known as constant error since it affects the measurement in a constant way.  Random error – Error in measurement due to temporary aspects of the person or measurement situation that affects the measurement in irregular ways. b. Exhibit 12.9: Components of an Observed Response c. Response set bias – A problem that arises when respondents answer questionnaire items in a similar way without thinking about the items.

12-4a. Validity a. Validity – The extent to which differences in scores on a measuring instrument reflect true differences among individuals, groups, or situations in the characteristic that it seeks to measure or true differences in the same individual, group, or situation from one occasion to another, rather than systematic or random errors. b. The higher the levels of systematic and random error, the lower the validity.

12-4b. Reliability a. Reliability – Ability of a measure to obtain similar scores for the same object, trait, or construct across time, across different evaluators, or across the items forming the measure. b. Consistency is the hallmark of reliability. c. A measure can be reliable but not necessarily valid because of systematic error. d. Exhibit 12.10: Illustration of Difference Between Random and Systematic Error

e. Polling Activity: 5–10 minutes total. (PPT Slide 27)

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



An important goal of marketing research is to minimize the errors in order to increase the validity of the results. Which of the following do you believe is the most serious threat to a study’s validity? (a) Systematic error (b) Random error (c) Response set bias Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response. In this particular activity, their answer can be related to how confident they are in being able to control the introduction of these errors into the survey process.

Appendix 12A: Interpreting Rating Scales: Raw Scores versus Norms Self-Assessment (PPT Slide 28) 

When you think about all the ways people rely on ratings scales such as customer satisfaction scores when purchasing various products, how reliable and/or valid do you believe those ratings to be?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

88. What are the scales of measurement? What comparisons among scores can be made with each?

The four scales of measurement are nominal, ordinal, interval, and ratio scales. For nominal scales, identity is the basic comparison; for ordinal, it is order; for interval, it is a comparison of intervals; for ratio, it is a comparison of absolute magnitudes.

89. What are the major ways that have been used to measure attitudes? How do they differ?

The summated-ratings scale and the semantic-differential scale are commonly used attitude scaling techniques in marketing research. Researchers also use other types of itemized-rating scales, along with graphic-rating scales and comparative-rating scales. The summated-ratings scale is a self-report technique in which respondents indicate their degree of agreement or disagreement with each of a number of statements. A semantic-differential scale is also a self-report technique in which subjects are asked to check which cell between a set of bipolar adjectives or phrases best describes their feelings toward the object.

90. What are some factors that may produce systematic errors? What factors may produce random errors?

Factors that may produce systematic errors include some personality traits or other stable characteristics of individuals. Factors that may produce random errors are due to temporary aspects of the person or measurement situation which can affect measurement in irregular ways.

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91. What is reliability? What information does it contribute to determining if a measure is accurate?

Reliability is the ability of a measure to obtain similar scores for the same object, trait, or construct across time, across different evaluators, or across the items forming the measure. Consistency is the hallmark of reliability; a reliable measure may not be measuring the right thing, but it returns consistent scores.

92. What is validity? What are two contributing factors to decreases in validity?

Validity is the extent to which differences in scores on a measuring instrument reflect true differences among groups, individuals, or situations in the characteristic that it seeks to measure or true differences in the same individual, group, or situation from one occasion to another, rather than systematic or random errors. The higher the levels of systematic and random error, the lower the validity, or correctness, of a measure. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

38. This chapter contains content that is centrally important to conducting solid marketing research.

Unfortunately, students often view the chapter as a (boring) set of definitions to be memorized, without ever really grasping the importance of measurement. Every question that appears on a survey is an attempt to measure something. An instructor might even consider showing a questionnaire (perhaps one of their own), explaining what was being measured. A review of the definition of measurement could stress that, when we assign numbers to represent quantities of attributes, we must continue to be aware of what the numbers represent and not to manipulate them as if they were divorced from their real-world context.

39. A discussion of psychological measurement and the central problem in marketing research of measuring invisible and intangible qualities such as attitudes and purchase intentions can introduce the essential elements of a theory, namely hypothetical constructs, linkages among and between the constructs, and data that connect the constructs with the empirical world. Conceptual and operational definitions are distinct from each other, and operational definitions are essential if we are ever going to investigate the frameworks we develop. It is as true for the marketing manager as it is for the social scientist investigating basic marketing phenomenon. It is helpful here to present some hypothesis and to ask the students how the variables of the hypothesis could be measured so that the hypothesis could be tested—for example, the greater the similarity of customer and salesperson, the greater the likelihood of a sale? How does one measure similarity so that similar and dissimilar dyads can be defined?

40. It is essential to appreciate the central role of attitude measurement in marketing. In discussing the various ways of measuring attitudes, you can point out that measurement is always an inference and that the different methods are subject to their own particular errors (e.g., halo effects, socially acceptable responses, yea saying, etc., with self-report techniques). Thus, the use of multiple measures, when feasible, makes sense.

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41. Using some example (e.g., customer satisfaction) as the attitude to be measured, have the class develop examples of how the attitude could be assessed employing each of the approaches. After an approach has been suggested, it is helpful to explore its associated pitfalls.

42. In a discussion of self-report scales and the general categories of self-report rating scales, excellent examples include the graphic-ratings scales, itemized-ratings scales, and comparative-ratings scales, respectively, that are provided in the text.

43. Because undergraduate students often have difficulty developing an effective presentation of research results, instructors may want to prompt students about how the results obtained using the various types of rating scales could best be presented to managers or other readers. The snake diagram is an example of presenting a series of semantic-differential scale scores as applied to multiple objects.

44. The issue of how best to interpret a rating scale score is one students often assume is simply a function of the scale used to obtain the score. That is, a score derives meaning solely from reference to its scale (e.g., anything above the scale midpoint is ―positive,‖ and anything below the scale midpoint is ―negative‖). Challenge this view by emphasizing the usefulness of norms for adding meaning to rating scale scores. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 13: Designing the Data Collection Form for Communication Data

TABLE OF CONTENTS Purpose and Perspective of the Chapter ............................................................................................... 106 Chapter Objectives ................................................................................................................................. 106 Complete List of Chapter Activities and Assessments......................................................................... 106 Key Terms................................................................................................................................................ 106 What's New in This Chapter .................................................................................................................. 107 Chapter Outline ...................................................................................................................................... 108 Review Questions .................................................................................................................................... 112 Additional Insights and Activities ......................................................................................................... 114

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to examine the steps involved in developing an effective questionnaire. The 10 steps in the process have been divided into five categories that include pre-design, question design, pre-analysis, questionnaire design, and final preparation before launch. Each of the steps is meant to limit error in the study while at the same time maximizing clarity and data quality. An exhibit at the end of the chapter provides a fairly inclusive checklist that walks a researcher through the questionnaire preparation process.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 13-1

Identify the two areas of a research study that should be revisited before designing a data collection form.

13-2

Name three areas of consideration during question design.

13-3

Discuss the primary reason for considering how data will be analyzed before the data are collected.

13-4

Explain why target information should be asked before classification information in a questionnaire.

13-5

Explain the role of pretesting in the questionnaire development process.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 13-1 13-2 13-4 13-5 13-1–13-5

PPT slide PPT slide 6 PPT slides 14–15 PPT slide 22 PPT slide 27 PPT slide 28

Activity/Assessment Knowledge Check 13.1 Group Activity Written Assignment Knowledge Check 13.2 Self-Assessment

Duration < 5 min 10–20 min 15–30 min < 5 min 10–20 min

[return to top]

KEY TERMS A problem that occurs when a question is not framed so as to clearly state the consequences, and thus it generates different responses from individuals who assume different consequences.

Assumed consequences

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Branching question

A question that routes people to different survey items based on their responses to the

question.

Classification information

Information used to classify respondents, typically for demographic breakdowns.

Double-barreled question

A question that calls for two responses and creates confusion for the respondent.

Dummy table

A table (or figure) used to show how the results of an analysis will be presented.

A question used to determine if a respondent is likely to possess the knowledge being sought; also used to determine if an individual qualifies as a member of the defined population.

Filter question

Funnel approach An approach to question sequencing that gets its name from its shape, starting with broad questions and progressively narrowing down the scope.

Leading question

A question framed so as to give the respondent a clue as to how they should answer.

Use of a questionnaire (or observation form) on a trial basis in a small pilot study to determine how well the questionnaire (or observation form) works.

Pretest

The tendency for earlier questions on a questionnaire to influence respondents’ answers to later questions.

Question order bias

An interviewing technique in which potentially embarrassing and relatively innocuous questions are paired, and the question the respondent answers is randomly determined but is unknown to the interviewer.

Randomized-response model

Recall loss

A type of error caused by a respondent’s forgetting that an event happened at all.

An error that occurs when the response to a question is influenced by the order in which the alternatives are presented.

Response order bias

A technique for combatting response order bias in which researchers use multiple versions of a survey, with different wordings of an item or different orders of response options.

Split-ballot technique

Target information The basic information that addresses the subject of the study.

A type of error resulting from the fact that most people remember an event as having occurred more recently than it did.

Telescoping error

Unstated alternative

An alternative answer that is not expressed in a question’s options.

[return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition: 

Moved the 10-step design process into five categories to add greater stage-based organizational structure to the discussion.



Updated material in Research Window 13.1.

[return to top]

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CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

13-1.

Data Collection Form Pre-Design Considerations (13-1, PPT Slides 3–6) ww.

Exhibit 13.1: Procedure for Developing a Questionnaire provides an overview of the steps discussed in this chapter. xx. Proper focus for any study includes:  The client’s managerial needs.  A firmly established method of administration.

13-1a. Step 1: Specify What Information Will Be Sought a. Have a clear understanding of the issue. b. Make sure the information is necessary to the study.

13-1b. Step 2: Determine the Method of Administration a. The degree of structure and disguise influence this decision, as does the specific research situation. b. The choice of method will influence the number and types of questions, the wording of questions and response categories, question sequencing, and so forth. c. Knowledge Check 13.1: < 5 minutes total. (PPT Slide 6)  You are preparing a survey for a well-defined problem. Which of the following statements about the information you will be seeking in the survey is true? (a) Questions whose answers are ―nice to know‖ should be included in the survey as long as the length of the survey is under ten questions. (b) Any information that could be helpful for understanding the research problem should have a question about it included in the survey. (c) Longer surveys are more likely to provide the best information for your research problem. (d) If the information is not central to the purpose of the study, the question need not be included in the survey. e. Answer: d—If the information simply represents something that might be ―nice to know‖ but isn’t central to the purpose of the study, forget about it. Including extra questions makes the survey longer, which often causes response rates to drop.

13-2.

Question Design Including Response Options and Clear Word Choices (13-2, PPT Slides 7– 15) 13-2a. Step 3: Determine Content of Individual Questions a. Consider number and value of questions as well as whether respondents have the answers. b. The goal is to capture needed data using as few questions as possible. c. To improve the chances that a respondent knows the answer to questions, ask a filter question – a question used to determine if a respondent is likely to possess the

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knowledge being sought; also used to determine if an individual qualifies as a member of the defined population. d. Two kinds of errors affect a respondent’s ability to provide accurate answers:  Telescoping error – A type of error resulting from the fact that most people remember an event as having occurred more recently than it did.  Recall loss – A type of error caused by a respondent’s forgetting that an event happened at all. e. Exhibit 13.2: Handling Sensitive Questions offers a variety of techniques.  Don’t ask unless absolutely necessary!  Guarantee anonymity.  Place sensitive questions near the end.  Include a counter-biasing statement.  Ask about how others might feel.  Ask for general, rather than specific, information.  Use randomized-response model – an interviewing technique in which potentially embarrassing and relatively innocuous questions are paired, and the question the respondent answers is randomly determined but is unknown to the interviewer.

13-2b. Step 4: Determine the Form of Response to Each Question a. Open-ended questions allow respondents to answer in their own words.  One type seeks factual information from respondents.  The other type is more exploratory and is designed to uncover motivations, feelings, and attitudes. b. Closed-ended questions have predetermined answers from which a respondent must choose.  Response categories must be exhaustive and mutually exclusive.  This type uses fixed-alternative response scales.  Does not permit individuals to explain their position.  Requires consideration of whether to add a ―don’t know‖ or ―no opinion‖ option to the scale. c. Response order bias – An error that occurs when the response to a question is influenced by the order in which the alternatives are presented. d. Split-ballot technique – A technique for combatting response order bias in which researchers use multiple versions of a survey, with different wordings of an item or different orders of response options.

13-2c. Step 5: Determine the Wording of Each Question a. Use simple words (language that reflects usage of the population). b. Avoid ambiguous words and questions.  Research Window 13.1: Some Problem Words and Possible Solutions c. Avoid any leading question – a question framed so as to give the respondent a clue as to how they should answer.  Remember that advocacy research is unethical. d. Avoid any unstated alternative – an alternative answer that is not expressed in a question’s options.

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e. Avoid assumed consequences – a problem that occurs when a question is not framed to clearly state the consequences, and thus it generates different responses from individuals who assume different consequences.  Avoid generalizations and estimates.  Exhibit 13.3: Illustration of Assumed Consequences f. Questions should always be asked in specific, rather than general, terms. g. Avoid double-barreled questions – a question that calls for two responses and creates confusion for the respondent.  Watch out for and and or. h. Group Activity: 10–20 minutes total. (PPT Slides 14–15)  Form groups of three to five participants. Choose an area of marketing research and create a hypothesis for a study. (Example: More people in their 20s exercise regularly without having a gym membership.) Then write several questions that might appear on a survey, giving a good example and a poor example of each of the following survey-writing guidelines: a. Use simple words. b. Avoid leading questions. c. Avoid generalizations and estimates.  As time allows, share your questions with the larger class.  Debrief: Which of the guidelines was easiest to write a good example of? Which was most difficult?  In creating your examples, what stood out to you as the biggest issues with poorly worded questions?  Note: Answers and examples will vary and provide a rich canvas on which to point out how critical getting the wording of questions right can be. Be sure to remind students that poor wording can cause less-than-honest answers or no answer at all, which is problematic and introduces errors in research results.

13-3.

Pre-Analysis: Know How Data Will Be Used Before It Is Collected (13-3, PPT Slides 16–17) 13-3a. Step 6A: Prepare Dummy Tables: Researcher’s Perspective a. Dummy table – A table (or figure) used to show how the results of an analysis will be presented. b. It is crucial to specify the exact values and categories to be investigated before collecting data. c. Exhibit 13.4: Dummy Table Examples 13-3b. Step 6B: Consider Data Summarization from a Manager’s Perspective a. Managers might seek a greater variety, including visual summaries of data. b. Researchers need to assess which data need to be in a table and which can be in a chart or graph.

13-4.

Questionnaire Design (13-4, PPT Slides 18–22) 13-4a. Step 7: Determine Question Sequence a. Use simple and interesting opening questions.

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b. Use the funnel approach – question sequencing that starts with broad questions and progressively narrows the scope. c. Question order bias – The tendency for earlier questions on a questionnaire to influence respondents’ answers to later questions. d. Use care when designing a branching question – a question that routes people to different survey items based on their response. e. Ask for classification information last.  Target information – The basic information that addresses the subject of the study.  Classification information – Information used to classify respondents, typically for demographic breakdowns. f. Place difficult or sensitive questions late in the questionnaire.

13-4b. Step 8: Determine Appearance of the Questionnaire a. The appearance of a questionnaire can influence respondents’ cooperation. b. Some guidelines to remember:  No clutter!  Keep it as short as possible.  Use care with branching questions.  Use graphics as needed to improve appearance.  Go easy on instructions, unless they are absolutely necessary. c. Written Assignment: 15–30 minutes total. (PPT Slide 22)  Choose a marketing research issue. (Hint: You could use the one you selected for the previous Group Activity, but keep it simple.) Then, design a brief survey (preferably fewer than 8–10 questions) to collect the information you need.  Note: You could randomly select one or two of these surveys to scrutinize in class.

You may also use this initial assignment in the next section for the students to practice making revisions.

13-5.

Final Preparation Before Launch: Recruiting, Review, and Pretesting (13-5, PPT Slides 23– 27) a. How a researcher recruits potential respondents is important.

13-5a. Step 9: Develop a Recruiting Message or Script a. Good cover letters and scripts are NOT written in a hurry. b. The usual things to include:  Who you are  Why you are contacting them  The request for help  How long it will take  Promise of anonymity or confidentiality  Any incentives 13-5b. Step 10: Reexamine Steps 1 through 9, Pretest Questionnaire, and Revise if Necessary a. Developing a questionnaire is a difficult and iterative process. b. It normally requires several revisions of the data collection form.

