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Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modelling Using Two New Strategies

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Author Info

  • Hussain, S

    ()
    (Division of Primary Care and General Practice, School of Medicine, University of Birmingham)

  • Mohamed, M. A.

    (Department of Public Health, University of Birmingham, UK)

  • Holder, R.

    (Division of Primary Care and General Practice, School of Medicine, University of Birmingham)

  • Almasri, A.

    (Department of Economics and Statistics, Karlstad University, Sweden)

  • Shukur, G

    (Jönköping International Business School)

Abstract

In this paper we propose a general framework for performance evaluation of organisations and individuals over time using routinely collected performance variables or indicators. Such variables or indicators are often correlated over time, with missing observations, and often come from heavy tailed distributions shaped by outliers. Two double robust strategies are used for evaluation (ranking) of sampling units. Strategy 1 can handle missing data using residual maximum likelihood (RML) at stage two, while strategy two handle missing data at stage one. Strategy 2 has the advantage that overcomes the problem of multicollinearity. Strategy one requires independent indicators for the construction of the distances, where strategy two does not. Two different domain examples are used to illustrate the application of the two strategies. Example one considers performance monitoring of gynaecologists and example two considers the performance of industrial firms.

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Bibliographic Info

Paper provided by Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies in its series Working Paper Series in Economics and Institutions of Innovation with number 114.

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Length: 20 pages
Date of creation: 26 Feb 2008
Date of revision:
Handle: RePEc:hhs:cesisp:0114

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Related research

Keywords: Ranking indicators; performance; robust statistics; multilevel estimation; Mahalanobis distance;

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