Universität Wien

234002 SE Statistics for Social Scientists 2 (2022S)

4.00 ECTS (2.00 SWS), SPL 23 - Soziologie
Continuous assessment of course work
MIXED
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

*** Please note that the sessions start at 14:00, NOT at 13:15 ***

  • Tuesday 01.03. 13:15 - 16:30 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 08.03. 13:15 - 16:30 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 15.03. 13:15 - 16:30 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 22.03. 13:15 - 16:30 Seminarraum 17, Kolingasse 14-16, OG02
  • Tuesday 29.03. 13:15 - 16:30 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 26.04. 13:15 - 16:30 Hörsaal 15 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 03.05. 13:15 - 16:30 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 10.05. 13:15 - 16:30 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 17.05. 13:15 - 16:30 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

*** Please note that the sessions start at 14:00, NOT at 13:15 as written in the above "Dates" ***

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 analysis

METHODS: 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: English

PREREQUISITES: 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.

Assessment and permitted materials

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.

Minimum requirements and assessment criteria

For a successful completion of the course, all performance components must be delivered in time. The final grade will be determined as follows

100%-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.

Examination topics

• Content of the lectures and the take-home assignment
• Weekly research articles
• Selected book chapters

Reading list

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)

Association in the course directory

Last modified: Fr 25.02.2022 07:29