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c. Pretest – Use of a questionnaire (or observation form) on a trial basis in a small pilot study to determine how well the questionnaire (or observation form) works.  The real test of a questionnaire is how it performs under actual conditions of data collection.  Data collection should never begin until you have pretested—and probably revised again—the questionnaire. d. Exhibit 13.5: Questionnaire Preparation Checklist e. Knowledge Check 13.2: < 5 minutes total. (PPT Slide 27)  Which of these activities would be part of the final step of questionnaire preparation? (a) Consider using open-ended questions that require short answers to begin a questionnaire. (b) Have members of the research team complete the surveys using the method of administration selected. (c) Ask yourself whether respondents will be informed about the issue the question is addressing. (d) Use the highest level of measurement possible for each question unless there is a solid reason to do otherwise.  Answer: b—The items in the checklist for step 10 include the following: o Examine each word of every question to ensure that the question is not confusing, ambiguous, offensive, or leading. o Have members of the research team complete the surveys using the method of administration selected. o Pretest the questionnaire first by personal interviews among respondents similar to those to be used in the actual study. o Obtain comments from the interviewers and respondents to discover any problems with the questionnaire and revise it if necessary. o Pretest the questionnaire using the method chosen for the study.

Self-Assessment (PPT Slide 28) 

Preparing a questionnaire consists of a number of fairly detailed steps. In the chapter, Exhibit 13.5 provides a checklist for each of the steps involved. Do you make lists for yourself when you have a more complex task or numerous tasks to do? What are the advantages of making a checklist? How might you incorporate checklists more effectively in your daily life?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

93. Suppose you wanted to determine the proportion of men in a geographic area who dye their hair. How could the information be obtained by open-ended questions and by closed-ended questions? Which would be preferable?

Respondents reply to open-ended questions in their own words and are not limited to a set of alternatives, while with close-ended questions, respondents choose their answers from a

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predetermined number of responses using fixed-alternative response scales. Because the answer to this research question is either ―yes‖ or ―no,‖ a closed-ended question survey would be preferred.

94. What criteria can a researcher use to determine whether a specific question should be included in a questionnaire?

Researchers may determine whether a specific question should be included in a questionnaire by considering the target respondents (age, gender, level of knowledge, etc.), the type of answers the respondents might have answers to, and how best to attain useful data.

95. What is telescoping error? What does it suggest about the period to be used when asking respondents to recall past experiences?

Telescoping error is the tendency to remember an event as having occurred more recently than it did.

It gets worse as the time periods respondents are asked to consider get shorter. Finding the optimal time frame for recalling prior behaviors is important.

96. What are some recommended ways for asking for sensitive information?

When asking sensitive questions, guarantee respondents that their answers will be completely anonymous, put any sensitive question near the end of the questionnaire, use a counter-biasing statement that indicates that the behavior or attitude in question is not unusual, phrase the question in terms of other people and how they might feel or act, ask for general (rather than specific) answers, and use the randomized-response model.

97. What is a split-ballot, and why is it used?

A split-ballot is a technique for combatting response order bias in which researchers use multiple versions of the same survey, with different wordings of an item or different orders of response questions.

98. What is an ambiguous question? A leading question? A question with unstated alternatives?

A question with assumed consequences? A double-barreled question?

An ambiguous question contains subjective words that may mean different things to different people and thus lead to inaccurate results. A leading question is one framed so as to give the respondent a clue as to how they should answer. An unstated alternative is a response alternative that is not expressed in the options. Assumed consequences are problems that occur when a question is not framed so as to clearly state the consequences, and thus it generates different responses from individuals who assume different consequences. A double-barreled question is one that calls for two responses and creates confusion for the respondent.

99. What is the proper sequence when asking for target information and classification information? Why? Target information refers to the subject of the study; classification information refers to the other data we collect to classify respondents in hopes of gathering more insights about the phenomenon of interest. The proper questionnaire sequence is to obtain target information first and classification information last on the data collection form.

100.

What is the funnel approach to question sequencing?

The funnel approach to question sequencing gets its name from its shape, starting with broad questions and progressively narrowing down the scope. This is important because asking for specific

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information early in a questionnaire will often influence respondents’ answers to later questions, a source or error known as question order bias.

101.

What is a branching question? Why are such questions used?

A branching question is one that initiates a skip pattern in a survey. Respondents are branched to different questions in a survey based on their answers to the branching question. Among other things, this allows respondents who (should) have information about a topic to provide that information, while those who don’t have the information skip those questions altogether.

102.

What is a cover letter? What key things should be included in a cover letter?

A cover letter is a written message that introduces paper-based and online surveys. It should include who you are, why you are contacting them, your request for their help in providing information, how long it will take, that their responses will be anonymous and/or confidential (if this is true), and any incentives they will be receiving for participating.

103.

What is a questionnaire pretest? Why should researchers pretest surveys?

A questionnaire pretest is the final step in the survey development process. It is the last chance that the researcher has to ensure that the data collection form is working properly prior to data collection; pretesting must not be overlooked. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

45. In elaborating the principles of good questionnaire design, it is helpful if students have an actual questionnaire to examine and critique. Actually, two questionnaires are helpful—an example of a relatively good one and an example of a poor one. If a poor one cannot be found in the literature, one can be constructed readily by having students submit a set of questions related to some topic one week or so before the class. Instructors can take some of these questions and integrate them into a questionnaire. The questionnaire can be made almost as bad as instructors wish, simply by the questions selected and the manner in which they are organized. It is helpful if the topic for the bad questionnaire corresponds to that for the available good questionnaire. The good questionnaire can then be distributed after the bad one has been critiqued to demonstrate to the students a better way of doing it.

46. An alternative approach to this activity is to provide the students with examples of actual questionnaires. These can be taken from the instructor's own experiences, or photocopies or slides can be made from questionnaires appearing in marketing case textbooks. A quick perusal of published marketing literature will produce as many examples as an instructor might desire.

47. A helpful approach to exposing students to the problems of developing observational data collection forms is by having each of them design a form to address specific objectives. Again, one or more of these forms could be selected at random for class discussion. [return to top]

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Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 14: Developing the Sampling Plan

TABLE OF CONTENTS Purpose and Perspective of the Chapter ............................................................................................... 116 Chapter Objectives ................................................................................................................................. 116 Complete List of Chapter Activities and Assessments......................................................................... 116 Key Terms................................................................................................................................................ 116 What's New in This Chapter .................................................................................................................. 117 Chapter Outline ...................................................................................................................................... 118 Review Questions .................................................................................................................................... 123 Additional Insights and Activities ......................................................................................................... 124

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to acquaint the reader with the steps in creating a sampling plan, which include defining the target population, identifying the sampling frame, selecting a sample procedure, and determining the sample size. The discussion includes what a population parameter is, understanding sampling frames, and distinguishing between nonprobability and probability sampling procedures.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 14-1 Explain the difference between a parameter and a statistic when defining the target population.

14-2 Discuss why perfect sampling frames are hard to produce.

14-3 Explain the difference between nonprobability and probability sampling procedures.

14-4 Cite three factors that influence the necessary sample size.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 14-1 14-2 14-3 14-4 14-1–14-4

PPT slide PPT slides 8–9 PPT slide 12 PPT slides 21–22 PPT slide 26 PPT slide 31

Activity/Assessment Discussion Activity 1 Group Activity Discussion Activity 2 Knowledge Check 14.1 Self-Assessment

Duration 5–10 min 10–20 min 5–10 min < 5 min 10–20 min

[return to top]

KEY TERMS Area sample A form of cluster sampling in which areas (e.g., census tracts, blocks) serve as the primary sampling units. Using maps, the population is divided into mutually exclusive and exhaustive areas, and a random sample of areas is selected.

Census A type of sampling plan in which data are collected from or about each member of a population.

Cluster sample A probability sampling plan in which (1) the parent population is divided into mutually exclusive and exhaustive subsets, and (2) a random sample of one or more subsets (clusters) is selected.

Confidence The degree to which one can feel confident that an estimate approximates the true value.

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Convenience sample A nonprobability sample in which population elements are included in the sample because they were readily available.

Judgment sample A nonprobability sample in which the sample elements are handpicked because they are expected to serve the research purpose.

Nonprobability sample A sample that relies on personal judgment in the element selection process.

Parameter A characteristic or measure of a population.

Population All cases that meet designated specifications for membership in the group.

Precision The degree of error in an estimate of a population parameter.

Probability sample A sample in which each target population element has a known, nonzero chance of being included in the sample.

Quota sample A nonprobability sample chosen so that the proportion of sample elements with certain characteristics is about the same as the proportion of the elements with the characteristics in the target population.

Sample Selection of a subset of elements from a larger group of objects.

Sampling error The difference between results obtained from a sample and results that would have been obtained had information been gathered from or about every member of the population.

Sampling frame The list of population elements from which a sample will be drawn; the list might consist of geographic areas, institutions, individuals, or other units.

Sampling interval (k) The number of population elements to count (k) when selecting the sample members in a systematic sample.

Simple random sample A probability sampling plan in which each unit included in the population has a known and equal chance of being selected for the sample.

Snowball sample A judgment sample that relies on the researcher’s ability to locate an initial set of respondents with the desired characteristics.

Statistic A characteristic or measure of a sample.

Stratified sample A probability sample in which (1) the population is divided into mutually exclusive and exhaustive subsets, and (2) a probabilistic sample of elements is chosen independently from each subset.

Systematic sample A probability sampling plan in which every kth element in the population is selected for the sample pool after a random start.

Total sampling elements (TSE) The number of population elements that must be drawn from the population and included in the initial sample pool to end up with the desired sample size. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

Consolidated chapter objectives for greater clarity and alignment with the section materials.

Updated data has been included for Exhibit 14.2.

A new example helps in the understanding of judgment samples.

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

Material and calculations for sampling distribution and sample size have been shifted to appendices that appear at the end of the chapter.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

14-1.

Defining the Target Population (14-1, PPT Slides 3–9) yy. Census – A type of sampling plan in which data are collected from or about each member of a population. zz. Sample – Selection of a subset of elements from a larger group of objects. aaa. Exhibit 14.1: Six-Step Procedure for Drawing a Sample bbb.

Population – All cases that meet designated specifications for membership in the group.  Researchers must be very clear and precise in defining the population.  Populations that are larger in number are easier to locate. o Exhibit 14.2: 2023 Participation Intention Rate in Various Sports Activities by Age ccc. Parameter – A characteristic or measure of a population. ddd.

Statistic – A characteristic or measure of a sample.  Exhibit 14.3: The Relationship Between Populations and Samples eee. Sampling error – The difference between results obtained from a sample and results that would have been obtained had information been gathered from or about every member of the population.  Is decreased by increasing sample size.  Can be estimated (assuming probability sample).  Is usually less troublesome than other kinds of error. fff. Discussion Activity 1: 5–10 minutes total. (PPT Slides 8–9)  Is it possible to avoid sampling error? Why or why not? How important is it to avoid sampling error?  Debrief: Sampling error is something that you need to consider, but unless you’re working with a really small sample, it’s probably not as much a problem as are other kinds of errors. Fortunately, you can estimate sampling error easily—provided that you’ve drawn the right kind of sample. Knowing how well (the probability) your sample results represent the target population can be important in making decisions, which is why researchers attempt to keep sampling error as low as possible.

14-2.

Identifying the Sampling Frame (14-2, PPT Slides 10–12) a. Sampling frame – The list of population elements from which a sample will be drawn; the list could consist of geographic areas, institutions, individuals, or other units. b. Commonly used sampling frames:  Customer database

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 Member directories  Lists developed by data compilers  Others c. Group Activity: 10–20 minutes total. (PPT Slide 12)  Form groups of three to five participants. Within your group, define two or three marketing research questions and their associated target population. Then, for each of the target populations, list the sampling frame you would use.  As time allows, share one or more of your examples with the larger class.

14-3.

Selecting a Sampling Procedure (14-3, PPT Slides 13–22) a. The basic types of samples are listed in Exhibit 14.4: Classification of Sampling Techniques.

14-3a. Nonprobability Samples a. Nonprobability sample – A sample that relies on personal judgment in the element selection process.  With nonprobability samples, sampling error cannot be estimated, and we cannot calculate the margin of sampling error. b. Convenience sample – A nonprobability sample in which population elements are included in the sample because they were readily available.  Sometimes referred to as ―accidental‖ sampling.  Commonly used with exploratory research.  Easy to conduct.  Need to be cautious about drawing important conclusions from convenience sample data. c. Judgment sample – A nonprobability sample in which the sample elements are handpicked because they are expected to serve the research purpose.  Snowball sample – A judgment sample that relies on the researcher’s ability to locate an initial set of respondents with the desired characteristics.  Appropriate for use at the early stages of research when ideas/insights are the goal. d. Quota sample – A nonprobability sample chosen so that the proportion of sample elements with certain characteristics is about the same as the proportion of the elements with the characteristics in the target population.

14-3b. Probability Samples a. Probability sample – A sample in which each target population element has a known, nonzero chance of being included in the sample.  A random component allows for greater objectivity in how elements are selected.  The analyst can statistically assess the level of sampling error and make projections to the population.  Probability samples depend on the sampling distribution of the particular statistic being considered for the ability to draw inferences about the larger population.

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b. Simple random sample – A probability sampling plan in which each unit included in the population has a known and equal chance of being selected for the sample.  Drawing a simple random sample depends mainly on having a good sampling frame.  A digital version of the sampling frame allows for easier implementation. c. Systematic sample – A probability sampling plan in which every kth element in the population is selected from the sample pool after a random start.  Sampling interval – The number of population elements to count (k) when selecting the sample members in a systematic sample. o k = Number of elements in sampling frame ÷ Total sampling elements  Total sampling elements (TSE) – The number of population elements that must be drawn from the population and included in the initial sample pool in order to end up with the desired sample size. o (TSE) = Sample size ÷ [(1 – BCI)(1 – I)(1 – R)(1 – NC)] o where BCI = proportion of bad contact information, I = proportion of ineligible elements, R = proportion of refusals, and NC = proportion that cannot be contacted after repeated attempts. d. Stratified sample – A probability sample in which (1) the population is divided into mutually exclusive and exhaustive subsets, and (2) a probabilistic sample of elements is chosen independently from each subset.  Most appropriate when strata are homogeneous within but heterogeneous between with respect to key variable(s).  Stratified samples can help ensure that particular categories of respondents are included in the final sample and receive adequate representation. e. Cluster sample – A probability sample in which (1) the parent population is divided into mutually exclusive and exhaustive subsets, and (2) a random sample of one or more subsets (clusters) is selected.  Strata should be heterogeneous within, homogeneous between.  Area sample – A form of cluster sampling in which areas (e.g., census tracts, blocks) serve as the primary sampling units. f. Discussion Activity 2: 5–10 minutes total. (PPT Slides 21–22)  Research Problem: Investigate undergraduate student attitudes toward a controversial technology fee. o Known population parameters: class (30% FR, 20% SO, 30% JR, 20% SR) and gender (50% male, 50% female) o 10 students will interview 10 friends each What should be the composition (class and gender) of those 100 students?  Debrief: Student interviewers assigned a ―quota‖ for which types of respondents they need. When all respondents from all interviewers are combined, the numbers will match those shown here.

15 FR men 15 FR women 10 SO men 10 SO women 14-4.

15 JR men 15 JR women 10 SR men 10 SR women

Determining How Big a Sample You Need (14-4, PPT Slides 23–30)

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a. Computer programs are routinely used to calculate the needed sample size in each situation. (The formulas for the calculations are included in Appendix 14B.) 14-4a. Basic Considerations in Determining Sample Size a. To determine the necessary sample size, we need three pieces of information:  How homogeneous the population is on the characteristic to be estimated.  The needed level of precision – the degree of error in an estimate of a population parameter.  The needed level of confidence – the degree to which one can feel confident that an estimate approximates the true value. o Precision and confidence are inversely related; as one increases, the other decreases, all else equal.

14-4b. Multiple Estimates in a Single Project a. Because sample size is calculated based on individual items, you will usually end up with different sample size requirements for many of the items when measuring multiple characteristics in a study. b. You have to establish an overall sample size for the project. c. The best approach is to focus on the most critical variables and select a sample big enough to estimate them with the required precision and confidence.

14-4c. Population Size and Sample Size a. Size of the population has no direct effect on the size of the sample. b. Desired precision, confidence, and variation of the characteristic in the population drive sample size, not the size of the population itself. c. The exception is when the calculated sample size is more than about 5–10% of the population.  The calculated sample size can safely be reduced using the finite population correction factor.  Computer programs can handle this calculation in practice.

