Automatic scoring of speeded interpersonal assessment center exercises via machine learning: Initial psychometric evidence and practical guidelines


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Assessment center (AC) exercises such as role-plays have established themselves as valuable approaches for obtaining insights into interpersonal behavior, but they are often considered the “Rolls Royce” of personnel assessment due to their high costs. The observation and rating process comprises a substantial part of these costs. In an exploratory case study, we capitalize on recent advances in natural language processing (NLP) by developing NLP-based machine learning (ML) models to investigate the possibility of automatically scoring AC exercises. First, we compared the convergent-related validity and contamination with word count of ML scores based on models that used different NLP methods to operationalize verbal behavior. Second, for the model that maximized convergence while minimizing contamination with word count (i.e., a model that used both n-grams and Universal Sentence Encoder embeddings as predictors), we investigated the criterion-related validity of its scores. Third, we examined how the interrater reliability of the AC role-play scores affects ML model convergence. To do so, we applied seven NLP methods to 96 assessees' transcriptions and trained 10 sets of ML models across 18 speeded AC role-plays to automatically score assessee performance. Results suggest that ML scores recovered most of the original variance in the overall assessment ratings, and replacing one or more human assessors with ML scores maintained criterion-related validity. Additionally, ML models seemed to exhibit higher convergence when assessors consistently detected and utilized observable behaviors to make ratings (i.e., when interrater reliability was higher). Finally, we provide a step-by-step guide for practitioners seeking to implement ML scoring in ACs.