Universität Wien
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400009 SE Experiments in the Social Sciences (2024S)

Methodenseminar

5.00 ECTS (2.00 SWS), SPL 40 - Doktoratsstudium Sozialwissenschaften
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

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

Details

max. 15 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Dienstag 05.03. 11:30 - 13:30 Seminarraum 12, Kolingasse 14-16, OG01
  • Dienstag 19.03. 11:30 - 13:30 Seminarraum 5, Währinger Straße 29 1.UG
  • Dienstag 09.04. 11:30 - 13:30 Seminarraum 11, Währinger Straße 29 2.OG
  • Dienstag 16.04. 11:30 - 13:30 Seminarraum 5, Währinger Straße 29 1.UG
  • Dienstag 23.04. 11:30 - 13:30 Seminarraum 2, Währinger Straße 29 1.UG
  • Dienstag 30.04. 11:30 - 13:30 Seminarraum 5, Währinger Straße 29 1.UG
  • Dienstag 07.05. 11:30 - 13:30 Seminarraum 2, Währinger Straße 29 1.UG
  • Dienstag 14.05. 11:30 - 13:30 Seminarraum 5, Währinger Straße 29 1.UG
  • Dienstag 21.05. 11:30 - 13:30 Seminarraum 11, Währinger Straße 29 2.OG
  • Dienstag 28.05. 11:30 - 13:30 Seminarraum 5, Währinger Straße 29 1.UG
  • Dienstag 04.06. 11:30 - 13:30 Seminarraum 11, Währinger Straße 29 2.OG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The scientific experiment is a central way to advance knowledge in the empirical social sciences. By randomly assigning participants to experimental groups, experiments allow us to make claims regarding causality. Hence, if our aim is to establish causal inferences, wherever possible it is recommended to conduct experiments.
In this course we will discuss the scientific experiment, how and when it can be used, what pitfalls to avoid, and how to interpret results. We will focus on statistical power, design of stimuli, and open science (with a focus on Registered Reports). We will also focus on how best to analyze results.
We will explore these topics by reading and discussing texts, through inputs by me, short summaries of the texts presented by you, and hands-on analyses in R. Throughout, we will engage with your phd projects and your individual perspectives.
Each student will give a short summary of one of the papers we discuss. Everyone is expected to participate actively in the discussions.
In the hands-on part of the seminar, we will analyze actual data/experiments. We will use the software R. Although not needed, a basic understanding of R is recommended. If R is new, I recommend reading introductory texts or watching online tutorials. Here are some helpful materials:
- https://r4ds.had.co.nz
- https://github.com/jobreu/r-intro-gesis-2021
- https://github.com/ccs-amsterdam/r-course-material
- https://www.youtube.com/watch?v=BvKETZ6kr9Q
1. Introduction
2. Basics: What’s an Experiment?
3. Planning an Experiment
4. Experimental Designs
5. Open Science & Registered Reports
6. Statistical Power
7. Analysis: t-Tests
8. Analysis: ANOVAs
9. Analysis: Regressions
10. Analysis: Multilevel Analyses
11. Conclusion

Art der Leistungskontrolle und erlaubte Hilfsmittel

Everyone participates actively in the course. Willingness and openness to take part in the discussion are a prerequisite.
During the semester, everyone picks a text and gives a short presentation. The presentation needs to be in English.
Essay at the end, in which you present your phd project and an experiment you are planning to conduct. In case you are not actually planning to conduct an experiment, this will then be a hypothetical task. The essays are recommended to be in English. However, it is also possible to write an essay in German.

Mindestanforderungen und Beurteilungsmaßstab

Active participation
Presentation of text (50%)
Essay (50%)

Prüfungsstoff

Participants need to follow all presentations and what is being discussed. Participants also need to read all texts and prepare for the course

Literatur

ield, A. P., & Hole, G. (2003). How to design and report experiments. Sage publications Ltd.
• Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and Other Stories (1st ed.). Cambridge University Press. https://doi.org/10.1017/9781139161879

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 31.07.2024 12:06