Liam Beiser-McGrath: Katalogdaten im Herbstsemester 2018 |
Name | Herr Liam Beiser-McGrath |
Departement | Geistes-, Sozial- und Staatswissenschaften |
Beziehung | Dozent |
Nummer | Titel | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|
857-0052-00L | Comparative and International Political Economy | 8 KP | 2S | V. Koubi, L. Beiser-McGrath | |
Kurzbeschreibung | This research seminar complements the MACIS core seminar in Political Economy. It covers topics such as international trade, environmental policy, international finance and foreign direct investment, and welfare state policy. Students will, based on reading assignments and discussions in class, develop a research question, present a research design, and write a paper. | ||||
Lernziel | Students will acquire an advanced understanding of some of the key issues and arguments in comparative and international political economy. They will also prepare the ground for a high-quality MA thesis in political economy. | ||||
Inhalt | Because the number of students will be very small, the Political Economy core course runs in parallel, and research interests will be heterogeneous, the general approach will be informal and decentralized. Before the seminar starts we will identify what research topics - within the broader field of Comparative and International Political Economy - the participating students are most interested in. In the first two weeks of the semester, we will meet twice for two hours each as a group to discuss how to write a good research seminar paper, and to identify more closely what each student will be working on. Each student will then receive a reading list, so that she/he can get familiar with the state-of-the-art in her/his area of interests and develop a research design in close consultation with Profs. Bernauer and Koubi as well as postdocs from Prof. Bernauer's group. The group as a whole meets again ca. in week 7 of the semester to discuss the provisional research designs. Research then continues in a decentralized fashion - again in consultation with Profs. Bernauer and Koubi as well as postdocs from Prof. Bernauer's group. The group as a whole meets again in the second to last week of the semester. Each student reports on progress in her/his research during that meeting. The research seminar paper must be finalized and submitted by the end of July 2015. | ||||
Voraussetzungen / Besonderes | This seminar is restricted to students enrolled in the MACIS program. | ||||
860-0006-00L | Essential Tools and Statistics for Impact and Policy Evaluation Number of participants limited to 20. Science, Technology, and Policy MSc and MAS students have priority. This lecture had been offered until autumn semester 2017 with the title "Applied Statistics and Policy Evaluation". Students who has completed that lecture cannot take credit points for this lecture again. | 3 KP | 2G | L. Beiser-McGrath | |
Kurzbeschreibung | This course aims to equip students with the basic knowledge and skills to both understand and conduct the evaluation of policies. This will involve both learning about statistical models and their appropriateness for estimating causal effects, as well as developing skills using statistical software to implement these models. | ||||
Lernziel | Students will: - know strategies to test causal hypotheses using regression analysis and/or experimental methods - be able to critically interpret results of applied statistics, in particular, regarding causal inference - be able to critically read and assess published studies on policy evaluation - learn to use the statistical software R | ||||
Inhalt | This course aims to equip students with the basic knowledge and skills to both understand and conduct the evaluation of policies. The first part of the course offers a thorough treatment of the classical linear regression model, the workhorse model for quantitative data analysis, and the program R that will be used for statistical analysis. The second part of the course focuses on more advanced methods that aim to estimate causal effects from observational data. |