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Latent Class Models for Diary Method Data: Parameter Estimation by Local Computations

Published online by Cambridge University Press:  01 January 2025

Frank Rijmen ,
Kristof Vansteelandt  and
Paul De Boeck
Frank Rijmen*
Affiliation:
VU Medical Center, Katholieke Universiteit Leuven
Kristof Vansteelandt
Affiliation:
UC Sint-Josef Kortenberg
Paul De Boeck
Affiliation:
Katholieke Universiteit Leuven
*
Requests for reprints should be sent to Frank Rijmen, Clinical Epidemiology and Biostatistics, VU Medical Center, De Boelelaan 1118, 1007 MB Amsterdam, The Netherlands. E-mail: [email protected]
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Abstract

The increasing use of diary methods calls for the development of appropriate statistical methods. For the resulting panel data, latent Markov models can be used to model both individual differences and temporal dynamics. The computational burden associated with these models can be overcome by exploiting the conditional independence relations implied by the model. This is done by associating a probabilistic model with a directed acyclic graph, and applying transformations to the graph. The structure of the transformed graph provides a factorization of the joint probability function of the manifest and latent variables, which is the basis of a modified and more efficient E-step of the EM algorithm. The usefulness of the approach is illustrated by estimating a latent Markov model involving a large number of measurement occasions and, subsequently, a hierarchical extension of the latent Markov model that allows for transitions at different levels. Furthermore, logistic regression techniques are used to incorporate restrictions on the conditional probabilities and to account for the effect of covariates. Throughout, models are illustrated with an experience sampling methodology study on the course of emotions among anorectic patients.

Keywords

graphical modelslatent Markov modelhierarchical latent Markov modeljunction tree algorithm
Type
Theory and Methods
Information
Psychometrika , Volume 73 , Issue 2 , June 2008 , pp. 167 - 182
Copyright
Copyright © 2007 Psychometric Society

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Footnotes

Frank Rijmen was partly supported by the Fund for Scientific Research Flanders (FWO).

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