Question:
I’m a manager with over 20 years of experience. We’re seeing some turnover among our engineers and project managers. We’re doing our best to get a handle on it. The reason for contacting you is to see what you think we should do to improve the odds of hiring good people in the first place. I hear about all the engineers that India and China are supposed to be turning out. I can assure you they’re not always what they’re cracked up to be. Some are good, some are not, but that’s no different than my experience with engineers from U.S. schools. I’m looking ahead. Making good hiring decisions is going to be more important going ahead. There are more choices for employees. There’s going to be more competition from employers for the really good ones. We need to do a better job in hiring top talent. Here’s my issue in a nutshell: What should we be doing to make sure we are hiring the best employees we possibly can?
Answer:
At the outset, let me say that this question is at the core of my consulting practice. Each day within the assessment service area of our practice, we deal with this matter with our clients, small and large, from all employment sectors.
Employment selection is serious business. Let’s be clear on that from the beginning. Making wise hiring decisions has serious bottom-line implications. Productive employees make for productive work areas. Productive work areas make for productive organizations.
So, how do you go about hiring a productive employee every single time?
I wish I had an easy answer for that one, but there are no easy answers when it comes to a complex matter like employment selection. However, within this article, I will talk about some of the “best practices” that are based on our current understanding of the cumulative findings from research on this topic.
One of the central issues attaching to this matter is the concept of employee productivity. Clearly, if all employees were equally productive, it would make no difference whom you hired – every employee would offer the same level of productivity. But, as you point out in your question, you have hired engineers with different preparation and found variation in productivity regardless of where they earned their degrees. All employees are most certainly not created equal.
Output as a percentage of mean (i.e., average) output is one way to measure the variability in productivity among employees. Basically, the thinking behind this computation is that in order to decide how productive Employee X is, you need to know how productive the average employee is. To compute this number, each employee’s output is divided by the output of workers at the 50th percentile (i.e., median or mid-point) of the productivity distribution and then multiplied by 100.
Meta-analytic research (i.e., large-scale statistical reanalysis of previous research) has documented that the standard deviation (average variation) of output as a percentage of mean output varies by job. That is, the average differences in productivity are greater or lesser for different kinds of work. For example, for unskilled and semi-skilled jobs the figure is 19 percent, for skilled work it is 32 percent, and for managerial and professional jobs it is 48 percent.
In practical terms, this means that if you hire a superior rather than an average or below average worker, very real differences in productivity will be obtained, over time. I’ll grant you that this probably just seems like a common sense conclusion (i.e., “Of course you get more productivity from a superior performer … who doesn’t know that?”). At the same time, I believe the research evidence behind this conclusion underscores its significance. After all, these are not minor differences that we are talking about.
Let me pause to emphasize what this truly means. Referencing your earlier question, you mentioned you are having turnover among engineers and project managers. These are professional assignments. Think about output as a percentage of mean output as you fill these vacancies. Think about whether you are hiring a superior employee to fill the void, an average employee or a less than average employee. The research mentioned above tells us that 48 percent more productivity attaches to hiring a superior as opposed to an average professional-level employee. Even a greater productivity gap exists between a superior and below average professional-level employee.
Think about this for a moment. What is the cost, in terms of lesser productivity, of filling all of your vacancies with average performers? What productivity costs attach to that over the next year? Five years? And so on. Now, fill all of your vacancies with below average performers. What are the corresponding costs? Too scary to ponder.
So, what can you do to increase your odds of selecting employees who turn out to be superior performers?
Ninety-plus years of research into this question documents that among all of the employee selection approaches (i.e., predictors) that are available, general intelligence (i.e., “g” factor) is the best predictor of job success.
Predictive validity (i.e., the statistical relationship between the selection measure and job success) can vary from + 1.0 to – 1.0. A + 1.0 is a perfectly positive relationship (i.e., the better the applicant does on the selection measure, the better he/she will tend to do on the job). A – 1.0 is a perfectly negative relationship (i.e., the better applicant does on the selection measure, the worse he/she is likely to do on the job). When the statistical relationship is in the middle, at or near zero, there is, essentially, no relationship between the selection measure and job success.
Meta-analysis has documented that + 0.51 is the predictive validity for measures of general intelligence. In practical terms, this says that if you are only able to use one factor to differentiate among candidates, you can do yourself a favor by hiring the one who is the brightest.
I should note that general intelligence is the best predictor of job success, whether we are talking about unskilled, semi-skilled, skilled, managerial, or professional-level candidates, and whether we are talking about inexperienced or experienced candidates.
So, at the bottom line, here is the best advice I can offer you regarding improving the predictive validity of your selection process. First, you will want to consider making use of a general intelligence test. Second, again based upon meta-analytic findings, in addition to the intelligence test, you will want to consider combining it with either (a) an integrity test or (b) a structured interview. These two combinations do the best job of predicting job performance. These combinations work equally well with inexperienced or experienced job applicants.
Of course, there may be times when you will want to gather additional information regarding candidates’ capabilities relative to a given work assignment. The research tells us that making use of personality tests, work sample tests and job knowledge tests are other sound options to consider on a situational basis.
On the other hand, if you’re using graphology (i.e., handwriting analysis) to make employment selection decisions, you’re wasting your time. The predictive validity coefficient for this approach is + 0.02! You’d be better off selecting employees by rolling a pair of dice.
Want to improve your odds of selecting a superior performer?
Then, don’t roll the dice. Instead, use some of the suggestions I offer here. Over time, I believe you’ll find that the results will be most satisfactory.
