Social Science Data Analysis

Author:

Scime Anthony1,Murray Gregg R.2

Affiliation:

1. State University of New York, USA

2. Texas Tech University, USA

Abstract

Social scientists address some of the most pressing issues of society such as health and wellness, government processes and citizen reactions, individual and collective knowledge, working conditions and socio-economic processes, and societal peace and violence. In an effort to understand these and many other consequential issues, social scientists invest substantial resources to collect large quantities of data, much of which are not fully explored. This chapter proffers the argument that privacy protection and responsible use are not the only ethical considerations related to data mining social data. Given (1) the substantial resources allocated and (2) the leverage these “big data” give on such weighty issues, this chapter suggests social scientists are ethically obligated to conduct comprehensive analysis of their data. Data mining techniques provide pertinent tools that are valuable for identifying attributes in large data sets that may be useful for addressing important issues in the social sciences. By using these comprehensive analytical processes, a researcher may discover a set of attributes that is useful for making behavioral predictions, validating social science theories, and creating rules for understanding behavior in social domains. Taken together, these attributes and values often present previously unknown knowledge that may have important applied and theoretical consequences for a domain, social scientific or otherwise. This chapter concludes with examples of important social problems studied using various data mining methodologies including ethical concerns.

Publisher

IGI Global

Reference46 articles.

1. Ankerst, M., Ester, M., & Kriegel, H. (2000). Towards an effective cooperation of the user and the computer for classification. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 179-188). Boston, MA.

2. Burn-Thornton, K., & Burman, T. (2009). Factors which influence the recovery of alcohol addicts: A second follow up study. In Proceedings of the 2009 International Conference on Data Mining (pp. 165-170). Las Vegas, NV.

3. Mining for the truth: Analyses of celebrity adjudication decisions.;B.Carroll;National Social Science Journal,2012

4. Applying data mining to predict college admissions yield: A case study

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