METHODOLOGY OF POLITICAL SCIENCE

1. Knowledge and understanding. By the end of the course, students will be able to identify theories, hypotheses, and methods used in empirical political science research. They will understand how big data and data science can contribute to the understanding of political and social problems and dynamics.

2. Applying knowledge and understanding. By the end of the course, students will apply different methods to political science research questions. They will be able to design and carry out a research project that uses innovative (big) data for understanding, describing, real-time monitoring and/or forecasting of political and social behaviour.

3. Making judgements. By the end of the course, students will analyze data to measure concepts, make comparisons, and draw inferences. They will be able to understand suitable and appropriate methodologies and designs for political and social science research.

4. Communication skills. By the end of the course, students will learn how to communicate political science concepts, theories, and methods in writing. They will also be able to present their research projects, findings and implications in front of an audience.

5. Learning skills. By the end of the course, students will learn how to recognise the most suitable method(s) for addressing research questions with the use of big data and data science methods.

Seminars will tipically include presentations by the lecturer as well as by students.

Normally, at each seminar we will first discuss topics in general terms. We may call these sections lectures, although I will make an effort to make them as interactive as possible.
We will then discuss one or two real pieces of research – research in focus – that employed big data/data science/computational social science methods. The discussion of research in focus will concentrate both on substantive elements (the question or problem that the study addresses and its main findings) and research design aspects (the data and design used). In this way, we will improve our capacity to understand and evaluate research while we will also learn how innovative data and methods are used in real computational social science.

While most or all seminars will include one or two student presentations, three seminars will be based entirely on the presentation and discussion of students’ research design proposals (see method of assessment). We will call these student workshops – more details in the next section.

NOTA BENE: Students should not be intimidated by the amount of readings! Before each seminar, you will normally be expected to read one chapter on the ‘general’ topic under discussion. One additional reading will normally be a piece of research (see research in focus below) that we will use to make sense of how ‘general’ questions are addressed ‘in practice’ in applied research.

NOTE that, should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

1. Introduction to the scientific study of politics (and to the course). What does political (or social) science mean. Approaching politics scientifically. Contents and structure of the course.

Readings:

• Kellstedt and Whitten 2018 chapter 1

• Toshkov 2016 chapter 1

 

2. Asking good research questions. Why bother with research questions. Types of research questions. Why good research questions are so important to good science, and how to formulate good research questions.

Readings:

• Toshkov 2016 chapter 2.

• Huntington-Klein 2022 chapter 2

Further readings:

• Roberts Clark, W. (2020) “Asking interesting questions”, in Curini and Franzese, eds. The Sage Handbook of Research Methods in Political Science and International Relations. Thousand Oaks: Sage Pubns Ltd.

• McCauley A and Ruggeri A (2020) “From Questions and Puzzles to Research Projects” , in Curini and Franzese, eds. The Sage Handbook of Research Methods in Political Science and International Relations. Thousand Oaks: Sage Pubns Ltd.

Optional topic: Literature review. Finding, selecting, assessing, organising and presenting science – ‘without getting buried in it’!

Readings:

• Knopf, J.W. (2006) ‘Doing a Literature Review’, PS: Political Science & Politics, 39(1), pp. 127–132. doi:10.1017/S1049096506060264.

• Slides + selective readings to be analysed and discussed in class.

 

3. Theory. The function of theory in social science – and the difference with theories in natural science. Paradigms, frameworks, theories and models. Developing theories. Assessing theories.

Readings:

• Toshkov 2016 chapter 3

• Kellstedt and Whitten 2018 chapter 2

Further readings:

• Roberts Clark, W. (2020) “Asking interesting questions”, in Curini and Franzese, eds. The Sage Handbook of Research Methods in Political Science and International Relations. Thousand Oaks: Sage Pubns Ltd.

• Kellstedt and Whitten 2018 chapter 2

 

4. Concepts and operationalisation. The role of concepts in social science, and the challenge of concept definition. Operationalising concepts to make them measurable.

Readings:

• Toshkov 2016 chapter 4.

Further readings:

• Gerring 2012 chapter 5

 

5. Measuring and describing variables. Measurement strategies and descriptive inference.

Readings:

• Toshkov 2016 chapter 5

• Kellstedt and Whitten 2018 chapter 5

Further readings:

• Gerring 2012 chapter 7

 

** FIRST STUDENT WORKSHOP. Presentation of research topics/questions for research design proposal.

 

6. Explanation and causal relations. Types of explanation: laws, probabilistic, functional, intentional and mechanistic explations. Notions of causality and causal inference.