14-4d. Other Approaches to Determining Sample Size a. Because marketing research is expensive, one approach when the research budget is limited is to take the remaining budget and divide it by the expected cost per contact of the method of administration. b. Another consideration is analysis to be conducted on the data—sample size must be big enough to ensure for the type of analysis’s minimum requirements. c. One final method is to use the sample size that others have used for similar studies in the past. d. Knowledge Check 14.1: < 5 minutes total. (PPT Slide 26)  Three factors influence the sample size to use in marketing research. Select the three options that apply from the following list: (a) The desired degree of precision (b) The desired degree of confidence (c) A nonrandom process of selecting sample members (d) The degree of ease with which the parameter in question can be observed (e) The degree of variability in the population on the parameter in question

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

Answers: a, b, and e—The three factors influencing sample size are the desired degree of precision, desired degree of confidence, and degree of variability in the population on the parameter in question.

Appendix 14A: Basics of the Sampling Distribution a. Population mean (μ) = Sum of population elements ÷ Number of population elements  Example data are given in Exhibit 14A.1: Population.

A-1. Derived Population a. Derived population consists of all possible samples that can be drawn from the population under a given sampling plan. b. Sample mean (x) = Sum of sample elements ÷ Number of elements in sample  Possible results are given in Exhibit 14A.2: Derived Population of All Possible Samples of Size n = 2 with Simple Random Selection.  The sample mean monthly income for samples 25, 62, 108, 147, and 189 are displayed in Exhibit 14A.3: Several Possible Samples and Their Respective Errors When Estimating the Population Mean.

A-2. Sample Mean Versus Population Mean a. The mean of all possible sample means is equal to the population mean. b. The variance of sample means is related to the population variance.

A-3. Central-Limit Theorem a. The sampling distribution is mound shaped, consistent with the central-limit theorem. b. The approximation will become more and more accurate as n becomes larger.

Appendix 14B: Calculating Sample Size a. Formula for sample size when estimating a population mean:  n = (z2/H2) × (est 2)  where n = required sample size, z = z-score corresponding to the desired degree of confidence, H = half-precision (or how far off the estimate can be in either direction), and σ2 = variance of the variable in the population. b. Formula for sample size when estimating a population proportion:  n = (z2/H2) × π(1 – π)  where n = required sample size, z = z-score corresponding to the desired degree of confidence, H = half-precision (or how far off the estimate can be in either direction), and π = estimated population proportion.

Self-Assessment (PPT Slide 31) 

How confident are you in being able to determine the needed sample size for marketing research?

How can you increase your level of confidence in assessing needed sample size, the level of acceptable sampling error, and the type of sample needed for the research?

[return to top]

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REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

104.

What is a census? What is a sample?

A census is a type of sampling plan in which data are collected from or about each member of a population. A sample is a selection of a subset of elements from a larger group of objects.

105.

Why is it important to carefully define the population?

It is important to carefully define the population because that data collected from your population will affect the subsequent steps and the results of your study.

106.

What is the difference between a parameter and a statistic? How are they related?

A parameter is a characteristic of a population; if it were possible to take measures from all population members without error, we could arrive at the true value of a parameter. A statistic is a characteristic or measure of a sample; statistics are used to estimate population parameters.

107.

What distinguishes a probability sample from a nonprobability sample?

In a probability sample, each member of the target population has a known, nonzero chance of being included in the sample. The chances of each member of the target population being included in the sample may not be equal, but everyone has a known probability of inclusion. With nonprobability samples, on the other hand, there is no way of estimating the probability that any population element will be included in the sample. Thus, there is no way of ensuring that the sample is representative of the target population. All nonprobability samples rely on personal judgment at some point in the sample-selection process.

108.

What are the main types of nonprobability samples? What are their differences?

The primary types of nonprobability samples are convenience samples, judgment samples, and quota samples. With convenience samples, people or objects are selected for the sample because they happen to be in the right place at the right time to be included. Judgment samples involve researchers handpicking the sample elements because the researchers believe those elements can serve the research purpose. With quota samples, researchers pull a sample that mirrors the population on one or more important aspects.

109.

What are the main types of probability samples? What are their differences?

The primary types of probability samples are simple random samples, systematic samples, stratified samples, and cluster samples. A simple random sample is a probability sampling plan in which each unit included in the population has a known and equal chance of being selected for the sample. The systematic sample involves a probability sampling plan in which every kth element in the population is selected for the sample pool after a random start. The stratified sample is where (1) the population is divided into mutually exclusive and exhaustive subsets, and (2) a probabilistic sample of elements is chosen independently from each subset. The cluster sample involves (1) the parent population being divided into mutually exclusive and exhaustive subsets, and (2) a random sample of one or more subsets (clusters) being selected.

110.

How do cluster samples differ from stratified samples?

A cluster sample is a probability sampling plan in which the parent population is divided into mutually exclusive and exhaustive subsets, and a random sample of one or more of the subsets is

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selected. A stratified sample is a probability sample in which the population is divided into mutually exclusive and exhaustive subsets, and a probabilistic sample of elements is chosen independently from each subset.

111.

What is the notion of total sampling elements (TSE)? Why is TSE calculated?

Because it is rare that all of the people who have been selected to participate in a study will do so, it is usually necessary to draw a larger pool of sample elements from the sampling frame than is actually needed in the study. The larger set drawing from the sampling frame is referred to as total sampling elements (TSE).

112.

In determining sample size, what three basic factors must you consider?

In determining sample size, you must consider the desired degree of precision, the desired degree of confidence, and the degree of variability in the population on the parameter in question.

113.

What effect would relaxing the precision with which a population mean or proportion is estimated have on sample size? What about decreasing the degree of confidence from 95% to 90%?

Increases in desired precision, confidence, or the variation of the characteristics in the population lead to increases in the necessary sample size.

114.

What is the relationship between population size and sample size?

In most instances, the size of the population has no direct effect on the size of the sample.

115.

What are some other methods of determining sample size?

One can determine sample size by taking the remaining budget of a project and dividing it by the expected cost per contact of the method of administration, by considering the type of analysis to be conducted on the data and making decisions accordingly, or by adopting the size that others have used for similar studies in the past. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

48. To set the stage for the various sampling techniques discussed in the chapter, it is helpful to review some actual examples of sampling plans such as those employed by the commercial marketing information services, e.g., Starch, Nielsen, and NFO. By asking students why a particular scheme might have been selected, they are prompted to think about these questions now, which helps them to appreciate the rationale for the various plans much better when they are finally discussed.

49. A helpful discussion includes the characteristics that distinguish probability sampling from nonprobability sampling—whether or not every target population element has a known, calculable chance of being included in the sample.

50. When covering nonprobability sampling, convenience sampling and judgment sampling can be covered rather quickly. Quota sampling, on the other hand, requires somewhat more time. It seems particularly helpful with quota sampling to trace the whole process with an example. Students can then be asked to specify the cell breakdown criteria and the categories to be used. Then, using these classifications and some assumed, hypothetical population proportions, one can specify how many

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sample observations of each type are to be selected. The difficulty of fulfilling a quota defined on multiple characteristics soon becomes obvious, as does the lack of probability associated with the selection process.

51. Employing some simplified example, e.g., a population with only six elements from which you are considering selecting a sample of size 3, review the basic notions of parameter versus statistic and parent population versus derived population. A population of this magnitude is in order because with a simple random sample of size n = 3, there are only 20 possible distinguishable samples when sampling without replacement. The students can then be asked to develop the derived population of all possible distinguishable samples of size 3. For example, if the parent population is an urn containing discs labeled with the letters A, B, C, D, E, F, e.g.,

the derived population of all possible distinguishable samples is ABC, ABD, ABE, ABF, ACD, ACE, ACF, ADE, ADF, AEF, BCD, BCE, BCF, BDE, BDF, BEF, CDE, CDF, CEF, DEF By changing the sampling plan from one without replacement to one with replacement or from n = 3 to n = 2, one quickly demonstrates how the derived population (and thus sampling distribution of the statistic) depends on the sampling plan.

52. Employing the example above and some assumed values for the discs, e.g., A = 3, B = 6, C = 9, D = 12, E = 15, F = 18, or some other simple example, review how the parameters of the two populations are related, for example: Derived Population

Mean

Parent Population µ

Variance

2

2/x

Relationship

(x)

(x) = µ

2/x = 2/x [(N – n)/(N – 1)

The calculations are detailed below. This example affords an opportunity to discuss what is meant by unbiasedness and the finite population correction.

Relationship Between Parameters of Parent Population and Derived Population Parent Population Element A

Value 3

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B C D E F

6 9 12 15 18

Mean: µ = xi /N = (3 + 6 + 9 + 12 + 15 + 18)/6 = 10.5 Variance: 2 = (xi – µ)2 = [(3 - 10.5)2 + . . . + (18 - 10.5)2]/6 = 157.5/6 = 26.25 Derived Population j= 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Sample ABC ABD ABE ABF ACD ACE ACF ADE ADF AEF BCD BCE BCF BDE BDF BEF CDE CDF CEF DEF

Total 18 21 24 27 24 27 30 30 33 36 27 30 33 33 36 39 36 39 42 45

Mean Xi 6 7 8 9 8 9 10 10 11 12 9 10 11 11 12 13 12 13 14 15

Mean: E(x) = xi /L = (6 + 7 + . . . + 15)/20 = 105/20 = 10.5 Variance: 2/x = [xi – (x)]2/L = [(6 - 10.5)2 +. . . + (15 - 10.5)2]/6 = 157.5/20 = 5.25 53. Construct the two distributions. Point out that the derived population produces a sampling distribution of some statistic, be it a mean, variance, mode, range or whatever, and that the sampling distribution determines the statistical test to be employed in making inferences.

Although this notion will be familiar to most students, the simple example serves as a review and also provides a nice introduction to the more complex probability sample designs.

54. Using the two plotted distributions, introduce and discuss the operation of the Central Limit Theorem and how it allows us to make inferences about a population mean regardless of the distribution of the variable in the parent population as long as the sample size is large enough.

Review the procedure for making inferences about a population mean and what the derived confidence interval means.

55. Review how a simple random sample is properly drawn using a table of random numbers.

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56. Review what stratified samples are, why they are used, and the issues that arise in designing them.

57. In discussing the two basic types of stratified samples—proportionate and disproportionate—it is helpful to emphasize the different knowledge requirements from the researcher. In the case of a proportionate stratified sample, the researcher only needs to know the relative sizes of each stratum in the population in order to determine the sample size and composition; in the case of a disproportionate stratified sample, however, the investigator needs to know the relative variability of the strata.

58. Progress to a review of the procedure for selecting a cluster sample: (a) divide the parent population into mutually exclusive and exhaustive subsets, (b) select a random sample of subgroups. It is useful here to drive home the difference between stratified and cluster samples using a simplified example.

Suppose the issue here is whether a new mass transit system would be favorably received (i.e., used) by workers for their daily commutes. Suppose further that there are variations in the amount of congestion and the length of the commutes from various areas in the city. Clearly, that could affect a person’s reaction to the proposed system as could the question of whether the person has rather rigid hours or has some flexibility when he or she commutes. Given these considerations, students can be asked how they would sample the population of workers using both stratified and cluster sampling techniques.

59. The notions of efficiency surrounding sampling include statistical efficiency, economic efficiency, and overall efficiency. Cluster samples in practices are typically least statistically efficient because of the way they are selected, but they produce smaller standard errors of estimate for a given cost than do either stratified or simple random samples.

60. For the systematic sample, illustrate the generation of the sampling fraction, the sampling interval, and the random start using some hypothetical example. After selecting a sample, have students point out why a systematic sample is a cluster sample by identifying the clusters.

61. Introduce and emphasize the basic principle underlying area sampling—a target group for which a list of population elements is not readily available (e.g., an up-to-date list by name of people living in a city) is transformed into a list of population elements, which is readily available in the form of areas on a map, so that a probability sample can be selected. [return to top]

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TABLE OF CONTENTS Purpose and Perspective of the Chapter ............................................................................................... 129 Chapter Objectives ................................................................................................................................. 129 Complete List of Chapter Activities and Assessments......................................................................... 129

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Key Terms................................................................................................................................................ 129 What's New in This Chapter .................................................................................................................. 130 Chapter Outline ...................................................................................................................................... 130 Review Questions .................................................................................................................................... 134 Additional Insights and Activities ......................................................................................................... 136

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to acquaint students with the types of errors that can enter a study and ways to handle them to reduce the effect they have on research results. The chapter also looks at response rates to communication-based studies. Formulas that calculate various response rates are explained. Then, the chapter concludes by discussing various ways to improve response rates.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 15-1. Describe the six types of error that can enter a study.

15-2. Give the general definition for response rate.

15-3. Discuss several ways in which response rates might be improved.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 15-1 15-2 15-3 15-1–15-3

PPT slide PPT slide 11 PPT slides 15–16 PPT slide 22 PPT slide 23

Activity/Assessment Knowledge Check 15.1 Discussion Activity Polling Activity Self-Assessment

Duration < 5 min 5–10 min 5–10 min 10–20 min

[return to top]

KEY TERMS Data merger error

Error resulting from the aggregation of data from or about a population element from multiple

sources.

Error that arises because of failure to include qualified elements of the defined population in the sampling frame.

Noncoverage error

Error from failing to obtain information from some elements of the population that were selected and designated for the sample.

Nonresponse error

Not-at-homes Office error

Nonresponse error that arises when respondents are not available when the interviewer calls.

Error due to data editing, coding, or analysis errors.

Mistakes made by humans or machines in the process of recording respondents’ communication- or observation-based data.

Recording error

Refusals

Nonresponse error resulting because some designated respondents refuse to participate in the study.

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Error that occurs when an individual provides an inaccurate response, consciously or subconsciously, to a survey item.

Response error

The number of completed interviews with responding units divided by the number of eligible responding units in the sample.

Response rate

[return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:  

New chapter title, Data Collection: Types of Error and Response Rate Calculation, better reflects the material contained in it.

Minor editorial updates throughout the chapter are meant to enhance comprehension.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

15-1.

Types of Error (15-1, PPT Slides 3–11) ggg.

Exhibit 15.1: Six Types of Error lists those that can bias results away from the truth about a population.

15-1a. Sampling Error a. The sampling error is the difference between results obtained from a sample and results that would have been obtained had information been gathered from or about every member of the population.  If you don’t use a probability sampling technique, it is impossible to estimate the degree of sampling error.  Sampling error usually isn’t the biggest problem – it’s all the other things that contribute error to a project, and these other sources of error can’t be accounted for statistically.

15-1b. Noncoverage Error a. Noncoverage error – Error that arises because of failure to include qualified elements of the defined population in the sampling frame.  It is essentially a sampling frame problem.  A smaller but related problem with sampling frames is overcoverage errors that occur when the same name ends up on the sampling frame more than once.

15-1c. Nonresponse Error

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a. Nonresponse error – Error from failing to obtain information from some elements of the population that were selected and designated for the sample.  This potential problem only occurs when those who respond are systematically different in an important way from those who don’t respond.  Refusals – Nonresponse error resulting because some designated respondents refuse to participate in the study.  Not-at-homes – Nonresponse error that arises when respondents are not available when the interviewer calls.  It is better to work hard at generating responses from a smaller sampling pool than to start with a much larger sampling pool to get the same number of respondents. b. Methods for diagnosing nonresponse errors include (see Exhibit 15.2: Two (Imperfect) Methods for Diagnosing Nonresponse Error):  Contact a sample of nonrespondents.  Compare respondent demographics against known demographics of population.  Conduct an analysis of late responders vs. early responders.

15-1d. Response Error a. Response error – Error that occurs when an individual provides an inaccurate response, consciously or subconsciously, to a survey item. b. Key considerations with response error:  Does the respondent understand the question?  Does the respondent know the answer to the question?  Is the respondent willing to provide the true answer to the question?  Is the wording of the question or the situation in which it is asked likely to bias the response?

15-1e. Recording Error a. Recording error – Mistakes made by humans or machines in the process of recording respondents’ communication- or observation-based data.  One challenge happens in interviews when trying to keep a respondent interested and engaged while at the same time recording their responses.  Online data collection can be subject to hardware and software glitches.

15-1f. Office Error a. Office error – Error due to data editing, coding, or analysis errors. b. Data merge error – Error resulting from the aggregation of data from or about a population element from multiple sources.

15-1g. Total Error Is the Key Controlling the overall amount of error, rather than any single type of error, is the key in a research project. b. Exhibit 15.3: Types of Error and Methods for Handling Them can be used as a checklist for evaluating the quality of research prior to making important decisions based on the research results. c. Knowledge Check 15.1: < 5 minutes total. (PPT Slide 11) a.

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



15-2.

When an error results because information from selected elements of the population and designated for the sample are missing, it is known as: (e) a nonresponse error. (f) a noncoverage error. (g) a recording error. (h) an office error.

