Publication

Model fit after pairwise maximum likelihood

Barendse, M. T., Ligtvoet, R., Timmerman, M. E. & Oort, F. J., 21-Apr-2016, In : Frontiers in Psychology. 7, 8 p., 528.

Research output: Contribution to journalArticleAcademicpeer-review

Copy link to clipboard

Documents

DOI

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.

Original languageEnglish
Article number528
Number of pages8
JournalFrontiers in Psychology
Volume7
Publication statusPublished - 21-Apr-2016

    Keywords

  • discrete data, pairwise maximum likelihood analysis, weighted least squares analysis, fit statistics, STRUCTURAL EQUATION MODELS, WEIGHTED LEAST-SQUARES, POLYTOMOUS VARIABLES, CONTINGENCY-TABLES, POLYCHORIC CORRELATIONS, EXPECTED FREQUENCIES, ORDINAL VARIABLES, PERFORMANCE

View graph of relations

Download statistics

No data available

ID: 32776431