Readings:

• Toshkov 2016 chapter 6

• Huntington-Klein 2022 chapters 6, 7

Further readings:

• Cunningham, chapter 3

• Huntington-Klein 2022 chapters 8, 9

• King, Keohane and Verba, selected chapters

 

7. Experimental research designs. The basics, goals and logic of experimental research; the design of experimental research; randomised controlled trials and quasi-experiments; analysis and limitations; experiments in political science and public policy research.

Readings:

• Toshkov 2016 chapter 7

 

8. Large-N research designs: logic and pitfalls. Conditions and strategies for causal inference: naturals experiments, instrumental variables, mediation analysis, conditioning. Common designs for causal inference: time series, cross-sectional, panel, multilevel designs. Estimating causal effects: varieties and size of association; uncertainty and statistical significance; linearity and beyond; limited outcomes. Design: variable and case selection; levels of analysis and observation: measurement error and missing data. Use and limitations.

Readings:

• Toshkov 2016 chapter 8

Further readings:

• Cunningham chapters 4-10

• Huntington-Klein chapters 14-20

 

** SECOND STUDENT WORKSHOP. Presentation of literature reviews and hypotheses for research design proposal.

 

9. ‘Standard’ research designs/I. Comparative designs: logic and types of small-n comparative research, most similar/most different designs; qualitative comparative analysis; use and limitations.

Readings:

• Toshkov 2016 chapter 9, 10

Further readings:

• King, Keohane and Verba, selected chapters

 

** RESEARCH IN FOCUS WORKSHOP. Your turn to present relevant research. See materials in separate document

 

10. ‘Standard’ research designs/II. Case studies: selecting evidence to observe; conducting case studies research; use and limitations. Mixed and nested designs: selecting and using cases in mixed and nested designs; use and limitations.

Readings:

• Toshkov 2016 chapter 10, 11

• King Keohane and Verba?

Further readings:

• King, Keohane and Verba, selected chapters

Optional topic. ‘Big data’ research designs. Social networks: understanding and analysing social interactions; statics and dynamics. Social complexity: origins, laws, theories. Simulations.

Readings:

• Cioffi-Revilla 2017 chapters 4-5.

Further readings:

• See readings in Appendix

 

** THIRD STUDENT WORKSHOP. Presentation of data sources and preliminary design for research design proposal.

 

APPENDIX. SOME COMPUTATIONAL POLITICAL SCIENCE RESEARCH

• Computational political science: applications of big data science to political science research/I.

• Big data in surveys for the study of elections, public opinion and representation (Warshaw in Alvarez 2016).

• Political event real time data (Beieler et al in Alvarez 2016).

• Network analysis (Sinclair in Alvarez 2016).

• Social media and protests (Tucker et al in Alvarez 2016).

• Social marketing for smart government (Griepentrog in Alvarez 2016).

• Machine learning algorithms for election fraud detection (Levin et al in Alvarez 2016).

• Social media for nowcasting and forecasting elections (Ceron/Curini/Iacus 2017).

 

• Computational political science: applications of big data science to political science research/II.

• International Trade with Big Data

C. A. Hidalgo, B. Klinger, A.-L. Barab´asi, R. Hausmann. “The Product Space Conditions the Development of Nations.” Science 317.5837 (2007): 482-487

• Lobbying and Campaign Contribution

In Song Kim. “Political Cleavages within Industry: Firm-level Lobbying for Trade Liberalization.” American Political Science Review, 111.1: 1-20.

Stephen Ansolabehere, John M. de Figueiredo, and James M. Snyder. “Why is There so Little Money in U.S. Politics?” Journal of Economic Perspectives, 17.1 (2003): 105-130

• Identifying Behavioral Patterns using Massive Data Reading:

Gary King, Jennifer Pan, and Margaret E Roberts. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review, 107.2: 326-343.

Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Ramachandran, V., Phillips, C., and Goel, S. (2017). “A large-scale Analysis of Racial Disparities in Police Stops across the United States.” arXiv preprint arXiv:1706.05678.

