Comparing RMSEA-Based Indices for Assessing Measurement Invariance in Confirmatory Factor Models

Fit indices are descriptive measures that can help evaluate how well a confirmatory factor analysis (CFA) model fits a researcher's data. In multigroup models, before between-group comparisons are made, fit indices may be used to evaluate measurement invariance by assessing the degree to which multiple groups' data are consistent with increasingly constrained nested models. One such fit index is an adaptation of the root mean square error of approximation (RMSEA) called RMSEAD. This index embeds the chi-square and degree-of-freedom differences into a modified RMSEA formula. The present study comprehensively compared RMSEAD to Delta RMSEA, the difference between two RMSEA values associated with a comparison of nested models. The comparison consisted of both derivations as well as a population analysis using one-factor CFA models with features common to those found in practical research. The findings demonstrated that for the same model, RMSEAD will always have increased sensitivity relative to Delta RMSEA with an increasing number of indicator variables. The study also indicated that RMSEAD had increased ability to detect noninvariance relative to Delta RMSEA in one-factor models. For these reasons, when evaluating measurement invariance, RMSEAD is recommended instead of Delta RMSEA.

Automated summary

This study compared two fit indices, RMSEP which is a modified version of the adequacy index, and the RMSEP difference Delta RMSEP between nested models. The findings showed that RMSEP has increased sensitivity compared to Delta RMSEP as the number of indicator variables increases in the same model. The study also indicated that RMSEP has increased ability to detect noninvariance relative to Delta RMSEP in one-factor models.