Key and additional literature

Key readings:

• Imai, K.: Quantitative Social Science, An Introduction. Princeton University Press. 2017

Readings on R-programming

• Grolemund, G. & Wickham, H.: R for Data Science
• Venables, W.N., Smith, D.M. & the R Core Team: An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics
• Verzani, J.: simpleR: Using R for Introductory Statistics

Readings for the annotated bibliography

First meeting:

• Goodman, S. N., Fanelli, D., & Ioannidis, J. P. (2016). What does research reproducibility mean?. Science translational medicine, 8(341).
• Iqbal, S. A., Wallach, J. D., Khoury, M. J., Schully, S. D., & Ioannidis, J. P. (2016). Reproducible research practices and transparency across the biomedical literature. PLoS Biol, 14(1).
• Stodden, V. (2010). Data sharing in social science repositories: facilitating reproducible computational research. NIPS 2010.

Second meeting:

• Mearsheimer, J. J., & Walt, S. M. (2013). Leaving theory behind: Why simplistic hypothesis testing is bad for International Relations. European Journal of International Relations, 19(3), 427-457.

Third meeting:

• Höfler, M. (2005). Causal inference based on counterfactuals. BMC medical research methodology, 5(1), 28.
• Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945-960.

Fourth meeting:

• Card, D., & Krueger, A. B. (1993). Minimum wages and employment: A case study of the fast food industry in New Jersey and Pennsylvania (No. w4509). National Bureau of Economic Research.

Seventh meeting:

• Powsner, S. M., & Tufte, E. R. (1994). Graphical summary of patient status. The Lancet, 344(8919), 386-389.
• Edward Tufte. (1983). The visual display of quantitative information. Chapter 9.

Twelweth meeting:

• Gelman, A., & Loken, E. (2014). The Statistical Crisis in Science Data-dependent analysis—a “garden of forking paths”—explains why many statistically significant comparisons don’t hold up. Am Sci, 102(6), 460.