Social science research often deals with the influence of social contexts on the individual. In comparative research, context is often understood as the country. For example: Does a nationalist election campaign jeopardize support for the welfare state? Why do people in some countries trust the police more than in others? Does income inequality lead to depression?
The course aims to provide students with the skills to carry out their own country comparison study. For this purpose, data from the European Social Survey (ESS) will be analyzed using the programming language R. This is an advanced methods course - basic statistical knowledge, knowledge of data analysis (e.g. with R or Stata), and a general interest in quantitative research are required. At the end of the course, students will conduct their own empirical-quantitative, country-comparative study in which they apply what they have learned during the semester to a topic of their choice.
This research training was originally conducted at Goethe University Frankfurt during the winter term of 2021/22 but is now openly accessible to anyone. Most sessions include a 90-minute lecture followed by a 90-minute exercise, ensuring a balanced blend of theoretical knowledge and practical application. For the original course, also see the course Syllabus.
| # | Topic | Lecture | Tutorial | Literature |
|---|---|---|---|---|
| 0 | Welcome | Slides | None | Slack introduction |
| 1 | Introduction to R | Slides | Solution | Wickham & Grolemund (2017). R for Data Science. O'Reilly. |
| 2 | Research process & descriptive statistics | Slides | Solution | Chapter 1 in: Bohrnstedt & Knoke (1982). Statistics for Social Data Analysis. Peacock Publishers. |
| 3 | Linear regression | Slides | Solution | Chapter 3 (pages 68-94) in: Wooldridge (2012). Introductory econometrics: A modern approach. Cengage Learning. |
| 4 | Linear and non-linear probability models | Slides | Solution | Breen, Karlson & Holm (2018). Interpreting and understanding logits, probits, and other nonlinear probability models. Annual Review of Sociology, 44, 39-54. |
| 5 | Introduction to comparative social research | Slides | Solution | Kohn (1987). Cross-National Research as an Analytic Strategy. American Sociological Review, 52 (6), 713-731. |
| 6 & 7 | Studies | None | See syllabus | |
| 8 | Random intercept models | Slides | Solution | Schmidt-Catran, Fairbrother & Andreß (2019). Multilevel models for the analysis of comparative survey data: Common problems and some solutions. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 71 (1), 99-128. |
| 9 | Random slope models | Slides | Solution | Heisig, Schaeffer & Giesecke (2017). The costs of simplicity: Why multi-level models may benefit from accounting for cross-cluster differences in the effects of controls. American Sociological Review, 82 (4), 796-827. |
| 10 | Cross-level interactions | Slides | Solution | Heisig & Schaeffer (2019). Why you should always include a random slope for the lower-level variable involved in a cross-level interaction. European Sociological Review, 35 (2), 258-279. |
| 11 | Logistic multi-level models | Slides | Solution | Hox (2002): Chapter 6 in: Multilevel Analysis. Techniques and Applications. Routledge. |
| 12 | Advanced multi-level structures | Slides | Solution | Schmidt-Catran & Fairbrother (2015). The random effects in multilevel models: Getting them wrong and getting them right. European Sociological Review, 32 (1), 23-38. |
| 13 | Multi-level models with pooled cross-sections | Slides | Solution | Fairbrother (2014). Two multilevel modeling techniques for analyzing comparative longitudinal survey datasets. Political Science Research and Methods, 2 (1), 119-140. |
| 14 | Final | None | Academy of Sociology (2020). Checklist for Quantitative Social Science Articles. |