You believe in analytics and evidence-based management. So you have invested in the latest pre-employment assessments to let science inform hiring decisions. You have a handful of test scores, but what do you do with them? Do you use a mathematical formula to combine the scores, or do you come up with minimum passing scores that applicants must reach? In other words, do you use equations or hurdles in selection?
Analytics is a numbers game. You have applicants take assessments that provide numeric scores that indicate how much knowledge, skill, ability, or other characteristics—the KSAOs—applicants have. You then link those scores to job performance by conducting a validation study. In other words you collect assessment scores and performance scores on a sample of employees and run statistical analyses to see if those scores can predict performance. For example, you take a sample of 100 sales representatives and have them take an assessment designed to predict sales performance. You then check records to see how much product each one sold, and run statistical analyses to see if the scores relate to sales. If they do, you would want to use that assessment to help hire salespeople, as those who score well on the assessment would be likely to perform well on the job. (For more on how scientific selection works, see my I-O Psychology textbook).
This all sounds good, but you have say 8 scores on each applicant. What exactly do you do with them? Do you use equations or hurdles in selection?
When you run your statistical analysis in a validation study, you will get an equation that allows you to predict performance from a combination of the assessment scores. For example, if you have 2 scores, your equation might be something like:
Performance = 2 x Score1 + 3 x Score2
You would take the two scores for each applicant and combine them using this equation to get a total score. If an applicant scored 5 on the first assessment and 4 on the second, their total score would be 22 (10 + 12). Each applicant would have a total score and you could rank order those totals from highest to lowest. You would then do further screening with those highest on the list, and hope to hire those with the highest scores.
Although equations are appealing, they have two problems. First, they allow very high scores on one assessment to compensate for low scores on another. If a particular job requires 6 critical skills, someone who is very high on one, but very low on another might get hired even though there is a vital skill deficiency that might prevent good performance. Second, there is the assumption with this approach that the higher the score on each assessment the better, and that is not always the case. Many jobs require the use of math but excess skill is not at all helpful. A bank teller, for example, needs to balance a drawer at the end of the day, so basic math is essential. Having a PhD in mathematics would not make someone a better teller.
A hurdle is a minimum or “cut” score that indicates an applicant has an adequate level of each KSAO being assessed. If an assessment is scored from 1 to 10, the cut score might be 6, meaning anyone who scores at least 6 is considered further. This approach means that every person hired had passed the test for each KSAO being assessed. Thus for a bank teller, the goal is to merely determine that each applicant has sufficient skill in math to do the job. If a greater level of math skill is needed than reflected in a score of 6, the cut score can be raised.
Setting cut scores can be tricky. Validation studies show that higher scores mean higher performance and generally do not indicate what a sufficient score might be. The cut score is determined in consideration of how many people apply for each position, and how critical each KSAO might be. Once set they provide an easy way to screen applicants and focus on those who show the most promise.
Equations or Hurdles in Selection?
So should you use equations or hurdles? The answer depends on your situation. Equations provide an easy mathematical way to compare individuals, as each applicant gets a single score. Multiple hurdles provide a list of applicants who have an adequate level of each KSAO, but do not indicate who among those finalists is most likely to be successful. Of course, it is possible to use both—use multiple hurdles as a first screen to eliminate from further consideration those individuals who have one or more KSAO deficiencies. Of those who survive this initial screen, an equation is applied to indicate which ones have the highest overall potential. That list of finalist would be advanced to the next step in the selection process—likely being invited to an interview.
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