Recently we had a discussion with an organization about where to place pre-employment assessments in the hiring process. Some recruiters felt that the assessments should be at the end of the process after the candidate had been reviewed and interviewed by a recruiter. Other recruiters felt that the assessments should be at the beginning of the process. They are objective, calibrated against performance, and always available given online access. In our experience, letting pre-employment assessments handle the “heavy lifting” to filter candidates at the beginning of the process has more value and better predictive results.
An interesting article in the Harvard Business Review confirms this approach based on an analysis of 17 studies of applicant evaluations. In the May 2014 issue of the Harvard Business Review, Nathan Kuncel, David Klieger, and Deniz Ones discuss their analysis and the results which show that an algorithm, or equation, outperforms human only decisions by at least 25% when it comes to candidate selection.
The results below show the percentages of above-average employees hired through algorithmic systems versus human judgment. The results below represent improvement over chance.
The authors also discuss additional research they conducted with Brian Connelly of the University of Toronto. The authors concluded“...people are easily distracted by things that might be only marginally relevant, and they use information inconsistently. They can be thrown off course by such inconsequential bits of data as applicants’ compliments or remarks on arbitrary topics—thus inadvertently undoing a lot of the work that went into establishing parameters for the job and collecting applicants’ data. So they’d be better off leaving selection to the machines.”
What do you think?
Topics: Talent Selection Ideas