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The Prophet Of Modern Constitutional Liberalism: John

Stuart Mill And The Supreme Court 1st Edition Edition John Lawrence Hill

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Summative Evaluation

Process Evaluation

Outcome Evaluation

Impact Evaluation

Evaluation: Logic Models

Summary

Case Study: Healthy Food/Healthy People

Goal Statement

Objectives

Case Study Discussion Questions

Student Activities

References

Chapter 2 Ethics

Chapter Objectives

Key Terms

Introduction

Historical Background

U.S. Public Health Service Syphilis Study in Tuskegee Henrietta Lacks

Basic Principles of Medical Ethics

Ethical Links Between Research and Evaluation

Confidentiality of Medical Information and Research Data

Healthcare Providers: Medical Care Versus Medical Research

Physical Therapy Example

Nursing Example

Medical Example

Hospital Administration Example

Hospital Ethics Committee Example

Evaluation Example

Institutional Review Board

Informed Consent

Risk/Benefit Assessment

Selection of Individuals and Special Populations

Summary

Case Study: Diaz vs. Hillsbourgh County Hospital Authority

An Inquiry

The Next Step

The Lawsuit

Doctors’ Perspective

The Settlement

A Postscript

Case Study Discussion Questions

Student Activity References

Chapter 3 Determinants of Health

Chapter Objectives

Key Terms

Introduction

Historical View of Achievement in Health

Health Disparities

Social Determinants of Health

Physical Activity

Education

Access to Health Care

Resources: Safe Food, Safe Housing, and Employment

Using Healthy People 2020 to Study Health Disparities and

Social Determinants of Health

Summary

Case Study

Case Study Discussion Questions

Answers

References

Chapter 6 Qualitative Data

Chapter Objectives

Key Terms

Introduction

The Qualitative-Quantitative Debate

Qualitative Methods: Validity and Reliability

Types of Qualitative Design

Interviews

Observations

Case Studies

Phenomenology

Historical Documents

Content Analysis

Ethnography

Grounded Theory

Ethical Issues in Qualitative Research

Analyses of Qualitative Data

Data Organization

Coding Data

Data Display

Summary

Case Study

Summary

Case Study Discussion Questions

Student Activity

Case Study 1

Case Study 2

Case Study 3

Chapter 9 Data Tools

Chapter Objectives

Key Terms

Introduction

Data Classification

Categorical Data

Continuous Data

Data Organization

Descriptive Data

Graphic Presentation

Measures of Central Tendency

Mean

Median Mode

Normal Curve

Standard Deviation and Variance

Summary

Case Study

Survey Questions

Case Study Discussion Questions

Student Activity

Answers

References

Chapter 10 Populations and Samples

Chapter Objectives

Key Terms

Introduction

Populations and Samples

Probability and Inferential Statistics

Research and Null Hypothesis

Level of Significance

Sample Size Considerations

Margin of Error

Population, Sample, and Variability

Confidence Level

Budget and Budget Justification

Timeline

Probability and Nonprobability Samples

Probability Sampling

Nonprobability Sampling

Sampling Bias

Item Nonresponse Bias

Unit Nonresponse Bias

Summary

Case Study

Case Study Discussion Questions

Student Activity

Answers

References

Chapter 11 Inferential Statistics

Chapter Objectives

Key Terms

Introduction

Types of Statistics

The Need for Statistics

Inferential Statistics

Scientific Hypothesis

Research Questions

Null Hypothesis and Alternate Hypothesis

Basic Inferential Statistical Tests

Description

Overview of Evaluation

Budget

Budget Justification

Personnel

Graduate Students

Consultants

Focus Groups

Travel

Indirect Costs

Evaluation

Clinical Staff and Clinical Support Staff

Laboratory

Nonclinical Staff

Central Supply, Technical Support, Physical Plant and Environmental Services (CTPES)

