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CESIS - Centre of Excellence for Science and Innovation Studies, Royal Institute of Technology KTH/CESIS Working Paper Series in Economics and Institutions of Innovation

No 114:
Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modelling Using Two New Strategies

S Hussain (), M. A. Mohamed, R. Holder, A. Almasri and G Shukur

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.

Keywords: Ranking indicators; performance; robust statistics; multilevel estimation; Mahalanobis distance; (follow links to similar papers)

JEL-Codes: C40; C51; C52; (follow links to similar papers)

20 pages, February 26, 2008

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