Answer: a—Nonresponse error is possible when some elements designated for inclusion in the sample did not respond and were systematically different from those who did respond on key characteristics.

Calculating Response Rates (15-2, PPT Slides 12–16) a. Response rate – The number of completed interviews with responding units divided by the number of eligible responding units in the sample.  Allows an assessment of the potential influence of nonresponse error on study results.  Indicates the overall quality of a data collection effort. b. General formula:  Response rate (RR) = Number of completed interviews with responding units ÷ Number of eligible responding units in the sample 15-2a. Online and Mail Surveys (No Eligibility Requirement) a. Response rate calculation is usually straightforward with this formula:  RR = Number of usable questionnaires ÷ (Number of contacts attempted – Number of wrong addresses) 15-2b. Telephone Interviews (No Eligibility Requirement) a. Attempted contacts are categorized into three groups: completed interviews, refusals, and not-at-homes. b. Applicable formula:  RR = Number of completed interviews ÷ (Number of completed interviews + Number of refusals + Number of not-at-homes) 15-2c. Online Surveys and Telephone Interviews (with Eligibility Requirement) a. Sometimes, sampling frames include response units that are not members of the population being studied, so the ineligible response units must be considered in the formula:  E% = Number of completed interviews ÷ (Number of completed interviews + Number of ineligibles)  Given the eligibility percentage, the response rate formula is:  RR = Number of completed interviews ÷ [Number of completed interviews + (E%)(Number of refusals + Number of not-at-homes)]

15-2d. Other Methods of Data Collection a. The same basic logic and formula apply when determining response rate, regardless of the type of data collection. b. Discussion Activity: 5–10 minutes total. (PPT Slides 15–16)

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



15-3.

You’ve conducted an online survey, attempting to contact 500 people; the 500 sample elements have been classified as follows: - Completed surveys: 250 - Refusals: 200 - Ineligibles: 50 Calculate the response rate.

Answer/Debrief: E% = 250 ÷ (250 + 50) = 83% RR = 250 ÷ [250 + (0.83)(200)] = 60%

Improving Response Rates (15-3, PPT Slides 17–22) a. The response rate on a project serves as an indicator of the overall quality of a data collection effort.  It also provides insight into the likely influence of nonresponse error on the project. b. Researchers must strive to obtain the highest response rates possible in a given situation. c. One factor, how interested the sample pool is in the topic, probably has more effect than any other on response rate.  Exhibit 15.4: Tips for Increasing Response Rates on Online Surveys 15-3a. Survey Length a. As surveys get longer, respondents get tired, lose focus, become inattentive, and speed through the survey to finish.

15-3b. Guarantee of Confidentiality or Anonymity a. Confidentiality is especially important when respondents may be sensitive to the topic or specific questions. b. Researchers are ethically bound to keep promises of confidentiality.

15-3c. Interviewer Characteristics and Training a. It is important to develop an effective recruiting script and then train interviewers to follow that script. b. Interviewers who can quickly convince potential respondents of the value of the research and of their participation are invaluable.

15-3d. Personalization a. Personalizing the collection process tends to improve response rates.

15-3e. Response Incentives a. Offering an incentive usually increases response rates. b. Response incentives can take a variety of forms (money, lotteries, donations to charity, etc.).

15-3f. Follow-Up Surveys and Reminders a. Circumstances around a contact’s refusal or nonparticipation may be temporary, which makes follow-up reminders useful. b. Timing is important; it can encourage participation before an invitation gets lost in an inbox. c. Polling Activity: 5–10 minutes total. (PPT Slide 22)

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

f.

Various techniques have been identified for improving response rates. Of the following, which do you feel is most important to attend to when collecting data? (a) Appropriate survey length (b) Guarantee of confidentiality or anonymity (c) Personalization (d) Response incentives Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response. In this particular activity, their answer can be related to why the technique might work and identifying the conditions under which the technique might be especially useful.

Self-Assessment (PPT Slide 23) 

The methods for improving response rates can be readily translated into improving a variety of situations involving interaction with others. Why is this so? What examples in your own life could be improved by employing these techniques?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

116.

What are the six general types of error that can enter a research project? How do they differ?

Sampling error occurs anytime that we work with a subset of the population instead of the population itself. Noncoverage errors occur because part of the population of interest was not included in the sampling frame. Nonresponse errors are possible when some elements designated for inclusion in the sample did not respond and were systematically different from those who did respond on key characteristics. Response errors occur because inaccurate information was secured from the sample elements. Recording errors occur when researchers or the technology they employ fail to accurately record observational data. Office errors occur when errors are introduced in the processing of the data or in reporting the findings.

117.

Why is sampling error potentially less troubling than other kinds of error?

Sampling error is potentially less troubling than other kinds of error because there are easy ways to reduce it (increase the sample size) or to account for it statistically (calculate the margin of sampling error).

118.

Why is noncoverage error considered to be a sampling frame problem?

Noncoverage error is considered to be a sampling frame problem because it occurs when the researcher fails to include qualified elements of the defined population in the sampling frame.

119.

Why is nonresponse error a ―potential‖ source of error?

Nonresponse error is a potential source of error because there is the possibility that those who respond to the survey are systematically different from those who didn’t respond; thus, the results would be biased upward.

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120.

What are the two primary sources of nonresponse error? Describe how each source could result in nonresponse error.

The two primary sources of nonresponse error are refusals (designated respondents refuse to participate in the study) and not-at-homes (respondents are not at home with the interviewer calls).

121.

Why might it be better to work with smaller total sampling elements (TSE) and work diligently to get responses than to start with a much larger TSE but obtain a lower response rate?

The nonresponse problem is important to the accuracy of most communication-based projects.

Unless 99% who do not respond are similar to the 1% who do respond on the key issues, the risk of nonresponse error is probably too great.

122.

What are the basic considerations underlying response error?

The basic considerations underlying response error include the following: Does the respondent know how to answer the question? Does the respondent understand the question? Is the respondent willing to provide the true answer to the question? Is the wording of the question or the situation in which it is asked likely to bias the response?

123.

What is recording error? How might it apply to both communication- and observation-based studies?

Recording error is the mistakes made by humans or machines in the process of recording respondents’ communication- or observation-based data. Communication-based data rely on the interviewer to record the actual responses of the subjects correctly. Observation-based data recording error can occur when observers fail to record important behaviors.

124.

Why can office error be described as the most frustrating kind of error?

The most frustrating type of error is office error because it can show up even after the data are collected (during the process of editing, coding, and analyzing the data).

125.

What causes data merger errors?

Data merger errors are caused by the aggregation of data from or about a population element from multiple sources. It often results in data that are lost, combined with the wrong cases, not readable because they are in the wrong format, or a host of other problems.

126.

What is a response rate?

Response rate is the number of completed interviews with responding units divided by the number of eligible responding units in the sample.

127.

How should response rates be calculated for the different methods of data collection?

For online and mail surveys with no eligibility requirements, the response rate should be calculated by the number of usable questionnaires divided by the number of contacts attempted minus the number of wrong addresses. For online and mail surveys with eligibility requirements, the response rate should be calculated by the number of completed interviews divided by the number of completed interviews plus the eligibility percentage times the number of refusals plus the number of not-at-homes. For telephone interviews, the response rate should be calculated by the number of completed interviews divided by the number of completed interviews plus the number of refusals plus the number of not-at-homes.

128.

What are some of the techniques for improving response rates?

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There are several approaches for improving response rates, including framing the study to enhance respondent interest, keeping the survey as short as possible, guaranteeing confidentiality or anonymity, training interviewers well and matching their characteristics to those of the subject pool, personalizing the recruiting message when possible, using an incentive, and sending follow-up surveys. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

62. The distinction between sampling error and nonsampling error provides a discussion starting point.

Whereas sampling error is the difference between the observed values of a variable and the long-run average of the observed values in repetitions of the measurement, nonsampling error encompasses the many other errors that arise in research. When students realize that sampling error simply reflects the spread in the sampling distribution of the statistic, they seem to appreciate better the argument that the answers to important research questions do not necessarily lie with larger samples, as many students seem to feel from their beginning statistics courses.

63. The distinction between nonsampling and sampling error can be more firmly implanted by asking the students to associate each research plan with each sample distribution in the following example: Research Plan: A: n = 50; ask students for overall grade point average (GPA).

B: n = 50; ascertain GPAs using registrar's records.

C: n = 300; ask students for overall GPA. D: n = 300; ascertain GPAs using registrar's records.

Sampling Distributions:

The correspondence is:

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Research Plan

Sampling Distributions

A

2

B

1

C

4

D

3

because when asked, the students might be expected to "round upward" their actual GPA, and the nonsampling bias is the difference between the vertical solid line and the vertical dashed line.

With this example, the following key points can be made readily: (a)

Nonsampling errors are not as well behaved as sampling errors. Sampling errors can be estimated if probability sampling plans are employed. Nonsampling errors cannot be estimated typically, as we quite often do not even know their direction, much less their size; e.g., we "felt" the students would round their GPAs upward when asked, although it is possible they might report them accurately or even round downward.

(b)

Sampling errors can be reduced simply by increasing the sample size. Nonsampling errors are not necessarily reduced this way and may in fact increase with increases in sample size.

Their reduction depends on better methods rather than increased sample size.

64. The following points can all be made about nonsampling errors: (a)

The definition of each type of nonsampling error

(b)

How the errors arise with the various methods of data collection

(c)

How the magnitude of the error is estimated

(d)

What can be done to minimize it, allow for it, or reduce its impact

65. For presenting the three possible methods of diagnosing nonresponse error, instructors might begin with a scenario in which nonresponse error is likely (e.g., salary surveys, studies of outdoor activities conducted by telephone), and then discuss how each approach might help identify the presence of nonresponse bias.

66. Response error can originate with the respondent, the interviewer, and/or the situation. In addition, it is important to make the point that reducing response error involves better data collection efforts (e.g., questionnaire design, pre-testing, interviewer training) rather than increasing sample size—in fact, increasing sample size will likely increase response error.

67. We strongly encourage numerous reminders of the value of exploratory research and questionnaire pre-testing for (a) estimating the likely levels of refusal and not-at-homes in the population and (b) helping to decrease response errors by identifying poorly worded questions, response categories, and instructions, and providing an opportunity for interviewer training and practice.

68. The overall goal continues to be to reduce the total error in a project, rather than any single type of error. Sampling error is only one of five major categories of error, and decreasing sampling error by increasing sample size will likely increase some of the other types of errors (especially response error and office error). Again, total error is the key.

69. Working through actual examples that calculate response rate for various types of data collection methods helps to clarify the formulas. Using an actual survey project can be useful in presenting these concepts. This is also an appropriate opportunity to discuss the issue of what constitutes a

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―good‖ response rate as well as the rationale for why it may be more important to achieve a higher response rate in a smaller sample than a lower response rate in a larger sample. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 16: Data Preparation for Analysis

TABLE OF CONTENTS Purpose and Perspective of the Chapter ............................................................................................... 139 Chapter Objectives ................................................................................................................................. 139 Complete List of Chapter Activities and Assessments......................................................................... 139 Key Terms................................................................................................................................................ 139 What's New in This Chapter .................................................................................................................. 140 Chapter Outline ...................................................................................................................................... 140 Review Questions .................................................................................................................................... 144 Additional Insights and Activities ......................................................................................................... 145

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to provide a discussion of the preliminary steps of marketing research that include data editing, coding, and aggregating, as well as how data are cleaned. The chapter spends time explaining how to handle missing data and the value of a codebook. At the end of the chapter is a project example that illustrates key points made in the chapter.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 16-1

Explain the purpose of the data editing process.

16-2

Define data coding.

16-3

Explain why data must sometimes be aggregated.

16-4

Discuss how data are cleaned.

16-5

Discuss options for dealing with missing data.

16-6

Describe a data file and explain its connection to the codebook.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 16-1 16-2 16-4 16-5 16-6 16-1–16-6

PPT slide PPT slide 6 PPT slides 12–13 PPT slides 18–19 PPT slides 23–24 PPT slide 27 PPT slide 31

Activity/Assessment Knowledge Check 16.1 Group Activity 1 Discussion Activity 1 Discussion Activity 2 Group Activity 2 Self-Assessment

Duration < 5 min 10–20 min 5–10 min 5–10 min 15–30 min 10–20 min

[return to top]

KEY TERMS Blunder

An office error that arises during editing, coding, or data entry.

A document that contains explicit directions about how data from data collection forms are coded in the data file.

Codebook

Coding

The process of transforming raw data into symbols (usually numbers).

Data aggregation

The process of creating summary data for a particular repeated behavior over a specified period

of time.

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Data entry procedure in which data are entered separately by two people in two data files, and the data files are compared for discrepancies.

Double-entry

Editing

The inspection and correction of the data received from each element of the sample (or census).

A source of error that arises when a respondent agrees to an interview but refuses, or is unable, to answer specific questions.

Item nonresponse

Merging

The process of combining data from different data sources into a single database.

The use of scanner technology to ―read‖ responses on paper surveys and to store these responses in a data file.

Optical scanning

[return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:  

Consolidated chapter objectives for greater clarity and alignment with the section materials.

Minor editorial updates throughout the chapter were made to enhance comprehension.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

16-1.

Editing Data (16-1, PPT Slides 3–6) hhh.

Editing – The inspection and correction of the data received from each element of the sample (or census).  Its basic purpose is to make certain that raw data meet minimum quality standards.  Rule of thumb: If half or more of responses are missing on a survey, drop that case entirely.  Beware of response set bias. iii. Exhibit 16.1: Primary Tasks in the Editing Process  Convert all responses to consistent units.  Assess degree of nonresponse.  Where possible, check for consistency across responses.  Look for evidence that the respondent wasn’t really thinking about the answers provided.  Add any needed codes. jjj. Knowledge Check 16.1: < 5 minutes total. (PPT Slide 6)  Choose the following statement that best expresses the purpose of the data editing process. (i) Eliminating any questionnaire in which pieces of data are missing is the primary purpose of editing.

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

16-2.

(j) Creating additional codes for data collection forms occupies the bulk of editing tasks. (k) Editing is primarily a concern for observation-based studies. (l) Ensuring data meets minimum quality criteria is the basis of data editing.

Answer: d—The basic purpose of editing is to make certain that the raw data meet minimum quality standards. Editing involves the inspection and, if necessary, correction of the data received from or about each case to be included in the analysis.

Editing is primarily a concern for communication-based studies.

Coding Data (16-2, PPT Slides 7–13) a. Coding – The process of transforming raw data into symbols (usually numbers).

16-2a. Coding Closed-Ended Items a. In descriptive research, most questionnaire items are likely to be closed-ended with a limited number of response categories, which are generally simple to code. b. The researcher uses one variable for the question and assigns a number to each possible response. c. For more complex items, a coder can use the binary 0 for no and 1 for yes when respondents can indicate more than one answer.

16-2b. Coding Open-Ended Items a. When coding factual open-ended items that seek concrete responses, numeric answers are typically recorded as given by the respondent, while other types of responses are given a specific code number. b. Exploratory open-ended items seeking less structured responses are much more difficult to code. c. Exhibit 16.2: Steps in Coding Exploratory Open-Ended Items  Step 1: Highlight each separate response given by each individual.  Step 2: Specify the categories for responses.  Step 3: Sort responses into categories, using multiple coders.  Step 4: Compare results and, after discussing code differences between coders, assign a final code for each response. d. Group Activity 1: 10–20 minutes total. (PPT Slides 12–13)  Form groups of three to five participants and write two example closed-ended items for a questionnaire. Then, write two example open-ended items. Describe how you would code each of the items.  As time allows, compare your examples within the larger class.  Debrief: In writing your examples, which was more difficult: closed-ended items or open-ended items?

In determining coding for each item’s responses, did the closed-ended items or openended items present more of a challenge?

16-3.

Aggregating Data (16-3, PPT Slides 14–15) a. Data aggregation – The process of creating summary data for a particular repeated behavior over a specified period of time.

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  16-4.

Applies to longitudinal behavioral data collected by mechanical observation.

Must be accompanied by careful data coding.

Cleaning the Data (16-4, PPT Slides 16–19) a. Blunder – An error that arises during editing, coding, or data entry.  Blunders are usually due to researcher carelessness.  Occur less frequently with online surveys. b. How to identify blunders:  Run frequency analysis on all variables.  Double-entry – Data entry procedure in which data are entered separately by two people in two data files, and the data files are compared for discrepancies.  Optical scanning – The use of scanner technology to ―read‖ responses on paper surveys and to store these responses in a data file. c. Discussion Activity 1: 5–10 minutes total. (PPT Slides 18–19)  Discuss the benefits and costs of using double-entry and optical scanning as approaches to reduce the incidence of blunders.  Debrief: These two approaches differ somewhat in the level of time, effort, and money required. Using double-entry helps to quickly identify where two people doing data entry of the same surveys have differences in their input. The correct data can be quickly determined. Of course, the labor of two individuals is required. With optical scanning, the cost of scanners or of a company’s services to provide them must be considered.

