Date of Award
Doctor of Philosophy
Educational Leadership and Policy Studies
Combining institutional data and measures with predictive analyses is a viable means by which to determine where and how to allocate all too limited institutional resources and programming. There are not many among us who would argue against the richness of data and depth of understanding of a phenomenon that are gained through focus groups and interviews and other qualitative research methods. However, these techniques are both time-consuming and expensive. Furthermore, it has been my experience that these types of research methods make for great research publications but rarely lead to timely, substantive, or continually evolving structural change within the academy. I propose that we be probabilistic, using our data to tell us where we will get the most bang for our retention and matriculation efforts. Using the knowledge of what has happened to establish both causal links between outcomes for departments, programs, and discount rates based on valid and reliable measures of success allows us to quickly and efficiently explore what is working as well as providing valuable information about the areas that we need to improve upon.
The research presented within explores the effects of departmental, programmatic, and financial aid leveraging strategies employed by institutions of higher education and the effect that they have on retention and or graduation. Utilizing secondary data reflective of a Research One University, a private not-for-profit liberal arts college and four-year public and private not-for-profit baccalaureate degree or higher-granting IPEDS institutions we explore the effects of structural manipulations on success outcomes. The author hopes that this work will add to the corpus of research on retention as well as provide new insights into how institutions can most effectively maximize retention and graduation efforts.
Narren J. Brown
Brown, Narren J., "Administrative Structural Variables: Towards greater Retention and Efficiencies" (2013). Graduate Theses and Dissertations. 13340.