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.
CITATION STYLE
Pornprasertmanit, S., Wu, W., & Little, T. D. (2013). A monte carlo approach for nested model comparisons in structural equation modeling. In Springer Proceedings in Mathematics and Statistics (Vol. 66, pp. 187–197). Springer New York LLC. https://doi.org/10.1007/978-1-4614-9348-8_12
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