The most effective method we have for selling our pre-employment testing software, HireSelect, is our 30-day free trial. It allows prospective customers to try the tests, preview the software, and ask our sales team questions about how to best use HireSelect. We also encourage people to evaluate HireSelect by administering the tests to a group of their existing employees. Since companies have a good idea of how their existing employees are performing, testing incumbents can be an effective way to analyze the accuracy and predictive validity of our tests. Most testing companies won’t let potential customers preview their tests in such a comprehensive way, but for us it’s a great sales tool-we have plenty of evidence about the predictive accuracy of our tests, and we want to make sure people see the value in our assessments before they invest in using our service.
But one scenario that we face is when we get comments like, “I don’t know… one of my top performers failed your tests.” Our sales staff will hear comments like this from people who doubt the effectiveness of the tests because of a notable case where the results don’t correspond with what they know about a particular employee. When this happens, we often ask if they’d be willing to share performance data for the employees they tested. We often get back something like this (the data below is not real, but is pretty typical of the data sets we frequently review):
|Employee #||CCAT Percentile||SalesAP Score||Monthly Sales|
In this case it seems clear that employee #2, who is one of the company’s top performing salespeople, didn’t do very well on either the Criteria Cognitive Aptitude Test (CCAT) OR the SalesAP, our sales personality test. In employee #2’s case, the test “didn’t work” in the sense that it dramatically under-predicted her potential. But in any sample of any size, there can always be cases where the test results “didn’t work”; no test is a crystal ball. But the way we should evaluate the predictive accuracy of selection tools is by looking at the whole data set, to see how well the tests predicted performance across the sample population. With this in mind, take another look at the table above.
If you are looking for instances the test “didn’t work” you might also notice that employee #19 got good scores on both tests, but evidently can’t sell a lick. But other than these two outliers, the correlation between test results and job performance (as measured in this case by monthly sales) is pretty strong. How can we be sure of this? (Besides noticing that the scores at the top of the chart, which is sorted by monthly sales, tend to be higher than those at the bottom.) Organizational psychologists measure the predictive validity of a test by calculating a correlation coefficient — a measure statisticians use to represent the strength of a relationship between two things: in this case test scores and job performance. The correlations for the two tests in this case are .34 and .25, respectively. A correlation coefficient can range from -1 (perfectly uncorrelated) to 1 (perfectly correlated): for a pre-employment test a correlation of .21 -.35 is likely to be useful–anything higher than .35 is very beneficial as a predictor. Correlation coefficients of .34 and .25 are respectable: although this particular sample is small, a 20 person sample is much more representative than a one person sample. Calculating the correlation coefficient is a great way to combat “the curse of the anecdote;” letting one prominent data point obscure the trend that is the real story of this data set. The scatter plot below provides another way to visualize this data — it shows that as CCAT scores increase, so does performance — with the two notable outliers as exceptions to the rule. Remember, don’t look at anecdotal evidence if you have a whole data set to examine.