Expectancy-Value Model: Investigating Academic Success and Retention Predictors of First-Year STEM Majors

The gap in supply (i.e., shortage) and demand of the STEM workforce have prompted extensive research on identifying factors that predict STEM outcomes and retention of students. Few studies, however, have examined the relationships between STEM outcomes and predictors in an integrated model, taking into account measurement errors in the predictors. Drawing upon the Expectancy-Value Model of Achievement Related Performance and Choice, I conducted a structural equation modeling (SEM) analysis to examine the relationships among academic support, academic engagement, mathematics readiness, and hours worked and first-year STEM students’ academic success and retention. The SEM allowed me to investigate the relationships between predictors and outcomes simultaneously while accounting for the measurement error. The sample consisted of first-year STEM majors who took the National Survey of Student Engagement during 2016, 2018, and 2020 academic years. Results indicated that academic support was a statistically significant predictor of first-year STEM students’ academic success and retention. Additionally, mathematics readiness was found to be a statistically significant predictor of first-year retention. Last but not the least, results suggested that female students on average were more likely than their male counterparts to engage in academic support and academic engagement activities even though females had longer on-campus work hours than males. These results have implications for policies and practices aimed at improving STEM retention. Areas of further research are also identified.