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Workflows for quantitative data analysis in the social sciences

  • Kenneth J. Turner
  • Paul S. Lambert
Regular Paper

Abstract

The background is given as to how statistical analysis is used by quantitative social scientists. Developing statistical analyses requires substantial effort, yet there are important limitations in current practice. This has motivated the authors to create a more systematic and effective methodology with supporting tools. The approach to modelling quantitative data analysis in the social sciences is presented. Analysis scripts are treated abstractly as mathematical functions and concretely as web services. This allows individual scripts to be combined into high-level workflows. A comprehensive set of tools allows workflows to be defined, automatically validated and verified, and automatically implemented. The workflows expose opportunities for parallel execution, can define support for proper fault handling, and can be realised by non-technical users. Services, workflows and datasets can also be readily shared. The approach is illustrated with a realistic case study that analyses occupational position in relation to health.

Keywords

e-Social science Quantitative data analysis Scientific workflow Service-oriented architecture Statistical analysis 

Notes

Acknowledgments

The work reported in this paper was conducted on the Dames project, which was led by the second author. Dames was funded by the Economic and Social Research Council under grant number RES-149-25-1066. The authors are grateful to their colleagues on Dames for collaboration on the general topic of techniques for e-social science. Support of social science workflows was jointly developed by Koon Leai, Larry Tan and the first author as part of the Dames project. Guy C. Warner built the occupational and educational portals for Dames, and created the server infrastructure needed for the work reported here. He also provided technical advice on using workflows in conjunction with the Dames servers.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Computing Science and MathematicsUniversity of StirlingStirlingScotland, UK
  2. 2.Applied Social ScienceUniversity of StirlingStirlingScotland, UK

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