As information about COVID-19 safety behavior changed, people had to judge how likely others were to protect themselves through mask-wearing and vaccination seeking. In a large, campus-wide survey, we assessed whether University of Kansas students viewed others’ protective behaviors as different from their own, how much students assumed others shared their beliefs and behaviors, and which individual differences were associated with those estimations. Participants in our survey (N = 1, 704; 81.04% white, 64.08% female) estimated how likely they and others were to have worn masks on the University of Kansas campus, have worn masks off-campus, and to seek a vaccine. They also completed measures of political preference, numeracy, and preferences for risk in various contexts. We found that participants estimated that others were less likely to engage in health safety behaviors than themselves, but that their estimations of others were widely shared. While, in general, participants saw themselves as more unique in terms of practicing COVID-19 preventative behaviors, more liberal participants saw themselves as more unique, while those that were more conservative saw their own behavior as more similar to others. At least for masking, this uniqueness was false—estimates of others’ health behavior were lower than their actual rates. Understanding this relationship could allow for more accurate norm-setting and normalization of mask-wearing and vaccination.

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. This creates a challenge for those studying these decisions, as most theories of behavioral economics specifically focus on decisions made from highly structured information. The goal of this study was to evaluate how structured and unstructured information are used within graduate admissions decisions. We examined a uniquely comprehensive dataset of 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, an extension of correlated topic modeling that allows topic content and prevalence to covary based on other metadata (e.g., department of study). This allowed us to examine not only what information the letters and statements contain, but also the effects of gender, race, and department on how that information was conveyed. We found that most topics in the unstructured data related to specific fields of study and were difficult to generalize outside of that field. Consequently, we found that admissions committees behaved as if they prioritized structured numeric metrics, using unstructured information to check for disqualifications if at all. Furthermore, we found that applicant race and gender influenced the prevalence of topics in their letters and statements.