540008 SE Forschungsseminar (2023S)
Multivariate and Complex Data Analysis
Prüfungsimmanente Lehrveranstaltung
Labels
Persönliche Anmeldung über die LV-Leitung
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mi 01.03.2023 11:06 bis Di 14.03.2023 11:02
- Abmeldung bis Di 14.03.2023 11:02
Details
Sprache: Deutsch, Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Bitte um persönliche Anmeldung per mail an die LV-Leitung.
Mittwoch
15.03.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
22.03.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
29.03.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
19.04.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
26.04.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
03.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
10.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
17.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
24.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
31.05.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
07.06.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
14.06.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
21.06.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Mittwoch
28.06.
11:30 - 13:00
Hörsaal C Psychologie, NIG 6.Stock A0618
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
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.
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 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.