Predictors and Socio-Demographic Disparities in STEM Degree Outcomes: A ten-year UK study using Hierarchical Logistic Regression

This research study uses hierarchical logistic regression to identify predictors of first-class STEM degree outcomes at a research-intensive Russell Group university in the UK between 2012 and 2022. By building a multivariate binary logistic model with random intercepts for different STEM degree subjects, we find that prior academic attainment, ethnicity, gender, socioeconomic status, age, and course duration are statistically significant predictors of achieving a first-class degree. By determining the odds ratios and average marginal effects of socio-demographic predictors, we find evidence for the existence of age, ethnicity, gender, and socioeconomic awarding gaps. The largest awarding gap exists between Black and White students, with Black students having 0.45 (95\% CI: 0.30-0.68) times the odds, and a 14\% lower probability, of achieving a first-class degree compared to White students, holding all other variables constant. Students who graduate from 4-year degrees are found to have, on average, a 27\% higher probability of achieving a first-class degree than students on 3-year degrees. Despite raw data suggesting that male students outperform female students, the multivariate hierarchical analysis revealed higher odds for female students after controlling for other factors and accounting for nested data structures. Analysis using year-specific average marginal effects indicates that awarding gaps have not significantly changed between 2012 and 2022. This research study provides a robust analytical framework for use by other departments and institutions aiming to identify and address awarding gaps.