9781593853938

While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study's conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study results. When statistical remedies are needed - such as deletion procedures, augmentation methods, and single imputation and multiple imputation procedures - the book also explains how to make sound decisions about their use.

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Patrick E. McKnight, PhD, is Assistant Professor in the Department of Psychology at George Mason University, Fairfax, Virginia. The majority of his work focuses on health services outcomes and, in particular, on measuring those outcomes to make them readily interpretable. He has worked and published in the health-related areas of asthma, arthritis, cancer, speech, pain, low vision, and rehabilitation. Dr. McKnight is an active member of the American Evaluation Association, serving as co-chair of the quantitative methods topical interest group for the past 4 years. Katherine M. McKnight, PhD, teaches statistics at George Mason University, Fairfax, Virginia, and is Director of Evaluation for LessonLab Research Institute, part of Pearson Achievement Solutions. She has published numerous articles reflecting a wide range of interests, with the common underlying framework of the thoughtful use of research methods, measurement, and data analysis for addressing research and evaluation questions. She is a member of the American Evaluation Association and the Association for Psychological Science. Souraya Sidani, PhD, RN, is Professor in the Faculty of Nursing, University of Toronto. Her areas of expertise are in quantitative research methods, intervention design and evaluation, and measurement. Her research areas of interest focus on evaluating interventions and on refining research methods and measures for determining the clinical effectiveness of interventions. She is a member of the American Evaluation Society and the Canadian Evaluation Society. Aurelio José Figueredo, PhD, is Professor of Psychology at the University of Arizona. He is the director of the graduate program in Ethology and Evolutionary Psychology (EEP), a cross-disciplinary program integrating the studies of comparative psychology, ethology, sociobiology, and behavioral ecology, genetics, and development. His major areas of research interest are the evolutionary psychology and behavioral development of life-history strategy and sex and violence in human and nonhuman animals, and the quantitative ethology and social development of insects, birds, and primates. In the EEP he regularly teaches the graduate year-long course in Statistical Methods in Psychological Research.

A Gentle Introduction to Missing Data	p. 1
The Concept of Missing Data	p. 2
The Prevalence of Missing Data	p. 2
Why Data Might Be Missing	p. 5
The Impact of Missing Data	p. 6
A Cost-Benefit Approach to Missing Data	p. 10
Missing Data-Not Just for Statisticians Anymore	p. 13
Purpose of This Book	p. 14
Consequences of Missing Data	p. 17
Three General Consequences of Missing Data	p. 18
Consequences of Missing Data on Construct Validity	p. 20
Consequences of Missing Data on Internal Validity	p. 25
Consequences for Causal Generalization	p. 35
Summary	p. 39
Classifying Missing Data	p. 40
"The Silence That Betokens"	p. 41
The Current Classification System: Mechanisms of Missing Data	p. 42
Expanding the Classification System	p. 52
Summary	p. 62
Preventing Missing Data by Design	p. 65
Overall Study Design	p. 66
Characteristics of the Target Population and the Sample	p. 72
Data Collection Methods	p. 75
Treatment Implementation	p. 84
Data Entry Process	p. 85
Summary	p. 85
Diagnostic Procedures	p. 88
Traditional Diagnostics	p. 89
Dummy Coding Missing Data	p. 90
Numerical Diagnostic Procedures	p. 92
Diagnostic Procedures Using Graphing	p. 110
Summary	p. 114
The Selection of Data Analytic Procedures	p. 116
Preliminary Steps	p. 117
Decision Making	p. 127
Summary	p. 138
Data Deletion Methods for Handling Missing Data	p. 140
Data Sets	p. 140
Complete Case Method	p. 141
Available Case Method	p. 147
Available Item Method	p. 148
Individual Growth Curve Analysis	p. 152
Multisample Analyses	p. 156
Summary	p. 158
Data Augmentation Procedures	p. 160
Model-Based Procedures	p. 161
Markov Chain Monte Carlo	p. 166
Adjustment Methods	p. 168
Summary	p. 172
Single Imputation Procedures	p. 173
Constant Replacement Methods	p. 175
Random Value Imputation	p. 181
Nonrandom Value Imputation: Single Condition	p. 185
Nonrandom Value Imputation: Multiple Conditions	p. 190
Summary	p. 194
Multiple Imputation	p. 196
The MI Process	p. 199
Summary	p. 210
Reporting Missing Data and Results	p. 213
APA Task Force Recommendations	p. 213
Missing Data and Study Stages	p. 214
TFSI Recommendations and Missing Data	p. 218
Reporting Format	p. 221
Summary	p. 223
Epilogue	p. 225
References	p. 229
Author Index	p. 237
Subject Index	p. 241
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Missing Data A Gentle Introduction

Missing Data A Gentle Introduction

1593853939

Summary

Author Biography

Table of Contents

Supplemental Materials