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Predicting Latent Class Scores for Subsequent Analysis

Published online by Cambridge University Press:  01 January 2025

Janne Petersen ,
Karen Bandeen-Roche ,
Esben Budtz-Jørgensen  and
Klaus Groes Larsen
Janne Petersen*
Affiliation:
Copenhagen University Hospital, Hvidovre
Karen Bandeen-Roche
Affiliation:
Johns Hopkins University
Esben Budtz-Jørgensen
Affiliation:
University of Copenhagen
Klaus Groes Larsen
Affiliation:
H. Lundbeck A/S
*
Requests for reprints should be sent to Janne Petersen, Copenhagen University Hospital, Hvidovre, Denmark. E-mail: petersen.janne@gmail.com
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Abstract

Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose a new method for predicting class scores that, in contrast to posterior probability-based methods, yields consistent estimators of the parameters in the third step. Additionally, in simulation studies the new methodology exhibited only a minor loss of efficiency. Finally, the new and the posterior probability-based methods are compared in an analysis of mobility/exercise.

Keywords

classificationlatent class regressionlatent class scoresthree-step procedureleast squares class
Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 2 , April 2012 , pp. 244 - 262
Copyright
Copyright © 2012 The Psychometric Society

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