Description
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. Patrick E. McKnight's website offers a periodically updated annotated bibliography on missing data and links to other Web resources that address missing data.
Chapter
CH$1. A Gentle Introduction to Missing Data
The Concept of Missing Data
The Prevalence of Missing Data
Why Data Might Be Missing
The Impact of Missing Data
A Cost–Benefit Approach to Missing Data
Missing Data—Not Just for Statisticians Anymore
CH$2. Consequences of Missing Data
Three General Consequences of Missing Data
Consequences of Missing Data on Construct Validity
Consequences of Missing Data on Internal Validity
Consequences for Causal Generalization
CH$3. Classifying Missing Data
“The Silence That Betokens”
The Current Classification System: Mechanisms of Missing Data
Expanding the Classification System
CH$4. Preventing Missing Data by Design
Characteristics of the Target Population and the Sample
CH$5. Diagnostic Procedures
Dummy Coding Missing Data
Numerical Diagnostic Procedures
Diagnostic Procedures Using Graphing
CH$6. The Selection of Data Analytic Procedures
CH$7. Data Deletion Methods for Handling Missing Data
Individual Growth Curve Analysis
CH$8. Data Augmentation Procedures
CH$9. Single Imputation Procedures
Constant Replacement Methods
Nonrandom Value Imputation: Single Condition
Nonrandom Value Imputation: Multiple Conditions
CH$10. Multiple Imputation
CH$11. Reporting Missing Data and Results
APA Task Force Recommendations
Missing Data and Study Stages
TFSI Recommendations and Missing Data
Missing Data
：A Gentle Introduction
( Methodology in the Social Sciences )
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