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Author
M.T. Barendse
R. Ligtvoet
M.E. Timmerman
F.J. Oort
Date
4-2016
Title
Model fit after pairwise maximum likelihood
Journal
Frontiers in Psychology
Volume
7
Article number
528
Number of pages
8
Document type
Article
Faculty
Faculty of Social and Behavioural Sciences (FMG)
Institute
Research Institute of Child Development and Education (RICDE)
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.
URL
go to publisher's site
Link
Final publisher version
Language
English
Note
With supplemental data
Permalink
http://hdl.handle.net/11245.1/43d3d58d-0f85-4597-a7e2-a9ab0767eb5f
Downloads
  • fpsyg-07-00528

  • Model fit after pairwise maximum likelihood_data sheet 1

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