Abstract
To evaluate model fit in structural equation modeling, researchers commonly compare fit indices against fixed cutoff values (e.g., CFI ≥ .950). However, methodologists have cautioned against overgeneralizing cutoffs, highlighting that cutoffs permit valid judgments of model fit only in empirical settings similar to the simulation scenarios from which these cutoffs originate. This is because fit indices are not only sensitive to misspecification but are also susceptible to various model, estimation, and data characteristics. As a solution, methodologists have proposed four principal approaches to obtain so-called tailored cutoffs, which are generated specifically for a given setting. Here, we review these approaches. We find that none of these approaches provides guidelines on which fit index (out of all fit indices of interest) is best suited for evaluating whether the model fits the data in the setting of interest. Therefore, we propose a novel approach combining a Monte Carlo simulation with receiver operating characteristic (ROC) analysis. This so-called simulation-cum-ROC approach generates tailored cutoffs and additionally identifies the most reliable fit indices in the setting of interest. We provide R code and a Shiny app for an easy implementation of the approach. No prior knowledge of Monte Carlo simulations or ROC analysis is needed to generate tailored cutoffs with the simulation-cum-ROC approach.