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A General Approach to Categorical Data Analysis with Missing Data, using Generalized Linear Models with Composite Links

Published online by Cambridge University Press:  01 January 2025

David Rindskopf
David Rindskopf*
Affiliation:
City University of New York Graduate Center
*
Requests for reprints should be sent to David Rindskopf, Educational Psychology Program, CUNY Graduate Center, 33 West 42nd Street, New York, NY 10036.
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Abstract

A general approach for analyzing categorical data when there are missing data is described and illustrated. The method is based on generalized linear models with composite links. The approach can be used (among other applications) to fill in contingency tables with supplementary margins, fit loglinear models when data are missing, fit latent class models (without or with missing data on observed variables), fit models with fused cells (including many models from genetics), and to fill in tables or fit models to data when variables are more finely categorized for some cases than others. Both Newton-like and EM methods are easy to implement for parameter estimation.

Keywords

missing datageneralized linear modelscategorical data
Type
Original Paper
Information
Psychometrika , Volume 57 , Issue 1 , March 1992 , pp. 29 - 42
Copyright
Copyright © 1992 The Psychometric Society

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Footnotes

The author thanks the editor, the reviewers, Laurie Hopp Rindskopf, and Clifford Clogg for comments and suggestions that substantially improved the paper.

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