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To publish or not to publish? On the aggregation and drivers of research performance

  • De Witte, K.
  • Rogge, N.

This paper presents a methodology to aggregate multidimensional research output. Using a tailored version of the non-parametric Data Envelopment Analysis model, we account for the large heterogeneity in research output and the individual researcher preferences by endogenously weighting the various output dimensions. The approach offers three important advantages compared to the traditional approaches: (1) flexibility in the aggregation of different research outputs into an overall evaluation score; (2) a reduction of the impact of measurement errors and atypical observations; and (3) a correction for the influences of a wide variety of factors outside the evaluated researcher’s control. As a result, research evaluations are more effective representations of actual research performance. The methodology is illustrated on a data set of all faculty members at a large polytechnic university in Belgium. The sample includes questionnaire items on the motivation and perception of the researcher. This allows us to explore whether motivation and background characteristics (as age, gender, retention, etc.) of the researchers explain variations in measured research performance.

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Paper provided by Top Institute for Evidence Based Education Research in its series Working Papers with number 33.

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Date of creation: 00 2010
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Handle: RePEc:tir:wpaper:33
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  6. Daouia, Abdelaati & Simar, Leopold, 2007. "Nonparametric efficiency analysis: A multivariate conditional quantile approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 375-400, October.
  7. Knox Lovell, C. A. & Pastor, Jesus T. & Turner, Judi A., 1995. "Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries," European Journal of Operational Research, Elsevier, vol. 87(3), pages 507-518, December.
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  10. Zhou, P. & Ang, B.W. & Poh, K.L., 2007. "A mathematical programming approach to constructing composite indicators," Ecological Economics, Elsevier, vol. 62(2), pages 291-297, April.
  11. Laurens Cherchye & Willem Moesen & Nicky Rogge & Tom Puyenbroeck, 2007. "An Introduction to ‘Benefit of the Doubt’ Composite Indicators," Social Indicators Research, Springer, vol. 82(1), pages 111-145, May.
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  13. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
  14. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355, April.
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  20. Garey C. Durden & Patricia Gaynor & Kellie Maske, 2002. "Determinants of Scholarly Productivity Among Male and Female Economists," Working Papers 02-12, Department of Economics, Appalachian State University.
  21. Johnes, Geraint & Johnes, Jill, 1993. "Measuring the Research Performance of UK Economics Departments: An Application of Data Envelopment Analysis," Oxford Economic Papers, Oxford University Press, vol. 45(2), pages 332-47, April.
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  23. Levin, Sharon G & Stephan, Paula E, 1991. "Research Productivity over the Life Cycle: Evidence for Academic Scientists," American Economic Review, American Economic Association, vol. 81(1), pages 114-32, March.
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