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Quantitative Applications in the Social Sciences Ser.: Missing Data by Paul D. Allison (2001, Trade Paperback)
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About this product
Product Identifiers
PublisherSAGE Publications, Incorporated
ISBN-100761916725
ISBN-139780761916727
eBay Product ID (ePID)1911719
Product Key Features
Number of Pages104 Pages
LanguageEnglish
Publication NameMissing Data
Publication Year2001
SubjectProbability & Statistics / General, Research
TypeTextbook
AuthorPaul D. Allison
Subject AreaMathematics, Social Science
SeriesQuantitative Applications in the Social Sciences Ser.
FormatTrade Paperback
Dimensions
Item Height0.3 in
Item Weight5 Oz
Item Length8.5 in
Item Width5.5 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2001-001295
Dewey Edition21
Series Volume Number136
IllustratedYes
Dewey Decimal001.4/22
Table Of ContentSeries Editors Introduction1. Introduction2. Assumptions Missing Completely at Random Missing at Random Ignorable Nonignorable3. Conventional Methods Listwise Deletion Pairwise Deletion Dummy Variable Adjustment Imputation Summary4. 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 Conclusion5. 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 16. 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 37. Nonignorable Missing Data Two Classes of Models Heckmans Model for Sample Selection Bias ML Estimation With Pattern-Mixture Models Multiple Imputation With Pattern-Mixture Models8. Summary and ConclusionNotesReferencesAbout the Author
SynopsisUsing 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., 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.
LC Classification NumberQA276.A55 2002
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