ISBN:
978-1-4625-4986-3
Language:
English
Pages:
xiv, 546 Seiten :
,
Diagramme ;
,
254 mm.
Edition:
Second edition
Series Statement:
Methodology in the Social Sciences
Keywords:
Sozialwissenschaften.
;
Statistik.
;
Methodologie.
;
Fehlende Daten.
;
Datenauswertung.
;
Sozialwissenschaften
;
Statistik
;
Methodologie
;
Fehlende Daten
;
Datenauswertung
Abstract:
The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website (www.appliedmissingdata.com) includes datasets and analysis examples from the book, up-to-date software information, and other resources.New to This Edition*Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables.*New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics.*Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples.*Most of the book is entirely new.
Description / Table of Contents:
1. Introduction to Missing Data; 1.1 Chapter Overview; 1.2 Missing Data Patterns; 1.3 Missing Data Mechanisms; 1.4 Diagnosing Missing Data Mechanisms; 1.5 Auxiliary Variables; 1.6 Analysis Example: Preparing for Missing Data Handling; 1.7 Older Missing Data Methods; 1.8 Comparing Missing Data Methods via Simulation; 1.9 Planned Missing Data; 1.10 Power Analyses for Planned Missingness Designs; 1.11 Summary and Recommended Readings; 2.-
Description / Table of Contents:
Maximum Likelihood Estimation; 2.1 Chapter Overview; 2.2 Probability Distributions versus Likelihood Functions; 2.3 The Univariate Normal Distribution; 2.4 Estimating Unknown Parameters; 2.5 Getting an Analytic Solution; 2.6 Estimating Standard Errors; 2.7 Information Matrix and Parameter Covariance Matrix; 2.8 Alternative Approaches to Estimating Standard Errors; 2.9 Iterative Optimization Algorithms; 2.10 Linear Regression; 2.11 Significance Tests; 2.12 Multivariate Normal Data; 2.13 Categorical Outcomes: Logistic and Probit Regression; 2.14 Summary and Recommended Readings; 3.-
Description / Table of Contents:
Maximum Likelihood Estimation with Missing Data; 3.1 Chapter Overview; 3.2 The Multivariate Normal Distribution Revisited; 3.3 How Do Incomplete Data Records Help?; 3.4 Standard Errors with Incomplete Data; 3.5 The Expectation Maximization Algorithm; 3.6 Linear Regression; 3.7 Significance Testing; 3.8 Interaction Effects; 3.9 Curvilinear Effects; 3.10 Auxiliary Variables; 3.11 Categorical Outcomes; 3.12 Summary and Recommended Readings; 4. Bayesian Estimation; 4.1 Chapter Overview; 4.2 What Makes Bayesian Statistics Different?; 4.3 Conceptual Overview of Bayesian Estimation; 4.4 Bayes’ Theorem; 4.5 The Univariate Normal Distribution; 4.6 MCMC Estimation with the Gibbs Sampler; 4.7 Estimating the Mean and Variance with MCMC; 4.8 Linear Regression; 4.9 Assessing Convergence of the Gibbs Sampler; 4.10 Multivariate Normal Data; 4.11 Summary and Recommended Readings; 5.-
Description / Table of Contents:
Bayesian Estimation with Missing Data; 5.1 Chapter Overview; 5.2 Imputing an Incomplete Outcome Variable; 5.3 Linear Regression; 5.4 Interaction Effects; 5.5 Inspecting Imputations; 5.6 The Metropolis–Hastings Algorithm; 5.7 Curvilinear Effects; 5.8 Auxiliary Variables; 5.9 Multivariate Normal Data; 5.10 Summary and Recommended Readings; 6. Bayesian Estimation for Categorical Variables; 6.1 Chapter Overview; 6.2 Latent Response Formulation for Categorical Variables; 6.3 Regression with a Binary Outcome; 6.4 Regression with an Ordinal Outcome; 6.5 Binary and Ordinal Predictor Variables; 6.6 Latent Response Formulation for Nominal Variables; 6.7 Regression with a Nominal Outcome; 6.8 Nominal Predictor Variables; 6.9 Logistic Regression; 6.10 Summary and Recommended Readings; 7.-
Description / Table of Contents:
Multiple Imputation; 7.1 Chapter Overview; 7.2 Agnostic versus Model-Based Multiple Imputation; 7.3 Joint Model Imputation; 7.4 Fully Conditional Specification; 7.5 Analyzing Multiply-Imputed Data Sets; 7.6 Pooling Parameter Estimates; 7.7 Pooling Standard Errors; 7.8 Test Statistic and Confidence Intervals; 7.9 When Might Multiple Imputation Give Different Answers?; 7.10 Interaction and Curvilinear Effects Revisited; 7.11 Model-Based Imputation; 7.12 Multivariate Significance Tests; 7.13 Summary and Recommended Readings; 8. Multilevel Missing Data; 8.1 Chapter Overview; 8.2 Random Intercept Regression Models; 8.3 Random Coefficient Models; 8.4 Multilevel Interaction Effects; 8.5 Three-Level Models; 8.6 Multiple Imputation; 8.7 Joint Model Imputation; 8.8 Fully Conditional Specification Imputation; 8.9 Maximum Likelihood Estimation; 8.10 Summary and Recommended Readings; 9.-
Description / Table of Contents:
Missing Not at Random Processes; 9.1 Chapter Overview; 9.2 Missing Not at Random Processes Revisited; 9.3 Major Modeling Frameworks; 9.4 Selection Models for Multiple Regression; 9.5 Model Comparisons and Individual Influence Diagnostics; 9.6 Selection Model Analysis Examples; 9.7 Pattern Mixture Models for Multiple Regression; 9.8 Pattern Mixture Model Analysis Examples; 9.9 Longitudinal Data Analyses; 9.10 Diggle–Kenward Selection Model; 9.11 Shared Parameter (Random Coefficient) Selection Model; 9.12 Random Coefficient Pattern Mixture Models; 9.13 Longitudinal Data Analysis Examples; 9.14 Summary and Recommended Readings; 10.-
Description / Table of Contents:
Special Topics and Applications; 10.1 Chapter Overview; 10.2 Descriptive Summaries, Correlations, and Subgroups; 10.3 Non-Normal Predictor Variables; 10.4 Non-Normal Outcome Variables; 10.5 Mediation and Indirect Effects; 10.6 Structural Equation Models; 10.7 Scale Scores and Missing Questionnaire Items; 10.8 Interactions with Scales; 10.9 Longitudinal Data Analyses; 10.10 Regression with a Count Outcome; 10.11 Power Analyses for Growth Models with Missing Data; 10.12 Summary and Recommended Readings; 11. Wrap-Up; 11.1 Chapter Overview; 11.2 Choosing a Missing Data-Handling Procedure; 11.3 Software Landscape; 11.4 Reporting Results from a Missing Data Analysis; 11.5 Final Thoughts and Recommended Readings; Appendix. Data Set Descriptions; Author Index; Subject Index; About the Author;
Note:
References S. 493-517, Author Index S. 519-528, Subject Index S. 529-545
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