As with many decisions to be made in data research, the purpose of the study and the methods that will be used must be factored in.

16-5.

Handling Missing Data (16-5, PPT Slides 20–24) a. Item nonresponse – A source of error that arises when a respondent agrees to an interview but refuses, or is unable, to answer specific questions.  If a particular case has a significant amount of item nonresponse, it should probably be eliminated during the editing process. b. Strategies for handling missing data include:  Eliminate the case with the missing item(s) from all further analyses.  Eliminate the case with the missing item in analyses using the variable.  Substitute values for the missing items.  Contact the respondent again. c. There is no ―right‖ answer as to how missing items should be handled; it depends on:  The purposes of the study.  The incidence of missing data.  The methods to be used in analyzing the data.

d. Discussion Activity 2: 5–10 minutes total. (PPT Slides 23–24)

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 

16-6.

Discuss the difference between response set bias and item nonresponse. Which do you believe to be the bigger problem when evaluating and/or coding data?

Debrief: Response set bias is difficult to detect in the process of editing data, but when it is obvious, the answers don’t contribute to the validity of results. You have to decide whether the respondent has taken the survey seriously.

Item nonresponse can indicate the quality of the research. It can often be avoided with careful exploratory research and pretesting.

Building the Data File and Codebook (16-6, PPT Slides 25–28) a. To take advantage of computer analysis of data, the data codes for respondents’ answers must be placed in a file the computer can read.  Data may be collected through online data collection tools.  Behavioral data can be downloaded into spreadsheets.  Manual entry of data is sometimes necessary. b. A preliminary task in analyzing data is merging – the process of combining data from different data sources into a single database. c. Codebook – A document that contains explicit directions about how data from data collection forms are coded in the data file.  For an example, see Exhibit 16.6: Codebook for Avery Fitness Center Project. d. Standards for coding:  Assign a separate variable to each response option.  Use only numeric codes.  Use standard codes for ―no information‖ throughout the study. e. Group Activity 2: 15–30 minutes total. (PPT Slide 28)  Form groups of three to five participants and create a codebook for the following questions found on a survey of college students. a. How many semesters have you been at this university? b. How many classes are you taking currently? c. How difficult is your favorite class this term? d. How difficult is your least favorite class this term? e. How much free time do you have? f. How do you occupy that free time?  As time allows, compare your examples within the larger class.

16-7.

Avery Fitness Center Project (16-6, PPT Slides 29–31) a. Key points from this chapter are illustrated through the Avery Fitness Center example.  Exhibit 16.4: Data Sources on Avery Fitness Center Project b. The research problems:  Discover existing member demographics and usage patterns.  Investigate how members initially learn about AFC and their motivations for using the center. c. Multi-source data needs to be analyzed.  Exhibit 16.3: Avery Fitness Center Application Form  Exhibit 16.5: Avery Fitness Center Questionnaire

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

Exhibit 16.6: Codebook for Avery Fitness Center Project

Self-Assessment (PPT Slide 32) 

In what ways does the data editing process increase the validity and reliability of survey results?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

129.

What should a researcher do with incomplete answers? Obviously wrong answers?

Answers that reflect a lack of interest?

Unless you are using an online survey that forces respondents to complete each item before they can move on in the survey (and we don’t usually recommend using such a strategy), it is likely that some respondents won’t complete all of the items on a survey. Some surveys will have complete sections omitted. It’s not a formal rule, but if half or more of the responses are missing on a survey, we usually recommend dropping that case entirely.

130.

How might a researcher best code ―please check all that apply‖ questions?

When a question allows multiple responses, assign separate variables for each response option.

When using ―please check all that apply,‖ the researchers must assign separate variables for each option in the data file.

131.

What are the two kinds of open-ended questions, and why is one more difficult to code than the other?

The two kinds of open-ended questions are factual open-ended items and exploratory open-ended items. A factual open-ended question is easy to code by simply coding the actual response (or, if the actual responses aren’t numeric, converting the responses to numbers). An exploratory openended question is more difficult and expensive to code. For many open-ended questions, there are multiple legitimate responses, some of which you might not anticipate in advance.

Why should multiple coders be used to establish categories and code responses for openended questions? Does this apply to all open-ended questions?

Multiple coders help reduce bias in interpreting different responses, a form of office error. Each coder will individually decide which category is appropriate for a response and then assign the numerical code for that category to the response. This applies to coding exploratory open-ended items.

132.

What is a blunder?

An office error that arises during editing, coding, or data entry.

133.

What is double-entry of data?

Data are entered into two separate data files and then compared for discrepancies.

134.

What are the possible ways for dealing with missing data? Which strategy would you recommend?

Several options exist, including (a) eliminating the case with missing information from all analyses, (b) eliminating the case with missing information from only analyses using variables

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with missing information, (c) substituting values for the missing items, and (d) contacting the respondent again.

135.

What methods are available for building a data file?

There are numerous methods, including creating text data files in word processing software, using spreadsheet software, using database software, entering data directly into statistical software packages, such as SPSS, or using optical scanning. Regardless of how the data input process will be handled, it helps to visualize the input in terms of a multiple-column record, where columns represent different variables and rows represent different cases.

136.

What is the purpose of the codebook?

The codebook contains general instructions indicating how each item of data was coded. It contains the variable names, sources of the data (if the data came from multiple sources), a description of how each variable is coded, and an explanation of how missing data are treated in the data file. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

70. To begin, present the total research process so as to point out where we are in the process. With this diagram, you can then make several points: g. The particular analysis one employs will depend on the decisions previously made with respect to research problem, research design, sampling procedure, and so on. These decisions dictate the appropriate analysis. Although the specific statistical analyses will vary from study to study, the initial aspects of data analysis are common across studies. h. The preliminary steps are editing, coding, developing a codebook, inputting data, ―cleaning‖ data after input, and dealing with missing data. These issues are relevant to practically all studies.

71. Discussion of the specifics of these preliminary data analysis steps can focus on such questions as: i.

Editing o

j.

What to do with incomplete answers, obviously wrong answers, and answers that reflect a lack of interest and how one goes about detecting such problems.

Coding o

The establishment of categories.

o

The coding of open-ended versus fixed-alternative/closed-ended questions.

o

Different types of open-ended questions.

o

The coding of ―check all that apply‖ types of items.

o

Codes that must be added by the researcher (e.g., respondent identification numbers).

o

The division of responsibilities when a large number of long questionnaires is involved.

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o

The coding of data for subsequent computer analysis. Here, it is helpful to distribute a completed sample questionnaire for a study with clearly stated objectives. Class discussion can then be productively directed at how each student would code the information on this questionnaire and why.

k. Cleaning the Data

l.

o

Techniques for identifying blunders, including frequencies and double-entry.

o

New techniques for reducing blunders, including optical scanning.

Handling Missing Data o

Decisions about how to handle missing data. It is useful to remind students that the goal should normally be to try to retain and use as much data as is possible.

o

The use of a respondent’s other answers to replace a missing value vs. using answers from other respondents.

m. Developing the Codebook o

Decisions about what type of data file to use.

o

Information to be included in the codebook (i.e., variable names, column locations, code description, explanation of how missing data are handled). A sample questionnaire is an effective tool for demonstrating how to develop a codebook. A reproduction of a section of a codebook is available in the PowerPoint slides.

72. Instructors can choose to further elaborate on these topics on the basis of student backgrounds and their own experiences and interests. Our experience suggests that students sometimes have difficulty grasping what the codebook represents and how it is developed, but once they have begun working with a codebook (either the one from the text or, even better, one for a project they are pursuing for the course), they quickly understand. [return to top]

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TABLE OF CONTENTS Purpose and Perspective of the Chapter ............................................................................................... 148 Chapter Objectives ................................................................................................................................. 148 Complete List of Chapter Activities and Assessments......................................................................... 148 Key Terms................................................................................................................................................ 148 What's New in This Chapter .................................................................................................................. 149 Chapter Outline ...................................................................................................................................... 149

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Review Questions .................................................................................................................................... 154 Additional Insights and Activities ......................................................................................................... 157

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to present common types of univariate data analysis techniques and introduce the concept of hypothesis testing. The discussion begins with frequency analysis and confidence intervals for proportions before moving into descriptive statistics and confidence intervals for means. The Avery Fitness Center Project introduced in the previous chapter is used to illustrate the concepts in the chapter. Hypothesis testing as the way to evaluate study results is also covered.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 17-1

Discuss frequency analysis and confidence intervals for proportions.

17-2

Discuss descriptive statistics and confidence intervals for means.

17-3

Detail the basic purpose of hypothesis testing.

17-4

Describe the primary hypothesis tests for univariate variables.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 17-1 17-2 17-3 17-4 17-1–17-4

PPT slide PPT slides 12–13 PPT slides 20–21 PPT slide 26 PPT slide 30 PPT slide 31

Activity/Assessment Discussion Activity 1 Discussion Activity 2 Group Activity Knowledge Check 17.1 Self-Assessment

Duration 5–10 min 5–10 min 10–20 min < 5 min 10–20 min

[return to top]

KEY TERMS Alternative hypothesis

The hypothesis that a proposed result is true for the population.

Categorical measures

A commonly used expression for nominal and ordinal measures.

A statistical test to determine whether some observed pattern of frequencies corresponds to an expected pattern.

Chi-square goodness-of-fit test

A projection of the range within which a population parameter will lie at a given level of confidence, based on a statistic obtained from a probabilistic sample.

Confidence interval

Continuous measures

A commonly used expression for interval and ratio measures.

A technique for converting a continuous measure into a categorical measure. The categories are formed based on the cumulative percentages obtained in a frequency analysis.

Cumulative percentage breakdown

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Statistics that describe the distribution of responses on a variable. The most commonly used descriptive statistics are the mean and standard deviation.

Descriptive statistics

Frequency analysis A count of the number of cases that fall into each of the possible response categories.

A form of column chart on which the values of the variable are placed along the x-axis and the absolute or relative frequency of the values is shown on the y-axis.

Histogram

Hypothesis

An unproven proposition about some phenomenon of interest.

A technique for converting a continuous measure into a categorical measure with two approximately equal-sized groups. The groups are formed by ―splitting‖ the continuous measure at its median value.

Median split

Null hypothesis The hypothesis that a proposed result is not true for the population. Researchers typically attempt to reject the null hypothesis in favor of some alternative hypothesis.

Outlier An observation so different in magnitude from the rest of the observations that the analyst chooses to treat it as a special case.

The probability of obtaining a given result if the null hypothesis were true in the population. A result is regarded as statistically significant if the p-value is less than the chosen significance level of the test.

P-value

Sample mean

The arithmetic average value of the responses on a variable.

A measure of the variation of responses on a variable. The standard deviation is the square root of the calculated variance on a variable.

Sample standard deviation

α) The acceptable level of error selected by the researcher, usually set at 0.05. The level of error refers to the probability of rejecting the null hypothesis when it is actually true for the population.

Significance level (

A technique for converting an interval-level rating scale into a categorical measure, usually used for presentation purposes. The percentage of respondents choosing one of the top two positions on a rating scale is reported.

Two-box technique

[return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:  

Consolidated chapter objectives for greater clarity and alignment with the section materials.

Minor editorial updates throughout the chapter were made to enhance comprehension.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

Introduction (PPT Slide 3) 

If you can answer these two questions, data analysis is easy:

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1. Is the variable to be analyzed by itself (univariate analysis) or in relationship to other variables (multivariate analysis)?

2. What level of measurement was used?

17-1.

Univariate Categorical Measures: Frequency Analysis (17-1, PPT Slides 4–13) kkk.

Categorical measures – A commonly used expression for nominal and ordinal measures.

17-1a. Frequency Analysis a. Frequency analysis – A count of the number of cases that fall into each of the possible response categories.  A simple but important tool.  Statistical programs (SPSS) and even spreadsheets (Excel) can provide the frequencies for any of a study’s variables. b. Exhibit 17.1: Avery Fitness Center: Gender presents a frequency analysis for the gender identity of the individuals in the AFC study data set.  SPSS Menu Sequence: Analyze > Descriptive Statistics > Frequencies c. Exhibit 17.2: Avery Fitness Center: Level of Education presents frequency results for the highest level of education reported by the AFC members. d. Researchers almost always work with ―valid‖ percentages, which are simply percentages after taking out cases with missing data on the variable being analyzed. e. Other uses for frequencies:  Outlier – An observation so different in magnitude from the rest of the observations that the analyst chooses to treat it as a special case.  Histogram – A form of column chart on which the values of the variable are placed along the x-axis and the absolute or relative frequency of the values is shown on the y-axis.  Exhibit 17.3: Avery Fitness Center: Histogram of Respondent Age (SPSS Output) f. SPSS frequency analysis output is also presented in Exhibit 17.4: Avery Fitness Center: Age (SPSS Output).  With ordinal-, interval-, or ratio-level measures, it is often useful to identify the median, which is the value at the 50th percentile. g. Exhibit 17.5: Avery Fitness Center: Services Used Within the Past 30 Days 17-1b. Confidence Intervals for Proportions a. Confidence interval – A projection of the range within which a population parameter will lie at a given level of confidence, based on a statistic obtained from a probabilistic sample.





where z = z score associated with the desired level of confidence; p = the proportion obtained from the sample; and n = the number of valid cases overall on which the proportion was based.

To calculate the confidence interval:

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b. Margin of sampling error for the proportion of AFC members with at least a 4-year college degree:  (0.60 – 0.06 ≤ π ≤ 0.60 + 0.06), or (0.54, 0.66)  Result: We can be 95% confident that the actual percentage of AFC members with at least a 4-year college degree in this population (π) lies between 54% and 66%. c. Discussion Activity 1: 5–10 minutes total. (PPT Slides 12–13)  What cautions are important to keep in mind when using confidence intervals?  Debrief: The confidence interval only takes sampling error into account. It does not account for other common types of error (e.g., response error, nonresponse error).

The goal is to reduce total error, not just one type of error.

17-2.

Univariate Continuous Measures: Descriptive Statistics (17-2, PPT Slides 14–21) a. Continuous measures – A commonly used expression for interval and ratio measures.

17-2a. Descriptive Statistics a. Descriptive statistics – Statistics that describe the distribution of responses on a variable; the most commonly used descriptive statistics are the mean and standard deviation.  Sample mean – The arithmetic average value of the responses on a variable.

o



Although mean values can be calculated for any variable in a data set, they are only meaningful for continuous (i.e., interval, ratio) measures. o Be careful about just how precise a mean value can be. o It is better to report the median value when there are outliers in a distribution because it more accurately represents the vast majority of cases.

Sample standard deviation – A measure of the variation of responses on a variable; the standard deviation is the square root of the calculated variance on a variable.

o

In almost all cases, it’s important to report standard deviations along with mean values.

17-2b. Converting Continuous Measures to Categorical Measures a. Sometimes, it is useful to convert continuous measures to categorical measures (refer to Exhibit 17.3). b. Median Split Technique  Median split – A technique for converting a continuous measure into a categorical measure with two approximately equal-sized groups; the groups are formed by ―splitting‖ the continuous measure at its median value.

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

Cumulative percentage breakdown – A technique for converting a continuous measure into a categorical measure; the categories are formed based on the cumulative percentages obtained in a frequency analysis. c. Two-Box Technique  Two-box technique – A technique for converting an interval-level rating scale into a categorical measure, usually used for presentation purposes; the percentage of respondents choosing one of the top two positions on a rating scale is reported.  Exhibit 17.6: Avery Fitness Center: Reasons for Participation ―How Important to You Personally Is Each of the Following Reasons for Participating in AFC Programs?‖ Number (Percentage) of Respondents Selecting Each Response Category d. Converting from continuous to categorical measures results in the loss of information about a variable.  A simple solution for many univariate analyses is to provide both types of results (refer to Exhibit 17.7: Avery Fitness Center: Two-Box Results, With Descriptive Statistics).

17-2c. Confidence Intervals for Means The sample mean ( ) is an important piece of information about a variable.

Because managers care more about populations than samples, projections about where the population mean (μ) is likely to fall are needed. c. To establish the confidence interval, we must estimate the degree of sampling error for the sample mean. a. b.

d. where z = z score associated with the desired level of confidence; s = the sample standard deviation; and n = the total number of cases used to calculate the mean. e. To calculate the confidence interval:

f.

