Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish

Confirmatory Factor Analysis (CFA) has been widely used to assess the fit of theoretical measurement models to observed data. We introduce the InterModel Vigorish (IMV) to the field; a predictive fit index that offers novel perspectives for model comparison. The IMV complements traditional fit indices by offering additional information to support model evaluation, with a particular emphasis on a model's generalizability to the hold-out data. It also yields an interpretable and intuitive metric that facilitates meaningful comparisons. We extend it into the CFA framework with binary outcomes and conduct four simulation studies to evaluate its effectiveness. The simulation results suggest that IMV effectively gauges model misspecification, offering insights both at the scale and item levels. As designed, it is insensitive to changes in sample size. By focusing on predictive accuracy, the IMV discourages overfitting. It also enables item-level comparisons, offering richer diagnostic information. To facilitate the practical application of IMV, we offer an empirical example that demonstrates its efficacy in applied research. The paper is accompanied by an R package to further advance the use of the IMV in the CFA space.