Advantages of Using Unweighted Approximation Error Measures for Model Fit Assessment.

ABSTRACT: Fit indices are highly frequently used for assessing the goodness of fit of latent variable models. Most prominent fit indices, such as the root-mean-square error of approximation (RMSEA) or the comparative fit index (CFI), are based on a noncentrality parameter estimate derived from the model fit statistic. While a noncentrality parameter estimate is well suited for quantifying the amount of systematic error, the complex weighting function involved in its calculation makes indices derived from it challenging to interpret. Moreover, noncentrality-parameter-based fit indices yield systematically different values, depending on the indicators' level of measurement. For instance, RMSEA and CFI yield more favorable fit indices for models with categorical as compared to metric variables under ot

SUBMITTER: Lubbe D 

PROVIDER: S-EPMC10188575 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature