UID:
almafu_9961900683702883
Format:
1 online resource (vi, 91 p.) :
,
ill.
ISBN:
9781412985079 (ebook) :
,
9781452207902
,
1452207909
,
9781412985079
,
1412985072
Series Statement:
Quantitative applications in the social sciences ; 136
Content:
Using numerous examples and practical tips, this book offers a non-technical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer methods, maximum likelihood and multiple imputation.
Note:
"A SAGE university paper".
,
Cover -- CONTENTS -- Series Editor's Introduction -- Chapter 1 - Introduction -- Chapter 2 - Assumptions -- Missing Completely at Random -- Missing at Random -- Ignorable -- Nonignorable -- Chapter 3 - Conventional Methods -- Listwise Deletion -- Pairwise Deletion -- Dummy Variable Adjustment -- Imputation -- Summary -- Chapter 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 -- Chapter 5 - Multiple Imputation: Basics -- 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 Versus Parallel Chains of Data Augmentation -- Using the Normal Model for Nonnormal or Categorical Data -- Exploratory Analysis -- MI Example 1 -- Chapter 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 -- Chapter 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 -- Chapter 8 - Summary and Conclusion -- Notes -- References -- About the Author.
,
English
Additional Edition:
ISBN 9780761916727
Additional Edition:
ISBN 0761916725
Language:
English
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