Factors Influencing Cross-Validation of Confirmatory Factor Analysis Models

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Abstract
This study investigated, by means of a Monte Carlo study, the use of four cross-validation indices with confirmatory factor analysis models. The influence on the cross-validation indices of three design factors: sample size, loading size, and the degree of model misspecification, was studied. Sample size was varied at 200 and 600, and loading size at .4, .6, and .8. Model misspecifications were introduced by setting nonzero factor loadings to equal zero. A modified version of the Akaike Information Criterion (AIC) obtained very accurate estimates of the two-sample index under all sets of conditions. As expected, larger sample sizes and better specified models resulted in better cross-validation results. The presence of larger factor loadings increased the number of times the cross-validation indices yielded optimal values for the correctly specified model.