Moving from exploratory to confirmatory network analysis: An evaluation of structural equation modeling fit indices and cutoff values in network psychometrics.

= 1 time-series cases (graphical autoregressive models). Using simulations, we analyzed the performance of fit indices to test hypothesized network structures and evaluate stationarity, under varying number of variables (nodes), sample sizes, and measurement waves. Most fit indices performed well, except that Type I incremental fit indices showed high false rejection rates. Conventional SEM cutoffs are largely generalizable to CNA as preliminary assessment criteria when dynamical cutoffs are unavailable. However, we recommend stricter cutoff values (e.g., 0.03/0.04 for the root-mean-square error of approximation [RMSEA] and 0.96/0.97 for incremental fit indices) in hypothesis testing or direct replication studies if researchers aim for more precise testing or exact replications. For detecting network structure non-stationarity, stricter RMSEA cutoffs (0.03/0.04) are advised. This study validates the use of SEM fit criteria for confirmatory network psychometrics and encourages theory-testing and replication studies in network research, providing practical recommendations for using SEM fit indices in confirmatory network testing. (PsycInfo Database Record (c) 2025 APA, all rights reserved).