Easing the Inferential Leap in Competency Modelling: The Effects of Task‐related Information and Subject Matter Expertise*
通过三项研究（1项实验室和2项现场研究）检验能力建模中推断的质量，发现加入任务相关信息能提高评分者信度和区分效度，对实践者改进能力建模有指导意义。
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 competency modeling to raise the quality of competency inferences. Study 1 showed that competency modeling resulted in poor interrater reliability 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 organization. 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 modeling should not be taken for granted, and that practitioners can improve competency modeling efforts by incorporating some of the methodological rigor inherent in job analysis.