Finance and Accounting

Board of Directors, Executive Administration, and Management

Patient Satisfaction

Draft of the Final Report

Evaluation of Clinical Staff and Clinical Support Staff

Evaluation of Laboratory

Evaluation of Nonclinical Staff

Evaluation of CTPES Staff

Evaluation of Finance and Accounting

Evaluation of Board of Directors, Executive Administration, and Management

Evaluation of Patient and Visitor Satisfaction

Recommendations for the Final Report

Student Activity

current literature on how the theories and models have been used as the framework for project development. The list of theories and models is not intended to be comprehensive, but rather an introduction to examples that are commonly used. Once the research questions or evaluation goals and objectives and an appropriate theory are selected, it is time to explore types of data. Chapter 5 defines the concepts of reliability and validity as well as random and systematic errors. The chapter ends with a detailed description of how to conduct a pilot test and why they are essential. Chapter 6 provides a detailed discussion of qualitative data including types qualitative designs, potential ethical issues, and analyses utilized in qualitative data. Chapter 7 presents some basic elements of research including the difference between basic and applied research, variables, group assignment, constructs, and operational definitions. After these concepts are understood, the three basic types of research design (true experimental, quasi-experimental, and nonexperimental) are defined and examples are provided to enhance understanding. Chapter 8 focuses on survey design, including types of surveys and how to select them. Various tests, inventories, and scales are introduced along with examples and reasons for selecting one survey type over another. A discussion of how culture and diversity influences data collection is included near the end of the chapter.

Chapters 9, 10, and 11 focus on basic skills related to data. Chapter 9 introduces how data are classified as categorical or continuous and then organized using frequency distributions. Building on this knowledge, the concepts of measures of central tendency, the normal curve, standard deviation, and variance are explained in detail with plenty of examples. This chapter serves as the foundation for understanding the next two chapters. Chapter 10 describes terms related to population and samples. There are three main topics covered: sample size considerations, probability and nonprobability samples, and sampling bias. Each topic deserves important consideration when determining the sample size needed for any evaluation or research project. Chapter 11 introduces inferential statistics and defines the terms scientific hypothesis, research questions, null hypothesis, and alternative hypothesis. The next section presents basic statistical tests (e.g., chi-square, t-tests, and correlation coefficients). The chapter ends with a discussion of type I and type II errors.

The last chapters provide skills related to budgets, reports, and presentations, and the text culminates with a case study. Chapter 12 is divided into two sections. The first section describes various types of budgets with examples to practice basic skills. Budget justifications are also presented. The second section defines the types of cost analyses and how each type is used. Chapter 13 illustrates several ways to present results including abstracts, executive summaries, reports, manuscripts, posters, and verbal presentations. Chapter 14 is a lengthy case study reinforcing all aspects presented in this book.

Acknowledgments

During process of writing this book, I received assistance and support from numerous family members, friends, and colleagues.

Kevin, my husband, supported me through the many weekends that were consumed with writing. He allowed me the opportunity to fulfill my dream.

Laura Merrell edited each chapter with great attention to detail. Her superb skills greatly contributed to the quality of this text.

Dr. Richard Riegelman provided guidance and mentoring as I embarked on writing my first textbook. His constructive comments improved the overall quality of this text.

Dr. R. Clifford Blair offered valuable advice throughout the process. As a biostatistician and author, he offered humor and encouragement whenever I got tired of writing.

Over decades of teaching, hundreds of students taught me the skills and expertise needed to write this text.

The University of South Florida College of Public Health granted me time to complete this text.

Most educators say that the best way to learn a subject is to teach it. After writing this text, I have revised this advice to say:

“The best way to learn a subject is not to teach it, but rather to write a book about it.”

(Qualitative data)

6. What type of numerical data will you obtain online? (Quantitative data)

7. What questions do you ask your friends about their computers? (Qualitative data)

8. Are there budget constraints or stakeholders influencing your decision? (Budget and stakeholders)

9. What type of evaluation is most appropriate? (Evaluation)

10. How will you merge the data and make your final decision? (Data analysis)

11. Purchase a computer, install software, and read the manual. (Final report)

Once you realize the usefulness that research and evaluation skills play in daily life, this text becomes more practical and beneficial. You will learn skills and methods to assist with a wide range of program planning, research, and evaluation methods in your personal and professional lives.

