print

Empirical challenges and methodological approaches in comparative politics (through the lens of the Political Atlas of the Modern World 2.0)

Melville A.Yu.,

HSE University, Moscow, Russia, amelville@hse.ru

elibrary_id: 251142 | ORCID: 0000-0002-1414-5783 | RESEARCHER_ID: B-1152-2014

Malgin A.V.,

MGIMO University, Moscow, Russia, artemmalgin@gmail.com

elibrary_id: 376145 |

Mironyuk M.G.,

HSE University, Moscow, Russia, mmironyuk@hse.ru

elibrary_id: 251177 | ORCID: 0000-0002-8183-3084 | RESEARCHER_ID: K-9102-2015

Stukal D.K.,

HSE University, Moscow, Russia, denis.stukal@gmail.com

elibrary_id: 1096522 | ORCID: 0000-0001-6240-5714 | RESEARCHER_ID: ABA-3314-2020

Article received: 2023.03.22. Accepted: 2023.04.18


DOI: 10.17976/jpps/2023.05.10
EDN: NOWUCL

Rubric: Theory and methodology of political studies

For citation:

Melville A.Yu., Malgin A.V., Mironyuk M.G., Stukal D.K. Empirical challenges and methodological approaches in comparative politics (through the lens of the Political Atlas of the Modern World 2.0). – Polis. Political Studies. 2023. No. 5. P. 153-171. (In Russ.). https://doi.org/10.17976/jpps/2023.05.10. EDN: NOWUCL

The article was prepared within the consortium of MGIMO University and HSE University and funded by the grant for the implementation of the Priority 2030 Strategic Academic Leadership Program. The authors thank the anonymous reviewers for their valuable comments.

Abstract

In recent decades, the expanding volume, diversity and coverage of data have created new or have transformed existing areas of research. They have also turned data into a key element of politics today. In this context, the status of empirical research that became the political science mainstream at the turn of the 20th - 21st centuries is not unambiguous. On the one hand, empirical research claims to produce rigorous scientific knowledge. On the other hand, it is plagued with distrust of both data sources and methods of analyzing them. Implementation of the Political Atlas of the Modern World 2.0 project by the consortium of MGIMO-University and the HSE-University involves a collection of large amounts of country data, and in order to avoid falling into the trap of data manipulation, it is necessary to use the state-of-art approaches to data collection, verification and analysis. This article discusses the general problems associated with the collection of empirical databases for comparative studies, and offers a tentative typology. These problems are investigated in relation to the Political Atlas of the Contemporary World 2.0 project. An overview of the key weaknesses of existing databases is given. Then key practices that claim to be the up-to-date “gold standard” for ensuring the quality of data collected are considered. The article also discusses the advantages and disadvantages of a number of popular data analysis methods in comparative studies. Methods previously employed in the Political Atlas of the Modern World project are also discussed.

Keywords

consortium, MGIMO University, HSE University, state, database, index, quantitative methods, statistical analysis, principal component analysis, discriminant analysis, training sample, factor analysis, Bayesian statistics.

 Full Text

Aleskerov, F., Meshcheryakova, N., & Shvydun, S. (2020). Indirect influence assessment in the context of retail food network. In Bychkov, I., Kalyagin, V., Pardalos, P., Prokopyev, O. (Ed.), Network Algorithms, Data Mining, and Applications. NET 2018. Springer Proceedings in Mathematics & Statistics. Vol 315. Cham: Springer. https://doi.org/10.1007/978-3-030-37157-9_10

Aleskerov, F.T., Gavrilenkova, I., Shvydun, S., & Yakuba, V.I. (2020). Power distribution in the networks of terrorist groups: 2001-2018. Group Decision and Negotiation, 29(3), 399-424. https://doi.org/10.1007/s10726-020-09674-2

Ambrozik, C. (2019). Countering violent extremism globally: a new global CVE dataset. Perspectives on Terrorism, 13(5), 102-111.

Daxecker U., Amicarelli E., & Jung A. (2019). Electoral contention and violence (ECAV). Journal of Peace Research, 56(5), 714-723. https://doi.org/10.1177/0022343318823870

Fienberg, S.E. (2006). When did Bayesian inference become “Bayesian”? Bayesian Analysis, 1(1), 1-40. https://doi.org/10.1214/06-BA101

Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (Ed.). (2013). Bayesian data analysis. Chapman and Hall/CRC. https://doi.org/10.1201/b16018

Gill, J. (2021). Political science is a data science. The Journal of Politics, 83(1), 1-7. https://doi.org/10.1086/711611

Gleditsch, N.P., Wallensteen P., Eriksson M., Sollenberg M., & Strand H. (2002). Armed conflict 1946-2001: a new dataset. Journal of Peace Research, 39(5), 615-637. https://doi.org/10.1177/0022343302039005007

Hastie, T., Tibshirani, R. & Friedman, J. (2016). The elements of statistical learning: data mining, inference, and prediction. New York, NY: Springer.

