Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-26T03:01:32.288Z Has data issue: false hasContentIssue false
  • English
  • Français

Capturing causation in political science: the perspective of research design

Published online by Cambridge University Press:  30 July 2021

Luigi Curini Open the ORCID record for Luigi Curini [Opens in a new window]  and
Alessia Damonte Open the ORCID record for Alessia Damonte [Opens in a new window]
Luigi Curini
Affiliation:
Social and Political Sciences, Università degli Studi di Milano, Milano, Italy
Alessia Damonte*
Affiliation:
Social and Political Sciences, Università degli Studi di Milano, Milano, Italy
*
*Corresponding author. Email: [email protected]
Get access

Abstract

In the last decades, ‘research design’ has become a strategic topic across political science. An emerging discourse relies on it to encompass paradigmatic oppositions and cultivate a pluralist approach to causation. As an introduction to the special issue on the topic, we offer an outline of the roles that the discipline recognizes to design in its relation to models and contend that, in a time of fascination for predictors, political science pluralism allows for balancing interpretability and validity of findings at once.

Keywords

Causal inferencecausal modelpolitical scienceresearch design
Type
Research Article
Information
Italian Political Science Review / Rivista Italiana di Scienza Politica , Volume 51 , Special Issue 2: Capturing Causation: Issues of Research Design in Political Science , July 2021 , pp. 157 - 163
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Società Italiana di Scienza Politica

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Austen-Smith, D and Banks, JS (2005) Positive Political Theory II: Strategy and Structure. Michigan: Michigan University Press.CrossRefGoogle Scholar
Bennett, A (2015). Disciplining our conjectures: systematizing process tracing with Bayesian analysis. In Bennett, A and Checkel, JT (eds), Process Tracing: From Metaphor to Analytic Tool. Cambridge, UK: Cambridge University Press, pp. 276–298.Google Scholar
Blair, G, Cooper, J, Coppock, A and Humphreys, M (2019) Declaring and diagnosing research designs. American Political Science Review 113, 838–859.CrossRefGoogle ScholarPubMed
Blatter, J and Haverland, M (2012) Designing Case Studies: Explanatory Approaches in Small-N Research. London, UK: Palgrave Macmillan.CrossRefGoogle Scholar
Brannen, J (2005) Mixing methods: the entry of qualitative and quantitative approaches into the research process. International Journal of Social Research Methodology 8, 173–184.CrossRefGoogle Scholar
Campbell, D and Stanley, J (1963) Experimental and Quasi-Experimental Designs for Research. Chicago, IL: Rand-McNally.Google Scholar
Collier, D, Seawright, J and Munck, GL (2004) The quest for standards: King, Keohane, and Verba's ‘Designing Social Inquiry’. In Brady, HE and Collier, D (eds), Rethinking Social Inquiry: Diverse Tools, Shared Standards. Plymouth, UK: Rowman and Littlefield, pp. 33–63.Google Scholar
Cook, TD and Campbell, DT (1979) Quasi-Experimentation: Design and Analysis Issues for Field Settings. Boston, MA: Houghton Mifflin Company.Google Scholar
Costalli, S and Negri, F (2021) Looking for twins: how to build better counterfactuals with matching. Italian Political Science Review/Rivista Italiana di Scienza Politica, 1–16. https://doi.org/10.1017/ipo.2021.1CrossRefGoogle Scholar
Cox, DR and Reid, N (2000) The Theory of the Design of Experiments. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Creswell, JW and Creswell, JD (2017) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. London, UK: Sage.Google Scholar
Damonte, A (2021) Modeling configurational explanations. Italian Political Science Review/Rivista Italiana di Scienza Politica, 1–16. https://doi.org/10.1017/ipo.2021.2CrossRefGoogle Scholar
Della Porta, D and Keating, M (2008) How many approaches in the social sciences? An epistemological introduction. In Della Porta, D and Keating, M (eds), Approaches and Methodologies in the Social Sciences A Pluralist Perspective. Cambridge, UK: Cambridge University Press, pp. 19–39.CrossRefGoogle Scholar
Di Salvatore, J and Ruggeri, A (2021) Spatial analysis for political scientists. Italian Political Science Review/Rivista Italiana di Scienza Politica, 1–17. https://doi.org/10.1017/ipo.2021.7CrossRefGoogle Scholar
Ermakoff, I (2019) Causality and history: modes of causal investigation in historical social sciences. Annual Review of Sociology 30, 581–606.CrossRefGoogle Scholar
Fairfield, T and Charman, A (2017) Explicit Bayesian analysis for process tracing. Political Analysis 25, 363–380.CrossRefGoogle Scholar
Franzese, R Jr (2007) Multicausality, context-conditionality, and endogeneity. In Boix, C and Stokes, SC (eds), The Oxford Handbook of Comparative Politics. Oxford, UK: Oxford University Press, pp. 27–72.Google Scholar
Geddes, B (1990) How the cases you choose affect the answers you get: selection bias in comparative politics. Political Analysis 2, 131–150.CrossRefGoogle Scholar
George, AL and Bennett, A (2004) Case Studies and Theory Development in the Social Sciences. Boston, MA: The MIT Press.Google Scholar
Gerring, G (2001) Social Science Methodology. A Criterial Framework. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Gschwend, T and Schimmelfennig, F (2007) Introduction: designing research in political science – a dialogue between theory and data. In Gschwend, T and Schimmelfennig, F (eds), Research Design in Political Science: How to Practice What They Preach. Basingstoke: Palgrave MacMillan, pp. 1–18.CrossRefGoogle Scholar
Guba, EG and Lincoln, YS (1989) Fourth Generation Evaluation. London: Sage.Google Scholar
Hammersley, M (2008) Troubles with triangulation. In Bergman, M (ed.), Advances in Mixed Methods Research: Theories and Applications. Thousand Oaks, CA: Sage, pp. 22–36.Google Scholar
Holland, PW (1986) Statistics and causal inference. Journal of the American Statistical Association 81, 945–960.CrossRefGoogle Scholar
Imbens, GW (2020) Potential outcome and directed acyclic graph approaches to causality: relevance for empirical practice in economics. Journal of Economic Literature 58, 1129–79.CrossRefGoogle Scholar
Kaaber, N (2020) Women's empowerment and economic development: a feminist critique of storytelling practices in ‘randomista’ economics. Feminist Economics 26, 1–26.CrossRefGoogle Scholar
King, G (1995) Replication, replication. PS: Political Science and Politics 28, 444–452.Google Scholar
King, G, Keohane, RO and Verba, S (1994) Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
King, G, Keohane, RO and Verba, S (2004) The importance of research design. In Brady, HE and Collier, D (eds), Rethinking Social Inquiry: Diverse Tools, Shared Standards . Plymouth, UK: Rowman and Littlefield, pp. 111–123.Google Scholar
Laver, M and Sergenti, E (2011) Party Competition: An Agent-Based Model. Princeton: Princeton University Press.CrossRefGoogle Scholar
Levy, JS (2007) Qualitative methods and cross-method dialogue in political science. Comparative Political Studies 40, 196–214.CrossRefGoogle Scholar
Lieberman, ES (2005) Nested analysis as a mixed-method strategy for comparative research. American Political Science Review 99, 435–452.CrossRefGoogle Scholar
Mahoney, J and Goertz, G (2006) A tale of two cultures: contrasting quantitative and qualitative research. Political Analysis 14, 227–249.CrossRefGoogle Scholar
Martelli, P (2009) Narrazioni analitiche. Quaderni di Scienza Politica 3, 553–566.Google Scholar
Martini, S and Olmastroni, F (2021) From the lab to the poll: The use of survey experiments in political research. Italian Political Science Review/Rivista Italiana di Scienza Politica, 1–19. https://doi.org/10.1017/ipo.2021.20Google Scholar
McCarty, N and Meirowitz, A (2007) Political Game Theory: An Introduction. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Onwuegbuzie, AJ, Johnson, RB and Collins, KMT (2009) Call for mixed analysis: a philosophical framework for combining qualitative and quantitative approaches. International Journal of Mixed Research Approaches 3, 114–139.Google Scholar
Pearl, J (2009) Causal inference in statistics: an overview. Statistics Surveys 3, 96–146.CrossRefGoogle Scholar
Pemberton, J and Cartwright, N (2014) Ceteris paribus laws need machines to generate them. Erkenntnis 79, 1745–1758.CrossRefGoogle Scholar
Ragin, CC (2008) Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Rohlfing, I (2008) What you see and what you get: Pitfalls and principles of nested analysis in comparative research. Comparative Political Studies 41, 1492–1514.CrossRefGoogle Scholar
Rohlfing, I (2012). Case Studies and Causal Inference: An Integrative Framework. Basingstoke, UK: Palgrave Macmillan.CrossRefGoogle Scholar
Ruffa, C and Evangelista, M (2021) Searching for a middle ground? A spectrum of views of causality in qualitative research. Italian Political Science Review/Rivista Italiana di Scienza Politica, 1–18. https://doi.org/10.1017/ipo.2021.10CrossRefGoogle Scholar
Schmitter, P (2008). The design of social and political research. In Della Porta, D and Keating, M (eds), Approaches and Methodologies in the Social Sciences A Pluralist Perspective. Cambridge, UK: Cambridge University Press, pp. 264–295.Google Scholar
Toshkov, D (2016) Research Designs in Political Science. London: Palgrave.CrossRefGoogle Scholar
Valentim, V, Ruipérez Núñez, A and Dinas, E (2021) Regression discontinuity designs: a hands-on guide for practice. Italian Political Science Review/Rivista Italiana di Scienza Politica, 1–19. https://doi.org/10.1017/ipo.2021.27CrossRefGoogle Scholar