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Description: In graduate admissions, as in many multiattribute decisions, evaluators must judge candidates from a flood of information, including recommendation letters, personal statements, grades, and standardized test scores. Some of this information is structured, while some is unstructured. Yet most studies of multiattribute decisions focus on decisions from structured information. This study evaluated how structured and unstructured information is used within graduate admissions decisions. We examined a uniquely comprehensive dataset of N = 2,231 graduate applications to the University of Kansas, containing full application packages, demographics, and final admissions decisions for each applicant. To make sense of our documents, we applied structural topic modeling (STM), a model that allows topic content and prevalence to covary based on other metadata (e.g., department of study). STM allowed us to examine what information the letters and statements contain, and the relationships between variables like gender and race and textual information. We found that most topics in the unstructured data related to specific fields of study. The STMs did not uncover strong differences among applicants regarding race and gender, though recommendation letters and personal statements for international applicants did show some different topic profiles than domestic applicants. We also found that admissions decision-makers behaved as if they prioritized structured numeric metrics, using unstructured information to check for disqualifications, if at all. However, we found that topics were less reliable than admissions documents, meaning that additional ways of using them cannot be completely ruled out. The implications of our findings on graduate admissions decisions are discussed.

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