Universität Wien FIND

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220078 SE SE Advanced Data Analysis 3 (2021W)

Prüfungsimmanente Lehrveranstaltung
VOR-ORT

An/Abmeldung

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

Details

max. 30 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Montag 18.10. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 25.10. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 08.11. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 15.11. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 22.11. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 29.11. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 06.12. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 13.12. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 10.01. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 17.01. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 24.01. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG
Montag 31.01. 09:45 - 11:15 Seminarraum 4, Währinger Straße 29 1.UG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Course Objectives and Summary:

This is an interdisciplinary data analysis seminar in workshop form focused on the application of principles of linear modeling in the context of linear regression analysis to exploring questions about mediated (i.e., indirect) and moderated (i.e., interaction) effects. We will spend part of the course talking about partitioning effects into direct and indirect components and how to quantify and test hypotheses about indirect effects, part talking about estimating, testing, and probing interactions in linear models, and part integrating moderation and mediation as conditional process analysis by discussing and how to conceptualize and test the contingencies of a mechanism (moderated mediation) and whether moderated effects are mediated (mediated moderation). Computer applications will focus on R statistical language. No knowledge of matrix algebra is required or assumed, yet a moderate proficiency in R and R programming is preferred.

By the end of this course, participants will be able to:
- interpret the results of basic moderation and mediation models within regression framework
- know how test competing theories of mechanisms statistically through the comparison of indirect effects in models with multiple mediators,
- have the ability to visualize and probe interactions in regression models in order to interpret interaction effects in the appropriate ways,
- have learned how to estimate the contingencies of mechanisms through the computation and inference about conditional indirect effects,
- and use R language to run and understand moderation, mediation, and conditional process models.

Following the University motto “welcome (back) to the University of Vienna”, this is an on-site course.

Art der Leistungskontrolle und erlaubte Hilfsmittel

There are two take-home exams that will be distributed at some points during the semester. The two assignments deal with critically demonstrating your understandings of key concepts in linear modeling fundamentals and its extensions. This constitutes a total of 30% of the final grade.

The rest of your grade (70%) will be based on a final data analysis project that you complete using either your own data or data available to you through an advisor or through a public archive (I do not place any restrictions on the scope of possible data you could use).

Mindestanforderungen und Beurteilungsmaßstab

Ongoing in-class participation and additional readings are basic requirements.

Your grade will be calculated based on largely a percentage based system where 90%+ = A (=1), 80% - 90%+ = B (=2), 70% - 80%+ = C (=3), 60% - 70%+ = D (=4), less than 60% = E (=5).

I reserve the right to modify this system downward or upward depending on the distribution of grades. In other words, if only one student exceeds the 90% threshold, but five hit 89%, I may choose to move the cutoff for an A to 89%.

For successfully passing the course, participants have to achieve at least 51% of the total points. Full details on the course grading (e.g., grading system) will be given in the first session. Ongoing in-class participation is required.

Prüfungsstoff

Required knowledge and practical skills will be conveyed in the workshop sessions and tutorials. In addition, participants are expected to read widely on the subject. Here, participants are required to consult the required basic reading and the additional literature in order to successfully complete the assignments.

Literatur

Readings will be provided by the teacher during the course to help understanding the statistical concepts and techniques that will be described.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Di 07.09.2021 15:28