Research ArticleOpen AccessGoogle Scholar indexed
Cluster Analysis for Political Scientists
Department of Political Science, Federal University of Pernambuco, Recife, Brazil
Department of Political Science, Federal University of Pernambuco, Recife, Brazil
Institute of Social Science, Federal University of Alagoas, Maceió, Brazil
Institute of Social Science, Federal University of Alagoas, Maceió, Brazil
Department of Political Science, Federal University of Pernambuco, Recife, Brazil
Department of Political Science, Federal University of Pernambuco, Recife, Brazil
- 1 Department of Political Science, Federal University of Pernambuco, Recife, Brazil
- 2 Department of Political Science, Federal University of Pernambuco, Recife, Brazil
- 3 Institute of Social Science, Federal University of Alagoas, Maceió, Brazil
- 4 Institute of Social Science, Federal University of Alagoas, Maceió, Brazil
- 5 Department of Political Science, Federal University of Pernambuco, Recife, Brazil
- 6 Department of Political Science, Federal University of Pernambuco, Recife, Brazil
Applied Mathematics·Volume 05 (2014)·Pages 2408–2415·Published 5 August 2014·DOI10.4236/am.2014.515232
Copy link · social · email
Abstract
This paper provides an intuitive introduction to cluster analysis. Our targeting audience are both scholars and students in Political Science. Methodologically, we use basic simulation to illustrate the underlying logic of cluster analysis and we replicate data from Coppedge, Alvarez and Maldonado (2008) [1] to classify political regimes according to Dahl’s (1971) [2] polyarchy dimensions: contestation and inclusiveness. With this paper, we hope to help novice scholars to understand and employ cluster analysis in Political Science empirical research.
KeywordsCluster AnalysisQ AnalysisPolitical Regimes
- Coppedge, M., Alvarez, A. and Maldonado, C. (2008) Two Persistent Dimensions of Democracy: Contestation and Inclusiveness. Journal of Politics, 70, 632-647. http://dx.doi.org/10.1017/S0022381608080663
- Dahl, R. (1971) Poliarquia: Participação e Oposição. Edusp, São Paulo.
- Alquist, J.S. and Breunig, C. (2011) Model-Based Clustering and Typologies in the Social Sciences. Political Analysis, 20, 92-112.
- Krueger, J. and Lewis-Beck, M. (2008) Is OLS Dead? The Political Methodologist, 15, 2-4.
- Tabachnick, B. and Fidell, L. (2007) Using Multivariate Analysis. Allyn & Bacon, Needham Heights.
- Aldenderfer, M.S. and Blashfield, R.K. (1984) Cluster Analysis. Quantitative Applications in the Social Science, Sage University Paper Series.
- Burns, R. and Burns, R. (2008) Cluster Analysis. In: Business Research Methods and Statistics Using SPSS. Sage Publications.
- Hair, J., et al. (2009) Multivariate Data Analysis. 17th Edition, Prentice Hall, Upper Saddle River.
- Tan, P., Steinbach, M. and Kumar, V. (2005) Cluster Analysis: Basic Concepts and Algorithms. In: Introduction to Data Mining, Addison-Wesley, Boston.
- Bartlett, M.S. (1947) Multivariate Analysis. Journal of the Royal Statistics Society, 9, 176-197.
- Dolnicar, S. (2002) A Review of Unquestioned Standards in Used Cluster Analysis for Data Driven Market Segmentation. Faculty of Commerce, Papers.
- de O. Bussab, W., Miazaki, S.E. and Andrade, D.F. (1990) Introdução à análise de agrupamento. In: IX Simpósio Brasileiro DE Probabilidade E Estatísitica, IME-USP, São Paulo, 105 p.
- Pohlmann, M.C. (2007) Análise de Conglomerados. In: Corrar, L.J., Edílson, P. and Dias Filho, J.M., Eds., Análise Multivariada, Atlas, São Paulo.