Structural Equation Modeling

Master 2025/2026

Type: Compulsory course (Data Analytics and Social Statistics)

Delivered by: International Laboratory for Applied Network Research

When: 2 year, 1, 2 module

Online hours: 40

Open to: students of one campus

Instructors: Irina Pavlova

Language: English

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Abstract

This course is designed for students in applied statistics and data analysis who would like to acquire a significant familiarity with the statistical techniques known collectively as "structural equation modeling," "causal modeling," or "analysis of covariance structures."

Learning Objectives

To provide you with an understanding of the basic principles of latent variable structural equation modeling and lay the foundation for future learning in the area. To explore the advantages and disadvantages of latent variable structural equation modeling, and how it relates to other methods of analysis . To develop your familiarity, through hands on experience, with the major structural equation modeling programs, so that you can use them and interpret their output. To develop and/or foster critical reviewing skills of published empirical research using structural equation modeling.

Expected Learning Outcomes

Be able to use the major SEM programs to estimate common types of models: Models with latent variable interactions.
Be able to use the major SEM programs to estimate common types of models: Models with multiple mediating effects.
Be able to use the major SEM programs to estimate common types of models: Multi-equation path analysis models
Be able to use the major SEM programs to estimate common types of models: Multi-group models with mean structures.
Be able to use the major SEM programs to estimate common types of models: Multi-level models (If time permits).
Be able to use the major SEM programs to estimate common types of models: Path models with fixed, non-zero error terms
Have a working knowledge of the different ways to analyze models with covariance structures.
Have an understanding common problems related to model specification, identification, and estimation.
Know how to translate conceptual thinking into models that can be estimated.
Know the basic idea of implied matrices and what is happening in SEM.
Know the major structural equation modeling programs.

Course Contents

Section 1. Introduction and Structural Models (Path Analysis)
Section 2. Measurement Model and General Model
Section 3. Measurement
Section 4. Measurement Invariance
Section 5. Testing construct validity
Section 6. MTMM approach for network measurement

Assessment Elements

SEM Project 1
SEM Project 3
SEM Project 2

Interim Assessment

2025/2026 2nd module
0.33 * SEM Project 1 + 0.33 * SEM Project 2 + 0.34 * SEM Project 3

Bibliography

Recommended Core Bibliography

Netemeyer, R. G., Sharma, S., & Bearden, W. O. (2003). Scaling Procedures : Issues and Applications. Thousand Oaks, Calif: SAGE Publications, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=321358
Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (Vol. 2nd ed). Mahwah, NJ: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=188193

Recommended Additional Bibliography

Byrne, B. M. (1998). Structural Equation Modeling With Lisrel, Prelis, and Simplis : Basic Concepts, Applications, and Programming. Mahwah, N.J.: Psychology Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=582749

Authors

Pavlova Irina Anatolevna

Course Syllabus