Predicting and Explaining Assessment Center Judgments: A Cross-Validated Behavioral Approach to Performance Judgments in Interpersonal Assessment Center Exercises

Although Assessment Center (AC) role-play assessments have received ample attention in past research, their reliance on actual behavioral information is still unclear. Uncovering the behavioral basis of AC role-play assessments is, however, a prerequisite for the optimization of existing and the development of novel automated AC procedures. This work provides a first data-driven benchmark for the behavioral prediction and explanation of AC performance judgments. We used machine learning models trained on behavioral cues (C = 36) to predict performance judgments in three interpersonal AC exercises from a real-life high-stakes AC (selection of medical students, N = 199). Three main findings emerged: First, behavioral prediction models showed substantial predictive performance and outperformed prediction models representing potential judgment biases. Comparisons with in-sample results revealed overfitting of traditional approaches, highlighting the importance of out-of-sample evaluations. Second, we demonstrate that linear combinations of behavioral cues can be strong predictors of assessors' judgments. Third, we identified consistent exercise-specific patterns of individual cues and cross-exercise consistent behavioral patterns of behavioral dimensions and interpersonal strategies that were especially predictive of the assessors' judgments. We discuss implications for future research and practice.