Missing Data

Paul D. Allison - University of Pennsylvania, University of Pennsylvania, USA

Volume: 136

Series:
Quantitative Applications in the Social Sciences

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.

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ISBN: 9780761916727	Paperback	Suggested Retail Price: $42.00	Bookstore Price: $33.60
ISBN: 9781071962510	Electronic Version	Suggested Retail Price: $36.00	Bookstore Price: $28.80

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Series Editor's Introduction

1. Introduction

2. Assumptions

Missing Completely at Random

Missing at Random

Ignorable

Nonignorable

3. Conventional Methods

Listwise Deletion

Pairwise Deletion

Dummy Variable Adjustment

Imputation

Summary

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

Conclusion

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

Notes

References

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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Missing Data

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