Comparison of weighted least squares and robust estimation in structural equation modeling of ordinal categorical data with larger sample sizes

Abstract

The effect of different sample sizes on estimation methods such as weighted least squares and robust weighted least squares that are used in structural equation modeling was studied and compared using information criteria such as Akaike Information Criteria in this study. The simulations were repeated 1000 times with two estimation methods and the average values of criteria were calculated with different sample sizes. The study includes a construct of four factors, with four questions of each that are measured on a five-point Likert scale. Different sample sizes, ranging from 300 to 5000 were selected. According to the simulations results, it is concluded that the robust estimation method provides more effective results at lower sample size. In addition, it was found that as the sample size increases, the efficiency difference between two methods gradually decreases. Moreover, it was detected that there is almost no difference between the two methods for sample sizes over 3000.