Quantitative Research in Political Science: How to Apply the Classical Toolbox

The seminar provides students with a comprehensive overview of the main quantitative tools used in empirical political science and aims to enhance their ability to develop scientifically rigorous research designs. The course will begin by refreshing students' familiarity with the R programming language and basic statistical methods. It will then introduce techniques for replicating existing research on topics presented in Prof. Dr. Daniela Braun's seminar 'Current Developments in Political Science Research on Europe: "Legitimacy Problems and Polarisation in European Societies"'. Finally, students will be guided in developing their own research designs, with particular emphasis on translating concepts into measures, moving from research questions to empirically testable hypotheses, and selecting appropriate analytical tools to examine their data.

This seminar will run in parallel to ‘How to Analyse Digital Content for Political Science Research’ by Dr. Alexander Hartland. Students should opt to participate in one or the other course after the initial classes.

Literature:

  • De Mesquita, E. B., & Fowler, A. (2021). Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis. Princeton University Press.

Optional:

  • Brambor, T., Clark, W. R., & Golder, M. (2005). Understanding interaction models: improving empirical analyses. Political Analysis, 14(1), 63–82. https://doi.org/10.1093/pan/mpi014
  • Cinelli, C., Forney, A., & Pearl, J. (2022). A crash course in good and bad controls. Sociological Methods & Research, 53(3), 1071–1104. https://doi.org/10.1177/00491241221099552
  • Heiss, A. (2022, May 20). Marginalia: A guide to figuring out what the heck marginal effects, marginal slopes, average marginal effects, marginal effects at the mean, and all these other marginal things are. andrewheiss.com. doi.org/10.59350/40xaj-4e562
  • King, G. (1998). Unifying political methodology. https://doi.org/10.3998/mpub.23784
  • Long, J. S. (1997). Regression models for categorical and limited dependent variables. SAGE.
  • Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What is your estimand? Defining the target quantity connects statistical evidence to theory. American Sociological Review, 86(3), 532–565. doi.org/10.1177/00031224211004187

Event no. 156687 in the LSF

Porträtfoto Giuseppe Carteny
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Lecturer: Dr Giuseppe Carteny

Dr. Giuseppe Carteny is a postdoctoral researcher at the Chair of Political Science with a focus on European Integration and International Relations at Saarland University.

Previously, he was a postdoctoral researcher at the Mannheim Centre for European Social Research (MZES) at the University of Mannheim for the ProConEU project, a research project investigating the growing gap between supporters and opponents of European integration in relation to party politics, civic politics and social media communication, funded by the German Federal Ministry of Education and Research (BMBF).

In July 2021, he completed his PhD in Political Science at the Network for the Advancement of Social and Political Studies (NASP) at the University of Milan with a dissertation dedicated to the individual and contextual antecedents of individual trust in public institutions in the East Asian region.

His work focuses the study of political attitudes, electoral behaviour, party politics, and comparative politics in Europe and East Asia, with a particular focus on institutional confidence, value and ideological orientations, Euroscepticism, far-right party agency, cleavage politics, and related topics.  His methodological interests include classical quantitative methods for the social sciences, quantitative text analysis methods and survey methodology.