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Confirmatory factor analysis (CFA) is a statistical method that allows researchers to quantify how well the hypothesized factorial structure of a psychological measure fits the associated data. Fit indices are statistical measures that evaluate the factorial structure by quantifying the degree of model/data fit or misspecification. Researchers generally use traditional or fixed cutoff values of fit indices to judge the appropriateness of their CFA models. However, many authors have discussed the limitations of using these cutoffs, given that they cannot be generalized to all models. Recently, McNeish and Wolf (2023) developed the Dynamic Fit Index (DFI) approach, which generates cutoff values of fit indices that are tailored to the characteristics of a specific model. In this tutorial, a CFA of the Attainment of School Achievement Goal Scale (A-SAGS) was conducted using the lavaan package in R. Next, cutoff values for the fit indices were generated using the dynamic package in R. The model fit of the A-SAGS was mostly satisfactory when interpretations were based on fixed cutoff values. However, the hypothesized model did not reach the dynamic cutoff values, prompting a more thorough investigation into the possible sources of model misspecification. The DFI approach is easily implemented, highly promising, and offers researchers valuable insights as they attempt to improve the construct validity of a given psychological measure.

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