234002 SE Statistics for Social Scientists 2 (2024S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Do 01.02.2024 09:00 bis Di 20.02.2024 09:00
- Abmeldung bis Fr 15.03.2024 09:00
Details
max. 30 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
*** Please note that the session on March 15th starts at 11:00, and that the session on April 8th starts at 10:00 ***
- Mittwoch 06.03. 15:00 - 17:30 Seminarraum 19, Kolingasse 14-16, OG02
- Freitag 08.03. 15:00 - 17:30 Seminarraum 19, Kolingasse 14-16, OG02
- Mittwoch 13.03. 15:00 - 17:30 Seminarraum 19, Kolingasse 14-16, OG02
- Freitag 15.03. 09:45 - 13:30 Seminarraum 7, Kolingasse 14-16, OG01
- Mittwoch 20.03. 15:00 - 17:30 Seminarraum 19, Kolingasse 14-16, OG02
- Freitag 22.03. 15:00 - 17:30 Seminarraum 19, Kolingasse 14-16, OG02
- Montag 08.04. 09:45 - 12:30 PC-Seminarraum 1, Kolingasse 14-16, OG01
- Mittwoch 10.04. 15:00 - 17:30 Seminarraum 19, Kolingasse 14-16, OG02
- Mittwoch 17.04. 15:00 - 17:30 PC-Seminarraum 1, Kolingasse 14-16, OG01
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
The performance components consist of (i) an exam, (ii) one take-home assignment, and (iii) active class participation:(i) Exam (40%): The exam will take place during the final session (digital or on-site, depending on the COVID-19-related regulations).(ii) Take-home assignment (30%): Students will apply what they have learned to a dataset provided by the lecturer, using a statistical software of their choice (ideally R or Stata). They will submit their code and answer questions based on their analysis (potentially in groups). The assignment will be discussed in class after the submission deadline and before the final exam.(iii) Active class participation (30%): Students are asked to actively participate in class. Moreover, they are expected to read and critically review research articles each week.
Mindestanforderungen und Beurteilungsmaßstab
For a successful completion of the course, all performance components must be delivered in time. The final grade will be determined as follows100%-91%: Excellent (1)
90%-81%: Good (2)
80%-71%: Satisfactory (3)
70%-60%: Sufficient (4)
< 60%: Unsatisfactory (5)Attendance is compulsory; up to two absences will be excused if the lecturer is informed beforehand.
90%-81%: Good (2)
80%-71%: Satisfactory (3)
70%-60%: Sufficient (4)
< 60%: Unsatisfactory (5)Attendance is compulsory; up to two absences will be excused if the lecturer is informed beforehand.
Prüfungsstoff
• Content of the lectures and the take-home assignment
• Weekly research articles
• Selected book chapters
• Weekly research articles
• Selected book chapters
Literatur
The weekly research articles will be provided in class in due time. In addition, selected chapters from the following books will help students to prepare for class, the take-home assignment, and the exam. Access to these chapters will be provided in due time as well.• Mastering 'Metrics: The Path from Cause to Effect (Angrist & Pischke, Princeton University Press)
• Mostly harmless econometrics: An empiricist's companion (Angrist & Pischke, Princeton University Press)
• A Guide to Econometrics (Kennedy, Wiley)
• Survey methodology (Groves et al., Wiley)
• Mostly harmless econometrics: An empiricist's companion (Angrist & Pischke, Princeton University Press)
• A Guide to Econometrics (Kennedy, Wiley)
• Survey methodology (Groves et al., Wiley)
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mi 31.07.2024 12:06
This seminar is targeted at students with an interest in applied empirical research and will prepare them to answer their own research questions by carefully choosing their data and employing appropriate survey and microeconometric methods.The course will start by introducing students to different data types and data sources frequently used for microeconometric analyses in the social sciences. Next, we continue to discuss the most important quality criterions of these data and explore how potential flaws can be accounted for with survey methods. We then proceed to the largest part of the course, which will deepen students’ knowledge about quantitative data analysis, focusing on microeconometrics. More specifically, we will cover• General linear models for different data types
• Decomposition methods
• Panel data analysis with fixed and random effects models
• Causal inference from instrumental variables, difference-in-difference analyses, and regression discontinuity designs
• Survival analysisMETHODS: The lecturer will introduce students to different data sources, survey methods, and estimation techniques, thereby mainly focusing on the intuition behind the respective methods. In addition, students will learn how to critically evaluate the implementation of these methods by reading and discussing topical research articles. They will apply the newly learned methods by analysing data during a take-home assignment.LEARNING OUTCOME: After this course, students will (i) know the most important data types and sources for microeconometric analyses in the social sciences, (ii) know how flawed data can be accounted for using survey methods, (iii) be able to identify appropriate estimation techniques to analyse these data, in particular microeconometric methods, and (iv) be able to critically evaluate research designs considering data, methods, and interpretation of the results.COURSE AND EXAMINATION LANGUAGE: EnglishPREREQUISITES: Students should have basic training in statistics including hypothesis testing, probability distributions, and linear regression. Basic knowledge of a statistical software (e.g., Stata or R) is necessary to complete parts of the take-home assignment (see performance components below). The software will not be covered in class.