Universität Wien

540008 SE Forschungsseminar (2021S)

Multivariate and Complex Data Analysis for Psychological Science

Prüfungsimmanente Lehrveranstaltung
DIGITAL

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

Sprache: Deutsch, Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

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.:
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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 12.05.2023 00:27