Universität Wien

540008 SE Research Seminar (2024S)

Multivariate and Complex Data Analysis

4.00 ECTS (2.00 SWS), SPL 54 - Doktoratsstudium Psychologie und Biologie mit Schwerpunkt CoBeNe
Continuous assessment of course work

Persönliche Anmeldung über die LV-Leitung
Moodle

Details

Language: German, English

Lecturers

Classes (iCal) - next class is marked with N

Bitte um persönliche Anmeldung per mail an die LV-Leitung.

  • Wednesday 13.03. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 20.03. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 10.04. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 17.04. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 24.04. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 08.05. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 15.05. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 22.05. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 29.05. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 05.06. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 12.06. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 19.06. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618
  • Wednesday 26.06. 11:30 - 13:00 Hörsaal C Psychologie, NIG 6.Stock A0618

Information

Aims, contents and method of the course

Guided by the students’ individual research interests and needs, this seminar offers guidance and assistance in the application of multivariate and complex statistical models (cross-sectional and longitudinal) in psychology and related fields (the seminar is open to all CoBeNe doctoral students). 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)
- Meta-analysis

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/JASP, where applicable, will be explained and discussed. Some prior experience with R is recommended, but not mandatory.

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.:
Brown, V. A. (2021). An introduction to linear mixed-effects modeling in R. Advances in Methods and Practices in Psychological Science, 4(1), 1-19. https://doi.org/10.1177/2515245920960351
Finch, W. H., Boley, J. E., & Kelley, K. (2019). Multilevel modeling using Mplus (2nd ed.). CRC Press.
Gana, K., & Broc, G. (2019). Structural equation modeling with lavaan. Wiley.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford.
Rosseel, Y. (n.d.). The lavaan project. http://lavaan.ugent.be
Wang, J., & Wang, X. (2020). Structural equation modeling: Applications using Mplus. Wiley.

Association in the course directory

Last modified: We 13.03.2024 10:27