The Analysis of Social Science Data with Missing Values

742Citations
Citations of this article
389Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Methods for handling missing data in social science data sets are reviewed. Limitations of common practical approaches, including complete-case analysis, available-case analysis and imputation, are illustrated on a simple missing-data problem with one complete and one incomplete variable. Two more principled approaches, namely maximum likelihood under a model for the data and missing-data mechanism and multiple imputation, are applied to the bivariate problem. General properties of these methods are outlined, and applications to more complex missing-data problems are discussed. The EM algorithm, a convenient method for computing maximum likelihood estimates in missing-data problems, is described and applied to two common models, the multivariate normal model for continuous data and the multinomial model for discrete data. Multiple imputation under explicit or implicit models is recommended as a method that retains the advantages of imputation and overcomes its limitations. © 1989, SAGE PUBLICATIONS. All rights reserved.

Cite

CITATION STYLE

APA

Little, R. J. A., & Rubin, D. B. (1989). The Analysis of Social Science Data with Missing Values. Sociological Methods & Research, 18(2–3), 292–326. https://doi.org/10.1177/0049124189018002004

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free