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Research Design and Analysis: A Primer for the Non-Statistician

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Research Design and Analysis: A Primer for the Non-Statistician

Leslie D. Rosenstein

ISBN: 978-1-119-56361-7 May 2019 272 Pages

Description

A concise, straightforward overview of research design and analysis, helping readers form a general basis for designing and conducting research

The practice of designing and analyzing research continues to evolve with advances in technology that enable greater technical analysis of data—strengthening the ability of researchers to study the interventions and relationships of factors and assisting consumers of research to understand and evaluate research reports. Research Design and Analysis is an accessible, wide-ranging overview of how to design, conduct, analyze, interpret, and present research. This book helps those in the sciences conduct their own research without requiring expertise in statistics and related fields and enables informed reading of published research.

Requiring no background in statistics, this book reviews the purpose, ethics, and rules of research, explains the fundamentals of research design and validity, and describes how to select and employ appropriate statistical techniques and reporting methods. Readers gain knowledge central to various research scenarios, from sifting through reports of meta-analyses and preparing a research paper for submission to a peer-reviewed journal to discussing, evaluating, and communicating research results. This book:

  • Provides end-to-end guidance on the entire research design and analysis process
  • Teaches readers how to both conduct their own research and evaluate the research of others
  • Offers a clear, concise introduction to fundamental topics ideal for both reference and general education functions
  • Presents information derived from the author’s experience teaching the subject in real-world classroom settings
  • Includes a full array of learning tools including tables, examples, additional resource suggestions, complete references, and appendices that cover statistical analysis software and data sets

Research Design and Analysis: A Primer for the Non-Statistician is a valuable source of information for students and trainees in medical and allied health professions, journalism, education, and those interested in reading and comprehending research literature.

