First-year undergraduate student attrition

Patrick, William John (2004) First-year undergraduate student attrition. PhD thesis, University of Glasgow.

Full text available as:
Download (17MB) | Preview
Download (11MB) | Preview
Printed Thesis Information:


This is a study of student attrition amongst full-time, first year undergraduates at the University of Glasgow during the 1999-2000 academic session.

The thesis contains an initial assessment of the importance of research in this area (Chapter 1), followed by a review of the literature, focusing in particular on the theories and explanations of student attrition that have been advanced by other authors (Chapter 2), and on appropriate research methodologies and data collection techniques (Chapter 3). The investigation then progresses through a succession of different empirical and data-analytic phases.

Because of his function within the organisation, the author had uniquely good access to the student records system maintained centrally by the University. This made it practical to sift through this information in such a way as to determine first the simple concomitances of retention (Chapter 4), and then to use it in a more sophisticated manner to develop logistic regression models of retention (Chapters 5 and 8).

The challenge was then to decide which new, additional data should be gathered in order to improve upon these quantitative models. The solutions were found partly by recourse to some focus group work with students and staff (Chapter 6). This resulted in two questionnaires being developed to discover students’ attitudes believed to be relevant to retention (Chapter 6). The first survey instrument was administered to all first-year students as part of the matriculation process. The other was completed on-line in the course of the session as an adjunct to the IT Induction Programme for all first-year students.

Chapter 10 contains the first outcomes of the attempt to improve the logistic regression models described in Chapter 5 by the introduction of attitudinal constructs, first on their own, and then in combination with the original background and prior academic characteristics in order to model summer retention.

The amount of data available in this study is considerable and, consequently, some large-sample structural equation techniques were then used to develop some new, more comprehensive models of retention (Chapter 11). These are more informative, demonstrating how trade-offs can occur between different variables in an overall model of retention, and identifying particular areas where practical policy interventions are likely to be successful in ameliorating student attrition. It is demonstrated that summer retention is affected in roughly equal measure by academic and non-academic factors. On the academic side, it is shown that extra effort and additional academic help and feedback can benefit those students having relatively low entry point scores, for example. Social integration, at least in moderation, is beneficial, and it is positively influenced by living in university accommodation. However, various extraneous problems harm retention through the mediating variables of social integration and commitment. The models have a temporal dimension, and it is argued that students’ attitudes whilst on course owe their origins to those detected at the time of matriculation and, ultimately, back to levels of family support.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Subjects: H Social Sciences > HA Statistics
L Education > LC Special aspects of education
Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics > Statistics
College of Social Sciences > School of Education
Supervisor's Name: Baron, Stephen and McColl, Prof. John
Date of Award: 2004
Depositing User: Elaine Ballantyne
Unique ID: glathesis:2004-2592
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 12 May 2011
Last Modified: 10 Dec 2012 13:57

Actions (login required)

View Item View Item


Downloads per month over past year