James G., Witten D., Hastie T., Tibshirani R. (2021). An introduction to statistical learning: with applications in R. New York, NY: Springer. https://doi.org/10.1007/978-1-0716-1418-1

Jolliffe, I.T. (2002). Principal component analysis. New York, NY: Springer.

Jolliffe, I.T. & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society, 374(2065). https://doi.org/10.1098/rsta.2015.0202

Leemann, L., Stoetzer, L.F. & Traunmueller, R. (2021). Eliciting beliefs as distributions in online surveys. Political Analysis, 29(4), 541-553. https://doi.org/10.1017/pan.2020.42

Liu, L. (2021). The rise of data politics: digital China and the world. Studies in Comparative International Development, 56(1), 45-67. https://doi.org/10.1007/s12116-021-09319-8

Loyle C.E., & Binningsbo H.M. (2018). Justice during armed conflict. The Journal of Conflict Resolution, 62(2), 442-466. https://doi.org/10.1177/0022002716655441

Lutmar, C., & Terris, L.G. (2019). Introducing a new dataset on leadership change in rebel groups, 1946-2010. Journal of Peace Research, 56(2), 306-315. https://doi.org/10.1177/0022343318802979

Melville, A., Akhremenko, A. & Mironyuk, M. (2019). What Russia can teach us about power and influence in world politics. Russian Politics, 4(2), 137-167. https://doi.org/10.1163/2451-8921-00402001

Mulder, J. & Hoff, P.D. (2021). A latent variable model for relational events with multiple receivers. arXiv. https://doi.org/10.48550/arXiv.2101.05135

Nohlen, D., Grotz, F., & Hartmann, Ch.  (Ed.). (2002). Elections in Asia and the Pacific: a data handbook. Vol. 2. Oxford; New York: Oxford University Press.

Palmer, G., D'Orazio, V., Kenwick, M., & Lane, M. (2015). The MID4 Dataset, 2002-2010: procedures, coding rules and description. Conflict Management and Peace Science, 32(2), 222-242. https://doi.org/10.1177/0738894214559680

Regan, P. (2002). Third-party interventions and the duration of intrastate conflicts. Journal of Conflict Resolution, 46(1), 55-73. https://doi.org/10.1177/0022002702046001004

Shvydun, S. (2020). Influence of countries in the global arms transfers network: 1950-2018. In H. Cherifi, S. Gaito, J. Mendes, E. Moro, & L. Rocha (Ed.), Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence. Vol 882. Cham: Springer. https://doi.org/10.1007/978-3-030-36683-4_59

Smith, A.F.M. (1984). Present position and potential developments: some personal views: Bayesian statistics. Journal of the Royal Statistical Society. Series A (General), 147(2), 245-259. https://doi.org/10.2307/2981680

Walsh, J.I., Conrad, J.M., Whitaker, B.E., & Hudak, K.M. (2018). Funding rebellion. Journal of Peace Research, 55(5), 699-707. https://doi.org/10.1177/0022343317740621

Wellman E.I., Allen N.W., & Nyblade B. (2022). The extraterritorial voting rights and restrictions dataset (1950-2020). Comparative Political Studies, 56(6), 897-929. https://doi.org/10.1177/00104140221115169

Maltsev A. (2020). Network dynamics of technology diffusion in international arms transfers. International Trends, 18, 4 (63), 36-61. (In Russ.) https://doi.org/10.17994/IT.2020.18.4.63.5

Melville, A.Yu. & Mironyuk, M.G. (2020). “Political Atlas of the Modern World” Revisited. Polis. Political Studies, 6, 41-61. (In Russ.) https://doi.org/10.17976/jpps/2020.06.04

Melville, A.Yu., Malgin, A.V., Mironyuk, M.G., & Stukal, D.K. (2023). “Political Atlas of the Modern World 2.0”: formulation of the research problem. Polis. Political Studies, 2, 72-87. (In Russ.) https://doi.org/10.17976/jpps/2023.02.06

Prikladnaya statistika i osnovy ekonometriki. Pod red. A. S. Ajvazyan, V. S. Mhitaryan [Applied statistics and foundations of econometrics Ed. by A. S. Ajvazyan, V. S. Mhitaryan]. Moscow: Izdatel’skogo ob’edinenie «YUNITI». (In Russ.)

Sartori, G. (2003). Concept misformation in comparative politics. Polis. Political Studies, 3, 67-77. (In Russ.) https://doi.org/10.17976/jpps/2003.03.07

Stukal, D.K., Belenkov, V.E., & Philippov, I.B. (2021). Data science methods in political science research: analyzing protest activity in social media. Political Science (RU), 1, 46-75. (In Russ.) https://doi.org/10.31249/poln/2021.01.02

Vybory vo vsem mire. Elektoral'naya svoboda i obshchestvennyy progress. Entsiklopedicheskiy spravochnik. Sost. A.A. Tanin-L'vov [Elections around the World. Electoral Freedom and Social Progress. Encyclopedic reference book. Comp. by A.A. Tanin-L’vov]. 2001. Moscow: ROSSPEN Publ. (In Russ.) 

See also: