A Mixed-Method Approach of Exploring the Ways Food Access Impacts the Quality of Life of College of Agriculture and Life Science Students at Virginia Tech

Food insecurity has been documented on college campuses. Exploring the barriers that factor into food access can provide insight into how to combat food insecurity in higher education. Furthermore, exploring how these barriers play a role in students' Quality of Life (QoL) can help improve a student's success while in school. Little research has been done on food access and its impact on QoL, and research is completely absent in regard to its effects on agriculture students specifically. As the desire for degrees increases, students from more diverse backgrounds are attracted to college. Many students from underrepresented populations experience barriers to attaining a degree that other students may not. First-generation students, being the first in their family to attend college and have little to no knowledge of how to navigate the challenges of degree attainment., International students experience living in a completely different culture and having to adjust to a new country and a new educational environment. This mixed-method study focused on how food access barriers impact the QoL of first-generation, international, rural, and urban College of Agriculture and Life Science students at Virginia Tech. The study involved a concurrent phase (1) of a cross-sectional survey consisting of multiple choice and open-ended questions. The sequential phase (2) of the study constructed interview questions from themes found in phase 1 and then were used to interview students. Once interviews were completed, phase 1 and phase 2 results were then compared to provide a broad view of how food access impacts QoL for agriculture students. The results from the study showed an adjusted R square explained .277 or 27% of the variance in the dependent variable QoL to be affected by the independent variables. The independent variables of food access, first-generation, Asian, and undergraduate classification of senior were shown to be significant in the first stepwise linear regression model. In the 6 stepwise linear regression models ...