Choosing the right tools to find the right people

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Jacob B. Hirsh looks at performance prediction, an area with some of the strongest relationships in psychological research.

One of the classic goals of psychometric assessment has been to predict performance based on psychological characteristics. Two major categories of psychometric instruments are tests of intelligence and personality, both of which have a long history in predicting behavioural outcomes. Intelligence tests, for example, were originally used to identify learning disabilities among schoolchildren, while personality tests were geared primarily toward the prediction of dysfunctional behaviour. Following their broader adoption during the two world wars, these techniques gained prominence as tools for assessing performance ability and facilitating job placement. Importantly, the goals of psychometric assessment have expanded over time and now include not only the prediction of dysfunctional behaviour, but also performance differences across the normal range of psychological characteristics. While psychometric testing and performance prediction have evolved considerably over the past 100 years, their value is often underappreciated. In the current article, two critical lessons from this broad field of research are highlighted. Namely, research on performance prediction has taught us the importance of (a) choosing the right people, and (b) using the right tools to do so.

Choosing the right people

Most people would agree that in a competitive environment, the most qualified individual should be chosen for a given position. However, there are many obstacles to the real-world implementation of this meritocratic ideal. One such obstacle is the fact that people tend to underestimate the massive performance and productivity differences that exist between individuals.
A powerful illustration of such differences is codified in ‘Price’s law’, which describes the unequal distribution of productivity in any creative domain (Price, 1963). According to this formula, the square root of the number of people working within a field produce 50 per cent of the total creative output. For example, if there were 100 scientists working on a problem, the 10 most productive researchers within this group would produce the same amount of material as the remaining 90. This concentration of creative work becomes even more pronounced at the highest ends of the productivity distribution, such that the most prolific individuals within a domain generate disproportionately larger shares of the total output. Similar analyses have shown, for instance, that the 10 most prolific composers produced 40 per cent of the ‘masterworks’ in classical music (Moles, 1958). 
Although Price’s law was originally used to describe the unequal distribution of creative output, the substantial between person variability in productivity and performance outcomes extends to noncreative work domains as well. Metaanalytic studies of performance variability indicate that as the work domain becomes more complex, the variability in performance across individuals becomes larger. One way to examine this variability is as a percentage of an average employee’s output levels. Zero variability would indicate that all employees perform at the same level, whereas higher values indicate greater differences between individuals.
For unskilled and semi-skilled work, the standard deviation of work output as a percentage of average output is 19 per cent; for skilled work it is 32 per cent; and for managerial and professional work it is 48 per cent (Schmidt & Hunter, 1998). What this means is that a professional who performs at the 84th percentile (one standard deviation above the mean) will be 96 per cent more productive than an individual performing at the 16th percentile (one standard deviation below the mean). In financial terms, this performance difference would result in a £48,000 yearly productivity bonus, based on a £50,000 yearly salary. These productivity differences become even more pronounced when they are summed across multiple people. Organisations that are able to identify and recruit high-performing individuals thus have a considerable economic and strategic advantage.
While selecting the best people is an important goal in itself, a parallel goal of no less importance is screening out undesirable candidates. The consequences of choosing the wrong people are substantial, as they lead to increased turnover rates, recruitment costs, and training expenses, along with lost productivity and decreases in morale. The high costs associated with replacing poorly performing individuals make it all the more important to identify and select the best performers in the first place. 

Using the right tools

Because there are almost always more applicants than there are open positions, it is inevitable that some selection process is used. While the previous section highlighted the importance of identifying and selecting the right people, we turn now to the importance of using the right tools to do so.