Missing data

by Allison, Paul David

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Title Statements

Full title :Missing data / Paul D. Allison.

Summary

"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."--Pub. desc.

Edition Statements

Publication Date:©2002.

Publication Statements

Publisher(s):SAGE Publications
Place of Publication:Thousand Oaks, Calif
Language:eng
Publication Date:©2002.

Notes

"A SAGE university paper"--Cover.

The Physical Object

Description:vi, 93 pages : : illustrations ; : 22 cm

Edition Identifiers

ISBN:0761916725, 9780761916727
oclc:ocm46364640
LC Classification:QA276
Dewey Decimal:519.54

1. Introduction -- 2. Assumptions ; Missing Completely at Random ; Missing at Random ; Ignorable ; Nonignorable -- 3. Conventional Methods ; Listwise ; Deletion; Pairwise Deletion ; Dummy Variable Adjustment ; Imputation -- 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 -- 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 -- 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…

Subjects

Analyse des données Données statistiques Fehlende Daten Mathematical statistics Méthodes statistiques Missing observations (Statistics) Modèles économiques Observations manquantes (statistique) Ontbrekende gegevens Sciences sociales statistiek Statistik Statistique mathématique