摘要 : Model fit assessment is a central component of evaluating confirmatory factor analysis models and the validity of psychological assessments. Fit indices remain popular and researchers often judge fit with fixed cutoffs derived by ... 展开 Model fit assessment is a central component of evaluating confirmatory factor analysis models and the validity of psychological assessments. Fit indices remain popular and researchers often judge fit with fixed cutoffs derived by Hu and Bentler (1999). Despite their overwhelming popularity, methodological studies have cautioned against fixed cutoffs, noting that the meaning of fit indices varies based on a complex interaction of model characteristics like factor reliability, number of items, and number of factors. Criticism of fixed cutoffs stems primarily from the fact that they were derived from one specific confirmatory factor analysis model and lack generalizability. To address this, we propose a simulation-based method called dynamic fit index cutoffs such that derivation of cutoffs is adaptively tailored to the specific model and data characteristics being evaluated. Unlike previously proposed simulation-based techniques, our method removes existing barriers to implementation by providing an open-source, Web based Shiny software application that automates the entire process so that users neither need to manually write any software code nor be knowledgeable about foundations of Monte Carlo simulation. Additionally, we extend fit index cutoff derivations to include sets of cutoffs for multiple levels of misspecification. In doing so, fit indices can more closely resemble their originally intended purpose as effect sizes quantifying misfit rather than improperly functioning as ad hoc hypothesis tests. We also provide an approach specifically designed for the nuances of 1-factor models, which have received surprisingly little attention in the literature despite frequent substantive interests in unidimensionality. Translational Abstract Evaluating confirmatory factor model fit through the lens of "approximate fit" has enjoyed widespread - though not universal - adoption in empirical studies and is valued for its goal to assess whether the model may be practically useful, even if fit is not exact. An obstacle with approximate fit is that there is ambiguity regarding what is considered "practically useful". Hu and Bentler (1999) addressed this issue with an expansive study to provide guidelines for values indicating that a model demonstrates reasonable approximate fit. Though their suggestions remain widely used today and are engrained in the literature, their suggested cutoffs have unfortunately been shown to vary widely depending on context. That is, values that indicate great fit in one context may indicate poor fit in another. The inherent problem is that the Hu and Bentler cutoffs are fixed values, so they arbitrarily benefit some models and arbitrarily punish others. Previous literature has suggested custom simulation methods that take the logic of Hu and Bentler's approach and apply it to the individual model being evaluated. In this way, researchers can obtain values indicative of good approximate fit in their specific circumstances to avoid fixed cutoffs. Though a clever solution, such an approach has seen little uptake presumably because many researchers are not well-versed in conducting simulations. This paper addresses the issue by providing a method and software application that builds and executes a Hu-and- Bentler-style simulation from model output. In this way, researchers can benefit from modern computational resources without a deep programming background. 收起