319414 Introduction to Structural Equation Modeling (V) (SoSe 2025)

Contents, comment

The course "Introduction to Structural Equation Modeling" provides a comprehensive introduction to the fundamentals of structural equation modeling (SEM), beginning with the motivation for using SEM for complex data analysis. Students begin with a recap of basic statistical concepts to ensure a solid foundation. The course then moves on to path analysis, a preliminary stage of SEM that helps to understand causal relationships between variables. Students will then learn about model specification, which involves defining the model structure and hypotheses. The course covers model identification, focusing on the conditions necessary to obtain unique parameter estimates. In the model estimation section, different estimation techniques and software tools are introduced. Next, model evaluation is discussed, where students learn to evaluate model fit and diagnose potential problems of their model. Finally, the course covers advanced topics in structural equation modeling, providing insight into more complex models and applications. This structured approach ensures a deep and practical understanding of SEM.

Bibliography

Bollen, K. (1989) Structural Equations with Latent Variables, Wiley (New York)
Kline, R. B. (2016) Principles and Practice of Structural Equation Modeling, Guilford Press (New York)
Henseler, J. (2021) Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables, Guilford Press (New York)
Jöreskog K.G. (1969) A General Approach to Confirmatory Maximum Likelihood Factor Analysis, Psychometrika, 34(2): 183 - 202
Rosseel, Y. (2012) lavaan: An R Package for Structural Equation Modeling, Journal of Statistical Software, 48(2): 1 - 36

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  
weekly Di 14-16 U2-135 07.04.-18.07.2025

Subject assignments

Module Course Requirements  
31-M-ASM1 Advanced Statistical Methods I Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (I.) Student information
Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (II.) Student information
31-M-ASM2 Advanced Statistical Methods II Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (I.) Graded examination
Student information
Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (II.) Graded examination
Student information
31-M-El1 Elective Courses 1 Gewählte Veranstaltungen aus dem Bereich "Spezialkenntnisse in ökonomischer Theorie und/oder quantitativen Methoden" 4 LP Student information
31-M-El2 Elective Courses 2 Gewählte Veranstaltungen aus dem Bereich quantitativen Methoden 4 LP Student information
31-MM15 Empirische Wirtschaftsforschung und Quantitative Methoden Veranstaltungen aus dem Bereich "Angewandte Ökonometrie" (bspw. Methoden der Ökonometrie, etc.) oder aus dem Bereich "Angewandte Statistik" (bspw. GLM, MVV, etc.) oder aus dem Bereich "DV-Technik" (bspw. A&D, Simulationstechniken, etc.) Student information
31-MM15-WiMa Empirische Wirtschaftsforschung und Quantitative Methoden Veranstaltungen aus dem Bereich "Angewandte Ökonometrie" (bspw. Methoden der Ökonometrie etc.) oder aus dem Bereich "Angewandte Statistik" (bspw. GLM, MVV etc.) oder aus dem Bereich "DV-Technik" (bspw. A&D, Simulationstechniken etc.) Student information
31-SW-AKStat Ausgewählte Kapitel der Statistik Veranstaltung aus dem Bereich Statistik oder einem methodisch verbundenen Gebiet 4 LP Graded examination
Student information
Veranstaltung aus dem Bereich Statistik oder einem methodisch verbundenen Gebiet 4 LP Graded examination
Student information

The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.

Degree programme/academic programme Validity Variant Subdivision Status Semester LP  
Economics and Management (BiGSEM) / Promotion Data Science; Electives   4  
Economics and Management (BiGSEM) / Promotion Economics; Prerequisites   4  
Studieren ab 50    
No more requirements
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Registered number: 36
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SS2025_319414@ekvv.uni-bielefeld.de
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Last update basic details/teaching staff:
Friday, November 22, 2024 
Last update times:
Wednesday, February 12, 2025 
Last update rooms:
Wednesday, February 12, 2025 
Type(s) / SWS (hours per week per semester)
lecture (V) / 2
Language
This lecture is taught in english
Department
Faculty of Business Administration and Economics
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508179260