Evaluating Perceptions and Motivations of Statistics Students

Over the past decade, the demand for skilled statisticians and data scientists has grown dramatically, driving a need to attract diverse talent to the field. To foster a more diverse and inclusive data science workforce, it is critical to understand how students perceive the field and see themselves within it. This study examined how 168 undergraduate and master’s-level statistics students perceive data scientists, and how those perceptions relate to students’ own gender and racial identities. We further assessed key motivational factors (self-efficacy, expectancy, domain identification, persistence) and attitudes (social dominance orientation, gendered associations of statistics). Results revealed a predominant perception of data scientists as White and male. However, female students in more advanced courses were significantly more likely to report a female data scientist. Male students reported stronger social dominance orientation and a greater tendency to associate statistical terms with masculinity. Both domain identification and class level predicted persistence intentions. These findings underscore the role of stereotypes, motivation, and attitudes in relation to student trajectories, offering critical insights for developing interventions to promote inclusivity in statistics education.