A simulation study comparing model fit measures of structural equation modeling with multivariate contaminated normal distribution

Abstract

SEM is a very popular analytical method for quantitative studies in many disciplines. The goal of SEM is to determine the model that best fits the data. The data sets were generated from the multivariate contaminated normal distribution in this study. The aim of the study was to determine the best goodness of fit measures not affected by data structures and sample sizes. ADF, ML, and GLS estimation methods were applied under the multivariate nonnormality. According to the ADF method, AGFI, GFI, and RMSEA were not affected by sample size and correlation changes. CFI, NFI, and NNFI values were affected by both sample size and correlation changes. RMR always received values outside the acceptable fit limits in all sample sizes and correlation values. Consequently; if the data are multivariate nonnormality, the sample size should be greater than 250 units and AGFI, GFI and RMSEA fit measures should be used.

Keywords:

• ADF method
• Confirmatory factor analysis
• Goodness of fit measures
• Multivariate nonnormality

MATHEMATICS SUBJECT CLASSIFICATION:

• 62D05
• 62E10
• 62H25
• 62J10