Guiding decisions in empirical research: A precision-driven app with context-specific criteria for confirmatory factor analysis and structural equation modeling

The problem of assessing model misspecification and precision for decision making in areas such as business, cyber security, and medicine remains a pivotal issue. Current state-of-the-art methods in structural equation modeling rely on fixed fit criteria combinations and cut-offs, regardless of model or data characteristics. This approach is often questioned as variations in criteria often depend on these characteristics, raising doubts about the reliability of current decision-making processes and research insights. To address this limitation, we applied a precision-based approach to develop context-specific rules for model assessment and provide them in an easy-to-use Shiny App. This approach is crucial because high precision mitigates the risk of misleading or erroneous insights. Our context-specific rules represent a novel and relevant advancement in model evaluation. They are based on the insight that the performance of criteria combinations depends not only on the characteristics of the data and the model, but also on the specific class of structural equation model employed, such as confirmatory factor analysis or covariance-based structural equation modeling. As a result of our optimization, our developed rules have lower Type I error rates (with an average reduction of almost 50%) than state-of-the-art general combinations while keeping Type II error rates at a low level. Our Shiny App is designed to be user-friendly and accessible, requiring no prior expertise in simulation or programming. This precision-driven, adaptive approach aligns with the goals of decision support systems, which aim to provide context-sensitive insights to support informed, data-driven decisions.