Much of social science research explores how social contexts shape individual attitudes and behaviors. In comparative studies, context is often defined at the country level. For example: Does a nationalist campaigning reduce support for the welfare state? Does the size of the national police force influence migrants’ trust in the police? Does income inequality lead to depression?
This course trains participants to conduct their own comparative analyses using data from the European Social Survey (ESS) in R.
This research training was originally taught at Goethe University Frankfurt but is now openly accessible to anyone. Most sessions combine a 90-minute lecture with a 90-minute hands-on tutorial.
| # | Topic | Lecture | Tutorial | Literature |
|---|---|---|---|---|
| 0 | Welcome | Slides | - | 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 | - | - | Overview |
| 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 | - | - | Academy of Sociology (2020). Checklist for Quantitative Social Science Articles. |