title: Model Fit after Pairwise Maximum Likelihood.
abstract: Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log-likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two-way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations.
authors.affiliation: Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University Ghent, Belgium.
authors.forename: M T
authors.initials: MT
authors.lastname: Barendse
authors.affiliation: Department of Education, Research Institute of Child Development and Education, University of Amsterdam Amsterdam, Netherlands.
authors.forename: R
authors.initials: R
authors.lastname: Ligtvoet
authors.affiliation: Department Psychometrics and Statistics, Heymans Institute for Psychological Research, Psychometrics and Statistics, University of Groningen Groningen, Netherlands.
authors.forename: M E
authors.initials: ME
authors.lastname: Timmerman
authors.affiliation: Department of Education, Research Institute of Child Development and Education, University of Amsterdam Amsterdam, Netherlands.
authors.forename: F J
authors.initials: FJ
authors.lastname: Oort
keywords: discrete data; fit statistics; pairwise maximum likelihood analysis; weighted least squares analysis