Técnicas de correção do teste qui-quadrado para amostras não normais

Abstract

Techniques for Correction the Chi-Square Test for Non-Normal Samples This paper aims to evaluate techniques for correcting the chi-square test (χ2) as applied to Confirmatory Factor Analysis (CFA) models in non-normal data. In a simulated and exploratory approach, distinct distributions were analyzed in terms of multivariate kurtosis. In most situations, it was observed a tendency of the analyzed tests to produce differing corrections on the χ2 values, as well as for the CFI and RMSEA values. Among other tests evaluated, this study suggested the use of the Elliptical Test with Least Squares (Elliptical Theory), Heterogeneous Kurtosis Test with Reweighted Least Squares (Heterogeneous Kurtosis Theory) and Satorra-Bentler Scaled Test with Maximum Likelihood estimation (for distributions with excessive univariate asymmetry and/or kurtosis). However, due to the correction factor, the Satorra-Bentler Scaled test can accept moderately poorly specified models in the presence of extreme kurtosis.