Discussion Activity 2: 5–10 minutes total. (PPT Slides 20–21)  Question: How many times per month do AFC members visit the center? Compute the 95% confidence interval based on the mean number of visits (10.0, with a standard deviation of 7.3) to the Center reported by 198 sample respondents.  Debrief:

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Therefore, we would be 95% confident that the mean number of trips per month in the AFC population lies between 9 and 11, inclusive.

17-3.

Hypothesis Testing (17-3, PPT Slides 22–26) a. The issue: How can we tell if a particular result in the sample represents the true situation in the population or simply occurred by chance? b. Hypothesis – An unproven proposition about some phenomenon of interest.  Through hypothesis testing, we can establish standards about whether to accept sample results as valid for the overall population.

17-3a. Null and Alternative Hypotheses a. Null hypothesis – The hypothesis that a proposed result is not true for the population (H0). b. Alternative hypothesis – The hypothesis that a proposed result is true for the population (Ha). c. Frame the null hypothesis so that its rejection leads to the tentative acceptance of the alternative hypothesis.

17-3b. Hypothesis Testing in Practice a. Significance level (α) – The acceptable level of error selected by the researcher, usually set at 0.05.  The level of error refers to the probability of rejecting the null hypothesis when it is actually true for the population. b. p-value – The probability of obtaining a given result if in fact the null hypothesis were true in the population.  A result is regarded as statistically significant if the p-value is less than the chosen significance level of the test.

17-3c. Issues in Interpreting Statistical Significance a. Viewing p-values as if they represent the probability that the null hypothesis is true.  For example, p = .05 implies that there is only a .05 probability that the results were caused by chance. b. Viewing the  or p levels as if they are somehow related to the probability that the research hypothesis is true.  For example, a p-value such as p > .001 is ―highly significant‖ and therefore more valid than p < .05. c. Be careful not to misinterpret what a test of significance reveals.

d. Group Activity: 10–20 minutes total. (PPT Slide 26)

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

 17-4.

Form groups of three to five participants. Within the group, come up with two or three hypotheses you might like to test. Then, create the null and alternative hypotheses for your scenarios.

As time allows, share one or more of your examples with the class.

Note: This activity can also be an individual assignment.

Testing Hypotheses About Individual Variables (17-4, PPT Slides 27–30) 17-4a. Testing Statistical Significance with Categorical Variables a. Exhibit 17.8: AFC Customer Education Level Versus Education Level in Trade Area b. Chi-square goodness-of-fit test – A statistical test to determine whether some observed pattern of frequencies corresponds to an expected pattern.

17-4b. Testing Statistical Significance with Continuous Variables a. A one-sample t-test can be used to compare a sample mean against an external standard. b. The analysis is easy to implement in a standard statistical software analysis package. c. Knowledge Check 17.1: < 5 minutes total. (PPT Slide 30)  When would you use a chi-square goodness-of-fit test? (a) When you want to determine the measure of central tendency for data (b) When you want to test the difference between your expected data results and the data you collected from observation (c) When your confidence interval fails to satisfy the null hypothesis (d) When you have been unable to calculate the estimated sampling error  Answer: b—The chi-square goodness-of-fit test tests whether an observed sample frequency for a categorical variable corresponds to an expected pattern.

Self-Assessment (PPT Slide 31) 

Think about what you understood about opinion polls and sampling variables before reading this chapter. How has your understanding of frequency analysis and descriptive statistics changed?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

137.

What types of variables might be analyzed with frequency analysis?

A frequency analysis is a univariate technique that involves counting the number of responses that fall into various response categories. This is a simple analytic tool, yet it is incredibly important and commonly used to report the overall results of marketing research studies. You can produce frequencies for any of the variables in a study. Any packaged statistical program, such as SPSS, and even spreadsheet programs, such as Excel, can perform frequency analysis. As a general rule, you should run frequencies for all the variables in a study before you do anything else.

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138.

What is an outlier?

Outliers are valid observations that are so different from the rest of the observations that they ought to be treated as special cases.

139.

How many decimal places should normally be reported with percentages?

Percentages should be rounded to whole numbers because whole numbers are easier to read and because decimals might make the results look more accurate or ―scientific‖ than they really are, especially in a small sample. In some cases, it might be reasonable to report percentages to one decimal place (rarely two decimal places), but the general rule is to use whole numbers.

140.

What is a histogram? What information does it provide?

A histogram is a form of column chart on which the values of the variable are placed along the xaxis and the absolute or relative frequency of the values is shown on the y-axis.

141.

Why do analysts often construct confidence intervals? What is their purpose?

A confidence interval is the range within which the true proportion or mean for the population will fall, with a given level of confidence (usually 95%). For means, the confidence interval is equal to the sample mean (x) plus or minus the estimated sampling error. For proportions, the confidence interval equals the sample proportion (p) plus or minus the estimated sampling error.

142.

What type of error do confidence intervals take into account?

The confidence interval only takes sampling error into account. To the extent that other types of error have entered the study—and you can be sure that they have to some degree—the confidence interval is less likely to have ―captured‖ the population parameter within its bounds.

Unfortunately, there is no quantitative way of adjusting the confidence interval to reflect these types of errors.

143.

What are the most commonly used descriptive statistics?

The most commonly used descriptive statistics for continuous measures (interval- or ratio-level measures) are the mean, or arithmetic average, and the standard deviation. The mean is a measure of central tendency; the standard deviation provides a convenient measure of the dispersion of responses.

144.

Why must the distribution of responses be taken into account when deciding which type of ―average‖ to present?

With ordinal-, interval-, or ratio-level measures, it is often useful to identify the median point as a measure of ―average‖ for the distribution. The distribution of responses on a variable include measures of central tendency (mean, median, and mode), measures of the spread, or variation, in the distribution (range, variance, standard deviation), and various measures of the shape of the distribution (e.g., skewness, kurtosis).

145.

Why might an analyst choose to convert a continuous measure to a categorical measure?

Occasionally, a client will have a predetermined structure for categories. In other cases, the data themselves determine category division. If you want to convert a continuous measure into two approximately equal-sized groups, you’ll probably create the categories based on a median split.

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146.

What is a two-box technique? A median split? A cumulative percentage breakdown?

The two-box technique converts an interval-level rating scale into a categorical measure, usually for presentation purposes. The percentage of respondents choosing one of the top two positions on a rating scale is reported. The median split is a technique for converting a continuous measure into a categorical measure with two approximately equal-sized groups. The groups are formed by ―splitting‖ the continuous measure at its median value. The cumulative percentage breakdown is a technique for converting a continuous measure into a categorical measure. The categories are formed based on the cumulative percentages obtained in a frequency analysis.

147.

What is the difference between the null and alternative hypotheses?

The null hypothesis means the result isn’t true for the population and can be rejected in favor of the alternative hypothesis (i.e., the particular result holds for the population).

148.

Why can a hypothesis be rejected but never fully accepted?

Marketing research studies can’t ―prove‖ results. At best, they can indicate which of two mutually exclusive hypotheses is more likely to be true, based on the results of the study.

149.

What is a p-value? Do researchers typically want to obtain higher or lower pvalues?

The p-value represents the likelihood of obtaining the particular value of a test statistic if the null hypothesis were true. Once you have the p-value, it is a simple matter to compare it with the significance level of the test to determine whether the result can be considered ―statistically significant‖ (i.e., the sample results can be projected to the population). If the p-value is less than the significance level established, you can reject the null hypothesis and tentatively accept the alternative hypothesis. A statistically significant result simply means that the probability that you could have obtained a particular result if the null hypothesis were really true (the p-value) is less than the level of error that you’re willing to tolerate (the significance level). Researchers virtually always want to obtain low p-values, and, as long as the p-value is lower than the significance level (typically α = 0.05), the results can be applied to the population. If the p-value isn’t lower than the established significance level, then there is just too much risk that the results were a fluke produced by chance.

150.

What is the basic use of a chi-square goodness-of-fit test?

The chi-square goodness-of-fit test is a statistical test used to determine whether some observed pattern of frequencies corresponds to an expected pattern.

151.

How would you compare a sample mean against a standard?

In almost all cases, it is important to report standard deviation along with mean value. The sample mean is the arithmetic average value of the responses on a variable. The sample standard deviation is a measure of the variation of responses on a variable. The standard deviation is the square root of the calculated variance on a variable. If everyone were basically the same on some characteristic or felt the same way about some topic or object, then the standard deviation would be small. If, on the other hand, responses were different—some high, some low—then the

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standard deviation for the variable would be larger. If you don’t take the variation of responses into account, you’ll sometimes end up making bad decisions. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

73. There are several approaches to take with this chapter, depending on an instructor’s interests and the students' statistical backgrounds. For some classes, the techniques will already be familiar, and the instructor will simply want to emphasize the marketing research context in which these statistical tools can be applied. In this case, it would be helpful to present various marketing research scenarios to the students and ask them to indicate which statistical tests are appropriate, why, and how they would proceed to develop the answers. In other situations, the instructor may need to start by reviewing the statistical material in detail and assigning problems to be worked, from either the end of the chapter or other sources. Then, the solutions should be discussed in class.

74. We occasionally remind students that data analysis is usually straightforward if two key issues are kept in mind. First, is the variable to be analyzed by itself (univariate analysis) or in relationship to other variables (multivariate analysis)? Second, what level of measurement was used in assessing the variable?

75. Instructors may find it useful to demonstrate some of the analyses presented in the chapter during class sessions using SPSS or another statistical software package. For example, several of the text examples revolve around the Avery Fitness Center data set (see Exhibits starting with 17.1 in the text). We have included this data in a file (visit www.login.cengage.com) in the supplemental materials so that our analyses can be reproduced or other analyses conducted.

76. Given the usefulness and prevalence of simple univariate analyses in industry settings, instructors will likely want to spend some time during class reinforcing the importance of these simple analyses (e.g., frequency counts and percentages, means, standard deviations). Particular importance should be placed on the need to effectively communicate the results. One approach is to focus on the differences and similarities between mean scores and two-box percentages in terms of statistical power and ability to communicate results.

77. In our experience, it seems that some undergraduate students have difficulty with the concept of a confidence interval. While they have no doubt been exposed to the concept in earlier statistics classes, the marketing research course affords them the opportunity to understand how confidence intervals may be applied in practice. Students are likely to be familiar with the notion of sampling error from public opinion polls reported in the media, almost always reported in terms of percentages. When most students grasp the concept as applied to proportions, it should make the transition to confidence intervals for means a simpler process. We think it is important to stress to the students, however, that confidence intervals are taking into account only sampling error and that other types of error may actually be more dangerous.

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78. Some instructors desire to present the typical hypothesis testing procedure, while other instructors may wish to simply introduce the nature of hypothesis testing and move on to specific tests, allowing students to focus on the p-values reported by the statistical software. In either case, we believe that this information can be taught much more effectively if the instructor emphasizes two key points. First, for this information to make any sense to them, students must understand that we are working with sample data and that our goal is to decide whether the results we obtain in the sample would be true for the population. The second point to be emphasized is the proper interpretation of the p-value, including the idea that statistical significance and managerial significance are not the same thing. It is easy for first-time researchers (and even experienced researchers, for that matter) to get caught up in their analyses and lose sight of what the p-value really represents. [return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 18: Analysis and Interpretation: Multiple Variables Simultaneously

TABLE OF CONTENTS Purpose and Perspective of the Chapter ................................................................................................. 74 Chapter Objectives ................................................................................................................................... 74 Complete List of Chapter Activities and Assessments........................................................................... 74 Key Terms.................................................................................................................................................. 74 What's New in This Chapter .................................................................................................................... 75 Chapter Outline ........................................................................................................................................ 75 Review Questions ...................................................................................................................................... 78 Additional Insights and Activities ........................................................................................................... 80

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to present some commonly used multivariate analysis techniques. Although it barely scratches the surface of the full range of techniques available and used in industry, it provides enough detail to get started on most common analyses. As with the univariate analyses presented in the previous chapter, the computer does most of the work—including providing tests of statistical significance—but the key is to know when to use each of the approaches and how to interpret the results.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 18-1 Explain the purpose and importance of cross tabulation.

18-2 Describe a technique for comparing groups on a continuous dependent variable.

18-3 Explain the difference between an independent sample t-test for means and a paired sample ttest for means.

18-4 Discuss the Pearson product-moment correlation coefficient.

18-5 Discuss regression analysis as a technique for examining the influence of one or more predictor variables on an outcome variable.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 18-1 18-3 18-4 18-5 18-1–18-5

PPT slide PPT slides 9–10 PPT slides 19–20 PPT slides 24–25 PPT slides 32–33 PPT slide 34

Activity/Assessment Discussion Activity 1 Discussion Activity 2 Discussion Activity 3 Discussion Activity 4 Self-Assessment

Duration 5–10 min 5–10 min 5–10 min 5–10 min 10–20 min

[return to top]

KEY TERMS Coefficient of multiple determination (R2) A measure representing the relative proportion of the total variation in the dependent variable that can be explained or accounted for by the fitted regression equation. When there is only one predictor variable, this value is referred to as the coefficient of determination.

Cramer’s V A statistic used to measure the strength of the relationship between categorical variables.

Cross tabulation A multivariate technique used for studying the relationship between two or more categorical variables. The technique considers the joint distribution of sample elements across variables.

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Independent samples t-test for means A technique commonly used to determine whether two groups differ on some characteristic assessed on a continuous measure.

Paired sample t-test A technique for comparing two means when scores for both variables are provided by the same sample.

Pearson chi-square (X2) test of independence A commonly used statistic for testing the null hypothesis that categorical variables are independent of one another.

Pearson product-moment correlation coefficient A statistic that indicates the degree of linear association between two continuous variables. The correlation coefficient can range from –1 to +1.

Regression analysis A statistical technique used to derive an equation representing the influence of a single (simple regression) or multiple (multiple regression) independent variables on a continuous dependent, or outcome, variable. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:  

Consolidated chapter objectives for greater clarity and alignment with the section materials.

Incorporated feedback from a faculty adopter to aid student interpretation of data analysis.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

18-1.

Cross Tabulation (18-1, PPT Slides 3–10) lll. Exhibit 18.1: Univariate versus Multivariate Analysis: Enhanced Meaning  Multivariate analyses allow researchers a closer look at their data than is possible with univariate analyses. mmm.

Cross tabulation – A multivariate technique used for studying the relationship between two or more categorical variables.  This technique considers the joint distribution of sample elements across variables.  Exhibit 18.2: Avery Fitness Center: Therapy Pool Usage by Doctor’s Recommendation (SPSS Output)  Cross-tab analysis allows examination of joint distribution of variables. o AFC question: Does being referred by a doctor to AFC lead to greater usage of the therapy pool? o Two categorical variables: - Doctor referral (yes, no) - Pool usage (yes, no)

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o

In this situation, doctor referral would be considered the independent, or causal, variable, and pool usage would be considered the dependent, or outcome, variable.  The usefulness of percentages comes into play with row percentages and column percentages. o Always calculate percentages in the direction of the causal variable. o Hint: Which variable might have caused the other to occur? nnn.

To test whether two variables are independent of one another, use the Pearson chi-square (χ2) test of independence – A commonly used statistic for testing the null hypothesis that categorical variables are independent of one another. ooo.

To measure the strength of the relationship between the two categorical variables, use Cramer’s V – a statistic used to measure the strength of the relationship between categorical variables.

ppp.  

18-2.

Discussion Activity 1: 5–10 minutes total. (PPT Slides 9–10) In performing applied data analysis, what are the two fundamental questions a researcher needs to consider?

Debrief: 1. How many variables do we need to consider?

2. What level of measurement was used to assess each variable?

The relevance of the choice of analytic technique cannot be overstated. Once an appropriate technique is selected, it is usually a relatively easy process of applying formulas or interpreting computer output.

Independent Samples T-Test for Means (18-2, PPT Slides 11–14) a. Independent samples t-test for means – A technique commonly used to determine whether two groups differ on some characteristic assessed on a continuous measure.  Examples: o Satisfaction ratings, men vs. women o Age in years, customers vs. noncustomers  AFC question: Does utilizing the exercise circuit (categorical independent variable) lead to an increased number of visits to the center (continuous dependent variable)? b. Exhibit 18.3: Avery Fitness Center: Number of Visits (Past 30 Days) by Exercise Circuit Usage presents the output from an SPSS analysis (SPSS Menu Sequence: Analyze > Compare Means > Independent Samples T-Test).

18-3.

Paired Sample T-Test for Means (18-3, PPT Slides 15–20) a. Paired sample t-test – A technique for comparing two means when scores for both variables are provided by the same sample.  Examples: o Before and after measures o Applying same measure to different objects  AFC question: Do the mean attribute importance levels, provided by the same respondents, differ from one another?

