Structural equation modeling (SEM) is a very general statistical technique, as it has regression analysis, path analysis, and factor analysis as special cases. It is also possible to combine the advantages of these techniques, which makes SEM one of the most general and most flexible techniques available to researchers. This course will introduce you to the fundamentals of SEM, serving as a foundation to discover its full potential via future SEM courses, independent learning, or practical research applications.
GSSS – Structural Equation Modeling
Dr. Terrence Jorgensen
Course Description
Structural equation modeling (SEM) is a very general statistical technique, as it has regression analysis, path analysis, and factor analysis as special cases. It is also possible to combine the advantages of these techniques, which makes SEM one of the most general and most flexible techniques available to researchers. As a result, SEM is presently also one of the most widely used techniques in the social and behavioral sciences.
This course will introduce you to the fundamentals of SEM by first translating some familiar methods (t tests and ANOVA, regression and correlation) into mean and covariance structure (MACS) analyses. The first week will show how path analysis is more general than the general linear model and better able to facilitate testing hypotheses about mediation. The second week will introduce measurement models for latent variables (factor analysis) and show how a “full SEM” consists of both measurement and structural components (i.e., factor and path models). Lastly, we will cover tactics for evaluating data–model correspondence, ending with how to accommodate common types of nonideal data.
Each meeting will consist of a lecture component, followed by time to work on practical exercises. All instruction and example syntax will utilize the latest version of the statistical software environment R, as well as the latest versions of add-on packages lavaan and semTools. Students are encouraged to reproduce analyses using the example data provided, as well as using their own data whenever possible. Exercises will be used to provide opportunities for practical questions that arise during applications
Study Characteristics
- Discipline: Social Sciences
- Language: English
- ECTS: 3
- Type of education: In class, but participants may follow a lecture via Teams if a scheduling conflict prevents attending in person.
- Academic skill: Quantitative Methods
- Graduate School: Graduate School of Social Sciences
- Start date: 6 October 2025
- End date: 6 November 2025
- Schedule:
09.30-12.30
Monday 6 October
Thursday 9 October
Monday 13 October
Thursday 16 October
Thursday 23 October (first draft of paper due)
Monday 27 October
Thursday 30 October
Thursday 6 November (“exam”—final paper due)
- Minimum number of students: 5
- Maximum number of students: 15
- Admission criteria:
Required: Besides familiarity and some experience with R, students are expected to be familiar with the fundamental statistical concepts (e.g., descriptive and inferential statistics, null-hypothesis significance testing) as well as the general(ized) linear model (GLM) and its special cases: regression, t tests, ANOVA, and correlation. Therefore, at least one previous course covering multiple regression is a prerequisite. Students should also have passed at least one statistics course using R, or have used R to conduct any kind of statistical analysis for a paper (under review or published).
Recommended but optional: Familiarity with basic psychometrics (classical test theory, reliability, and validity) are helpful, especially for the portion of the course involving latent variables. Given the frequency with which SEMs are communicated using matrices (even in applied literature), some familiarity with basic matrix algebra is advantageous but not strictly necessary.
- Assessment type: One written paper applying one of the types of SEM covered in the course, to be completed on the student’s own time.
- Concluding assessment: Yes
- With Certificate: Yes, upon request
- Registration deadline: 4 weeks before the start of the course
- Available for: PhD candidates interested in applying SEM in their research. Free of charge for VU-GSSS, AISSR, and ZU PhD candidates. A fee of €750 applies to other PhD candidates.
- Name of teacher: Dr. Terrence Jorgensen, University of Amsterdam
- Link to profile: http://www.uva.nl/profile/t.d.jorgensen
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Course Description & Study Characteristics
Course Description
Structural equation modeling (SEM) is a very general statistical technique, as it has regression analysis, path analysis, and factor analysis as special cases. It is also possible to combine the advantages of these techniques, which makes SEM one of the most general and most flexible techniques available to researchers. As a result, SEM is presently also one of the most widely used techniques in the social and behavioral sciences.
This course will introduce you to the fundamentals of SEM by first translating some familiar methods (t tests and ANOVA, regression and correlation) into mean and covariance structure (MACS) analyses. The first week will show how path analysis is more general than the general linear model and better able to facilitate testing hypotheses about mediation. The second week will introduce measurement models for latent variables (factor analysis) and show how a “full SEM” consists of both measurement and structural components (i.e., factor and path models). Lastly, we will cover tactics for evaluating data–model correspondence, ending with how to accommodate common types of nonideal data.
Each meeting will consist of a lecture component, followed by time to work on practical exercises. All instruction and example syntax will utilize the latest version of the statistical software environment R, as well as the latest versions of add-on packages lavaan and semTools. Students are encouraged to reproduce analyses using the example data provided, as well as using their own data whenever possible. Exercises will be used to provide opportunities for practical questions that arise during applications
Study Characteristics
- Discipline: Social Sciences
- Language: English
- ECTS: 3
- Type of education: In class, but participants may follow a lecture via Teams if a scheduling conflict prevents attending in person.
- Academic skill: Quantitative Methods
- Graduate School: Graduate School of Social Sciences
- Start date: 6 October 2025
- End date: 6 November 2025
- Schedule:
09.30-12.30
Monday 6 October
Thursday 9 October
Monday 13 October
Thursday 16 October
Thursday 23 October (first draft of paper due)
Monday 27 October
Thursday 30 October
Thursday 6 November (“exam”—final paper due)
- Minimum number of students: 5
- Maximum number of students: 15
- Admission criteria:
Required: Besides familiarity and some experience with R, students are expected to be familiar with the fundamental statistical concepts (e.g., descriptive and inferential statistics, null-hypothesis significance testing) as well as the general(ized) linear model (GLM) and its special cases: regression, t tests, ANOVA, and correlation. Therefore, at least one previous course covering multiple regression is a prerequisite. Students should also have passed at least one statistics course using R, or have used R to conduct any kind of statistical analysis for a paper (under review or published).
Recommended but optional: Familiarity with basic psychometrics (classical test theory, reliability, and validity) are helpful, especially for the portion of the course involving latent variables. Given the frequency with which SEMs are communicated using matrices (even in applied literature), some familiarity with basic matrix algebra is advantageous but not strictly necessary.
- Assessment type: One written paper applying one of the types of SEM covered in the course, to be completed on the student’s own time.
- Concluding assessment: Yes
- With Certificate: Yes, upon request
- Registration deadline: 4 weeks before the start of the course
- Available for: PhD candidates interested in applying SEM in their research. Free of charge for VU-GSSS, AISSR, and ZU PhD candidates. A fee of €750 applies to other PhD candidates.
- Name of teacher: Dr. Terrence Jorgensen, University of Amsterdam
- Link to profile: http://www.uva.nl/profile/t.d.jorgensen