Article1987

Robust Structural Equation Models: Implications for Developmental Psychology

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

Abstract

HUBA, GEORGE J., and HARLOW, LISA L. Robust Structural Equation Models: Implications for Developmental Psychology. CHILD DEVELOPMENT, 1987, 58, 147-166. Advances in structural equation modeling techniques have made it possible to test models in data that are not normally distributed. This can lead to more realistic model testing in developmental psychology. Several alternate techniques are illustrated in structural equation models here in order to compare the results that are obtained. Maximum-likelihood and generalized least-squares estimators for normally distributed data are compared with Browne's asymptotically distribution-free technique for continuous nonnormally distributed data and Muthen's estimator for dichotomous indicators. While different critical ratios are found for some parameters estimated in models, the results are generally comparable so long as one does not consider absolute fit to be a critical factor in accepting a causal model as a good one.

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References

1984A General Structural Equation Model with Dichotomous, Ordered Categorical, and Continuous Latent Variable Indicators 1984Asymptotically distribution‐free methods for the analysis of covariance structures 1987"How Big Is Big Enough?": Sample Size and Goodness of Fit in Structural Equation Models with Latent Variables 1967Some Contributions to Maximum Likelihood Factor Analysis 1985A fit index for covariance structure models under arbitrary GLS estimation 1978Contributions to Factor Analysis of Dichotomous Variables 1982COVARIANCE STRUCTURES 1983Latent variable structural equation modeling with categorical data

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