Dette repository ('repo') samler materialer til faget "Videregående kvantitative metoder i studiet af politisk adfærd".
data: datasæt til brug i undervisningen
examples: kodeeksempler til illustration af forskellige funktioner
extra: ekstramaterialer såsom supplerende slides og præsentationer
midterm: materialer ifm. fagets midterm-opgave
scripts: R-scripts anvendt i undervisningen
slides: slides til undervisningsgangene
workshop: materialer ifm. fagets workshop
| Gang | Dato | Tema | Litteratur | Case |
|---|---|---|---|---|
| 1 | 5/9 | Introduktion til R | Imai kap 1 | |
| 2 | 12/9 | Regression I: OLS | GH kap 3, MM kap 2 | Gilens & Page (2014) |
| 3 | 26/9 | Regression II: Paneldata | GH kap 11 | Larsen et al. (2016) |
| 4 | 29/9 | Regression III: Multileveldata, interaktioner | GH kap 12 | Berkman & Plutzer (2011) |
| 5 | 3/10 | Introduktion til kausal inferens | Hariri (2012), Samii (2016) | |
| 6 | 10/10 | Matching | Justesen & Klemmensen (2014) | Ladd & Lenz (2009) |
| 17/10 | Efterårsferie | |||
| 7 | 24/10 | Eksperimenter I | MM kap 1, GG kap 1+2 | Gerber, Green & Larimer (2008) |
| 8 | 31/10 | Eksperimenter II | GG kap 3+4+5 | Gerber & Green (2000) |
| 9 | 14/11 | Instrumentvariable | MM kap 3 | Arunachalam & Watson (2016) |
| 10 | 14/11 | Regressionsdiskontinuitetsdesigns | MM kap 4 | Eggers & Hainmueller (2009) |
| 11 | 21/11 | Difference-in-differences | MM kap 5 | Enos (2016) |
| 12 | 28/11 | 'Big data' og maskinlæring | Harford (2014), Grimmer (2015), Varian (2014), Athey & Imbens (2016) | |
| 13 | 5/12 | Scraping af data fra online-kilder | MRMN kap 9+14 | |
| 14 | 12/12 | Tekst som data | Grimmer & Stewart (2013), Imai kap 5, Benoit & Nulty (2016) | Hjorth et al. (2015) |
Undervisningen finder sted mandage 10-12 i lokale 1.0.10. Bemærk dog flg. undtagelser:
- Gang 1 og 7 finder dog sted kl. 16-18, lokale 2.2.42.
- Gang 4 finder sted torsdag d. 29. september kl. 12-14, lokale 2.0.30.
- Gang 10 finder sted mandag d. 14. november kl. 13-15, lokale 2.1.02
GH: Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
GG: Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. WW Norton.
Imai: Imai, K. (2016): A First Course in Quantitative Social Science. Unpublished manuscript.
MM: Angrist, J. D., & Pischke, J. S. (2014). Mastering 'metrics: The path from cause to effect. Princeton University Press.
MRMN: Munzert, S., Rubba, C., Meißner, P., & Nyhuis, D. (2014). Automated data collection with R: A practical guide to web scraping and text mining. John Wiley & Sons.
Athey, S., & Imbens, G. (2016). The State of Applied Econometrics-Causality and Policy Evaluation. arXiv preprint arXiv:1607.00699.
Benoit, K., & Nulty, P. (2016) Getting Started with quanteda
Grimmer, J. (2015). We are all social scientists now: how big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(01), 80-83.
Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267-297.
Harford, T. (2014). Big data: A big mistake?. Significance, 11(5), 14-19. Chicago
Hariri, J. G. (2012). Kausal inferens i statskundskaben. Politica, 44(2), 184-201.
Justesen, M. K., & Klemmensen, R. (2014). Sammenligning af sammenlignelige observationer. Politica, 46(1), 60-78.
Samii, C. (2016). Causal empiricism in quantitative research. Journal of Politics 78(3): 941–955.
Varian, H. R. (2014). Big data: New tricks for econometrics. The Journal of Economic Perspectives, 28(2), 3-27.
Arunachalam, R., & Watson, S. (2016). Height, Income and Voting. British Journal of Political Science, 46(03), 1–20.
Berkman, M. B., & Plutzer, E. (2011). Defeating creationism in the courtroom, but not in the classroom. Science, 331(6016), 404-405.
Eggers, A. C., & Hainmueller, J. (2009). MPs for sale? Returns to office in postwar British politics. American Political Science Review, 103(04), 513-533.
Enos, R. D. (2016). What the demolition of public housing teaches us about the impact of racial threat on political behavior. American Journal of Political Science, 60(1), 123-142.
Gerber, A. S., & Green, D. P. (2000). The effects of canvassing, telephone calls, and direct mail on voter turnout: A field experiment. American Political Science Review, 94(03), 653-663.
Gilens, M., & Page, B. I. (2014). Testing theories of American politics: Elites, interest groups, and average citizens. Perspectives on politics, 12(03), 564-581.
Gerber, A. S., Green, D. P., & Larimer, C. W. (2008). Social pressure and voter turnout: Evidence from a large-scale field experiment. American Political Science Review, 102(01), 33-48.
Hjorth, F., Klemmensen, R., Hobolt, S., Hansen, M. E., & Kurrild-Klitgaard, P. (2015). Computers, coders, and voters: Comparing automated methods for estimating party positions. Research & Politics, 2(2).
Ladd, J. M., & Lenz, G. S. (2009). Exploiting a rare communication shift to document the persuasive power of the news media. American Journal of Political Science, 53(2), 394-410.
Larsen, M. V., Hjorth, F., Dinesen, P. & Sønderskov, K. M. (2016). Housing Bubbles and Support for Incumbents. Annual Meeting of the American Political Science Association.
Kleinberg, J., Ludwig, J., Mullainathan, S. (2016). A Guide to Solving Social Problems with Machine Learning. Harvard Business Review.
Stegmueller, D. (2013). How many countries for multilevel modeling? A comparison of frequentist and Bayesian approaches. American Journal of Political Science, 57(3), 748-761.