Logic of Quantitative Research in Political Science

This repo contains information about and materials for "Logic of Quantitative Research in Political Science", a five-day graduate-level course held at the University of Copenhagen, February 6-10, 2017. The course is taught by postdoc Frederik Hjorth, associate professor Asmus Leth Olsen, and associate professor Jacob Gerner Hariri.

Description

The course will use illustrative examples from the political science literature, and emphasizes the logic of research designs rather than their implementation in statistical software. The course equips students with concepts needed to understand the reasoning behind research designs and modeling in quantitative political science research.

The course is structured around five themes, one covered each day:

1. Logic of quantitative research

• Regression
• Natural Experiments
• Experiments
• Content analysis

The course covers the key methodological approaches within each theme as well as canonical research articles applying the relevant approach. For more details, see Course schedule below.

Students will also have the opportunity to present and receive feedback on their own ongoing work (see Research paper below).

Signing up

To sign up for the course, please send an email to [email protected].

Course schedule

Block	Day	Time	Theme	Instructor
1	Monday	9-12	Logic 1: Quantitative research designs	Frederik Hjorth
2		13-16	Logic 2: Controversies about the quantitative approach	Frederik Hjorth
3	Tuesday	9-12	Regression 1: Linear regression	Frederik Hjorth
4		13-16	Regression 2: Panel data and interaction models	Frederik Hjorth
5	Wednesday	9-12	Natural experiments 1: IV, difference-in-difference	Jacob Gerner Hariri
6		13-16	Natural experiments 2: Natural experiments and RDD	Asmus Leth Olsen
7	Thursday	9-12	Experiments 1: Simple randomization	Frederik Hjorth
8		13-16	Experiments 2: Clustering, blocking, noncompliance	Frederik Hjorth
9	Friday	9-12	Content analysis 1: Introduction, uses	Frederik Hjorth
10		13-16	Content analysis 2: Designs, reliability & validity	Frederik Hjorth

For readings for each block, see the Literature section below.

Dates

Monday February 6 - Friday February 10, 2017.

Location

University of Copenhagen, Department of Political Science, Øster Farimagsgade 5, 1353 Copenhagen K. Teaching takes place in room 4.2.50.

Class participation

It is expected that you have read the texts for each day and participate actively in class discussions.

Research paper

Deadline for submitting a research paper is Wednesday, February 2 at noon. The research paper should reflect a quantitative/comparative/methodological aspect of your research and be no longer than 10 pages. It is expected that you prepare comment to all papers. The papers will be distributed before the course.

Meals

Lunch and coffee will be provided every day. On Tuesday, February 7, there will be a dinner for all course participants at Madklubben Nørrebro.

Price

For students enrolled at University of Copenhagen or political science departments at other Danish universities, course participation is free. For students at other departments, the fee is 1500 DKK.

Literature

1: Logic 1

• Lijphart, A. (1971) Politics and the Comparative method. American Political Science Review. 65 (3):682-693. (search for his interpretation of the core idea of PS)
• Nørgaard. A. S. (2008) Political Science: Witchcraft or Craftsmanship? Standards for Good Research. World Political Science Review. 4(1):1-28. (A must read)
• Dahler-Larsen, P., & Sylvest, C. (2013). Hvilken pluralisme?: Betragtninger om det kausale design og definitionen af god samfundsvidenskab. Politik, 16(2), 59-68.
• Laitin, D. D. (2003). The perestroikan challenge to social science. Politics & Society, 31(1), 163-184.
• Flyvbjerg, B. (2004). A perestroikan straw man answers back: David Laitin and phronetic political science. Politics & Society, 32(3), 389-416.

2: Logic 2

• The Journal Editors' Transparency Statement (JETS), available at dartstatement.org/#!blank/c22sl
• Isaac, J. C. (2015). For a more public political science. Perspectives on Politics, 13(02), 269-283.
• Leeper, T. J. (2016). Really Introductory Introduction to R, available at github.com/leeper/Rcourse/raw/gh-pages/Intro2R/Intro2R.pdf

3: Regression 1

• Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton University Pres, chapter 2.
• Gilens, M., & Page, B. I. (2014). Testing theories of American politics: Elites, interest groups, and average citizens. Perspectives on politics, 12(03), 564-581.
• Bashir, O. S. (2015). Testing Inferences about American Politics: A Review of the “Oligarchy” Result. Research & Politics, 2(4).

4: Regression 2

• 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.
• Steenbergen, M. R., & Jones, B. S. (2002). Modeling Multilevel Data Structures. American Journal of Political Science, 46(1), 218-237.

5: Natural experiments 1

• Hariri, Jacob (2012): Kausal inferens i statskundskaben, Politica.
• Acemoglu, Daron, Simon Johnson, and James A. Robinson (2001): The Colonial Origins of Comparative Development: An Empirical Investigation, American Economic Review, 91 (5): 1369-1401.

• Miguel, E., Satyanath, S., & Sergenti, E. (2004). Economic shocks and civil conflict: An instrumental variables approach. Journal of political Economy, 112(4), 725-753.

6: Natural experiments 2

• Dunning, T. (2008). Improving Causal Inference: Strengths and Limitations of Natural Experiments. Political Research Quarterly, 61 (2), 282–293.
• Verrier, Diarmuid B. (2012). Evidence for the influence of the mere-exposure effect on voting in the Eurovision Song Contest. Judgment and Decision Making 7 (5), 639-643.
• Eggers, A. C., & Hainmueller, J. (2009). MPs for sale? Returns to office in postwar British politics. American Political Science Review, 103(04), 513-533.

7: Experiments 1

• Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton University Pres, chapter 1.
• Campbell, D. T., & Stanley, J. C. (1996): Experimental and Quasi-experimental Designs for Research. Chicago: Rand McNally. pp. 1-16. (a must read)
• 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.
• Gerber, A. S., & D. P. Green (2012): Field Experiments: Design, Analysis, and Interpretation. New York: W.W. Norton. Chapter 1. (a general intro to experiment)

8: Experiments 2

• Gerber, A. S., & D. P. Green (2012): Field Experiments: Design, Analysis, and Interpretation. New York: W.W. Norton. Chapters 3-5. (blocking, clustering, covariate adjustment, one-sided noncompliance)
• Nickerson, D. W. (2008): Is Voting Contagious? Evidence from Two Field Experiments. American Political Science Review 102 (February): 49-57. (focus on the design and the experiment)

9: Content analysis 1

• Neuendorf, Kimberly A. (2002): The Content Analysis Guidebook, Sage. Chapters: 1, 3-7 (p. 1-26, 26 pages)
• Krippendorff, Klaus (2008): Testing the Reliability of Content Analysis Data, in Krippendorff & Bock: The Content Analysis Reader, Sage (p. 350-357, 8 pages)
• Carney, D. R., Jost, J. T., Gosling, S. D., & Potter, J. (2008). The secret lives of liberals and conservatives: Personality profiles, interaction styles, and the things they leave behind. Political Psychology, 29(6), 807-840 (34 pages)

10: Content analysis 2

• Hansen, K. M., & Pedersen, R. T. (2008). Negative campaigning in a multiparty system. Scandinavian Political Studies, 31(4), 408-427 (20 pages)
• King, G., Pan, J., & Roberts, M. E. (2013). How censorship in China allows government criticism but silences collective expression. American Political Science Review, 107(02), 326-343 (18 pages)