The purpose of this qualitative study focused on the experiences of first-generation students attending a technical college in rural West Tennessee. The results of this study may identify barriers faced by students from these special populations and may also provide insight into the support systems that motivate these students to persevere. Although significant research has been conducted on first-generation and underrepresented students attending community colleges and four-year universities, there is a gap in the published literature pertaining to the lived experiences of students attending technical colleges, especially those living in rural areas. First-generation college students face many challenges as they strive to integrate and acclimate to the college campus environment, such as having poor study skills or habits, lack of peer support, and fear of the college environment. In addition, the lack of specific social and cultural capital contributes to their inability to navigate the college experience and complete their degree or certificate attainment. This research involved interviews with 17 students attending a technical college in rural West Tennessee. Participants discussed their experiences while enrolled at the technical college, backgrounds which included prior educational experiences, any barriers they faced, and identified support systems that increased their academic success. The findings indicate additional campus programs and initiatives may be needed at the technical colleges, especially those in rural areas, to help low-income students with their educational outcomes.

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The purpose of the study was to compare a freshman cohort of first and non first-generation college students enrolled in an urban university and to identify characteristics that distinguish the two groups in terms of selected demographics, pre-college behaviors and beliefs (expectations and personal traits). Moreover, the study sought to identify variables whose distribution indicated a significant difference between the two groups and rank those variables by order of the strength of association. Data analysis for this study consisted of a combination of chi-square and descriptive discriminate analysis using logistic regression. Chi-square analysis was the preliminary statistical procedure used in this study. I relied on a sequence of chi-square analyses to help identify a list of statistically significant variables to be used in the subsequent descriptive discriminate logistic regression model. Descriptive discriminate analysis was used because its primary function is designed to reveal projected differences among groups (Huberty, 1994). The results revealed seven important characteristics (Reading for pleasure (Hpw0111), Household income (Income), Asked teacher for advise (Act0114), Rate computer skills (Rate0103), Get a bachelor's degree (Futact11), Change major field of study (Futact01) and Obtain recognition by colleague (Goal0103) were commonly statistically significant student characteristics across all race/ethnicity groups, and three (Gain a general education (Reason05), High school grade point average (HSGPA) and Felt overwhelmed (Act0110) were unique to one or some of the groups. These variables can also be viewed as predictors that help identify the likelihood that a student is first-generation. Results of this study had implications for the practice of high school guidance counselors, student and academic affairs practitioners and specifically support services personnel and financial aid officers. / Ph. D.

In the United States context, there is a particularly prevalent dialogue about the transformative power of an engineering degree for underserved students. Long positioned as a mechanism for moving up the social ladder, engineering education is often discussed as a mechanism for upward mobility, promising underserved students the opportunity to climb. However, a critical examination of who enrolls and persists in engineering degree programs suggests not everyone can equitably leverage the transformative power of an engineering degree, with persistent inequities for underserved students. Though literature highlights systemic barriers faced by underserved engineering students, much less is known about how underserved students navigate barriers to pursue an engineering bachelor's degree. Accordingly, the purpose of my study was to explore how students from underserved backgrounds navigate their educational trajectories, focusing on the interplay between structures and agency. Using a Bourdieusian lens, my study was guided by the overarching research question: In their narratives, how do students from underserved backgrounds describe navigating their educational trajectories towards a bachelor's engineering degree? I used a single case study methodology with embedded units of analysis to explore this research question. My primary data sources included narrative interviews with 32 underserved engineering students and geospatial community-level data extrapolated from students' home zip codes. My results indicate that underserved engineering students describe a variety of strategies to enact agency by planning, optimizing, and, at times, redirecting their educational trajectories. This study also highlights the influence of family, community, economic, and political environments on the educational journeys of underserved engineering students, as students described navigating and adapting to these various social environments. Students also describe their environments as dynamic, with trajectories changing based on critical incidents such as a parent illness or loss of work. Lastly, students' narratives highlight a diverse range of reasons for pursuing engineering, which often extended beyond private goods approaches to engineering education. My results present implications for engineering education, the most notable of which is that underserved students are not a monolithic group and represent a diverse range of lived experiences. My results also highlight agency as a collective endeavor, challenging popular notions that agency is operationalized at the level of a single individual. Lastly, students' lived experiences with material hardship highlight the dynamic and multidimensional nature of economic disadvantage. Such insights compel engineering educators to reexamine how we conceptualize and measure economic disadvantage in higher education. Ultimately, this research highlights opportunities to increase access and equity in engineering education for underserved students. / Doctor of Philosophy / In the United States, engineering is often viewed as a transformative career for underserved students. Long positioned as a mechanism for moving up the social ladder, engineering education is positioned to underserved students as an opportunity to climb. However, inequities in engineering education persist, with low income and first generation students underrepresented in engineering. The purpose of my study was to explore how students from underserved backgrounds navigate their educational trajectories, focusing on the interplay between societal forces (i.e., structures) and individual decision-making (i.e., agency). My study was guided by the overarching research question: In their narratives, how do students from underserved backgrounds describe navigating their educational trajectories towards a bachelor's engineering degree? My primary data sources included narrative interviews with 32 underserved engineering students and geospatial community-level data. My results indicate that underserved engineering students describe a variety of strategies to plan, optimize, and, at times, redirect their educational trajectories. This study highlights the influence of family, community, economic, and political environments on the educational journeys of underserved engineering students. Additionally, students describe their environments as dynamic, with trajectories changing based on critical incidents such as a parent illness or loss of work. Lastly, students' narratives highlight a diverse range of reasons for pursuing engineering, which often extended beyond private goods approaches to engineering education. My results highlight agency as a collective family endeavor, challenging popular notions that agency is operationalized at the level of a single individual. Lastly, students' lived experiences with material hardship highlight the dynamic and multidimensional nature of economic disadvantage. Such insights compel engineering educators to reexamine how we conceptualize and measure economic disadvantage in higher education. Ultimately, this research highlights opportunities to increase access and equity in engineering education for underserved students.

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 68-71). Also available on the World Wide Web.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 210-223).

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Background: The firm’s choice of the optimal financial structure remains an unsolved problem within finance. The reasoning behind family firms’ specific financial structure differs within various research. The GGVV-region is composed of four small municipalities: Gnosjö, Gislaved, Värnamo, and Vaggeryd. This region is seen as the best dynamic counties in all of Sweden, it is also considered the most successful area of the countryside in terms of its economic contribution. Because of these aspects, it is therefore of great importance to investigate the difference of the financial structure within generations of family firms. Purpose: This paper studies whether there is a correlation between the generation in charge of family businesses within the GGVV-region and the financial structure of the businesses. Aim: The aspiration is that this research will be a good addition to the understanding of family businesses in the GGVV-region along their financial policies within different generations running the firm. Method: This study will contain 42 family firms within the GGVV-region defined as family firms through a questionnaire. The financial structure of the first-generation and non-first-generation family firm will be investigated using their debt ratios throughout the years 2015-2019. The testing is performed through Panel Data Model using Random Effects Model, along with descriptive statistics of the data and a Difference-in-Difference test. Conclusion: No significant difference can be found at any level between the 1GFF and the Non-1GFF when it comes to their financial structure.