Recent developments in maximum likelihood estimation of MTMM models for categorical data - Jeon, Minjeong; Rijmen, Frank (Frontiers in Psychology, 2014)
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, Alternating imputation posterior, and Monte Carlo local likelihood. Each method is briefly described and its applicability for MTMM models with categorical data are discussed. An illustration is provided using an empirical example.