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

Datos Bibliográficos

ID2331793
AutoresLawrence R Landerman (Duke Medical Center), K C Land (0000-0002-9551-7314, Duke University), Carl F Pieper (0000-0003-4809-1725, Duke Medical Center)
Año1997
Volumen26
Número1
Páginas3-33
Fecha de publicación1997-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSociological Methods & Research (JOURNAL)
Identificadores de la revistaISSN: 0049-1241 • E-ISSN: 1552-8294
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/0049124197026001001
OpenAlexW2170299304
IdiomaEN
Citas recibidas34
Referencias citadas8

This article reports empirical explorations of how well the predictive mean matching method for imputing missing data works for an often problematic variable-income-when income is used as an explanatory variable in a substantive regression model. It is found that the performance of the predictive mean method varies considerably with the predictive power of the imputation regression model and the percentage of cases with missing data on income. In comparisons of single-value with multiple-imputation methods, it also is found that the amount of bias and the loss of precision associated with single-value methods is considerably less than that associated with a weak imputation model. Situations in which using imputed data can lead to seriously biased estimates of regression coefficients (and related statistics) and situations in which the bias is so minimal as to be nonproblematic are identified

Econometrics · Imputation (statistics) · Matching (statistics) · Missing data · Predictive power · Regression · Regression analysis · Statistics · Mathematics · Spatial and Panel Data Analysis · Statistical Methods and Bayesian Inference · Statistical Methods and Inference