Dynamical Fit Index Cutoffs for Gaussian Graphical Models

As exploratory network research continues to grow, there is heightened demand for confirmatory network analysis that can validate replicability and examine hypotheses. The quality of confirmatory testing depends critically on reliable fit index cutoffs that control type I and type II errors during hypothesis testing. However, conventional cutoffs developed by Hu and Bentler (1999; HB cutoffs) are inflexible and are vulnerable to sample and model characteristics, creating a demand for more flexible dynamic fit index (DFI) cutoffs that can be adapted to researchers’ specific designs. In this study, we attempted to develop a DFI algorithm for Gaussian graphical models, a widely implemented network model, and compared the performance of DFI cutoffs with HB cutoffsin terms of their specificity and sensitivity to misspecifications. In simulation studies based on both synthetic and empirical networks, DFI provided more flexible cutoffs with a transparent, fixed power/specificity to detect correctly specified models and was often more sensitive to misspecifications, especially in empirical networks. The DFI for network models provides a more reliable and flexible criterion for confirmatory network analysis, thereby marking an essential advancement in theory and hypothesis testing in network research. The algorithm has been implemented in an open-source, user-friendly R package, netDFI.