Distribution errors in the employee performance evaluation process

It is difficult to achieve rational engagement, directing and developing, motivating, rewarding, and improving organizational efficiency, without the evaluation of the quality of job performance of employees. In management literature, this technique is known as "employee performance evaluation". Performance or productivity may be either a measurable result that has been achieved, or behaviour or personal characteristics necessary for performing certain activities in a defined period of time. Recently, a special management approach "performance management" has been developed, in order to integrate the performance evaluation and the use of assessment results in management, aimed at improving performance. Since the employee performance evaluation is most often the result of subjective judgement of an evaluator about the quality of his/her work, one has to take into account possible errors that characterize such a way of judging. There are several types of errors that an evaluator can commit during the performance evaluation process, and in this paper, an approach to the identification and reduction of distribution errors of evaluators, most widely spread in organizations with a large number of employees, has been presented. Introduction The e mployee performance evaluation system is of great importance to the organization ; however, the three basic types of its application are: strategic, administrative and developmental. In addition to these three basic types of application, the employee performance evaluation system is tied to other human resource management activities, primarily to recruitment and selection. Within the employee performance evaluation process, among other things, it is necessary to take into account the errors that evaluators may commit. Estimation errors are errors of judgment that systematically occur when an individual observes and evaluates another individual. They can also be defined as a difference between the results of human judgment and an objective, accurate assessment, without prejudice and other subjective influences. Distribution Errors Distribution errors are the result of tendency of evaluators to use only one part of the evaluation scale. This means that almost all employees receive the same ratings - high, low or average. In this sense, there are two basic types of distribution errors: - Lenient and/or severe evaluation errors, - Central tendency error. Identifying Distribution Errors There are several ways for establishing the existence of any of these types of errors. One of the most popular approaches is to calculate the standard deviation. A small standard deviation indicates the existence of distribution errors, but for the detection of lenient, severe evaluation errors, or the central tendency error, it is necessary to compare the average value of ratings and the average value on the evaluation scale. If the average value of ratings is lower than the average value on the evaluation scale, then there is a severe evaluation error. If the average value of ratings is higher than the average value on the evaluation scale, then there is a lenient evaluation error. Finally, if the average value of ratings approximates the average value on the evaluation scale, then this is the case of a central tendency error. In this paper, the approach of checking the existence of any of these types of distribution errors of evaluators, while evaluating performance of drivers of military vehicles in transportation units of the Serbian Army, was presented. Based on the statistical analysis of ratings of drivers in program package SPSS 11.5 for Windows, and in accordance with the methodology described above for checking the presence of any of the above types of distribution errors, it was concluded that the evaluator in the transport unit that was the subject of this study committed a lenient evaluation error. Reducing Distribution Errors Despite numerous errors that evaluators commit in the employee performance evaluation process, there are ways to significantly reduce them to a level that can be tolerated. In order to accomplish that, appropriate measures and activities, which organization must take into account, are necessary, and the following suggestions may contribute significantly to a more objective and fair employee performance evaluation process: –  Behaviours related to the quality of job performance should be documented ; –  Larger number of evaluators should be used ; –  Evaluators should be trained ; –  Horizontal evaluation should be conducted. In accordance with the aforementioned suggestions for reducing errors of evaluators within this study, training of the person who previously held the role of an evaluator in a transportation unit of the Serbian Army that was the subject of this check was conducted, as well as the horizontal evaluation of drivers, conducted by the same person. Based on the statistical analysis of new results in the program package SPSS 11.5 for Windows, it can be concluded that the training of the evaluator and the horizontal evaluation technique have significantly contributed to a reduction of the distribution error of lenient evaluation, previously committed by the evaluator. Also, it can be concluded that the inclusion of more evaluators in the evaluation process as well as keeping a journal to document the behaviour of employees further contributed to a fairer and more objective evaluation of employees' performance. Conclusion The most common evaluation errors in organizations with a large number of employees are "distribution errors of evaluators". Realization that the value of the standard deviation of ratings indicates the existence or non-existence of distribution errors of evaluators, as well as that the ratio of the average value of ratings and the average value on the evaluation scale determines the type of distribution errors, is the basis for conducting the analysis in order to identify these errors in the employee performance evaluation process. The use of appropriate program packages for statistical data processing, namely SPSS 11.5, contributes additionally to the efficiency of this procedure.