Missing Data, Issue 136 (Google eBook)

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Paul D. Allison
SAGE, 2002 - Mathematics - 93 pages
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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.

  

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Contents

Introduction
1
Assumptions
3
Missing at Random
4
Ignorable
5
Listwise Deletion
6
Pairwise Deletion
8
Dummy Variable Adjustment
9
Imputation
11
Sequential Versus Parallel Chains of Data Augmentation
37
Using the Normal Model for Nonnormal or Categorical Data
38
Exploratory Analysis
41
Multiple Imputation Complications
50
Compatibility of the Imputation Model and the Analysis Model
52
Role of the Dependent Variable in Imputation
53
Using Additional Variables in the Imputation Process
54
Other Parametric Approaches to Multiple Imputation
55

Summary
12
Review of Maximum Likelihood
13
ML With Missing Data
14
Contingency Table Data
15
Linear Models With Normally Distributed Data
18
The EM Algorithm
19
EM Example
21
Direct ML
23
Direct ML Example
25
Conclusion
26
Multiple Imputation Basics
27
Single Random Imputation
28
Multiple Random Imputation
29
Allowing for Random Variation in the Parameter Estimates
30
Multiple Imputation Under the Multivariate Normal Model
32
Data Augmentation for the Multivariate Normal Model
34
Convergence in Data Augmentation
36
Nonparametric and Partially Parametric Methods
57
Sequential Generalized Regression Models
64
Linear Hypothesis Tests and Likelihood Ratio Tests
65
MI Example 2
68
MI for Longitudinal and Other Clustered Data
73
MI Example 3
74
Nonignorable Missing Data
77
Two Classes of Models
78
Heckmans Model for Sample Selection Bias
79
ML Estimation With PatternMixture Models
82
Multiple Imputation With PatternMixture Models
83
Summary and Conclusion
84
Notes
87
References
89
About the Author
93
Copyright

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About the author (2002)

Paul D. Allison is Professor of Sociology at theUniversityofPennsylvania, where he teaches advanced graduate courses on event history analysis, categorical data analysis, and structural equation models with latent variables. He is the author of seven books and more than 50 journal articles. Every summer he teaches 5-day workshops on survival analysis and logistic regression analysis that draw about 100 researchers from around theU.S. A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology.