Sure, it's easy to say engineering, legal, IT, or actuarial jobs require technical degrees. People in these professions need a substantial amount of education to practice their trade. But we all know from watching folks in these professions that it takes more than a sheepskin to be successful. Sometimes, it takes certain personality factors to make a good job fit.
2 u/ M& D) m2 v# J2 XJob performance is a two-sided coin. On one side, all the hard skills required for the job, and on the other, all the social factors. For the purpose of this article, we'll assume personality is on the social factor side.
1 M2 B) n4 X7 F: ~ _. A; U1 gThe important things to ask are, "How do we decide which personality factors are important?" and "How do we measure them?") _3 F3 D2 {8 P8 H
Wrong WayA pre-screen test (i.e., interviews, tests, resume review, application form) is supposed to determine whether the applicant is qualified. In a perfect world, good scores predict good performance, and bad scores predict bad performance (assuming we hired applicants with bad scores).
6 x5 T( x- w; Q+ ]0 AWell, there's a right way and a wrong way to link scores to future job performance. Unfortunately, the wrong way is the norm.
8 x% ^% D$ D0 ]' x9 N/ L' u) GThe wrong way comes in two varieties:
$ Z6 x+ h, Y) c- Someone likes XYZ test, gives it to every applicant, and plays amateur shrink.
- Someone gives a one-size-fits-all test to current employees and correlates scores with job performance.
Both varieties are filled with mistakes. I call these WW1 and WW2. 8 s2 O3 x) l9 }5 R
WW1 is common among most organizations I know. WW2 is common among folks who might have taken a class in statistics, but skipped the class in measuring human performance. Either way, both WW1 and WW2 turns away good employees and hires bad employees. Of course, you don't have to take my word for it. Just do a controlled study.- n: K: N" x' T! l% O- b, b
What is This Thing Called Performance?If we want to use a pre-hire test, common sense says to first determine what factor we want to measure. Second, choose an accurate and trustworthy test that measures this factor. Third, make sure the test predicts job performance. Sound simple? Read on.
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Some people think they can measure "performance" by looking at performance appraisals or supervisor ratings. However, we all know most performance ratings are primarily personal opinion shaped by friendships, power, ambition, or sucking- up (sucking-up is a technical term for getting other people to think you are actually competent at what you do).
: ?# v. E. e8 k9 |As a result, most performance review data means:4 d0 S7 d1 w4 D3 {+ g
- The employee was unskilled at shifting blame for his or her mistakes and now must wait for the rater to get Alzheimer's.
- The employee is highly skilled at shifting blame and charismatic…and thus, is well-liked by the boss.
- Employees who are skilled at corporate camouflage.
In general, any data that can only be evaluated at the end of a long performance period is error-prone. Sales volume, for example, is fuzzy because it's a function of persistence, fact-finding, learning, strategizing, presentations, adapting to buyer personalities, economic conditions, market conditions, and so forth.
& T$ b1 |, ?% W7 _* g+ X( RTo use an analogy, if you want to measure the quality of grapes in a fruit salad, you cannot put all the fruits into a blender, press mix, and come back six months later with your grape-o-meter. . i+ F1 n5 d4 h! M
Performance data should be easy to see and easy to measure.8 Z! T( M; @) l3 q7 m; I
Big Nets Often Contain Big Holes
: y1 q1 r# L' m4 L- W8 pThere are some folks who think they can give a multi-factor personality test to employees, and then examine the results to identify correlations. Sorry. No cigar for them, either.
4 k# @: b2 m7 _; E' a$ fLet's assume for a moment these folks got the performance thing right. Giving one great-big test to everyone and looking for results is a major mistake, primarily because a correlation is not always a causation. In other words, just because two things are statistically associated does not mean one causes the other.
& j( D1 m3 M% QConsider the famous correlation of rising skirt hemlines with rises in the stock market. If one actually caused the other, today's hemlines would probably be located somewhere above the waistline, causing most male traders to become so distracted a market crash would be inevitable.
- [. _7 D) y& k% l/ a* F; y+ _Statistics is deaf, dumb, and blind. It can show how two factors move with relationship to each other. Only a human being can decide whether one factor causes the other. For example, global organization or teamwork factors may correlate with performance, but if organization and teamwork don't cause performance, then hiring people based on these scores will reject qualified candidates and hire unqualified ones. |