Dominik Hangartner: Katalogdaten im Herbstsemester 2018

NameHerr Prof. Dr. Dominik Hangartner
LehrgebietPolitikanalyse
Adresse
Professur für Politikanalyse
ETH Zürich, LEH D 4
Leonhardshalde 21
8001 Zürich
SWITZERLAND
Telefon+41 44 632 02 67
E-Maildominik.hangartner@gess.ethz.ch
DepartementGeistes-, Sozial- und Staatswissenschaften
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
857-0091-00LMethods II: Quantitative Methods Belegung eingeschränkt - Details anzeigen
Nur für Comparative and International Studies MSc und UZH MA in Poltitikwissenschaften.
6 KP1U + 2SD. Hangartner
KurzbeschreibungThis 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.
LernzielAfter 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.
857-0104-00LTopics in Public Policy: The Politics and Policies of International Migration Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 18
MACIS Studierende haben Priorität.
8 KP3SD. Hangartner, J. Pianzola
KurzbeschreibungThis 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.
LernzielUpon 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.
LiteraturThe reading materials consist of a series of academic papers (see detailed syllabus)
Voraussetzungen / BesonderesEssential: 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.