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Data Mining in Social Sciences: A Decision Tree Application Using Social and Political Concepts

Author

Listed:
  • Massou Efthalia

    (Primary Care Unit, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK)

  • Prodromitis Gerasimos

    (Laboratory of Experimental and Social Psychology, Panteion University, Athens, Greece)

  • Papastamou Stamos

    (Laboratory of Experimental and Social Psychology, Panteion University, Athens, Greece)

Abstract

In this paper, we investigated the utility of data mining to classify individuals into predefined categories of a target variable, based on their social and political attitude. Data collected for a social psychology study conducted in Greece in 1994 were used for this purpose. We established the theoretical background of our analysis through explanatory factor analysis. We ran the decision tree algorithm CHAID in order to build a predictive model that classifies the study participants in terms of their attitude toward physical and symbolic violence. The CHAID algorithm provided a decision tree that was easily interpreted, and which revealed meaningful predictive patterns. CHAID algorithm showed satisfactory predictive ability and promising alternatives to social psychology data analysis. To the best of our knowledge, there is no other evidence in the literature that the decision tree algorithms can be used to identify latent variables.

Suggested Citation

  • Massou Efthalia & Prodromitis Gerasimos & Papastamou Stamos, 2022. "Data Mining in Social Sciences: A Decision Tree Application Using Social and Political Concepts," Statistics, Politics and Policy, De Gruyter, vol. 13(3), pages 297-314, November.
    • Handle: RePEc:bpj:statpp:v:13:y:2022:i:3:p:297-314:n:2
    DOI: 10.1515/spp-2022-0004
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