Advertisement

European Political Science

, Volume 18, Issue 4, pp 651–668 | Cite as

FAIR national election studies: How well are we doing?

  • Christina EderEmail author
  • Alexander Jedinger
Research

Abstract

Election studies are an important data pillar in political and social science, as most political research investigations involve secondary use of existing datasets. Researchers depend on high-quality data because data quality determines the accuracy of the conclusions drawn from statistical analyses. We outline data reuse quality criteria pertaining to data accessibility, metadata provision, and data documentation using the FAIR Principles of research data management as a framework (Findability, Accessibility, Interoperability, and Reusability). We then investigate the extent to which a selection of election studies fulfils these criteria using studies from Western democracies. Our results reveal that although most election studies are easily accessible and well documented and that the overall level of data processing is satisfactory, some important deficits remain. Further analyses of technical documentation indicate that while a majority of election studies provide the necessary documents, there is still room for improvement.

Keywords

Accessibility Data Documentation Election studies Findability Interoperability Research data management Reusability 

Notes

Acknowledgements

We thank Kristi Winters and the anonymous reviewers for their helpful comments on previous versions of the paper. We also like to thank Katharina Bühren, Paul Vierus, and Timo Hutflesz for research assistance.

References

  1. Box-Steffensmeier, J.M., and K. Tate. 1995. Data accessibility in political science: Putting the principle into practice. PS: Political Science and Politics 28(3): 470–472.Google Scholar
  2. Carsey, T.M. 2014. Making DA-RT a reality. PS: Political Science and Politics 47(1): 72–77.Google Scholar
  3. Corti, L., V. Van den Eynden, L. Bishop, and M. Woollard. 2014. Managing and sharing research data: A guide to good practice. London: Sage.Google Scholar
  4. Faniel, I.M., A. Kriesberg, and E. Yakel. 2016. Social scientists satisfaction with data reuse. Journal of the Association for Information Science and Technology 67(6): 1404–1416.CrossRefGoogle Scholar
  5. Gertler, A.L., and J.G. Bullock. 2017. Reference rot: An emerging threat to transparency in political science. PS: Political Science and Politics 50(1): 166–171.Google Scholar
  6. ICPSR. 2012. Guide to social science data preparation and archiving. Ann Arbor, MI: Inter-University Consortium for Political and Social Research.Google Scholar
  7. King, G. 1995. Replication, replication. PS: Political Science and Politics 28(3): 444–452.Google Scholar
  8. Kolsrud, K., K.K. Skjåk, and B. Henrichsen. 2007. Free and immediate access to data. In Measuring attitudes cross-nationally. Lessons from the European Social Survey, ed. R. Jowell, C. Roberts, R. Fitzgerald, and G. Eva, 139–156. London: Sage.Google Scholar
  9. Lupia, A., and C. Elman. 2014. Openness in political science: Data access and research transparency: Introduction. PS: Political Science and Politics 47(1): 19–42.Google Scholar
  10. Netscher, S., and C. Eder (eds.) 2018. Data processing and documentation: Generating high quality research data in quantitative social science research (GESIS Papers 2018/22). Cologne: GESIS.Google Scholar
  11. OECD. 2007. Principles and guidelines for access to research data from public funding. Paris: OECD Publications.Google Scholar
  12. Putnam, R.D. 2000. Bowling alone. The collapse and revival of American community. New York: Simon & Schuster.Google Scholar
  13. Strong, D.M., W.L. Yang, and R.Y. Yang. 1997. Data quality in context. Communication of the ACM 40(5): 103–110.CrossRefGoogle Scholar
  14. Vardigan, M.B., and P. Granda. 2010. Archiving, documentation, and dissemination. In Handbook of survey research, ed. P.V. Marsden and J.D. Wright, 707–729. Bingley: Emerald Group.Google Scholar
  15. Wilkinson, M.D., M. Dumontier, I.J. Aalbersberg, G. Appleton, et al. 2016. The FAIR guiding principles for scientific data management and stewardship. Scientific Data 3: 1–9.CrossRefGoogle Scholar

Copyright information

© European Consortium for Political Research 2018

Authors and Affiliations

  1. 1.GESIS – Leibniz Institute for the Social SciencesMannheimGermany
  2. 2.GESIS – Leibniz Institute for the Social SciencesCologneGermany

Personalised recommendations