540008 SE Research Seminar (2023S)
Multivariate and Complex Data Analysis
Continuous assessment of course work
Labels
Persönliche Anmeldung über die LV-Leitung
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).
- Registration is open from We 01.03.2023 11:06 to Tu 14.03.2023 11:02
- Deregistration possible until Tu 14.03.2023 11:02
Details
Language: German, English
Lecturers
Classes (iCal) - next class is marked with N
Bitte um persönliche Anmeldung per mail an die LV-Leitung.
Wednesday
15.03.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
22.03.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
29.03.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
19.04.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
26.04.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
03.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
10.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
17.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
24.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
31.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
07.06.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
14.06.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
21.06.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Wednesday
28.06.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Information
Aims, contents and method of the course
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.
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 15.03.2023 09:09
- 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/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.