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Missing Data: A Gentle Introduction

by Patrick E McKnight, Katherine M McKnight, Aurelio Jose Figueredo and Souraya Sidani Guilford Publications
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Pbk 251 pages
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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.

KEY FEATURES: * Goes beyond competitors by focusing on preventing missing data, then follows with practical remedies in case it does occur. * The first book to appeal to statistically challenged-or less advanced-researchers who must grapple with this problem. * Explains the roots of missing data so the problem can be minimized.

AUDIENCE: Students and researchers in psychology, education, sociology, management, public health, nursing, and other behavioral and social science disciplines.

COURSE USE: Serves as a supplemental text in graduate-level courses on quantitative analysis, survey research, experimental design, or research methods, or as a primary text in a specialized course on missing data.

Patrick E. McKnight, Department of Psychology, George Mason University; Katherine M. McKnight, LessonLab Research Institute; Souraya Sidani, Faculty of Nursing, University of Toronto; and Aurelio José Figueredo, Department of Psychology, University of Arizona CONTENTS: 1. A GENTLE INTRODUCTION TO MISSING DATA 1.1. The Concept of Missing Data 1.2. The Prevalence of Missing Data 1.3. Why Data Might Be Missing 1.4. The Impact of Missing Data 1.5. What's Missing in the Missing Data Literature? 1.6. A Cost-Benefit Approach to Missing Data 1.7. Missing Data-Not Just for Statisticians Anymore 2. CONSEQUENCES OF MISSING DATA 2.1. Three General Consequences of Missing Data 2.2. Consequences of Missing Data on Construct Validity 2.3. Consequences of Missing Data on Internal Validity 2.4. Consequences on Causal Generalization 2.5. Summary 3. CLASSIFYING MISSING DATA 3.1. 'The Silence That Betokens' 3.2. The Current Classification System: Mechanisms of Missing Data 3.3. Expanding the Classification System 3.4. Summary 4. PREVENTING MISSING DATA BY DESIGN 4.1. Overall Study Design 4.2. Characteristics of the Target Population and the Sample 4.3. Data Collection and Measurement 4.4. Treatment Implementation 4.5. Data Entry Process 4.6. Summary 5. DIAGNOSTIC PROCEDURES 5.1. Traditional Diagnostics 5.2. Dummy Coding Missing Data 5.3. Numerical Diagnostic Procedures 5.4. Graphical Diagnostic Procedures 5.5. Summary 6. THE SELECTION OF DATA ANALYTIC PROCEDURES 6.1. Preliminary Steps 6.2. Decision Making 6.3. Summary 7. DATA DELETION METHODS FOR HANDLING MISSING DATA 7.1. Data Sets 7.2. Complete Case Method 7.3. Available Case Method 7.4. Available Item Method 7.5. Individual Growth Curve Analysis 7.6. Multisample Analyses 7.7. Summary 8. DATA AUGMENTATION PROCEDURES 8.1. Model-Based Procedures 8.2. Markov Chain Monte Carlo 8.3. Adjustment Methods 8.4. Summary 9. SINGLE IMPUTATION PROCEDURES 9.1. Constant Replacement Methods 9.2. Random Value Imputation 9.3. Nonrandom Value Imputation: Single Condition 9.4. Nonrandom Value Imputation: Multiple Conditions 9.5. Summary 10. MULTIPLE IMPUTATION 10.1. The MI Process 10.2. Summary 11. REPORTING MISSING DATA AND RESULTS 11.1. APA Task Force Recommendations 11.2. Missing Data and Study Stages 11.3. TFSI Recommendations and Missing Data 11.4. Reporting Format 11.5. Summary 12. EPILOGUE


