Faculty
Department of International Economics, Government and Business (EGB)
Prerequisites
The course requires basic understanding of statistics, but does not require knowledge of structural equation modeling or any particular SEM software.
Aim
This course is designed to help participants understand the theoretical basis and practical application of structural equation modeling.
More specifically, we will address the following issues:
1. What is structural equation modeling and what types of research questions can it help answer?
2. Theoretical understanding of measurement models and structural models.
3. Conducting basic SEM analysis.
4. Reading SEM output and providing guidelines for reporting SEM results.
5. Analysis of interaction effects with continuous and categorical variables.
6. Latent growth modeling and overview of advanced analyses.
Course content
10 modules/4 hours each Sessions will run between 9 am -1 pm and 2 -6pm with an hour lunch break
Teaching style
Lectures and interactive lab-sessions
Lecture plan
Module 1 - Intro to SEM
Module 2 - Intro to Mplus
Module 3 - :Measurement Model 1
Module 4 - Measurement model 2
Module 5 - Structural Model 1
Module 6 - Structural model 2
Module 7 - Multigroup Analysis
Module 8 - Latent Growth Models
Module 9 - SEM Reporting
Module10 - Project work
Learning objectives
Exam
Course certificates will be issued based on participation and completion of project.
Course Literature
Main texts:
Rex B. Kline (2016). Principles and practices of structural equation modeling, 4thedition, New York: Guilford Press.
Barbara M Byrne(2012). Structural Equation Modeling with Mplus, Basic concepts, applications, and programming, Lawrence Erlbaum Associates.
Articles:
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
Anderson, J. C. and Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103: 411-423.
McCallum, R. C., Roznowski M. ≠cowitz L.B.(1992). Model modifications in covariancestructure analysis: the problem of capitalizationon chance. Psychological Bulletin. 111:490–504
Note: In case we receive more registrations for the course than we have place, the registrations will be prioritized in the following order: PhD students from CBS departments, students from other institutions than CBS.