Recruitment is tough. Once we’ve got our shortlist, how do we ensure our talent assessment process truly predicts performance? Our Omni Talent Assessment team has delved into recent findings by Sackett et al. (2022; 2023) that shed new light on this. Building on the classic work of Schmidt and Hunter (1998), these studies offer a fresh perspective on the effectiveness of various predictors, including personality traits, emotional intelligence, situational judgement tests (SJTs), and cognitive ability.
Schmidt and Hunter’s research has been a cornerstone in talent acquisition for decades, with over 6,500 citations highlighting its impact. Sackett and his team re-examined this foundational work, expanding the range of predictors studied to include the Big 5 personality traits, emotional intelligence, and Situational Judgment Tests. They also took a closer look at diversity and equitable outcomes by analysing White-Black mean differences in selection predictors.
Sackett et al. discovered that many commonly used predictors are not as reliable for predicting overall job performance as we once thought. However, structured interviews have emerged as a top predictor, even outpacing cognitive ability, which used to be the gold standard.
Using a mix of predictors can also address diversity concerns, leading to better talent acquisition and organisational performance.
It’s clear that a one-size-fits-all approach to TA TA doesn’t work. Tailoring your selection systems to the specific requirements of each role enhances the accuracy of your hiring decisions and boosts job performance.
Structured interviews, job knowledge tests, and work sample tests are proving to be more effective than general psychological assessments. These tools better reflect the unique skills and demands of each position.
By considering White-Black mean differences in selection predictors, Sackett’s research highlights the importance of diversity in recruitment. Structured interviews, for example, show smaller group mean differences compared to work samples and cognitive ability tests, making a strong case for using a mix of predictors to ensure fair outcomes.
The revised view on cognitive ability assessments is surprising. Recent studies, like those by Griebe et al. (2022), suggest these tests are less predictive of job performance than we thought. But they’re not without value:
High-Volume Screening: Cognitive ability tests are still useful for screening large numbers of candidates, such as in graduate schemes.
Learning Performance: These tests excel in predicting performance in learning environments and roles requiring extensive training.
Sackett and his team challenge our previous assumptions about predictor validity in candidate selection. While some predictors may not be as strong as once believed, it’s vital to consider the context and tailor your selection process to your performance objectives. Using a mix of predictors can also address diversity concerns, leading to better talent acquisition and organisational performance.
Want to chat more about this research or how to create bespoke talent assessment programmess? Reach out to us!
