AUTHOR=Jeon Minjeong , Rijmen Frank TITLE=Recent developments in maximum likelihood estimation of MTMM models for categorical data JOURNAL=Frontiers in Psychology VOLUME=5 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. 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 (e.g., Rijmen and Jeon, 2013), alternating imputation posterior (e.g., Cho and Rabe-Hesketh, 2011), and Monte Carlo local likelihood (e.g., Jeon et al., under revision). Each method is briefly described and its applicability for MTMM models with categorical data are discussed.