| Dozent/in |
Michele Fenzl |
| Veranstaltungsart |
Kolloquialvorlesung |
| Code |
HS221499 |
| Semester |
Herbstsemester 2022 |
| Durchführender Fachbereich |
Politikwissenschaft |
| Studienstufe |
Bachelor
Master |
| Termin/e |
Do, 22.09.2022, 14:15 - 16:00 Uhr, HS 5
Do, 22.09.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 29.09.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 06.10.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 13.10.2022, 14:15 - 16:00 Uhr, 3.A05
Do, 20.10.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 27.10.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 03.11.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 10.11.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 17.11.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 24.11.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 01.12.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 15.12.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1)
Do, 22.12.2022, 14:15 - 16:00 Uhr, HS 2 (Terminierung 1) |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
wöchentlich |
| Inhalt |
The course is beginner-friendly introduction to statistical reasoning, causal inference, and research design. The course will be developed with an applied approach. We will therefore focus on real-world applications rather than mathematical proofs. Students will also gain experience importing, transforming, and analysing data. Topics covered in this course include: data cleaning and preparation, data visualization, data transformation, statistical sampling, descriptive statistics, inferential statistics, causality and potential outcomes, research design, and statistical modeling (regression analysis). Applications will be based on the R statistical software. Stata codes may be additionally provided and discussed.
|
| Lernziele |
This course will introduce students to statistical reasoning for hypothesis testing. Students will additionally be introduced to topics in causal inference; data visualisation; and research design. The focus will be applied and students will gain familiarity with coding for statistical analyses (on R). The main objective is to enable students to critically understand and independently produce original empirical studies. |
| Voraussetzungen |
An intrinsic motivation to learn statistics and data science is the only hard requirement for this course: passive listening-only and credit-oriented participation is discouraged since it undermines effective and durable learning. Some basic statistics and programming skills (e.g., one previous course in statistics) are recommended but not required in the presence of a strong motivation to learn. |
| Sprache |
Englisch |
| Anmeldung |
Teilnahmebeschränkung vorbehalten: Studierende ab dem 3. Semester werden bevorzugt. |
| Prüfung |
Coursework: see syllabus for details.
Active participation, 2 assignments during the course, 1 final written assignment.
3 Credits |
| Abschlussform / Credits |
Active participation (20%), 2 assignments (40%), final assignment (40%) / 3 Credits
|
| Hinweise |
The course is recommended for BA students in their higher (3+) semesters and is open to MA students. The registration via the e-learning platform OLAT is required to attend the lecture. The students themselves are responsible for checking the creditability of the course to their course of study. Direct link to OLAT course: https://lms.uzh.ch/auth/RepositoryEntry/17061151191. |
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
fenzl@ipz.uzh.ch |
| Material |
Reading material will be circulated using Perusall. Students may use their laptops, but all software, exercises, and solutions are freely provided through RStudio Cloud. |
| Literatur |
-Imai, K. (2017). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.
-Wickham, H. and Grolemund, G. (2016). R for Data Science. Sebastopol, CA: O'Reilly Media.
-Angrist, J. and Pischke J.S. (2014). Mastering Metrics. Princeton: Princeton University Press.
-Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. CRC Press.
-Gerber A.S. and Green, D.P. (2012). Field Experiments: Design, Analysis, and Interpretation. W.W. Norton & Company |