African Americans and STEM: An examination of one intervention program

Throughout the last century African Americans have struggled against segregation and for the right to equal job opportunities with equal pay and the right to equal educational opportunities. While segregation is no longer legal, the United States (US) has yet to reach equality in all aspects of society for African Americans. They have historically been underrepresented in science, technology, engineering, and mathematics (STEM). As the US STEM workforce continues to grow faster than the overall work-force, the need for college-trained STEM workers continues to increase. African Americans could help fill this gap. While African Americans comprise 11.3 percent of the US population 18 years or older, they hold just 4.4 percent of the STEM degreed-workforce positions. If the goal is parity, African Americans have quite a way to go until their share of degree attainment and positions in the STEM workforce matches their share of the US population. Numerous intervention programs are geared towards increasing the number of underrepresented minorities in STEM. These programs were developed to increase undergraduate retention and attainment rates, graduate degree attainment rates, and the rate at which students were entering the STEM workforce. Although many of these programs have conducted small self-studies, few have undergone extensive external program evaluation. As millions of dollars continue to pour into these programs, evaluation of their effectiveness at increasing the numbers of African Americans in STEM needs to be addressed. And, specifically, it needs to be determined whether there are certain program components that contribute more towards increasing those numbers. The primary question that this work will address is whether or not this intervention program increases the number of underrepresented minorities that complete their undergraduate STEM studies and continue on to STEM graduate study and then work in the STEM workforce with respect to historical departmental numbers. A logistic regression model will be tested for the strength of the relationship between the predictor variables and the response variable, for the significance of predictor variables, and for goodness-of-fit of the model. Interviews with past and present program participants as well as other primary stakeholders are conducted in order to give a richer understanding of the program being studied.