Clinical data integration methods versus mechanical data integration in predicting job performance

In selection and recruitment, most organisations and talent decision makers invest substantial resources in utilizing assessment tools with the aim of attaining the best prediction of an individual’s job performance. These assessments often comprise of test batteries which produce a number of scores in differing areas of performance which are then combined to produce an overall picture of the individual’s match for a predefined role. However, few decision makers stop to consider the implications of their assessment integration method on the accuracy of predicting job success. 

When the aim is to recruit the best individual for the job and substantial resources have been invested toward this endeavour, this consideration is worthwhile as it has a direct bearing on your return on investment.

Kuncel and colleagues conducted comparative research between two commonly used methods in decision-making, the clinical and mechanical data integration methods. Typically, scores integrated clinically, require practitioners or hiring managers to subjectively apply their professional judgements by drawing on their collective insights and expertise to make predictions about an individuals’ fit for the role. What this study found, looking at the overall trend across 25 independent studies, was that when decisions were made clinically, these were far less accurate than those which were made mechanically. When each score was weighted by means of a pre-specified algorithm to produce an overall score, the predictions proved to be more accurate by up to 50% than those concluded clinically. Furthermore, the loss of validity in clinical data integration was consistent irrespective of the knowledgeability of the decision makers, the organisational context or the jobs in question.

These noteworthy insights, highlight the value of careful consideration when selecting a data integration method geared toward yielding the most optimum benefits in job performance predictions. The importance of job profiling and the understanding of job requirements is just once more emphasized by this study.

Summary from Kuncel, N. R., Klieger, D. M., Connelly, B. S., & Ones, D. S. (2013). Mechanical versus clinical data combination in selection and admissions decisions: A meta-analysis. Journal of Applied Psychology, 98(6), 1060.

June 25, 2015