No matter how similar candidates for a given job may appear on the surface, people are different. They are complex. They may behave in unpredictable ways once they’re hired. And that’s where the staffing waste problem known as performance variation comes in. You hired your best performer, and you hired your worst performer from the same hiring process. The range of productivity between these two extremes is a form of performance variation. See the ROI Calculators on our web page for an example of how performance variation can be documented.
If all employees perform at a very high level within a given role, then there is little opportunity for real strategic impact. But if differences persist, like they do in the majority of employee selection cases, it’s time to start developing methods which are better predictors of performance outcomes and close the performance gap. To use a manufacturing analogy, your goal is to reduce the number of faulty widgets coming off the assembly line. By implementing scientific employee selection methods you can more accurately hire people who perform like your top 80%, thereby reducing performance variation.
An essential tool for developing an employee selection process aimed at reducing performance variation is pre-employment testing. The chart below demonstrates the degree of performance variation within a group of employees. When you look at job performance and results from the Virtual Job Tryout, it is easy to see how hiring from those who score in the top 80% can make a significant contribution to the organization. This appraoch is quite conservative. Beginning your interviews with candidates who score in the top 50% can be transformative.
Contemporary pre-employment testing use multiple methods of candidate evaluation which provide a rich data set for analytical purposes, namely, validation analysis. A validation analysis is the method used to document which pre-employment data from candidate screening and evaluation actually adds value to how well the hiring decision predicts success on the job. For example, the outcome of a validation analysis can demonstrate the strength of relationships among variables such as work history and attendance, work style and time-to-proficiency, work samples and productivity, etc.
The result of approaching your staffing process with HR analytical tools is the ability to reduce performance variation. This means fewer hires that perform below average and a steady increase in your overall levels of productivity. By using this more calculated, data-centric approach, you can connect pre-employment assessment data to objective, on-the-job performance metrics. This places the full weight of statistical evidence over “gut feelings” on how well your employee selection process predicts better business outcomes.