The odds are you use psychometric tests as part of your interview process. If you don’t, experts probably tell you should. Unless you understand their assumptions and interpret them correctly then you will hire the wrong people. Here’s why.
Firstly, these tests are based on a number of assumptions. They assume that, fundamentally, we have a constant personality that predicts our behaviour. This might seem like common sense, but it’s good to question common sense occasionally. Do you behave the same when relaxed and when stressed? With strangers and with friends? At work and at home?
The tests also assume that we can all be measured against similar yardsticks. We are all points on a graph with axes corresponding to universal traits.
You need to be aware of how these tests are developed. Researchers ask thousands of people to describe themselves and others and then examine the words used. Assuming that words used correspond to characteristics, they search for correlations between words. If there are strong correlations, then one of the characteristics is eliminated. For example, if ‘nice’ always corresponds to ‘kind’ then both describe the same trait so one is discarded. Of course, there’s never a perfect correlation so information is lost.
Personality is therefore distilled down to between 3 and 16 factors. This results in gross over-simplification. Carry out the same exercise with physical characteristics and you’d end up describing everybody in terms of their height, weight and skin colour. That broad-brush picture would give us some information, but it’s the lost nuances that are important.
You should note that these tests ignore some realms of personality. Some of the missing traits are important to the workplace, some are not. These tests rarely tell you if you’re an asshole, evil, gullible, attractive, intelligent, a hard worker, promiscuous or religious. Although not always relevant to work, these are important characteristics that people have.
The last point is more subtle and is about interpreting the results. When evaluating the world, our minds use certain heuristics and take certain short cuts. Normally, this works well but it can introduce some biases. One of the most notorious is that we tend to ignore base rates. For example, say a doctor tests you for a disease. The disease affects one person in ten thousand and the test is 99% accurate. You test positive. How likely is it that you have the disease? Take a moment to think about it.
Most people – and this includes doctors and statisticians – will say 99%. This is the wrong answer. This becomes clear if you think in concrete rather than abstract terms. Take ten thousand people. One person is ill. The test is 99% accurate so 1% of people will have false positives. That means that 100 people test positive. Since only one of those people is actually ill, the odds of you being ill given a positive test result is actually only 1% (ignoring the negligible effect of false negatives). The important point here is that the base rate is important. The underlying incidence of the disease massively affects the probabilities.
Your mind can take you down a similar blind alley with psychometric test results. You can see this from the diagram below. Say you’re trying to hire a developer. Obviously you want to hire an outstanding one, and they are rare (the small circle). You’ve noticed that the outstanding developers you know almost all share similar traits (the red area) so you decide to test for that trait.
You find a developer with the required trait. This group is represented by the large circle. However, because many average developers may also have the required trait, you are much more likely to have picked somebody in the blue area (average developer with the trait) than the red area (outstanding with the trait). You have been misled by the base rate – the number of developers in the general developer population with the trait. In other words, if you hire on the basis of the test results, you will likely hire the wrong person.
In conclusion, by all means use psychometric tests but understand their assumptions and interpret them carefully.