AUTHOR=Jeon Minjeong , Rijmen Frank 

TITLE=Recent developments in maximum likelihood estimation of MTMM models for categorical data

JOURNAL=Frontiers in Psychology

VOLUME=Volume 5 - 2014

YEAR=2014

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2014.00269

DOI=10.3389/fpsyg.2014.00269

ISSN=1664-1078

ABSTRACT=Maximum likelihood (ML) estimation of categorical multitrait-multimethod (MTMM) data is challenging because the likelihood involves high-dimensional integrals over the crossed method and trait factors, with no known closed-form solution.<br/>The purpose of the study is to introduce three newly developed ML methods that are eligible for estimating MTMM models with categorical responses: Variational maximization-maximization, Alternating imputation posterior, and Monte Carlo local likelihood. Each method is briefly described and its applicability for MTMM models with categorical data are discussed.<br/>An illustration is provided using an empirical example.