'This book is full of useful information about methodological and statistical issues related to missing data. It includes clear definitions of types of missing data, ways to reduce their negative effects, and analytical strategies for maximizing the use of all data-even partial data-collected in a research study. A unique strength of the book is its focus on missing data as a threat to the validity of a study's conclusions. Unlike other sources on missing data analysis, design approaches for preventing missing data are emphasized. More advanced statistical approaches to missing data analysis are also described clearly. This is a valuable, practical resource.'--- David MacKinnon, Department of Psychology, Arizona State University

'This very important, interesting, and well-written book addresses a serious problem in contemporary social science research....This book systematically explores methods for classification, diagnosis, and prevention of missing data problems. It provides step-by-step instructions for analyzing data sets with some observations missing; reviews imputation methods; and advises investigators on how to report on analyses when some participants have been lost to follow-up. This is an excellent book that will help behavioral science investigators handle analytical problems for virtually every study they conduct.'--- Robert M. Kaplan, Department of Health Services, UCLA School of Public Health

''The authors effectively communicate the importance of missing data: ways to avoid it, ways to identify it, and ways to accommodate it when analyzing data....The book is complete, useful for everyone wanting to know more about missing data.''
- Drug and Alcohol Review (Jason A. Ferris, Vol. 28, January 2009)

1. A Gentle Introduction to Missing Data1.1. The Concept of Missing Data1.2. The Prevalence of Missing Data1.3. Why Data Might Be Missing1.4. The Impact of Missing Data1.5. What's Missing in the Missing Data Literature?1.6. A Cost-Benefit Approach to Missing Data1.7. Missing Data--Not Just for Statisticians Anymore2. Consequences of Missing Data2.1. Three General Consequences of Missing Data2.2. Consequences of Missing Data on Construct Validity2.3. Consequences of Missing Data on Internal Validity2.4. Consequences on Causal Generalization2.5. Summary3. Classifying Missing Data3.1. "The Silence That Betokens"3.2. The Current Classification System: Mechanisms of Missing Data3.3. Expanding the Classification System3.4. Summary4. Preventing Missing Data by Design4.1. Overall Study Design4.2. Characteristics of the Target Population and the Sample4.3. Data Collection and Measurement4.4. Treatment Implementation4.5. Data Entry Process4.6. Summary5. Diagnostic Procedures5.1. Traditional Diagnostics5.2. Dummy Coding Missing Data5.3. Numerical Diagnostic Procedures5.4. Graphical Diagnostic Procedures5.5. Summary6. The Selection of Data Analytic Procedures6.1. Preliminary Steps6.2. Decision Making6.3. Summary7. Data Deletion Methods for Handling Missing Data7.1. Data Sets7.2. Complete Case Method7.3. Available Case Method7.4. Available Item Method7.5. Individual Growth Curve Analysis7.6. Multisample Analyses7.7. Summary8. Data Augmentation Procedures8.1. Model-Based Procedures8.2. Markov Chain Monte Carlo8.3. Adjustment Methods8.4. Summary9. Single Imputation Procedures9.1. Constant Replacement Methods9.2. Random Value Imputation9.3. Nonrandom Value Imputation: Single Condition9.4. Nonrandom Value Imputation: Multiple Conditions9.5. Summary10. Multiple Imputation10.1. The MI Process10.2. Summary11. Reporting Missing Data and Results11.1. APA Task Force Recommendations11.2. Missing Data and Study Stages11.3. TFSI Recommendations and Missing Data11.4. Reporting Format11.5. Summary12. Epilogue

"The information in this book is practical, judicious, and empirically sound. The authors have managed to compile a very useful book on an often repressed topic among researchers. For students, this is a worthwhile introduction that manages to avoid being too technical. Yet, there are very functional suggestions both in terms of study design and the handling of missing data that would be useful for seasoned researchers. Weighted Numerical Score: 97 - 5 Stars!"
Patrick E. McKnight, George Mason University, USA; Katherine M. McKnight, LessonLab Research Institute, USA; Souraya Sidani, University of Toronto, Canada; and Aurelio Jose Figueredo, University of Arizona, USA