Recent developments in maximum likelihood estimation of MTMM models for categorical data.
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.
Department of Psychology, Ohio State University Columbus, OH, USA.
Minjeong
M
Jeon
CTB/McGraw-Hill, Psychometric Services Monterey, CA, USA.
Frank
F
Rijmen
alternating imputation posterior; crossed factors; maximum likelihood estimation; monte carlo local likelihood; multitrait-multimethod model; variational maximization-maximization