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b. Exhibit 18.4: Avery Fitness Center: Importance of Various Reasons for Participating c. Exhibit 18.5: Avery Fitness Center: Paired Sample T-Tests (SPSS Output) presents SPSS output for three different paired sample t-tests, one each for comparing the importance of social aspects with the importance of the other three possible reasons for participating in AFC programs (SPSS Menu Sequence: Analyze > Compare Means > Paired Sample T-Test). d. Discussion Activity 2: 5–10 minutes total. (PPT Slides 19–20)  What is the difference between independent samples and paired sample t-tests?  Debrief: Independent sample t-tests compare values on the same variable but for two different groups of cases.

Paired sample t-tests compare values on two different variables but for the same group of cases.

18-4.

Pearson Product-Moment Correlation Coefficient (18-4, PPT Slides 21–25) a. Pearson product-moment correlation coefficient – A statistic that indicates the degree of linear association between two continuous variables.  The correlation coefficient can range from -1 to +1.  It is a fundamental building block of data analysis.  AFC question: Is there a correlation between age (continuous independent variable) and fees paid (continuous dependent variable)?  Exhibit 18.6: Avery Fitness Center: Correlation Between Age and Revenues (SPSS Output) presents the output for the correlation between age and 1-year revenues for AFC members in the analysis data set. b. Discussion Activity 3: 5–10 minutes total. (PPT Slides 24–25)  Consider this statement: Ice cream purchases and murder rates are positively correlated.

How would you evaluate this statement? What do researchers need to keep in mind about correlation?  Debrief: Thankfully, correlation is not the same thing as causation.

Nothing in correlation analysis, or any other mathematical procedure, can be used to establish causality. All these procedures can do is measure the nature and degree of association between variables. Statements of causality must come from underlying knowledge and theories about the phenomena under investigation. They do not come from the mathematics.

18-5.

Regression Analysis (18-5, PPT Slides 26–33) a. Regression analysis – A statistical technique used to derive an equation representing the influence of a single (simple regression) or multiple (multiple regression) independent variables on a continuous dependent, or outcome, variable.  AFC question: What are some of the factors that drive revenues at AFC? o Regress revenues on (1) member age and the importance of (2) general health and fitness, (3) social aspects, (4) physical enjoyment, and (5) specific medical concerns as reasons for visiting AFC. b. Shown in Exhibit 18.7: Avery Fitness Center: Regression of Revenues on Several Predictors (SPSS Output), the analysis produces regression coefficients for each of the predictor variables.

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

These coefficients represent the average change in the outcome variable per unit change in the associated predictor variable, holding all other predictor variables constant. c. Coefficient of multiple determination (R2) – A measure representing the relative proportion of the total variation in the dependent variable that can be explained or accounted for by the fitted regression equation.  When there is only one predictor variable, this value is referred to as the coefficient of determination. d. Some key questions to keep in mind when interpreting multiple regression results:  Does the set of predictors explain a statistically significant portion of variation in the dependent variable? o Look at the ANOVA table results.  How much of the variation in the dependent variable does our set of predictors explain? o Look at the coefficient of multiple determination.  Which of the individual predictors explain variation in the dependent variable, and what is the direction of the relationship (positive or negative)? o Look at the t-values and p-values of the individual predictors. e. Discussion Activity 4: 5–10 minutes total. (PPT Slides 32–33)  What are the main purposes of regression analysis?  Debrief: The value of regression analysis includes the following: - to construct an equation that will allow us to predict the value of the criterion variable for certain assumed values of the predictor variables.

- to determine those predictor variables with a significant impact on the criterion variable.

- to measure the amount of variation in the criterion variable that can be explained by or is associated with the predictor variables.

Self-Assessment (PPT Slide 34) 

If asked, how would you explain to someone about the difference between univariate and multivariate analyses? What potential problems of applied data analysis do you need to keep in mind?

[return to top]

REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

152.

Why would a researcher consider conducting multivariate analyses? Why not just conduct overall univariate analyses?

Multivariate analysis often provides a much deeper understanding of the data. Univariate analyses produce broad, overall results. Multivariate analyses look for differences across groups or associations among variables.

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153.

What technique is typically used to investigate relationships between two categorical variables?

Cross tabulation is a commonly used multivariate technique. Its purpose is to study the relationships among and between categorical variables.

154.

How do you determine which set of percentages (i.e., row versus column percentages) to use on a cross-tab analysis?

Cross tabulation is a multivariate technique used for studying the relationship between two or more categorical variables. The technique considers the joint distribution of sample elements across variables. The ―row percentage‖ is calculated using the row total as the denominator. The next percentage in each cell is the ―column percentage‖ and is calculated using the column total as the denominator. To answer this question, think about which of the variables being studied is likely to be the independent variable (cause) and which is likely to be the dependent variable (effect). Percentages are always calculated in the direction of the causal variable. That is, the marginal totals for the causal variable are always used as the denominator when calculating percentages in cross tabulations.

155.

What would be the appropriate test to determine if men differed from women in their satisfaction with a meal served in a fast-food restaurant?

You will need to use multivariate analyses to get the information you need. Here’s an example: In an awareness test for an ice-cream shop, 58% of survey respondents could name the shop in a recall task. Closer analysis revealed several insights, however. For one thing, gender seemed to be related to the awareness level: Only 45% of male respondents could name the shop, compared with 71% of female respondents. Age also seemed to be related to awareness: 69% of respondents 20 years old and younger could name the shop, but only 54% of those who were 21 to 40 years old and 39% of those over 40 years old could do so. If you stopped with the univariate analysis result (i.e., 58% correct in recall task), you would miss important managerial insights about gender and age.

156.

How do you test for differences between scores on two continuous measures when each respondent provides both measures? Explain.

What happens when you need to compare two means when both measures are provided by the same people? In that case, you would use the paired sample t-test for means.

157.

What does the Pearson product-moment correlation coefficient measure? When is it appropriate to use?

The Pearson product-moment correlation coefficient assesses the degree of linear association between two continuous variables. Essentially, the Pearson product-moment correlation coefficient assesses the degree to which two continuous variables change consistently across cases.

158.

If two continuous measures are positively correlated with one another, does that mean that one of them caused the other? Why or why not?

Here’s an example: If we did the math, we would discover that ice cream purchases are positively correlated with murder rates. What? Does this mean that purchasing ice cream can cause someone to commit a murder? Of course not. What we know is that people purchase more ice cream when the weather is warmer and the days are longer. Also, murder rates tend to be higher when more people are outside. So, what do these two activities have in common? If you said they both happen during the summer, you are correct. Ice cream sales and murder rates are higher during

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summer months, so the relationship between the two is due to a third variable (i.e., time of year, which itself reflects temperature). Yes, ice cream purchases and murder rates are correlated, but this in no way provides evidence that one causes the other.

159.

What is the proper procedure for testing the influences of two different independent variables on a single continuous dependent variable simultaneously?

Regression analysis is a statistical technique used to derive an equation representing the influence of a single (simple regression) or multiple (multiple regression) independent variables on a continuous dependent, or outcome, variable.

160.

What is the difference between regression analysis and correlation analysis?

Sometimes, you will be tempted to assume that one variable caused the other one when you obtain a statistically significant correlation coefficient between two variables. Just because two variables are correlated doesn’t mean that one necessarily caused the other, however. There is nothing in correlation analysis, or any other mathematical procedure, that can be used to establish causality. All these procedures can do is measure the nature and degree of association between variables. Statements of causality must come from underlying knowledge and theories about the phenomena under investigation. Statements of causality do not come from the mathematics.

Regression analysis is a statistical technique used to derive an equation representing the influence of a single (simple regression) or multiple (multiple regression) independent variables on a continuous dependent, or outcome, variable.

161.

What is the coefficient of multiple determination, and what does it measure?

The coefficient of multiple determination is a measure representing the relative proportion of the total variation in the dependent variable that can be explained or accounted for by the fitted regression equation. When there is only one predictor variable, this value is referred to as the coefficient of determination. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

79. The first thing instructors should decide is whether they are going to employ the examples in the text to develop the discussion or use other examples taken from their own research. Some prefer to use a single dataset so that all analyses revolve around the same general example project.

Others prefer to use multiple datasets and examples to liven up the presentation; that is the approach we have taken in the text. It is helpful if a single ongoing example is used for any technique in which multiple independent variables are included in the analysis (e.g., factorial design ANOVA, multiple regression) so that students can make comparisons about what is happening with the introduction of each successive variable.

80. There are two general approaches that may be taken to the presentation of this material, depending upon the degree of detail the instructor wants to provide in the classroom. The first approach is to try to cover all of the techniques included in the chapter, more or less, using examples from the text or other sources. We expect that it would take 2–3 class sessions to present the multivariate techniques we have included in the text.

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A second approach to presenting the material is to select the most commonly used techniques for classroom instruction and note in passing the other techniques that are available when needed.

81. Because most multivariate analyses will be performed by computer, the discussion can also profitably be framed by having the students interpret the output provided by running one of the more standard computer regression programs on campus rather than simply following the development in the text.

82. It is obvious that we excluded many types of analysis from the text, instead choosing to include only common and/or straightforward techniques that we felt were appropriate for the target audience. The instructor may want to go further to include one or more additional techniques (e.g., conjoint analysis, factor analysis) or variations on the techniques we have included (e.g., dummy variable regression, t-tests with known population variances or with populations for which variances cannot be assumed to be equal). Similarly, we have chosen not to belabor the chapter material with discussions of assumptions that must be met for the technique and/or the associated inferential test statistic to be strictly appropriate. Most of the techniques we discuss are reasonably robust toward violations of assumptions, and we did not believe that including this material would help us achieve the goal of communicating the basics of marketing research to undergraduate students.

83. As always, we encourage instructors to make the course their own. Much more detailed information on various multivariate data analysis techniques is available in Churchill, Gilbert A., Jr., and Dawn Iacobucci, Marketing Research: Methodological Foundations, or in one of the many statistics texts that are available.

84. Many students seem to experience little difficulty in following the development of cross tabulation analysis in class but experience great difficulty when forced ―to go it alone.‖ Thus, it is recommended that the students be given an assignment to explore the relationship among the variables. If the instructor has one, an alternative database can be used.

85. If preferred, the instructor can next detail what can happen when a third variable is added to an initial cross-tab analysis. The instructor might ask the class ―Why stop with three variables?‖ with the goal of making several important points, including: 

The analyst never knows for sure when to stop, in that he or she is always in a position of INFERRING a relationship does or does not exist.



Because statements of relationships are always inferences, the distinguishing feature of scientific method (i.e., the accumulation of studies which support a given relationship) makes more sense.



The need to plan the analysis in advance so that the data allow the generation of the ―proper‖ tabulations and also afford sufficient observations in each cell so as to lend some confidence to the obtained relationships.

86. T-tests are quite common in research and can be obtained easily via computer analysis or relatively easily by hand calculation. As a result, we recommend that instructors spend at least a little time on them in class. Some students may already be familiar with the different types of ttests, but most will probably need a refresher. We think it is even more important that the instructor emphasize the marketing research context in which the techniques are appropriate via examples.

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87. The text includes discussions of three primary correlational techniques, (a) the Pearson productmoment correlation coefficient, (b) simple regression analysis, and (c) multiple regression analysis. As with the other techniques, we believe that there is greater value for undergraduate students through discussion of when they should be applied and how to interpret the results rather than through learning the mathematical calculations.

88. The instructor should point out the similarities between correlation and simple regression. For example, the instructor might note that the standardized beta in the simple regression of one variable on the other is equivalent to the correlation coefficient between the variables. Regression analysis, though, attempts to predict, or explain, the variance in one variable with the variance in the other, often designating one the independent variable and one the dependent variable.

89. Once the key interpretive quantities of a multiple regression analysis have been highlighted, the discussion can profitably be directed at some of the problems of multiple regression analysis that do not seem to be appreciated fully by beginning research students. Some of the points that can be made are: 

The statement of relationship is still an inference, and the nature of the inference might change drastically with the introduction of the "right" additional variable. The situation here parallels that for cross tabulation analysis.



The fact that we have assumed a linear relationship between the variables.



The problems associated with two-way causation.

[return to top]

Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 19: The Oral Research Presentation

TABLE OF CONTENTS Purpose and Perspective of the Chapter ............................................................................................... 169 Chapter Objectives ................................................................................................................................. 169 Complete List of Chapter Activities and Assessments......................................................................... 169 Key Terms................................................................................................................................................ 169 What's New in This Chapter .................................................................................................................. 170 Chapter Outline ...................................................................................................................................... 170 Review Questions .................................................................................................................................... 173 Additional Insights and Activities ......................................................................................................... 174

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to provide some ―best practice‖ suggestions for preparing a strong oral presentation. The chapter also describes quite a few different types of charts that you might find useful for presenting your results.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 19-1

Identify key considerations in preparing an oral research presentation.

19-2

Discuss two fundamental rules for delivering good oral presentations.

19-3

List some of the different kinds of charts that can be used in presenting study results.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 19-1 19-2 19-3 19-1–19-3

PPT slide PPT slide 9 PPT slides 12–13 PPT slides 23–24 PPT slide 25

Activity/Assessment Polling Activity Discussion Activity Group Activity Self-Assessment

Duration 5–10 min 5–10 min 20–30 min 10–20 min

[return to top]

KEY TERMS Bar chart

A chart in which the relative lengths of the bars show relative amounts of variables or objects.

The process of using graphic illustrations to understand and communicate important relationships in large data sets.

Data visualization

A two-dimensional chart with the x-axis representing one variable (typically time) and the y-axis representing another variable.

Line chart

Bar charts in which pictures represent amounts—for example, piles of dollars for income, pictures of cars for automobile production, people in a row for population.

Pictograms

A circle representing a total quantity that is divided into sectors, with each sector showing the size of the segment in relation to that total.

Pie chart

A set of line charts in which quantities are aggregated or a total is disaggregated so that the distance between two lines represents the amount of some variable.

Stratum chart

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[return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

Consolidated chapter objectives for greater clarity and alignment with the section materials.

Minor editorial updates throughout the chapter were made to enhance comprehension.

Updated Exhibit 19.3.

[return to top]

CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

19-1.

Preparing the Oral Research Presentation (19-1, PPT Slides 3–9) qqq. 

The goal of an oral report is to communicate to the audience at hand.

A quality presentation can disguise poor research, but quality research cannot improve a poor presentation. rrr. Exhibit 19.1: OPEN UP! Exceptional Presentation Skills contains some excellent ideas about becoming an effective presenter.  OPEN UP! is an acronym representing the six characteristics shared by exceptional presenters: o The exceptional presenter is Organized, Passionate, Engaging, and Natural. o As an exceptional presenter, you must Understand your audience and Practice.  The secret is understanding how to incorporate them into your presentation style. sss. Two popular forms of structure for presentations:  To build a logical case, the order is to state the general purpose, the research problems, the evidence, and the conclusion.  To grab attention and make a point, the order is the general purpose, the research problems, the conclusion, and the evidence. ttt. Exhibit 19.2: Ten Tips for Preparing Effective Presentation Slides  Effective visual aids can range from definitions and bulleted lists to maps, diagrams, tables, and figures.  Various presentation software are available, including PowerPoint, Keynote, and Prezi. uuu.

Polling Activity: 5–10 minutes total. (PPT Slide 9)  Among the ten tips for preparing effective slides as part of a presentation (presented in Exhibit 19.2), which do you believe is most important to keep in mind? (a) Keep them simple. (b) Make the slides easy to read.

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

19-2.

(c) Be careful with the use of color and slide backgrounds. (d) Build complex thoughts sequentially.

Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response. In this particular activity, their answer can be related to how each tip in some way relates back to the notion of keeping it simple. Further discussion can touch on how the distinction between simple and thorough sometimes is complicated.

Delivering the Oral Report (19-2, PPT Slides 10–13) a. Two fundamental rules for delivering good oral presentations:  Know your stuff. o Don’t forget to practice.  Know your audience. o Keep in mind the purpose of the research and the technical sophistication of the audience. o Allow time for interaction (Q&A). o Make certain that the technology is working correctly. b. Discussion Activity: 5–10 minutes total. (PPT Slides 12–13)  What benefits derive from oral presentations? What drawbacks, if any, might they have?  Debrief: A benefit of an oral presentation is that it allows interaction. In fact, the question-and-answer period may be the most important part of your presentation because it allows you to clear up any confusion that may have arisen. You are also able to emphasize points that deserve special attention as well as to discern issues of particular concern to the audience.

Any drawbacks are most likely to come from a lack of preparation. Therefore, if you know your stuff and know your audience, you should be able to handle anything your audience can throw at you.

19-3.

