Universität Wien

460008 SE Research Seminar (2020S)

Multivariate and Complex Data Analysis for Psychological Science

Continuous assessment of course work

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

Lecturers

Classes (iCal) - next class is marked with N

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

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

Course enrollment: via personal email (until March 9, 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.

Assessment and permitted materials

Presentations, attendance, and active participation

Minimum requirements and assessment criteria

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.

Examination topics

Presentations, discussions, peer feedback

Reading list

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.

Association in the course directory

Last modified: Mo 07.09.2020 15:22