IDEAS home Printed from https://ideas.repec.org/a/bpj/statpp/v13y2022i3p297-314n2.html
   My bibliography  Save this article

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
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/spp-2022-0004
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/spp-2022-0004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:statpp:v:13:y:2022:i:3:p:297-314:n:2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.