Parsimony‐based fit indices for multiple‐indicator models: Do they work?

Larry J. Williams · Patricia J. Holahan

253 citations0/0 files storedStructural Equation Modeling A Multidisciplinary Journal

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Psychometric Methodologies and Testing Statistical Methods and Applications Statistical Methods and Bayesian Inference

Abstract

A frequently used type of model in applications of covariance structure analysis is one referred to as a multiple‐indicator regression model. This study takes a simulation approach to investigate seven parsimony‐based indices used to evaluate this type of model. Four representative theoretical models were examined, and the number of indicators used to represent latent variables was varied with two of the models. Both correctly and incorrectly specified models were fit to the data. The results show that the Akaike information criteria, the root mean square index, and the Tucker‐Lewis index were the most effective indices. The implications of the findings for the model selection process are discussed.

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References

1988Structural equation modeling in practice: A review and recommended two-step approach.1990Comparative fit indexes in structural models.1980Significance tests and goodness of fit in the analysis of covariance structures.1990Structural Model Evaluation and Modification: An Interval Estimation Approach 1973A Reliability Coefficient for Maximum Likelihood Factor Analysis 1987Factor Analysis and AIC 1988Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size.1989Evaluation of goodness-of-fit indices for structural equation models.

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