364-1020-05L  Methods in Management Research: Quantitative Research - Structural Equation Modelling

SemesterSpring Semester 2019
LecturersS. Raeder
Periodicityyearly recurring course
Language of instructionEnglish


AbstractStructural equation modeling (SEM) is a technique to build models and test causal relationships including latent variables, several outcome variables and intervening variables. The course provides basic knowledge about the design, analysis and reporting of structural equation models.
ObjectiveThe course aims to support students in:
1) understanding design and statistics of structural equation models,
2) being able to design and calculate a structural equation model,
3) being able to interpret and report the results of the statistics.
ContentThe course provides basic knowledge about the design and analysis of structural equation models.

Session 1: Basics of structural equation modeling and confirmatory factor analysis (CFA)
- model identification,
- model fit,
- measurement model and structural model,
- calculation of basic CFA and SEM model in Mplus.

Session 2:
- Calculation of more complex model (e.g. with intervening variables),
- reporting of results.

Mplus is used in the course for the practical course work.
Lecture notesPower points and all material used in the course are available in Moodle.
LiteratureRecommended reading:
Byrne, B. M. (2012). Structural Equation Modeling with Mplus. Basic concepts, applications, and programming. New York: Rutledge.
Geiser, C. (2012). Data Analysis with Mplus. Guildford: New York.
Geiser, C. (2011). Datenanalyse mit Mplus. Eine anwendungsorientierte Einführung. Springer VS: Wiesbaden.
Bühner, M. (2006). Einführung in die Test- und Fragebogenkonstruktion (2. Aufl.). München: Pearson. Kapitel 6
Prerequisites / NoticeBasic knowledge in regression analysis is necessary for following the course.

Students work on three assignments:
1) before the course starts,
2) after session 1 and
3) after the end of the course.
Assignment 1 includes finding a sample paper with SEM and providing information on experience with the method. More detailed information will be given before the course starts. Assignment 2 and 3 consist of analyzing data and reporting results.

It is expected that participants attend 100% of the teaching and work on all assignments.