Universität Wien

460008 SE Forschungsseminar (2020S)

Multivariate and Complex Data Analysis for Psychological Science

Prüfungsimmanente Lehrveranstaltung

An/Abmeldung

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

Details

Sprache: Deutsch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Donnerstag 19.03. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 26.03. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 02.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 23.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 30.04. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 07.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 14.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 28.05. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 04.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 18.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Donnerstag 25.06. 09:45 - 11:15 Hörsaal E Psychologie, Liebiggasse 5 1. Stock

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, demonstrated, and discussed. Some prior experience with R is recommended.

Course enrollment: via personal email (until March 16, 2020) 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, attendance, and active participation

Mindestanforderungen und Beurteilungsmaßstab

All participating students must present their research questions and analytic strategy to provide input for the seminar (oral presentation + short exposé [max. 2 pages]). The presentation and active participation are equally weighted for grading.

Prüfungsstoff

Presentations, discussions, peer feedback

Literatur

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.
Further literature will be announced in the seminar.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 07.09.2020 15:22