Applied missing data analysis
著者
書誌事項
Applied missing data analysis
（Methodology in the social sciences）
Guilford Press, c2010
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注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
*The first accessible treatment of the tough statistics underlying missing data.
*Patiently walks readers through difficult concepts step by step.
*Companion website will provide data files for the examples in the book as well as up-to-date information on software.
*Written for those doing behavioral or social science studies instead of those trained in mathematical statistics.
*An important research tool that will appeal to both graduate students and applied researchers.
目次
1. An Introduction to Missing Data
1.1 Introduction
1.2 Chapter Overview
1.3 Missing Data Patterns
1.4 A Conceptual Overview of Missing Data Theory
1.5 A More Formal Description of Missing Data Theory
1.6 Why Is the Missing Data Mechanism Important?
1.7 How Plausible Is the Missing at Random Mechanism?
1.8 An Inclusive Analysis Strategy
1.9 Testing the Missing Completely at Random Mechanism
1.10 Planned Missing Data Designs
1.11 The Three-Form Design
1.12 Planned Missing Data for Longitudinal Designs
1.13 Conducting Power Analyses for Planned Missing Data Designs
1.14 Data Analysis Example
1.15 Summary
1.16 Recommended Readings
2. Traditional Methods for Dealing with Missing Data
2.1 Chapter Overview
2.2 An Overview of Deletion Methods
2.3 Listwise Deletion
2.4 Pairwise Deletion
2.5 An Overview of Single Imputation Techniques
2.6 Arithmetic Mean Imputation
2.7 Regression Imputation
2.8 Stochastic Regression Imputation
2.9 Hot-Deck Imputation
2.10 Similar Response Pattern Imputation
2.11 Averaging the Available Items
2.12 Last Observation Carried Forward
2.13 An Illustrative Simulation Study
2.14 Summary
2.15 Recommended Readings
3. An Introduction to Maximum Likelihood Estimation
3.1 Chapter Overview
3.2 The Univariate Normal Distribution
3.3 The Sample Likelihood
3.4 The Log-Likelihood
3.5 Estimating Unknown Parameters
3.6 The Role of First Derivatives
3.7 Estimating Standard Errors
3.8 Maximum Likelihood Estimation with Multivariate Normal Data
3.9 A Bivariate Analysis Example
3.10 Iterative Optimization Algorithms
3.11 Significance Testing Using the Wald Statistic
3.12 The Likelihood Ratio Test Statistic
3.13 Should I Use the Wald Test or the Likelihood Ratio Statistic?
3.14 Data Analysis Example 1
3.15 Data Analysis Example 2
3.16 Summary
3.17 Recommended Readings
4. Maximum Likelihood Missing Data Handling
4.1 Chapter Overview
4.2 The Missing Data Log-Likelihood
4.3 How Do the Incomplete Data Records Improve Estimation?
4.4 An Illustrative Computer Simulation Study
4.5 Estimating Standard Errors with Missing Data
4.6 Observed Versus Expected Information
4.7 A Bivariate Analysis Example
4.8 An Illustrative Computer Simulation Study
4.9 An Overview of the EM Algorithm
4.10 A Detailed Description of the EM Algorithm
4.11 A Bivariate Analysis Example
4.12 Extending EM to Multivariate Data
4.13 Maximum Likelihood Software Options
4.14 Data Analysis Example 1
4.15 Data Analysis Example 2
4.16 Data Analysis Example 3
4.17 Data Analysis Example 4
4.18 Data Analysis Example 5
4.19 Summary
4.20 Recommended Readings
5. Improving the Accuracy of Maximum Likelihood Analyses
5.1 Chapter Overview
5.2 The Rationale for an Inclusive Analysis Strategy
5.3 An Illustrative Computer Simulation Study
5.4 Identifying a Set of Auxiliary Variables
5.5 Incorporating Auxiliary Variables Into a Maximum Likelihood Analysis
5.6 The Saturated Correlates Model
5.7 The Impact of Non-Normal Data
5.8 Robust Standard Errors
5.9 Bootstrap Standard Errors
5.10 The Rescaled Likelihood Ratio Test
