A monte carlo approach for nested model comparisons in structural equation modeling

Sunthud Pornprasertmanit, Wei Wu, Todd D. Little

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations


This paper proposes a Monte Carlo approach for nested model comparisons. This approach allows for test of approximate equivalency in fit between nested models and customizing cutoff criteria for difference in a fit index. Different methods to account for trivial misspecification in the Monte Carlo approach are also discussed. A simulation study is conducted to compare the Monte Carlo approach with different methods of imposing trivial misspecification to chi-square difference test and change in comparative fit index (CFI) with suggested cutoffs. The simulation study shows that the Monte Carlo approach is superior to the chi-square difference test by correctly retaining the nested model with trivial misspecification. It is also superior to the change in CFI by offering higher power to detect severe misspecification.

Original languageEnglish
Title of host publicationNew Developments in Quantitative Psychology - Presentations from the 77th Annual Psychometric Society Meeting
EditorsL. Andries van der Ark, Roger E. Millsap, Daniel M. Bolt, Carol M. Woods
PublisherSpringer New York LLC
Number of pages11
ISBN (Print)9781461493471
StatePublished - 2013
Event77th Annual Meeting of the Psychometric Society, 2012 - Lincoln, United States
Duration: Jul 9 2012Jul 12 2012

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Conference77th Annual Meeting of the Psychometric Society, 2012
Country/TerritoryUnited States


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