Model Fit Evaluation in Exploratory Structural Equation Modeling (Poster 25)

• In Event: AERA Poster Session 9
  In Poster Session: Structural Equation Modeling (San Diego)

Abstract

Exploratory structural equation modeling (ESEM; Asparouhov & Muthén, 2009) allows items to crossly load on nontarget factors and provides flexibility in modeling multidimensional survey data. ESEM estimates a significantly greater number of parameters and thus increases model complexity relative to the conventional SEM. This study aims to evaluate the model fit measures including the goodness-of-fit indices and information criteria in ESEM through a simulation study. The design factors included the model complexity, factor correlations, magnitudes and percentages of cross-loadings, and sample sizes. Findings will provide empirical researchers some guidelines to choose proper fit measures for evaluating the model fit in ESEM.

Authors

• Ji Li, University of Arkansas for Medical Sciences

• Xinya Liang, University of Arkansas

• Chunhua Cao, University of Alabama

• Wen-Juo Lo, University of Arkansas

• Paula Castro Abbott, University of Arkansas at Fayetteville