Barrett, The Honors College Thesis/Creative Project Collection

The media often portrays and the public often percieves white women and women of color politicians experience feminist obstacles, such as the masculine-feminine double bind and being dehumanized in the same way. Many of these representations of women of color politicians in society do not incorporate the impact of intersectionality and confining gender schemas; therefore, women of color politician’s experiences are often lumped together with that of their white women counterparts. This phenomenon ultimately contributes to the persistence of color-blind racism in the United States, which negatively effects the life outcomes of women of color politicians and women of color in general. In effort to help lessen the effects of color-blind racism locally and in government, some tools on how to reflect on one’s own biases are provided and avenues for change are proposed.

Using a dataset of ASU students from the 2016-2017 cohort, we interact gender and parent education level to observe gaps in academic achievement. We see a statistically insignificant achievement gap for males across parent education level, but a statistically significant achievement gap for females across parent education level. We also observe dropout gaps among these interaction groups. We see the widest dropout gap being between males across parent education level, with the smallest dropout gap being between females across parent education level. So with males we see an insignificant achievement gap but the widest dropout gap across parent education level, and with females we see a significant achievement gap but the smallest dropout gap across parent education level. What is driving these gaps and causing more similarly performing students to drop out at wider rates? At the aggregate level, we see larger gaps in grade- associated dropout probability across parent education level for males which may be able to explain the larger difference in overall proportions of dropouts between males. However, when predicting dropout probability of the semester with the most first generation and non-first generation dropouts, we see that females have the largest differences across parent education level in grade-associated dropout probability. This suggests that our model may be best suited in using college achievement data to predict overall dropout probabilities, not next-semester dropout probabilities using current semester data. Our findings also suggest that first generation students’ dropout probability is more sensitive to the grades they receive than non-first generation students.