Easing the inferential leap in competency modeling: The effects of task-related information and subject matter expertise

Despite the rising popularity of the practice of competency modeling, research on competency modeling has lagged behind. This study begins to close this practice–science gap through 3 studies (1 lab study and 2 field studies), which employ generalizability analysis to shed light on (a) the quality of inferences made in competency modeling and (b) the effects of incorporating elements of traditional job analysis into compe-tency modeling to raise the quality of competency inferences. Study 1 showed that competency modeling resulted in poor interrater reliabil-ity and poor between-job discriminant validity amongst inexperienced raters. In contrast, Study 2 suggested that the quality of competency inferences was higher among a variety of job experts in a real organiza-tion. Finally, Study 3 showed that blending competency modeling efforts and task-related information increased both interrater reliability among SMEs and their ability to discriminate among jobs. In general, this set of results highlights that the inferences made in competency modelin