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A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses

Published online by Cambridge University Press:  01 January 2025

Vassilis G. S. Vasdekis ,
Silvia Cagnone  and
Irini Moustaki
Vassilis G. S. Vasdekis*
Affiliation:
Athens University of Economics and Business
Silvia Cagnone
Affiliation:
University of Bologna
Irini Moustaki
Affiliation:
London School of Economics and Political Science
*
Requests for reprints should be sent to Vassilis G.S. Vasdekis, Department of Statistics, Athens University of Economics and Business, 76 Patission Street, 10434 Athens, Greece. E-mail: vasdekis@aueb.gr
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Abstract

The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate ordinal items. Time-dependent latent variables are linked with an autoregressive model. Simulation results have shown composite likelihood estimators to have a small amount of bias and mean square error and as such they are feasible alternatives to full maximum likelihood. Model selection criteria developed for composite likelihood estimation are used in the applications. Furthermore, lower-order residuals are used as measures-of-fit for the selected models.

Keywords

composite likelihoodlongitudinalordinal datalatent variablesgoodness-of-fit measures
Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 3 , July 2012 , pp. 425 - 441
Copyright
Copyright © 2012 The Psychometric Society

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