Approaches to assessing job candidates and employees in talent management are experiencing rapid change. Much of the change is technologically driven by developments in machine learning and artificial intelligence (AI). For instance, automated video interview scoring, social media scraping, and gamification are relatively new methods deployed as assessments in talent acquisition.
These methods are fast, can be more engaging for candidates, and represent an important evolution in testing beyond 20th century approaches. But, however compelling these new methods, practitioners should not forget they are selection tests. They therefore need to meet stringent criteria related to group differences, bias, and standards for reliability and validity (e.g., evidence that they predict job performance or quality of hire).
In this paper, we give an overview of testing in talent acquisition and offer four specific guidelines for assessment practitioners evaluating new selection methods.
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