Abstract:

Structural Equation Modeling (SEM) is a relatively new method, having its roots in the 1970s. Most applications have been in psychology, sociology, the biological sciences, educational research, political science, and market research. SEM is a modeling technique that can handle a large number of endogenous and exogenous variables, as well as latent variables specified as linear combinations of the observed variables. Because of this reason, the assessment of model fit in structural equation modeling (SEM) has long been a difficult issue in SEM applications. The use of Monte Carlo (MC) simulations for empirical assessment of statistical estimators and model fit is becoming more common in structural equation modeling. In this study, a Monte Carlo simulation study was conducted to investigate the effects on structural equation modeling (SEM) fit indices of, model specification, sample size and estimation method

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