Data Mining in the Social Sciences and Iterative Attribute Elimination

Data Mining in the Social Sciences and Iterative Attribute Elimination

Anthony Scime (SUNY Brockport, USA), Gregg R. Murray (SUNY Brockport, USA), Wan Huang (SUNY Brockport, USA) and Carol Brownstein-Evans (SUNY Brockport, USA)
Copyright: © 2008 |Pages: 25
DOI: 10.4018/978-1-59904-960-1.ch012
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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.

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