How does AON's approach to assessment design enhance predictive validity?

AON's approach to assessment design enhances predictive validity primarily through the utilization of data-driven methods and continuous refinement of assessments. This strategy involves analyzing large volumes of performance data to identify the most effective assessment techniques and content that correlate with successful outcomes in the targeted roles. By leveraging analytics, AON can ensure that the assessments accurately measure the competencies that predict job performance, thereby increasing the reliability of the selection process.

Refining assessments is crucial, as it allows AON to adapt to changing job requirements and optimize tests based on performance data. This iterative process helps ensure that assessments remain relevant, valid, and aligned with organizational needs, ultimately leading to better hiring decisions and improved employee performance.

The practice of data-driven assessment design stands in contrast to uniformity in questions, scoring models, or an exclusive focus on academic history, which may not provide a comprehensive view of a candidate's abilities or predict their future performance effectively. By focusing on data and ongoing improvements, AON enhances the likelihood that its assessments can effectively identify candidates who will excel in their roles.