860-0006-00L Applied Statistics and Policy Evaluation
|Semester||Autumn Semester 2016|
|Lecturers||I. Günther, K. Harttgen|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Comment||Number of participants limited to 20. |
Science, Technology, and Policy MSc and MAS in Development and Cooperation have priority.
|Abstract||This course introduces students to key statistical methods for analyzing social science data with a special emphasis on causal inference and policy evaluation. Students learn to choose appropriate analysis strategies for particular research questions and to perform statistical analyses with the statistical Software Stata.|
- have a sound understanding of linear and logit regression
- know strategies to test causal hypotheses using regression analysis and/or experimental methods
- are able to formulate and implement a regression model for a particular policy question and a particular type of data
- are able to critically interpret results of applied statistics, in particular, regarding causal inference
- are able to critically read and assess published studies on policy evaluation
- are able to use the statistical software STATA for data Analysis
|Content||The topics covered in the first part of the course are a revision of basic statistics and linear and logit regression analysis. The second part of the course focuses on causal inference and introduces methods such as panel data analysis, difference-in-difference methods, instrumental variable estimation, and randomized controlled trials mostly used for policy evaluation. The course shows how the various methods differ in terms of the required identifying assumptions to infer causality as well as the data needs. |
Students will apply the methods from the lectures by solving weekly assignments using statistical software and data sets provided by the instructors. These data sets will cover topics at the interface of policy, technology and society. Solving the assignments contributes to the final grade with a weight of 30%. Students are assisted in solving the assignments during the exercises session.