Dominik Hangartner: Katalogdaten im Herbstsemester 2018 |
| Name | Herr Prof. Dr. Dominik Hangartner |
| Lehrgebiet | Politikanalyse |
| Adresse | Professur für Politikanalyse ETH Zürich, LEH D 4 Leonhardshalde 21 8001 Zürich SWITZERLAND |
| Telefon | +41 44 632 02 67 |
| dominik.hangartner@gess.ethz.ch | |
| Departement | Geistes-, Sozial- und Staatswissenschaften |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 857-0091-00L | Methods II: Quantitative Methods  Nur für Comparative and International Studies MSc und UZH MA in Poltitikwissenschaften. | 6 KP | 1U + 2S | D. Hangartner | |
| Kurzbeschreibung | This class provides an introduction to quantitative methods for social science and policy analysis. The class covers statistical inference, introductory probability, descriptive statistics, regression, and statistical and database programming. | ||||
| Lernziel | After this course, students should be able to assemble a dataset, prepare descriptive statistics, develop and test hypotheses, and present their results in a high-quality presentation or paper. | ||||
| 857-0104-00L | Topics in Public Policy: The Politics and Policies of International Migration Maximale Teilnehmerzahl: 18 MACIS Studierende haben Priorität. | 8 KP | 3S | D. Hangartner, J. Pianzola | |
| Kurzbeschreibung | This course covers both classic and recent topics of international migration, including: economic and political effects of immigration, explanations for anti-immigrant attitudes, methods to assess economic and political discrimination, integration policies (immigrant voting rights and naturalization), and asylum policies. | ||||
| Lernziel | Upon completion, course participants will have a through understanding of the politics and policy of migration as well as knowledge of how to apply advanced quantitative methods for migration policy analysis. | ||||
| Literatur | The reading materials consist of a series of academic papers (see detailed syllabus) | ||||
| Voraussetzungen / Besonderes | Essential: Familiarity with applied statistics (up to and including OLS regression). Ideal: Familiarity with statistical methods for causal inference from observational data, in particular difference-in-difference, instrumental variables, and regression discontinuity designs. | ||||