Missing Data

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 relied 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.

Paul D. Allison is Professor of Sociology at the University of Pennsylvania.

Series Editor's Introduction

v

Introduction

1

(2)

Assumptions

3

(2)

Missing Completely at Random

3

(1)

Missing at Random

4

(1)

Ignorable

5

(1)

Nonignorable

5

(1)

Conventional Methods

5

(7)

Listwise Deletion

6

(2)

Pairwise Deletion

8

(1)

Dummy Variable Adjustment

9

(2)

Imputation

11

(1)

Summary

12

(1)

Maximum Likelihood

12

(15)

Review of Maximum Likelihood

13

(1)

ML With Missing Data

14

(1)

Contingency Table Data

15

(3)

Linear Models With Normally Distributed Data

18

(1)

The EM Algorithm

19

(2)

EM Example

21

(2)

Direct ML

23

(2)

Direct ML Example

25

(1)

Conclusion

26

(1)

Multiple Imputation: Basics

27

(23)

Single Random Imputation

28

(1)

Multiple Random Imputation

29

(1)

Allowing for Random Variation in the Parameter Estimates

30

(2)

Multiple Imputation Under the Multivariate Normal Model

32

(2)

Data Augmentation for the Multivariate Normal Model

34

(2)

Convergence in Data Augmentation

36

(1)

Sequential Versus Parallel Chains of Data Augmentation

37

(1)

Using the Normal Model for Nonnormal or Categorical Data

38

(3)

Exploratory Analysis

41

(1)

MI Example 1

41

(9)

Multiple Imputation: Complications

50

(27)

Interactions and Nonlinearities in MI

50

(2)

Compatibility of the Imputation Model and the Analysis Model

52

(1)

Role of the Dependent Variable in Imputation

53

(1)

Using Additional Variables in the Imputation Process

54

(1)

Other Parametric Approaches to Multiple Imputation

55

(2)

Nonparametric and Partially Parametric Methods

57

(7)

Sequential Generalized Regression Models

64

(1)

Linear Hypothesis Tests and Likelihood Ratio Tests

65

(3)

MI Example 2

68

(5)

Ml for Longitudinal and Other Clustered Data

73

(1)

Ml Example 3

74

(3)

Nonignorable Missing Data

77

(7)

Two Classes of Models

78

(1)

Heckman's Model for Sample Selection Bias

79

(3)

ML Estimation With Pattern-Mixture Models

82

(1)

Multiple Imputation With Pattern-Mixture Models

83

(1)

Summary and Conclusion

84

(3)

Notes

87

(2)

References

89

(4)

About the Author

93

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Summary

Author Biography

Table of Contents

Supplemental Materials