Multiple Imputation for Missing Data

Dados Bibliográficos

AUTOR(ES) P.D. Allison
AFILIAÇÃO(ÕES) University of Pennsylvania
ANO 2000
TIPO Artigo
PERIÓDICO Sociological Methods and Research
ISSN 0049-1241
E-ISSN 1552-8294
EDITORA Annual Reviews (United States)
DOI 10.1177/0049124100028003003
CITAÇÕES 31
ADICIONADO EM 2025-08-18
MD5 0a51b6c12305d63aba6f0d7883403b41

Resumo

Two algorithms for producing multiple imputations for missing data are evaluated with simulated data. Software using a propensity score classifier with the approximate Bayesian bootstrap produces badly biased estimates of regression coefficients when data on predictor variables are missing at random or missing completely at random. On the other hand, a regression-based method employing the data augmentation algorithm produces estimates with little or no bias.

An Empirical Evaluation of the Predictive Mean Matching Method for Imputing Missing Values

(1997)

The Analysis of Social Science Data with Missing Values

(1989)

Ferramentas

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