5.11 Bootstrapping the Likelihood Ratio Statistic
5.12 Data Analysis Example 1
5.13 Data Analysis Example 2
5.14 Data Analysis Example 3
5.15 Summary
5.16 Recommended Readings
6. An Introduction to Bayesian Estimation
6.1 Chapter Overview
6.2 What Makes Bayesian Statistics Different?
6.3 A Conceptual Overview of Bayesian Estimation
6.4 Bayes' Theorem
6.5 An Analysis Example
6.6 How Does Bayesian Estimation Apply to Multiple Imputation?
6.7 The Posterior Distribution of the Mean
6.8 The Posterior Distribution of the Variance
6.9 The Posterior Distribution of a Covariance Matrix
6.10 Summary
6.11 Recommended Readings
7. The Imputation Phase of Multiple Imputation
7.1 Chapter Overview
7.2 A Conceptual Description of the Imputation Phase
7.3 A Bayesian Description of the Imputation Phase
7.4 A Bivariate Analysis Example
7.5 Data Augmentation with Multivariate Data
7.6 Selecting Variables for Imputation
7.7 The Meaning of Convergence
7.8 Convergence Diagnostics
7.9 Time-Series Plots
7.10 Autocorrelation Function Plots
7.11 Assessing Convergence from Alternate Starting Values
7.12 Convergence Problems
7.13 Generating the Final Set of Imputations
7.14 How Many Data Sets Are Needed?
7.15 Summary
7.16 Recommended Readings
8. The Analysis and Pooling Phases of Multiple Imputation
8.1 Chapter Overview
8.2 The Analysis Phase
8.3 Combining Parameter Estimates in the Pooling Phase
8.4 Transforming Parameter Estimates Prior to Combining
8.5 Pooling Standard Errors
8.6 The Fraction of Missing Information and the Relative Increase in Variance
8.7 When Is Multiple Imputation Comparable to Maximum Likelihood?
8.8 An Illustrative Computer Simulation Study
8.9 Significance Testing Using the t Statistic
8.10 An Overview of Multiparameter Significance Tests
8.11 Testing Multiple Parameters Using the D1 Statistic
8.12 Testing Multiple Parameters by Combining Wald Tests
8.13 Testing Multiple Parameters by Combining Likelihood Ratio Statistics
8.14 Data Analysis Example 1
8.15 Data Analysis Example 2
8.16 Data Analysis Example 3
8.17 Summary
8.18 Recommended Readings
9. Practical Issues in Multiple Imputation
9.1 Chapter Overview
9.2 Dealing with Convergence Problems
9.3 Dealing with Non-Normal Data
9.4 To Round or Not to Round?
9.5 Preserving Interaction Effects
9.6 Imputing Multiple-Item Questionnaires
9.7 Alternate Imputation Algorithms
9.8 Multiple Imputation Software Options
9.9 Data Analysis Example 1
9.10 Data Analysis Example 2
9.11 Summary
9.12 Recommended Readings
10. Models for Missing Not at Random Data
10.1 Chapter Overview
10.2 An Ad Hoc Approach to Dealing with MNAR Data
10.3 The Theoretical Rationale for MNAR Models
10.4 The Classic Selection Model
10.5 Estimating the Selection Model
10.6 Limitations of the Selection Model
10.7 An Illustrative Analysis
10.8 The Pattern Mixture Model
10.9 Limitations of the Pattern Mixture Model
10.10 An Overview of the Longitudinal Growth Model
10.11 A Longitudinal Selection Model
10.12 Random Coefficient Selection Models
10.13 Pattern Mixture Models for Longitudinal Analyses
10.14 Identification Strategies for Longitudinal Pattern Mixture Models
10.15 Delta Method Standard Errors
10.16 Overview of the Data Analysis Examples
10.17 Data Analysis Example 1
10.18 Data Analysis Example 2
10.19 Data Analysis Example 3
10.20 Data Analysis Example 4
10.21 Summary
10.22 Recommended Readings
11. Wrapping Things Up: Some Final Practical Considerations
11.1 Chapter Overview
11.2 Maximum Likelihood Software Options
11.3 Multiple Imputation Software Options
11.4 Choosing between Maximum Likelihood and Multiple Imputation
11.5 Reporting the Results from a Missing Data Analysis
11.6 Final Thoughts
11.7 Recommended Readings
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