New Developments in Quantitative Psychology pp 187-197
A Monte Carlo Approach for Nested Model Comparisons in Structural Equation Modeling
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- Pornprasertmanit S., Wu W., Little T.D. (2013) A Monte Carlo Approach for Nested Model Comparisons in Structural Equation Modeling. In: Millsap R., van der Ark L., Bolt D., Woods C. (eds) New Developments in Quantitative Psychology. Springer Proceedings in Mathematics & Statistics, vol 66. Springer, New York, NY
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.