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Missing Data

Missing Data

August 2001 | 104 pages | SAGE Publications, Inc
Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data. 

Series Editor's Introduction
1. Introduction
2. Assumptions
Missing Completely at Random  
Missing at Random  
3. Conventional Methods
Listwise Deletion  
Pairwise Deletion  
Dummy Variable Adjustment  
4. Maximum Likelihood
Review of Maximum Likelihood  
ML With Missing Data  
Contingency Table Data  
Linear Models With Normally Distributed Data  
The EM Algorithm  
EM Example  
Direct ML  
Direct ML Example  
5. Multiple Imputation: Bascis
Single Random Imputation  
Multiple Random Imputation  
Allowing for Random Variation in the Parameter Estimates  
Multiple Imputation Under the Multivariate Normal Model  
Data Augmentation for the Multivariate Normal Model  
Convergence in Data Augmentation  
Sequential Verses Parallel Chains of Data Augmentation  
Using the Normal Model for Nonnormal or Categorical Data  
Exploratory Analysis  
MI Example 1  
6. Multiple Imputation: Complications
Interactions and Nonlinearities in MI  
Compatibility of the Imputation Model and the Analysis Model  
Role of the Dependent Variable in Imputation  
Using Additional Variables in the Imputation Process  
Other Parametric Approaches to Multiple Imputation  
Nonparametric and Partially Parametric Methods  
Sequential Generalized Regression Models  
Linear Hypothesis Tests and Likelihood Ratio Tests  
MI Example 2  
MI for Longitudinal and Other Clustered Data  
MI Example 3  
7. Nonignorable Missing Data
Two Classes of Models  
Heckman's Model for Sample Selection Bias  
ML Estimation With Pattern-Mixture Models  
Multiple Imputation With Pattern-Mixture Models  
8. Summary and Conclusion
About the Author

"…an excellent resource for researchers who are conducting multivariate statistical studies."

Richard A. Chechile
Journal of Mathematical Psychology

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