Data mining in the social sciences and iterative attribute elimination

Anthony Scime, Gregg R. Murray, Wan Huang, Carol Brownstein-Evans

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

Immense public resources are expended to collect large stores of social data, but often these data are under-examined thereby missing potential opportunities to shed light on some of society's pressing problems. This chapter proposes and demonstrates data mining in general and an iterative attribute-elimination process in particular as important analytical tools to exploit more fully these important data from the social sciences. We use an iterative domain-expert and data mining process to identify attributes that are useful for addressing distinct and nontrivial research issues in social science-presidential vote choice and living arrangement outcomes for maltreated children-using the American National Election Studies (ANES) from political science and the National Survey on Child and Adolescent Well-Being (NSCAW) from social work. We conclude that data mining is useful for more fully exploiting important but under-evaluated data collections for the purpose of addressing some important questions in the social sciences.

Original languageEnglish (US)
Title of host publicationData Mining and Knowledge Discovery Technologies
PublisherIGI Global
Pages308-332
Number of pages25
ISBN (Print)9781599049601
DOIs
StatePublished - Dec 1 2008
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences(all)

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    Scime, A., Murray, G. R., Huang, W., & Brownstein-Evans, C. (2008). Data mining in the social sciences and iterative attribute elimination. In Data Mining and Knowledge Discovery Technologies (pp. 308-332). IGI Global. https://doi.org/10.4018/978-1-59904-960-1.ch013