Applications of Structural Equation Modeling in Psychological Research

2000/02/01 by Robert C. MacCallum, James T. Austin · 4 citations
Psychology · Decision Sciences · Engineering · #Behavioral Health and Interventions #Mental Health Research Topics #Psychometric Methodologies and Testing #Structural equation modeling #Psychology #Variety (cybernetics) #Focus (optics) #Management science #Applied psychology #Computer science #Engineering #Artificial intelligence

paper · doi:10.1146/annurev.psych.51.1.201

openalex publication_date 2000/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years. We focus first on the variety of research designs and substantive issues to which SEM can be applied productively. We then discuss a number of methodological problems and issues of concern that characterize some of this literature. Although it is clear that SEM is a powerful tool that is being used to great benefit in psychological research, it is also clear that the applied SEM literature is characterized by some chronic problems and that this literature can be considerably improved by greater attention to these issues.

Citations

• Measurement Invariance, Factor Analysis and Factorial Invariance
• Applications of structural equation modeling in marketing and consumer research: A review
• Single Sample Cross-Validation Indices for Covariance Structures
• Alternative Ways of Assessing Model Fit
• Convergent and discriminant validation by the multitrait-multimethod matrix.
• Factor analysis in the development and refinement of clinical assessment instruments.
• The Criteria for Convergent and Discriminant Validity in a Multitrait-Multimethod Matrix
• Multilevel Covariance Structure Analysis
• Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives
• Model modifications in covariance structure analysis: The problem of capitalization on chance.
• Specification searches in covariance structure modeling.
• Multivariate Analysis with Latent Variables: Causal Modeling

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• Structural equation modeling [wikipedia]