Difference Between Research and Evaluation

Let’s begin with some simple definitions followed by more details about the similarities and differences between research and evaluation. Research creates new knowledge2 with the intention of generalizing results from a sample to the population.3 Evaluations are conducted to improve an internal situation with no intent to generalize to another population.2 Now let’s compare a few more specific similarities and differences between research and evaluation. Because this text introduces the concepts of both research and evaluation, it is not expected that the reader already understands all of the information provided in Table 1-1. Understanding and proficiency will increase as you move through each chapter.

Table 1-1 A Comparison of Research and Evaluation

Research Evaluation

As previously stated, research focuses on answering research questions and gaining new knowledge, whereas evaluations concentrate on identifying unmet needs and services, planning effective program implementation, investigating how to improve program services, and assisting staff with program decisions based on cost-effectiveness. Theories are developed, established, and emphasized in research. Although evaluations benefit from using theories, theories are not developed as an evaluation product. So now that research and evaluation have been compared, let’s add one more category, called audit, that utilizes basic statistics, but is not research or evaluation. Audits are linked to compliance, accreditation, laws, and regulations. For example, the Government Accountability Office utilizes agency and institution data to determine level of culpability in an agency receiving federal funding. Another example is The Joint Commission (TJC, formerly known as the Joint Commission on Accreditation of Healthcare Organizations [JCAHO]). This organization conducts performance and standard measures, health services research, and accountability measures for healthcare institutions across the United States.8

The rest of this chapter introduces how to review the literature, conduct a needs assessment, develop questions, identify the type of evaluation, and determine the role of stakeholders in planning programs and evaluations.

Needs Assessment

The purpose of a needs assessment is to identify the strengths, weaknesses, opportunities, and threats (SWOT) in the organization or community. The needs assessment is conducted in collaboration with the stakeholders, such as staff members, community leaders, neighborhood organization members, and political leaders. The goal is to obtain a broad range of opinions about the benefits and worries, because a concern for one group may be an asset for another group.

The first step of the needs assessment is to form a partnership with individuals involved in the organization or community. It is advisable to include at least one member with expertise in conducting a needs assessment to offer an outside perspective to the process. At the introductory meeting, it is important that every member is treated equally and with respect. The

neighborhood housing member’s opinion and contribution are given the same weight as the city council member’s. After getting to know each other, they should coalesce around a few common goals for the needs assessment. It is advisable to focus on two or three goals, so the effort is not too ambitious. After establishing the goals, team members need to address the following questions:

• Who is the target audience for the needs assessment?

• Are there sufficient personnel to staff the needs assessment?

• What funds are available to finance the needs assessment?

• What are the roles, responsibilities, and expectations of each team member?

• Are there data available to ascertain what has been accomplished on this topic previously?

• What is the optimal timeline of the needs assessment?

Once the team answers these questions, it is time to move to the next step. Data collection begins with exploring what types of data are necessary to complete the needs assessment. Generally, data are divided into two categories: qualitative (words) and quantitative (numbers). Qualitative data include but are not limited to focus groups, interviews, town hall meetings, and public forums. The conversations at these events are recorded, transcribed, and analyzed for themes. On the other hand, with quantitative data, the survey responses are converted into numerical data for analysis. For example, the survey question “How would you rate your health today?” provides four scaled choices: excellent (4), good (3), fair (2), and poor (1). The responses are added together and divided by the number of individuals responding, to obtain a mean score or average. In addition, demographic data can be converted to numbers. When asking the respondents how long they have lived in the community, the choices would be: 10+ years (4); 5–9 years (3); 1–4 years (2); less than 1 year (1). As you can see, any type of data may be converted into numbers for quantitative analysis. Besides surveys, quantitative data may come from secondary data sources. When data is collected by another researcher or organization, it is called secondary data. Anyone using secondary data develops her own research objectives and