A Monte Carlo Approach for Nested Model Comparisons in Structural Equation Modeling

  • Sunthud Pornprasertmanit
  • Wei Wu
  • Todd D. Little
Conference paper

DOI: 10.1007/978-1-4614-9348-8_12

Volume 66 of the book series Springer Proceedings in Mathematics & Statistics (PROMS)
Cite this paper as:
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

Abstract

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.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Sunthud Pornprasertmanit
    • 1
  • Wei Wu
    • 1
  • Todd D. Little
    • 2
  1. 1.Department of Psychology and Center for Research Methods and Data AnalysisUniversity of KansasLawrenceUSA
  2. 2.Educational Psychology and Leadership Director, Institute for Measurement, Methodology Analysis and Policy College of EducationTexas Tech UniversityLubbockUSA