Despite being an effective predictor of job performance, empirically keyed biodata assessments have been criticized as black box empiricism unlikely to generalize to new contexts. This paper introduces a model that challenges this perspective, explicating how biodata content, job demands, and criterion variables collectively influence the construct validity, and generalizability of empirically scored biodata. Across two field studies, expected changes in scale correlations with external measures were found that coincided with changes in the contextual similarity between calibration and holdout contexts, the criteria used, and the content validity of biodata items. Collectively, this paper offers a framework that helps understand and optimize empirical biodata keying in practice, furthering confidence for their use in applied settings. K E Y W O R D Sbig data, biodata, empirical keying, selection, trait activation theory, validity How to cite this article: Speer AB , Siver SR , Christiansen ND . Applying theory to the black box: A model for empirically scoring biodata. Int J Select Assess. 2020;28:68-84.

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Despite an established body of research supporting the benefits of rater training on performance appraisal (PA) ratings, it is unclear whether PA training has been widely adopted in the field and what types of PA training are most frequently utilized. This study expanded upon previous research by using a sample of 229 active managers collected via Qualtrics panels to examine the prevalence of various types and instructional contexts of PA training. This research also examined the relationship between training and perceptions of rater preparedness. Collectively, results suggest that PA training is occurring in organizations, that training is perceived favorably, but that the nature and usage of training differs widely.

which has been updated to include asterisks indicating statistical significance for each numerical value within the table, per the original table footnote. Additionally, updates have been made to include all regression weights for relevant predictors and the correct R 2 values for the column pertaining to the Achieves Results outcome. Finally, the sample size for the data pertaining to this table is now included within the footnote.