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Warning! The directory is not yet complete and will be amended until the beginning of the term.

540008 SE Research Seminar (2021S)

Multivariate and Complex Data Analysis for Psychological Science

Continuous assessment of course work
REMOTE

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

Language: German, English

Lecturers

Classes (iCal) - next class is marked with N

The seminar will be conducted digitally. If the current situation of the pandemic changes, a switch to hybrid teaching can be possible.

Wednesday 10.03. 11:30 - 13:00 Digital
Wednesday 17.03. 11:30 - 13:00 Digital
Wednesday 24.03. 11:30 - 13:00 Digital
Wednesday 14.04. 11:30 - 13:00 Digital
Wednesday 21.04. 11:30 - 13:00 Digital
Wednesday 28.04. 11:30 - 13:00 Digital
Wednesday 05.05. 11:30 - 13:00 Digital
Wednesday 12.05. 11:30 - 13:00 Digital
Wednesday 19.05. 11:30 - 13:00 Digital
Wednesday 26.05. 11:30 - 13:00 Digital
Wednesday 02.06. 11:30 - 13:00 Digital
Wednesday 09.06. 11:30 - 13:00 Digital
Wednesday 16.06. 11:30 - 13:00 Digital
Wednesday 23.06. 11:30 - 13:00 Digital
Wednesday 30.06. 11:30 - 13:00 Digital

Information

Aims, contents and method of the course

Guided by the students’ individual research interests and needs, this seminar offers an introduction to the application of multivariate and complex statistical models (cross-sectional and longitudinal) in psychology. The seminar specifically covers:
- Structural equation modeling (SEM; with continuous latent variables)
- Path analysis
- Mediation and moderation analysis
- Confirmatory factor analysis
- Multi-group modeling
- Multilevel modeling
- Growth curve modeling
- Latent class analysis (LCA; with categorical latent variables)
- Latent profile analysis (LPA; with categorical latent variables)

Students present their research questions and analytical strategy. In class, we will deal with the nature, assumptions, and requirements of the various statistical analyses and models as needed; data formats (e.g., wide vs. long) and data management; and computational issues (e.g., regarding estimators). Data analysis with the free open-source software R (e.g., package lavaan), Mplus, and SPSS, where applicable, will be explained and discussed. Some prior experience with R is recommended.

Course enrollment: via personal email to ulrich.tran@univie.ac.at.

The seminar will be held either in German or English, depending on the language requirements of participating students.

Assessment and permitted materials

Presentations and active participation, peer feedback.

Minimum requirements and assessment criteria

All students present their research questions and analytic strategy to provide input for the seminar (oral presentation + short exposé [max. 2 pages]). All students provide peer feedback to the other students' presentations.

The presentation and active participation are equally weighted for grading.

Examination topics

Presentations, discussions, peer feedback

Reading list

Guided by the students' research interests and needs, e.g.:
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, NY: Guilford.
Muthén, L. K., & Muthén, B. O. (1998-2017). Mplus user’s guide (8th ed.). Los Angeles, CA: Muthén & Muthén.
Rosseel, Y. (n.d.). The lavaan project. http://lavaan.ugent.be
Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis (2nd ed.). Thousand Oaks, CA: Sage.

Association in the course directory

Last modified: Tu 25.05.2021 13:09