• Measuring Ideological and Political Preferences using Social Network Data

Robert Bond and Solomon Messing. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.” American Political Science Review 109.1 (2015): 62-78.

Pablo Barbera “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis 23.1 (2014): 76-91

• What do Politicians Do?

Justin Grimmer, Solomon Messing, and Sean Westwood. “How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation.” American Political Science Review, 106.4 (2012), 703-719

• Big Administrative Data: Promises and Pitfalls

Connelly, R., Playford, C.J., Gayle, V., Dibben, C., 2016. “The Role of Administrative Data in the Big Data Revolution in Social Science Research.” Social Science Research,

Special issue on Big Data in the Social Sciences 59, 112

Kopczuk, W., Saez, E., Song, J., 2010. “Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937.” The Quarterly Journal of Economics 125, 91128.

• Machine Learning Algorithms in Society

Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. “Human Decisions and Machine Predictions.” The Quarterly Journal of Economics 133 (1):23793

CORE TEXT – THIS IS THE MAIN TEXT WE USE FOR SEMINARS

• Toshkov, D. (2016). Research Design in Political Science. London New York, NY: Palgrave.

 

SUPPLEMENTARY READINGS – MAIN ONES, SOME CHAPTERS FROM THESE ARE USED FOR SOME SEMINARS (see section on “Detailed course contents” below)

• Cunningham, S. (2021) Causal Inference: The Mixtape. Yale University Press. Available as free bookdown at https://mixtape.scunning.com

• Huntington-Klein, Nick. (2022). The Effect. An Introduction to Research Design and Causality. Routledge & CRC Press. Available as free bookdown at theeffectbook.net

• Kellstedt, P.M. & Whitten, G.D. (2018). The Fundamentals of Political Science Research. Cambridge: Cambridge University Press.

 

OTHER SUPPLEMENTARY READINGS – NOT DIRECTLY USED FOR SEMINARS, LISTED FOR YOUR REFERENCE

• Alvarez, R.M. (2016). Computational Social Science: Discovery and Prediction. New York, NY: Cambridge University Press.

• Curini, Luigi, and Robert Franzese, eds. (2020). The Sage Handbook of Research Methods in Political Science and International Relations. Thousand Oaks: Sage Pubns Ltd.

• Ceron, A., Curini, L. & Iacus, S.M. (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. London ; New York, NY: Routledge.

• Cioffi-Revilla, C. (2017). Introduction to Computational Social Science: Principles and Applications (2 edition.). New York, NY: Springer-Verlag

• Jungherr, A. (2015). Analyzing Political Communication with Digital Trace Data: The Role of Twitter Messages in Social Science Research. Cham: Springer Verlag.

• King, G., Keohane, R.O. and Verba, S. (1994) Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton, N.J: Princeton University Press.

• Lowndes, V., Marsh, D. & Stoker, G. (2017). Theory and Methods in Political Science (4 edition). Basingstoke: Palgrave MacMillan.

NB: Students should not be intimidated by the amount of readings. Before each seminar, you will normally be expected to read one chapter on the ‘general’ topic under discussion, see below. One additional reading will normally be a piece of research (see research in focus below) that we will use to make sense of how ‘general’ questions are addressed ‘in practice’ in applied research.

Assessment is based on class participation, presentations (30%), final paper (40%), and final exam (30%).

 

The final paper is a 3000-word original research design proposal carried out by applying course materials. The research design proposal should include the basic elements of an original research project, namely a research question, theoretical contribution, testable hypotheses, and a description of the proposed data collection and analysis. This paper should focus narrowly on a topic of the student’s choice and display a greater depth of understanding of a smaller set of ideas raised in the course. More information in a separate document (see “Student deliverables”).

Students should work on the research design proposal over the full term. Indeed three of the course seminars – the student workshops – will include students’ presentations of 1) possible research topics/questions (tentatively in the sixth week of the course); 2) literature review and hypotheses (around the nineth week); 3) data and preliminar design (around the end of the course).

The final exam focuses largely on an oral discussion of the research design proposal, that will (also) be used as a basis for discussing the key topics of the course.

Illustrate the distinctive features of your research design proposal.

Motivate the choice of your research design.

Explain how you operationalise the concepts in proposals.

If you are going to propose and test a theory, discuss it. Otherwise explain why you will not design your own theory of the problem at hand.

Explain the logic behind case selection in your proposal