List of Figures xiii

List of Tables xv

Introduction xix

Section 1 The Purpose, Ethics, and Rules of Research 1

1 The Purpose and Ethics of Research 3

1.1 The Purpose and Risks of Research 3

1.2 History of Harm to Humans 4

1.3 Ethical Issues in the Social Sciences 9

1.4 History of Harm to Animal Subjects in Research 10

1.4.1 Summary 12

1.5 Ethics, Principles, and Guidelines 12

1.6 Statutes and Regulations Protecting Humans and Animals in Research 16

1.7 More About Informed Consent 18

1.8 The Importance of Freedom to Withdraw 22

1.9 Separation of Provider–Researcher Role 22

1.10 Undue Influence 24

1.11 Anonymity 24

1.12 Summary 25

Section 2 Basic Research Designs and Validity 27

2 Research Validity 29

2.1 Internal Validity 30

2.1.1 History 30

2.1.2 Maturation 31

2.1.3 Measurement Error 32

2.1.4 Selection Bias and Random Assignment 33

2.1.5 Attrition 35

2.1.6 Experimenter Bias 35

2.1.7 Expectation 36

2.1.8 Sensitization and Practice Effects 36

2.1.9 Incorrect Conclusions of Causality 37

2.2 External Validity 37

2.3 Summary 45

3 Research Designs 47

3.1 The Lingo 47

3.2 Between‐Subjects Designs 49

3.2.1 More Examples of Between‐Subjects Designs 49

3.2.2 Statistical Analyses for Between‐Subjects Designs 50

3.3 Within‐Subjects Designs/Repeated Measures 52

3.3.1 Statistical Analyses for Within‐Subjects Designs 53

3.4 Between–Within Subjects Designs (Mixed Factorial/Split‐Plot Designs) 54

3.4.1 Statistical Analyses for Between–Within Subjects Designs 55

3.5 Latin Square Designs 57

3.5.1 Summary 59

3.5.2 Double Latin Square Designs 59

3.5.3 Graeco‐Latin and Hyper Graeco‐Latin Square Designs 59

3.6 Nesting 60

3.7 Matching 60

3.8 Blocking 61

3.9 Nonexperimental Research 62

3.10 Case Studies 62

3.11 Summary 64

Section 3 The Nuts and Bolts of Data Analysis 65

4 Interpretation 67

4.1 Probability and Significance 67

4.2 The Null Hypothesis, Type I (α), and Type II (β) Errors 68

4.3 Power 69

4.4 Managing Error Variance to Improve Power 71

4.5 Power Analyses 72

4.6 Effect Size 72

4.7 Confidence Intervals and Precision 74

4.8 Summary 76

5 Parametric Statistical Techniques 77

5.1 A Little More Lingo 77

5.1.1 Population Parameters Versus Sample Statistics 78

5.1.2 Data 78

5.1.2.1 Ratio and Interval Data 78

5.1.2.2 Ordinal Data 78

5.1.2.3 Nominal Data 79

5.1.3 Central Tendency 79

5.1.3.1 Mode 79

5.1.3.2 Median 79

5.1.3.3 Mean 86

5.1.4 Distributions 86

5.1.5 Dependent Variables 92

5.1.5.1 To Scale or Not to Scale 95

5.1.6 Summary 97

5.2 t Tests 97

5.2.1 Independent Samples t Tests 97

5.2.2 Matched Group Comparison 98

5.2.3 Assumptions of t Tests 99

5.2.4 More Examples of Studies Employing t Tests 100

5.2.5 Statistical Software Packages for Conducting t Tests 101

5.3 The NOVAs and Mixed Linear Model Analysis 101

5.3.1 ANOVA 102

5.3.1.1 ANOVA with a Multifactorial Design 104

5.3.1.2 Main Effects and Interactions 104

5.3.1.3 More Illustrations of Interactions and Main Effects 106

5.3.1.4 Assumptions of ANOVA 107

5.3.2 ANCOVA 109

5.3.3 MANOVA/MANCOVA 111

5.3.4 Statistical Software Packages for Conducting ANOVA/ANCOVA/MANOVA 114

5.3.5 Repeated Measures: ANOVA‐RM and Mixed Linear Model Analysis 114

5.3.5.1 ANOVA‐RM 114

5.3.5.2 Mixed Linear Model Analysis 116

5.3.5.3 ANCOVA 117

5.3.5.4 Statistical Software Packages for Conducting Repeated Measures Analyses 117

5.3.6 Summary 119

5.4 Correlation and Regression 120

5.4.1 Correlation and Multiple Correlation 120

5.4.2 Regression and Multiple Regression 121

5.4.3 Statistical Software Packages for Conducting Correlation and Regression 124

5.5 Logistic Regression 126

5.5.1 Statistical Software Packages for Conducting Logistic Regression 128

5.6 Discriminant Function Analysis 128

5.6.1 Statistical Software Packages for Conducting Discriminant Function Analysis 128

5.7 Multiple Comparisons 129

5.8 Summary 131

6 Nonparametric Statistical Techniques 133

6.1 Chi‐Square 134

6.1.1 Statistical Software Packages for Conducting Chi‐Square 136

6.2 Median Test 137

6.2.1 Statistical Software Packages for Conducting Median Tests 137

6.3 Phi Coefficient 137

6.3.1 Statistical Software Packages for Calculating the Phi Coefficient 139

6.4 Mann–Whitney U Test (Wilcoxon Rank Sum Test) 139

6.4.1 Statistical Software Packages for Conducting a Mann–Whitney U Test 141

6.5 Sign Test and Wilcoxon Signed‐rank Test 142

6.5.1 Statistical Software Packages for Conducting Sign Tests 143

6.6 Kruskal–Wallis Test 144

6.6.1 Statistical Software Packages for Conducting a Kruskal–Wallis Test 144

6.7 Rank‐Order Correlation 145

6.7.1 Statistical Software Packages for Conducting Rank‐order Correlations 146

6.8 Summary 147

7 Meta‐Analytic Studies 149

7.1 The File Drawer Effect 150

7.2 Analyzing the Meta‐Analytic Data 151

7.3 How to Read and Interpret a Paper Reporting a Meta‐Analysis 153

7.4 Statistical Software Packages for Conducting Meta‐Analyses 155

7.5 Summary 155

Section 4 Reporting, Understanding, and Communicating Research Findings 157

8 Disseminating Your Research Findings 159

8.1 Preparing a Research Report 159

8.2 Presenting Your Findings at a Conference 167

8.3 Summary 168

9 Concluding Remarks 169

9.1 Why is it Important to Understand Research Design and Analysis as a Consumer? 169

9.2 Research Ethics and Responsibilities of Journalists 175

9.3 Responsibilities of Researchers 177

9.4 Conclusion 178

Appendix A Data Sets and Databases 179

Appendix B Statistical Analysis Packages 195

Appendix C Helpful Statistics Resources 217

Glossary 221

References 233

Index 243