Browsing by Author "Twigge, Liesle"

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    A psychometric investigation into the use of an adaptation of the Ghiselli predictability index in personnel selection
    (Stellenbosch : Stellenbosch University, 2003-03) Twigge, Liesle; Theron, C. C.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Industrial Psychology.
    ENGLISH ABSTRACT: The field of human resources involves continuous decision-making regarding the matching of the workforce with the workplace, since this match determines individuals' motivation to perform the actions associated with the workplace. If, at the time of the decision, the decision maker could obtain information on end performance, the chances of achieving the desired results would be increased. However, personnel selection is complicated by the obvious fact that information on end performance is not available at the time of the selection decision. All such decisions thus involve predictions about people's performance. The classic validity model forms the foundation of all prediction in as far as the strength of the relationship between the predictor of performance and the actual performance determines the accuracy of the predictor. Over time, numerous possibilities have been considered on how to increase the magnitude of this relationship as experienced through the validity coefficient, mostly involving modifications and/or extensions to the standard regression model. An interesting and challenging alternative to the usual multiple-regression based attempts may be found in the work of Ghise11i (1956, 1960a, 1960b). He has chosen to improve prediction directly through the development of a composite predictability index that explains variance in the prediction errors resulting from an existing prediction model. It would, however, appear as if the procedure has found very little, if any, practical acceptance, partly attributed to the fact that the predictability index failed to significantly explain unique variance in the criterion when added to a model already containing one or more predictors. Resultantly, based on the Ghiselli idea, this research investigates the possibility of modifying such a predictability index so that it does significantly explain unique variance in the criterion when added to a model already containing one or more predictors. In addition, the study investigates whether the expansion of the prediction model is warranted by examining the effect the increase in subject predictability has on the predictive validity of the selection procedure, as well as the monetary effect it has on the utility of the procedure. Hypotheses are tested to determine the possibility of developing an index from a personality measurement that shows a strong and significant correlation with the residuals computed from the regression of the criterion on an ability predictor; to determine if the addition of the index to an ability predictor significantly explains variance in the criterion measurement that is not yet explained by the ability predictor relationships, and to determine whether this ability is affected by the direction in which the index has been developed. Furthermore, hypotheses are tested to determine the increment on validity and selection utility. The data for the analysis was obtained from Psytech (SA), where a validation study was performed at the Gordon Institute of Business Science using the Apil-B ability test, the Critical Reasoning Test Battery and the Organisational Personality Profile measurements to predict the performance of 100 MBA students. The results of the analysis confirmed Ghiselli' s earlier findings that the traditional predictability index does not significantly explain variance in the criterion residual when added to the selection battery. However, by modifying the Ghiselli procedure, the study found that the index was able to significantly explain variance when added to a battery already containing the predictor. When the index is based on the real values of the residuals, the addition of the predictability index to the model significantly explains unique variance in the criterion, but not so when based on the absolute values of the residuals. It also indicated that the inclusion of the predictability index to the prediction model created a substantial increase in the validity of the selection procedure and that the increase in validity translated into a noteworthy improvement in utility. Conclusions are drawn from the obtained results and recommendations are made for future research.