Graphic Presentation of Results (19-3, PPT Slides 14–24) a. A picture can be worth a thousand words when it is appropriate to the presentation and well designed. b. To be effective, an illustration must give your audience an accurate understanding of comparisons or relationships. c. Graphics should be used only to illustrate key findings and when they allow insights into data that might not be seen otherwise.

19-3a. Pie Chart a. Pie chart – A circle representing a total quantity that is divided into sectors, with each sector showing the size of the segment in relation to that total.  When there are more than five levels of the variable, the pie chart can become confusing. b. Exhibit 19.4: Pie Chart: Personal Consumption Expenditures by Major Category (Year 12) 19-3b. Line Chart

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a. Line chart – A two-dimensional chart with the x-axis representing one variable (typically time) and the y-axis representing another variable.  Different colors of lines allow for comparisons of several variables over time. b. Exhibit 19.5: Line Chart: Personal Consumption Expenditures by Major Category (12 Years) 19-3c. Stratum Chart a. Stratum chart – A set of line charts in which quantities are aggregated or a total is disaggregated so that the distance between two lines represents the amount of some variable.  It is like a dynamic pie chart; it would require multiple pie charts to capture the same information. b. Exhibit 19.6: Stratum Chart: Personal Consumption Expenditures by Major Category (12 Years) 19-3d. Bar Chart a. Bar chart – A chart in which the relative lengths of the bars show relative amounts of variables or objects.  Can be drawn vertically or horizontally. b. Exhibit 19.7: Bar Chart: Personal Consumption Expenditures by Major Category (Year 12) c. Bar chart variations:  Variations include grouped bar charts and stacked bar charts.  Pictograms – Bar charts in which pictures represent amounts—for example, piles of dollars for income, pictures of cars for automobile production, people in a row for population.  Exhibit 19.8: Two Versions of a Pictogram 19-3e. Other Charts a. Data visualization – The process of using graphic illustrations to understand and communicate important relationships in large data sets.  When presenting data about more than one variable, be wary of them becoming too complicated and losing their effectiveness.  Regardless of the type of chart, the key point is the degree to which the chart helps communicate the important results. b. Group Activity: 20–30 minutes total. (PPT Slides 23–24)  Form groups of three to five participants. Using either a marketing research project you are working on for class or other data found in an online search of a subject of your choice, prepare two or more of the charts discussed in this section. As time allows, share your charts with the larger class.  Debrief: - Why did you choose these particular charts to display?

- What difficulties did you have in making the choice and/or in finding data to present?

- In what ways do your charts communicate the information from the data more effectively than words?

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

Note: This activity can also be an individual assignment.

Appendix 19A: Oral Research a. The appendix includes a set of slides prepared to present some of the results of the Avery Fitness Center project.  It includes an outline slide and introduction slide, followed by slides briefly describing the study’s methods, results, and limitations.  The final slides in the presentation provide the conclusions derived from the research, based on the question that prompted the study.

Self-Assessment (PPT Slide 25) 

How comfortable are you with making oral presentations? Does any discomfort come from lack of experience? How might you increase your effectiveness and decrease your discomfort when making presentations?

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REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

162.

What are the key considerations in preparing an oral report?

As noted, preparing a successful oral report requires advance knowledge of the audience. What is their technical level of sophistication? Their involvement in the project? Their interest? You may want to present more detailed reports to those who are deeply involved in the project or who have a high level of technical sophistication. In general, though, it is better to have too little technical detail than too much. Executives want to hear and see what the information means to them as managers of marketing activities. What do the data suggest with respect to marketing actions?

They can ask for the necessary clarification with respect to the technical details if they want it.

163.

What are the two rules for presenting an oral report?

When presenting oral reports, the two fundamental rules are (1) know your stuff, and (2) know your audience.

164.

What is a pie chart? For what kinds of information is it particularly effective?

A pie chart is a circle representing a total quantity that is divided into sectors, with each sector showing the size of the segment in relation to the total. Because the sections are presented as part of a whole, pie charts are effective for depicting relative size, for one variable, at one point in time.

165.

What is a line chart? For what kinds of information is it generally used?

The line chart is a two-dimensional chart that is particularly useful in depicting relationships over time. The line chart is probably used even more often than the pie chart. The x-axis normally represents time, and the y-axis represents values of the variable or variables. When more than one

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variable is presented, use different colors or types of lines (dots and dashes in various combinations) to represent the different variables. Also, be sure to identify the different variables, using a key, or legend.

166.

What is a stratum chart? For what kinds of information is it particularly appropriate?

The stratum chart is like a dynamic pie chart, in that it can be used to show relative emphasis by sector (e.g., quantity consumed by user class) at any one point in time. It goes further, however, to also show changes in relative emphasis over time. The stratum chart consists of a set of line charts whose quantities are grouped together (or a total that is broken into its components). It is also called a stacked line chart.

167.

What is a bar chart? For what kinds of problems is it effective?

The bar chart is a widely used chart that can take several different forms. Bar charts can be drawn either vertically or horizontally, but if you’re going to show change in a variable over time, standard procedure is to use a vertical chart and to track time along the horizontal axis.

168.

What is a pictogram?

A pictogram is a bar chart in which pictures represent amounts (e.g., piles of dollars for income, pictures of cars for automobile production, people in a row for population). Pictograms can be effective, but be careful, because they can easily mislead an audience. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

90. The presentation slides for Avery Fitness Center are provided in Appendix 19A. It could be helpful to continue this AFC theme all the way to the oral presentation materials.

91. One point that deserves special emphasis regarding verbal/oral reports is that only 1/3–1/2 of the allotted time for the presentation should be taken up by the formal report. This usually surprises students, who fear that no questions might ensue from the formal presentation and that there will then be substantial ―dead time.‖ It is helpful to point out that the audience will be keenly interested in the outcome of the research and will have invested a great deal of money in the process.

92. It is useful to overview some general do-and-don’t guidelines for presentation visuals, found in Exhibit 19.2.

93. An alternative way of discussing graphical presentation is to show misleading graphs and discuss why they are misleading and what can be done to improve the accuracy of the message they convey.

One can consult the daily press for examples. [return to top]

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Instructor Manual Brown, Basic Marketing Research: Customer Insights and Managerial Action, 9780357901847; Chapter 20: The Written Research Report

TABLE OF CONTENTS Purpose and Perspective of the Chapter ............................................................................................... 176 Chapter Objectives ................................................................................................................................. 176 Complete List of Chapter Activities and Assessments......................................................................... 176 Key Terms................................................................................................................................................ 176 What's New in This Chapter .................................................................................................................. 176 Chapter Outline ...................................................................................................................................... 177 Review Questions .................................................................................................................................... 179 Additional Insights and Activities ......................................................................................................... 181

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PURPOSE AND PERSPECTIVE OF THE CHAPTER The purpose of this chapter is to explain how a report achieves the goal of communicating effectively with readers, which means it is generally one that meets the standards of completeness, accuracy, and clarity. The second section of the chapter describes the standard elements of a written research report, what to include and what to leave out, and is followed by the appendix, containing an example of a written report for the Avery Fitness Center project.

CHAPTER OBJECTIVES The following objectives are addressed in this chapter: 20-1

Discuss three writing standards that a research report should meet to communicate effectively with readers.

20-2

Outline the main elements that make up a standard research report.

COMPLETE LIST OF CHAPTER ACTIVITIES AND ASSESSMENTS The following table organizes activities and assessments by objective, so that you can see how all this content relates to objectives and make decisions about which content you would like to emphasize in your class based on your objectives. For additional guidance, refer to the Teaching Online Guide.

Chapter Objective 20-1 20-2 20-1–20-2

PPT slide PPT slide 9 PPT slides 18–19 PPT slide 21

Activity/Assessment Polling Activity Group Activity Self-Assessment

Duration 5–10 min 10–20 min 10–20 min

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KEY TERMS Accuracy Clarity

The degree to which the reasoning in the report is logical and the information correct.

The degree to which the phrasing in the report is precise.

Completeness The degree to which the report provides all the information readers need in a language they understand. [return to top]

WHAT'S NEW IN THIS CHAPTER The following elements are improvements in this chapter from the previous edition:   

Consolidated chapter objectives for greater clarity and alignment with the section materials.

Minor editorial updates throughout the chapter were made to enhance comprehension.

Research Window 20.1 has been updated.

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CHAPTER OUTLINE The following outline organizes activities (including any existing discussion questions in PowerPoints or other supplements) and assessments by chapter (and therefore by topic), so that you can see how all the content relates to the topics covered in the text.

20-1.

Research Report Writing Standards (20-1, PPT Slides 3–9) vvv.

Research reports are evaluated based on one fundamental issue:  How well do they communicate with the reader? www.

Near-perfect research can get lost in the clutter of a poorly written report.

20-1a. Completeness a. Completeness – The degree to which the report provides all the information readers need in language they understand.  A written report must be complete, without being too complete.  The trick is to determine what really matters and what ought to be shifted to an appendix or left out entirely.

20-1b. Accuracy a. Accuracy – The degree to which the reasoning in the report is logical and the information correct.  Problems with accuracy are difficult to correct after the report has been distributed. b. Exhibit 20.1: Some Examples of Inaccuracy in Report Writing  Simple errors in addition or subtraction.  Confusion between percentages and percentage points.  Inaccuracy caused by grammatical errors.  Confused terminology, resulting in faulty conclusions.

20-1c. Clarity a. Clarity – The degree to which the phrasing in the report is precise. b. To achieve clarity:  Carefully organize your report.  Write in short sentences and paragraphs.  Write, rewrite, and rewrite again.  Shorten the report until every word has purpose.  No excessive or obtuse verbiage. xxx.

Polling Activity: 5–10 minutes total. (PPT Slide 9)  Of the three writing standards considered essential for a good research report, which do you believe should take priority? (m) Completeness (n) Clarity (o) Accuracy n. Note: This activity has no single correct answer. Students can be prompted to volunteer the reasoning for their response. In this particular activity, encourage them to justify their choice and provide any caveats they might have along with that choice.

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Research Window 20.1: Some Suggestions When Choosing Words for Marketing Research Reports 20-2.

Research Report Outline (20-2, PPT Slides 10–19) a. Research reports can be organized in different ways.  Reports need to contain key components.  Using a good outline can help you achieve clarity, accuracy, and completeness.  The report format should be guided by the nature and needs of the audience. b. Exhibit 20.2: Written Research Report Outline c. Title Page and Table of Contents  Provides subject/title of the study and researcher name and contact information.  See Appendix 20A: Written Research Report, Avery Fitness Center (PPT Slide 20). d. Executive Summary  The executive summary is the most important part of the report.  Think about what you would most want to communicate about the project if you only had 60 seconds to do so.  Includes: o a statement of the specific research problems. o how data were collected. o the most important of the results and a conclusion. e. Introduction  Sets up the project by providing background for the project and specifying the decision problem and research problem(s). f. Method  Although difficult to handle well, this section needs enough detail so that readers know how you conducted the research but not so much detail that they get lost and lose sight of the bigger story told in the report. g. Results  The results section should be organized to provide answers to the research problem(s) that motivated the project.  Because no projects are perfect, including a limitations section often adds credibility to the overall project.  Tables and figures that help communicate answers to research problems should be included in the results section to illustrate the key findings.  All other exhibits can be placed in an appendix and referenced in the text as needed. h. Conclusions and Recommendations  Conclusions are an interpretation of the research results. o Each of the research problems that motivated the study merit a stated conclusion.  Recommendations are suggestions about what to do next, following the conclusions. o Type and extent of recommendations depend on managers’ wishes.

- Are typically straightforward with strategy-oriented research.

- Are less straightforward with discovery-oriented research. i. Appendices  The research report provides an archive of the project.

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

j.

Items to include in the appendices: o Copies of data collection forms o Univariate results and/or data tables (often placed on the data collection form) o Codebook o Technical appendix or any additional exhibits not included in results (if needed) o Data file o Bibliography Group Activity: 15-30 minutes total. (PPT Slides 18–19)  Form groups of three to five participants. Using a research report you locate online or one provided by the instructor, review it together and evaluate what elements discussed in the chapter are included.

As time allows, share your observations with the larger class.  Debrief: - How easy (if applicable) was it to locate a sample research report online?

- What types of elements do you feel were missing from your sample report? What elements were you surprised to find in it?

- After examining the report in Appendix 20A and the sample report you found for this activity, how confident do you feel about being able to put together a report?

Appendix 20A: Written Research Report, Avery Fitness Center (PPT Slide 20) Self-Assessment (PPT Slide 21) 

What aspects of writing research reports are familiar to you? What aspects are unfamiliar?

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REVIEW QUESTIONS You can assign these questions several ways: in a discussion forum in your LMS; as whole-class discussions in person; or as a partner or group activity in class.

169.

What is the most important goal of a research report? Explain.

A report that achieves the goal of communicating effectively with readers is generally one that meets the standards of completeness, accuracy, and clarity.

170.

What is meant by the written report standards of completeness, accuracy, and clarity?

Completeness is the degree to which the report provides all the information readers need in language they understand. Accuracy is the degree to which the reasoning in the report is logical and the information correct. Clarity is the degree to which the phrasing in the report is precise.

171.

On the one hand, we argued that the research report must be complete and, on the other hand, that it must be clear. Are these two objectives incompatible? If so, how do you reconcile them?

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When it comes to completeness, there’s a bit of a paradox. A written report must be complete, without being too complete. You need to include all the relevant information, but if you include too much information, the report quickly becomes less usable. You certainly don’t want to force the reader to sift through page after page of nonessential results looking for the things that really matter. The trick is to determine what really matters and what ought to be included in appendices to the report or left out entirely. This isn’t always easy. Think carefully about the reader.

172.

What content appears in each of the following parts of the research report?

a. Title page The title page shows the subject/title of the report; the name of the organization, department, or individual for whom the report was written; the name of the organization, department, or individual submitting it; and the date. It is especially important to include the name and contact information of the researcher responsible for the project. b. Table of contents The table of contents lists the headings and subheadings of the report, with page references.

The table of contents might also include tables and figures and the pages on which they will be found. c. Executive summary The executive summary begins with a statement of who authorized the research and the specific research problems or hypotheses that guided it. Next comes a brief statement about how the data were collected, including the response rate. The most important results obtained in the study are included next, often in ―bullet‖ format, followed by conclusions (and maybe recommendations, depending upon what managers want to see). d. Introduction The introduction provides the background information readers need to appreciate the discussion in the remainder of the report. A little bit of background about the nature of the issue being studied is a good idea. The introduction should always state the specific research problems being addressed by the research (and include hypotheses where appropriate). e. Method In the methods section, readers should be told in general terms about the types of research used (that is, exploratory, descriptive, and/or causal). If your project involved multiple stages (e.g., a descriptive survey followed by an online experiment), you should present the method and results of the stages sequentially. f.

Results The results section presents the findings of the study in some detail, often including supporting tables and figures. This section usually makes up the bulk of the report. The results need to address the specific research problems posed and must be presented with some logical structure. Results that are interesting but irrelevant in terms of the specific research problems should be omitted.

g. Conclusions and recommendations The results lead to the conclusions and recommendations. Conclusions are based on an interpretation of the results; recommendations are suggestions about what managers should do next. There should be a conclusion for each of the research problems that motivated the study. One good strategy is to link research problems and conclusions so closely that the

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reader—after reviewing the research problems—can turn directly to the conclusions to find a specific conclusion for each objective. h. Appendices The appendices to the report contain material that is too complex, too detailed, too specialized, or not absolutely necessary for the text. The appendices will typically include a copy of the questionnaire or observation form used to collect the data. If there are more detailed calculations for such things as sample size justification or test statistics, these will often appear in a technical appendix. [return to top]

ADDITIONAL INSIGHTS AND ACTIVITIES The following are insights and activities meant as instruction suggestions—they are for you to use if you wish.

94. The written report for Avery Fitness Center is provided in Appendix 20A. It could be helpful to continue this AFC theme for the example written report materials.

95. Begin with a discussion of the purpose of the report, i.e., to communicate with the reader, and the writing criteria that promote effective communication: completeness, accuracy, and clarity.

96. If one wishes, it is possible to digress briefly into some communication models of encoder-decoder to make the point that communication does not simply take place because the report-writer (encoder) has placed words on paper. Rather, these words must be correctly interpreted by the decoder (report-reader) if accurate communication is to take place.

97. When outlining the structure of the report, a great deal of amplification of how each section should be organized is not usually necessary. However, it is helpful to show students examples of actual reports. Again, Appendix 20A is provided for this purpose.

98. This is a good place to reemphasize the importance of the report in the context of the entire project. All prior endeavors will be evaluated on the basis of the report and, in some cases, solely on the summary. [return to top]

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