Gender Stereotypes and Barriers in STEM: A Bayesian Statistical Analysis of Perceptions and Challenges

The study examines gender stereotypes in STEM fields using advanced statistical techniques within a Bayesian framework. The anal- ysis relies on a survey collected from Italian participants in 2021, inves- tigating perceptions of STEM subjects and professional careers. By employing supervised learning for predictive analysis based on a mul- tilevel ordinal regression model, we aim to enhance the understanding of the barriers women face in STEM, including biases and stereotypes that shape career expectations and opportunities. Additionally, we estimate the marginal effects of key predictors to quantify the impact of factors such as gender, age, education, and workplace environment on percep- tions of STEM. This approach not only enhances statistical methodolo- gies but also provides insights into real-world social challenges.