Measures of fit impacts: Application to the causal model of consumer involvement

Eric Rakotoasimbola and Sam Blili

Using the Monte Carlo simulation method, this study analyzes the impacts on fit indices by the degree of nonnormality of variables, the sample size, and the choice of estimation method.

Introduction

Quantitative statisticians often encounter situations where the collected data fail to comply with the assumptions underlying the method that they are using. For instance, in the social sciences, the assumption that variables are normally distributed is rarely satisfied (e.g., Gierl & Mulvenon, 1995; Harlow, 1985; Micceri, 1989). In the case of structural equation modeling (SEM), although recommended standards exist to address violation of assumptions, its reliability has not yet been formally determined. For example, researchers typically use maximum likelihood estimation (MLE) as the standard method for estimating the parameters of structural equations (e.g., Baumgartner & Homburg, 1996; Martínez-López, Gázquez-Abad,...

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