A Longitudinal Study of How Quality Mentorship and Research Experience Integrate Underrepresented Minorities into STEM Careers

Abstract

African Americans, Latinos, and Native Americans are historically underrepresented minorities (URMs) among science, technology, engineering, and mathematics (STEM) degree earners. Viewed from a perspective of social influence, this pattern suggests that URMs do not integrate into the STEM academic community at the same rate as non-URM students. Estrada and colleagues recently showed that Kelman's tripartite integration model of social influence (TIMSI) predicted URM persistence into science fields. In this paper, we longitudinally examine the integration of URMs into the STEM community by using growth-curve analyses to measure the development of TIMIS's key variables (science efficacy, identity, and values) from junior year through the postbaccalaureate year. Results showed that quality mentorship and research experience occurring in the junior and senior years were positively related to student science efficacy, identity, and values at that same time period. Longitudinal modeling of TIMSI further shows that, while efficacy is important, and perhaps a necessary predictor of moving toward a STEM career, past experiences of efficacy may not be sufficient for maintaining longer-term persistence. In contrast, science identity and values do continue to be predictive of STEM career pathway persistence up to 4 years after graduation.

FIGURE 1.

Conceptual model showing expected direct and indirect effects of quality of mentorship and research experiences on TIMSI variables and career outcomes, controlling for individual and institutional characteristics.

FIGURE 2.

Simplified model showing significant direct and indirect effects of quality of mentorship and research experiences on TIMSI variables and career outcomes. Values on paths/arrows represent standardized structural coefficients; values on curved double-headed lines represent correlation coefficients; values inside parentheses represent residual variance. Proportion of variance explained (R²) by the complete set of predictors was calculated for all TIMSI and career outcome variables and the values were as follows: R²_{Intercept science efficacy} = 0.17; R²_{Linear growth slope science efficacy} = 0.14; R²_{Intercept science identity} = 0.22; R²_{Intercept science values} = 0.19; R²_{STEM career} = 0.25; R²_{Medical career} = 0.18; R²_{Other career} = 0.29. *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001.