Missing Data

A Gentle Introduction

GUILFORD PUBLICATIONSISBN: 9781593853938

Price:
Sale price$107.00
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Re-stocking soon

Imprint: THE GUILFORD PRESS
By:
Patrick E. McKnight,Katherine M. McKnight,Souraya Sidani,Aurelio Jose Figueredo
Release Date:

Format:
PAPERBACK
Pages:
251

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Description

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 Procedures8.1. 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

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