Universität Wien

540008 SE Forschungsseminar (2024S)

Multivariate and Complex Data Analysis

4.00 ECTS (2.00 SWS), SPL 54 - Doktoratsstudium Psychologie und Biologie mit Schwerpunkt CoBeNe
Prüfungsimmanente Lehrveranstaltung

Persönliche Anmeldung über die LV-Leitung
Moodle

Details

Sprache: Deutsch, Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Presentations and active participation, peer feedback.

Mindestanforderungen und Beurteilungsmaßstab

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.

Prüfungsstoff

Presentations, discussions, peer feedback

Literatur

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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 13.03.2024 10:27