Influence of Sample Size, Estimation Method and Normality on Fit Indices in Confirmatory Factor Analysis

Abstract

In this study, Monte Carlo simulation is used to evaluate the characteristics of CFA fit indices under different conditions (such as sample size, estimation method and distributional conditions). The simulation study was performed using seven different samples where sample has a different sample size such as 50, 100, 200, 400, 800, 1600, 4000, four different estimation methods (Maximum Likelihood, Generalized Least Square, Least Square and Weighted Least Square) and three distribution conditions (normal, slightly non-normal and moderately non-normal). A simulation study was conducted with EQS software to examine the effect of these conditions on the most common eleven fit indices that are studied in CFA and SEM. As a result of this study, all of the factors studied are shown to have an influence on the fit indices.