Advancing Contingent Paradigms Evaluating Model Fit in Structural Equation Modeling

Structural equation modeling (SEM) is a cornerstone in social and behavioral research. A central aspect of SEM is assessing global model fit, which determines how well a model represents the data. While traditional approaches proposing rigid cutoffs (most importantly, Hu & Bentler, 1999) are widely used for decades, recent advancements have introduced more flexible and dynamic alternatives that simulate effective cutoffs. Two prominent approaches are Flexible Cutoffs (FCO; Niemand & Mai, 2018) and Dynamic Cutoffs (DFI; McNeish & Wolf, 2023), which aim to provide more accurate assessments of model fit by generating simulated cutoffs from a given model and data. Yet, they rest on notable conceptual differences for determining the cutoffs by emphasizing either Type I or Type II errors, with specific strengths but also limitations. While FCO only controls for Type I error, DFI corrects for both error types, yet does not provide cutoffs when the overlap between the simulated correct and misspecified models is substantial. The present study suggests incorporating both approaches through revising the decision models (FCO2) or minimizing Type I and II errors (cut point, CP). With a comprehensive simulation study, we conclude that FCO2 (mean = .837) and CP (mean = .866) outperform DFI (mean = .579) in overall accuracy, largely due to DFI not considering many sources of misfit. These findings highlight the importance of carefully considering the underlying conditions and a cutoff’s generalizability, ultimately guiding researchers in making informed decisions about their analyses.