Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modelling Using Two New Strategies
Résumé
In this paper we propose a general framework for performance evaluation of organisations and individuals over time using routinely collected performance variables or indicators. Two new double robust and model-free strategies are used for evaluation (ranking) of sampling units. Strategy 1 can handle missing data using (RML) at stage two, while strategy two handle missing data at stage one. Strategy 2 has the advantage that overcomes multicollinearity problem. 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.
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