Higher Education

, Volume 28, Issue 2, pp 241–264 | Cite as

Self and peer appraisal in higher education

  • John R. Doyle
  • Rodney H. Green


This paper applies the mathematical technique of Data Envelopment Analysis to the problem of appraisal in Higher Education. The technique can be understood as an idealised self and peer appraisal: evaluating others in the same way that we would optimally evaluate ourselves relative to those others. The technique is applied to examples of student and staff appraisal. Although the main focus of this article is on the technique itself (its rationale, the prerequisites for use, and the insights that it yields), we also discuss the wider implications of having and using such a technique to assist appraisal.


High Education Data Envelopment Analysis Mathematical Technique Wide Implication Staff Appraisal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Adolphson, D.L., Cornia, G.C., and Walters, L.C. (1991). ‘A unified framework for classifying DEA models’, in Bradley, H.E. (ed.),Operational Research '90. Oxford: Pergamon Press.Google Scholar
  2. Beasley, J.E. (1990). ‘Comparing university departments’,OMEGA, International Journal of Management Science 18(2), 171–183.Google Scholar
  3. Bessent, A.M., and Bessent, E.W. (1980). ‘Determining the comparative efficiency of schools through Data Envelopment Analysis’,Educational Administration Quarterly 16(2), 57–75.Google Scholar
  4. Bessent, A.M., Bessent, E.W., Charnes, A., Cooper, W.W., and Thorogood, N.C. (1983). ‘Evaluation of educational program proposals by means of DEA’,Educational Administration Quarterly 19(2), 82–107.Google Scholar
  5. Boisot, M.H. (1986). ‘Markets and hierarchies in a cultural perspective’,Organization Studies 7(2), 135–158.Google Scholar
  6. Carson, K.P., Cardy, R.L., and Dobbins, G.H. (1991). ‘Performance appraisal as effective management or deadly management disease’,Group and Organization Studies 16(2), 143–159.Google Scholar
  7. Doyle, J.R., and Green, R.H. (1991). ‘Comparing products using Data Envelopment Analysis’,OMEGA, International Journal of Management Science 19(6), 631–638.Google Scholar
  8. Doyle, J.R., and Green, R.H. (1994). ‘Efficiency and cross-efficiency in DEA: Derivations, meanings and uses’,Journal of the Operational Research Society, 45(5), 567–578.Google Scholar
  9. Johnes, G., and Johnes, J. (1993). ‘Measuring the research performance of UK economics departments: An application of Data Envelopment Analysis’,Oxford Economics Papers 45, 332–347.Google Scholar
  10. Hartley, R. (1985).Linear and Non-linear Programming: An Introduction to Linear Methods in Mathematical Programming. Chichester, UK: Ellis Horwood.Google Scholar
  11. Kahneman, D., Solvic, P. and Tversky, A. (1982).Judgement Under Uncertainty. Cambridge: Cambridge University Press.Google Scholar
  12. Kleinmutz, B. (1990). ‘Why we still use our heads instead of formulas: towards an integrative approach”,Psychological Bulletin 107(3), 296–310.Google Scholar
  13. Nisbett, R.E., and Ross, L. (1980).Human Inference: Strategies and Shortcomings of Social Judgement. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  14. Norman, M., and Stoker, B. (1991).Data Envelopment Analysis: The Assessment of Performance. Chichester, UK: Wiley.Google Scholar
  15. Nunamaker, T.R. (1985). ‘Using Data Envelopment Analysis to measure the efficiency of non-profit organizations: a critical evaluation’,Managerial and Decision Economics 6(1), 50–58.Google Scholar
  16. Oral, M., Kettani, O., and Lang, P. (1991). ‘A methodology for collective evaluation and selection of industrial R&D projects’,Management Science 37(7), 871–885.Google Scholar
  17. Roll, Y., and Golany, B. (1933). ‘Alternative methods of treating factor weights in DEA’,OMEGA, International Journal of Management Science 21(1), 99–109.Google Scholar
  18. Ross, L. (1977). ‘The intuitive psychologist and his shortcomings’, in Berkowitz, L. (ed.),Advances in Experimental Social Psychology (vol. 10). New York: Academic Press.Google Scholar
  19. Sexton, T.R., Silkman, R.H., and Hogan, A.J. (1986). ‘Data Envelopment Analysis: critique and extensions’, in Silkman, R.H. (ed.),Measuring Efficiency: An Assessment of Data Envelopment Analysis. San Francisco: Jossey-Bass.Google Scholar
  20. Smith, P., and Mayston, D. (1987). ‘Measuring efficiency in the public sector’,OMEGA International Journal of Management Science 15(3), 181–189.Google Scholar
  21. Stillwell, W.G., Barron, F.H., and Edwards, W. (1983). ‘Evaluating credit applications: a validation of multiattribute utility weight elicitation techniques’,Organizational Behavior and Human Performance 32, 87–108.Google Scholar
  22. Times Higher Educational Supplement (1993). 14 May.Google Scholar
  23. Tomkins, C.R., and Green, R.H. (1978). ‘An experiment in the use of Data Envelopment Analysis for evaluating the efficiency of UK university departments of accounting’,Financial Accountability and Management 4(2), 147–164.Google Scholar

Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • John R. Doyle
    • 1
  • Rodney H. Green
    • 1
  1. 1.School of ManagementUniversity of BathBathUK

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