Proposal
Overview
This page is intended to provide an overview of Ian S. Ray's dissertation proposal, including each major section and in-progress updates. Each section is linked to the corresponding location in the "Draft of Proposal" document. All updates occur in real time, so please be aware of the continually improving nature of the proposal.
Draft of Proposal
Overview
This chapter presents the framework for this dissertation, an overview of the topics focused upon, and the research hypotheses addressed.
This chapter presents the overall statistical analyses used and rationale for selecting these methods. Specific variables used will be examined in Chapter Three.
This chapter is structured in the format of typical "hard" science dissertations. A series of papers, consisting of one theoretical monograph and five research, is presented with tailored introduction, literature reviews, and methodological sections for each. The results, discussion, and conclusion section of each paper will be presented in the final dissertation.
This paper grounds a critical examination of the experience of the generations who came of age around, or after, the change in millennium (HE 12,000; CE 2,000) ( ; ). Previous iterations of social science theory, including such seminal works as Critical Race Theory (CRT) ( ) and Feminist Theory ( ), focus on indivdiual identities as the most salient for a given group of people. Calls to move beyond such essentialized-identity prioritization have resulted in the development of Intersectionality Theory ( ). In the following monograph, I apply intersectionality to (re)conceptualize variables that may be impacting the PkT social environment, ultimately leading to a need for latent trait measurement when examining social science statistics.
Determining the diversity present on a collegiate campus has become a major focus of scholarship, institutional research, and marketing departments at colleges and universities across the United States. This push has a historical basis in the Civil Rights movement of the 1960s and 70s, with desegregation and racial equity being called out in the public eye ( ).
Within the United States, the idea of racial classification of individuals can be traced back to Blumenthal’s racial typologies ( ). He classified observed traits into five categories ( ), which roughly correlate to the mandatory racial classifications reported to IPEDS each year ( ). These include Caucasoid (White or Caucasian), Ethiopian (Black or African American), Mongoloid (Asian), Malayan (Native Hawaiian or Pacific Islander), and American (American Indian or Alaska Native). To this, the US Department of Education(?) adds the Hispanic or Latino ethnicity, which we refer to as LatinX ( ).
We highlight this complexity not to negate the lived experience of racial discrimination and systemic inequities, but rather to contextualize our work in quantifying campus diversity within the data that are publicly available. We make no claim that any index of campus diversity will fully encapsulate the true breadth of the campus community. Instead, we propose the following methodology and calculations as a supplement to existing campus- or region-specific diversity metrics for policy-makers, campus administration, and potential students.
Quantifying individual researcher’s scholastic productivity is problematic in a number of ways. First, metrics assume that each individual has an equal chance of gathering funding, obtaining institutional and social support for research projects, and the cultural capital to carry out innovative research. As Social Justice theory lays out, this is not the case for minoritized individuals. Nonetheless, citation-indices, publication productivity, and authorship order all factor into the neoliberalism academy’s view of what makes an academic tenure-worthy or, more often, not so.
Similarly problematic is the quantification of campus diversity. The US Census Bureau’s 2010 census listed _ racial and ethnics classifications (see Table _), while the 2020 census listed _ (see Table _). This differs from the NCES’ annual survey of US-based institutions in which _ racial and ethnic categories are included ( ). Thus, even within the federal government of the USA, categorization and segmentation of individuals is not consistent. This paper will present common calculations of campus diversity and compare these calculations to revised metrics, incorporating variables identified using PkT ( ).
College and University campuses have been examined, evaluated, and ranked in a variety of ways over the past century ( ). Rankings have moved from focused on organizational prestige to post-graduation employment, and indices have moved from impacts of individual researchers to campus productivity ( ). However, such rankings and indices are fraught with problematic assumptions, disproportionately affecting communities of color (CoC) and other marginalized groups ( ).
The work of Higher Education scholars, such as Kiyama and _( ), exemplify how culture impacts the student experience. (Make a paragraph about Funds of Knowledge).
Furthermore, the plethora of critical theories that have been utilized to understand how specific identities impact the lived experiences of students, faculty, and staff. CRT ( ). TribalCrit ( ). LatinXCrit( ). DissCrit( ). HawaiianCrit ( ).
In this paper, we argue that PkT is the most appropriate theoretical framework to integrate these previous theoretical perspectives while also providing a method to introduce additional identities, without the need for generating entirely new theoretical perspectives.
Much work has been done to increase Diversity, Equity, and Inclusion (DEI) across college campuses ( ), as well as in public-facing institutions ( biden stuff ). This has resulted in the proliferation of critical theories to explain how DEI work can incorporate the variety of identities present within an organization. Insert similar sentences as Ppr 3.
Statistical programs are varied and often tailored to specific industries or applications. One of the most prevalent statistical programs in use, IBM’s SPSS, was originally _’s Statistical Package for the Social Sciences ( ). However, the underlying mathematics and statistical analyses are intended to function irrespective of discipline, content, or context ( ).
Previous work on open-source software in educational research has largely focused on the utility of such software within the classroom environment. (1-2 paragraphs here on those articles).
A brief review of literature found no articles specifically focused on how such different software packages and approaches may impact the reliability of published research. Some work has been done in fields such as _ and _ (1-2 paragraphs on those)
However, none of these relate directly to the use of open- versus closed-source software within educational research. We therefore propose the question: Do open-source statistical programs function identically to paid programs and, if not, does the program selected affect the ultimate research outcomes and interpretation?
To answer this question, we re-analyzed the results of Paper One to Four using Appendix A.
This chapter will focus on the reintegration of discrete paper findings presented in Chapter Three of the final dissertation. Final thoughts and recommendations for next steps will be provided throughout, along with an outline of additional follow-up research.