Sample Size and Non-normality Effects on Goodness of Fit Measures in Structural Equation Model

(2017) - A. K Ainur, M. D Sayang, Z Jannoo, B. W Yap

Journal: Pertanika Journal of Science & Technology
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Tags:: #paper #Methods #StructuralEquationModel
Cite Key:: [@Ainur2017]

Abstract

A Structural Equation Model (SEM) is often used to test whether a hypothesised theoretical model agrees with data by examining the model fit. This study investigates the effect of sample size and distribution of data (normal and non-normal) on goodness of fit measures in structural equation model. Simulation results confirm that the GoF measures are affected by sample size, whereas they are quite robust when data are not normal. Absolute measures (GFI, AGFI, RMSEA) are more affected by sample size while incremental fit measures such as TLI and CFI are less affected by sample size and non-normality.

Notes

“Simulation results confirm that the GoF measures are affected by sample size, whereas they are quite robust when data are not normal.” (Ainur et al., 2017, p. 575)

“Structural equation modeling (SEM) is a statistical technique that combines elements in traditional multivariate models, such as regression analysis, factor analysis, and simultaneous equation modeling” (Ainur et al., 2017, p. 575)