Essential validation criteria for rigorous covariance-based structural equation modeling

Abstract

Covariance-based structural equation modeling (CB-SEM) is a robust analytical technique for validating complex measurements and theoretical models. Despite criticisms regarding overfitting, misspecifica-tion, and sample size limitations, SEM remains invaluable for rigorous theoretical model testing when applied correctly. This Methods Article aims to streamline the extensive SEM criteria into essential con-siderations segmented across three critical stages: data preparation, measurement validation, and struc-tural modeling. This provides scholars with a comprehensive guide tailored to meet the stringent re-quirements of top-tier scientific journals. We outline data design considerations, progress through key SEM processes, and conclude with guidelines for testing specific hypotheses. We also illuminate rele-vant validation criteria for each stage, forming a foundational framework for rigorous SEM analysis. Ne-glecting any of these criteria can trigger irreversible analytical errors. We provide examples of how missing some criteria can drastically change results. We also demonstrate an ongoing issue with inade-quate reporting of these criteria in IS journals, exacerbating these issues. Currently, SEM instruction is dispersed across numerous books and articles across different fields and decades, often with complex explanations. Our principal contribution is consolidating a comprehensive set of validation criteria into an articulated guide for scholars not yet proficient in SEM. However, this is not a step-by-step walkthrough for advanced SEM users. We advocate for a structured, transparent reporting system for these criteria, shifting the responsibility for methodological clarity onto the author and facilitating a more precise understanding for readers. Our recommendations aim to enhance the integrity of SEM applications in research by elevating reporting standards.