DRL036 Covariance-Based Structural Equation Modeling: Theory and Applications (Autumn 2025)

In today’s data-rich environment, researchers and professionals are increasingly expected to analyse complex relationships among variables—far beyond what traditional statistical techniques can handle. Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) offer powerful tools to generate deep insights into latent constructs, measurement quality, and theoretical model testing. This hands-on, medium-advanced course is designed for PhD students in business administration and related fields. Focusing on covariance structures, this course equips participants to go beyond surface-level correlations and explore the underlying mechanisms driving human behaviour, market dynamics, and organisational performance. Through a structured five-day intensive format, participants will gain a solid foundation in matrix algebra, likelihood theory, and multivariate normality—before diving into applications like longitudinal modelling, mediation and moderation analysis, and simulation-based evaluation. You will learn the “how” and the “why” behind model selection, fit assessment, and diagnostic tools. The course literature includes cutting-edge research and applied examples demonstrating how SEM is used in top-tier publications.

• Course leader: Professor Ulf Henning Olsson, BI
• Internal contact: Professor Deodat Mwesiumo

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Course content

Day 1

• 1. The multivariate normal distribution
• 2. Some likelihood theory
• 3. Some covariance algebra
• 4. Some basic matrix algebra
• 5. Introduction to CFA models

Day 2

• 6. More CFA
• 7. Testing fit
• 8. Reliability
• 9. MTMM models

Day 3

• 10. The Classical LISREL model. The general SEM
• 11. Mediation and moderation

Day 4

• 12. How to tackle non—normality
• 13. There are many «chi-squares»
• 14. Some acceptable fit indices
• 15. Modification and power analysis

Day 5

• 16. Some special models
• 17. Longitudinal modeling
• 18. Multi group modeling
• 19. Ordinal estimation and polychoric correlations
• 20. A simulation example

This course would meet the criterion related to methodology-content of the course package if you are registered to the PhD Logistics program at Molde University College

• Lectures and exercises (cases), 5 days - 6 teaching hrs. per day.
• Compulsory Software: R/Lavaan
• Alternative softwares: LISREL 12 and Mplus

Textbook:

Jöreskog, K. G., Olsson, U. H., & Wallentin, F. Y. (2016). Multivariate analysis with LISREL. Basel, Switzerland: Springer.

Technical papers:

Foss, T., Jöreskog, K. G., & Olsson, U. H. (2011). Testing structural equation models: The effect of kurtosis. Computational Statistics & Data Analysis, 55(7), 2263-2275.

Kenny, D. A., & McCoach, D. B. (2003). Effect of the number of variables on measures of fit in structural equation modeling. Structural equation modeling, 10(3), 333-351.

Olsson, U. H., Foss, T., Troye, S. V., & Howell, R. D. (2000). The performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural equation modeling, 7(4), 557-595.

Olsson, U. H., Foss, T., & Troye, S. V. (2003). Does the ADF fit function decrease when the kurtosis increases?. British Journal of Mathematical and Statistical Psychology, 56(2), 289-303.

Saris, W. E., Satorra, A., & Van der Veld, W. M. (2009). Testing structural equation models or detection of misspecifications?. Structural equation modeling, 16(4), 561-582. Applied papers

Arnulf, J. K., Olsson, U. H., & Nimon, K. (2024). Measuring the menu, not the food:“Psychometric” data may instead measure “lingometrics”(and miss its greatest potential). Frontiers in Psychology, 15, 1308098.

Iversen, N. M., Hem, L. E., & Olsson, U. H. (2022). Willingness to buy US products in three Southeast European countries. Journal of East European Management Studies, 27(3), 487-518.

Kost, D., Kopperud, K., Buch, R., Kuvaas, B., & Olsson, U. H. (2023). The competing influence of psychological job control on family‐to‐work conflict. Journal of Occupational and Organizational Psychology, 96(2), 351-377.

Miller, F. G., Johnson, A. H., Yu, H., Chafouleas, S. M., McCoach, D. B., Riley-Tillman, T. C.,... & Welsh, M. E. (2018). Methods matter: A multi-trait multi-method analysis of student behavior. Journal of school psychology, 68, 53-72.

Software manual:

Rosseel, Y. (2014). The lavaan tutorial. Department of Data Analysis: Ghent University.