Registration ends on 12.10.2026 at 23:59.

This seminar offers a comprehensive, three-part introduction to research design in the social sciences.

Part I ("Navigating the Worlds of Science," Sessions 1–5) lays conceptual foundations: what distinguishes science from other knowledge production, key epistemological debates (positivism vs. subjectivism, qualitative vs. quantitative approaches), and different research goals (normative/positive, descriptive/explanatory/predictive). Students learn to identify and sharpen research questions, engage with theory through deduction, induction, and abduction, and move from abstract concepts to measurable operationalizations.

Part II ("Quantitative Research: From Data to Causal Claims," Sessions 6–9) focuses on causal inference. Students learn the potential outcomes framework, causal diagrams, and threats to causal claims (confounding, selection, reverse causality). Sessions progress from observational and quasi-experimental designs (difference-in-differences, instrumental variables, regression discontinuity) through a hands-on research design workshop to experiments and the distinctions among internal, external, construct, and statistical validity.

Part III ("Qualitative Research: Making Sense of Social Worlds," Sessions 10–13) shifts to interpretive methodologies. Students explore how different epistemological frameworks construct "data" and "the field" differently (ethnography, interviews, discourse analysis), practice hands-on data collection methods (participant observation, interviews, sensory/walking methods), learn multiple coding and interpretive strategies (thematic analysis, discourse analysis, Grounded Theory), and engage with the challenges of writing, representation, and ethics in qualitative research (thick description, reflexivity, positionality).

Throughout, the pedagogy is highly interactive: students analyze published papers, critique causal claims, conduct field exercises on campus, code their own collected material, and produce analytical texts for peer review—building practical skills alongside theoretical understanding.

By the end of the lecture students will be able to:

1. Distinguish between different research goals and questions (normative/positive, descriptive/causal) and recognize which methodological approaches—quantitative or qualitative—are appropriate for each.

2. Apply core causal inference concepts (confounding, counterfactuals, causal diagrams, identification strategies) to assess whether empirical claims are credible.

3. Critically evaluate published research by identifying its underlying epistemological assumptions, data construction choices, and threats to validity or interpretive rigor.

3. Practice both quantitative and qualitative data collection and analysis techniques, from identifying natural experiments to conducting participant observation and coding interview material.

4. Reflect on the researcher's position, ethical responsibilities, and the constructed nature of data and findings across different methodological traditions.

Further information on teaching evaluation: https://www.leuphana.de/en/teaching/quality-management/evaluation/course-evaluation.html