Predicting faculty membership – application of student choice logit model. Issue 1 (9th January 2017)

• Record Type: Journal Article
• Authors: Kopanidis, Foula Zografina; Shaw, Michael John
• Is Part Of: Education + training. Volume 59:Issue 1(2017)
• Page Start: 90
• Page End: 104
• Publication Date: 2017-01-09
• Subjects: Higher education -- Membership -- Logit model; Vocational education -- Periodicals; Occupational training -- Periodicals; Business and education -- Periodicals; 370.113
• DOI: 10.1108/ET-08-2015-0078
• Languages: English
• ISSNs: 0040-0912

Abstract

Abstract : Purpose: Educational institutions are caught between increasing their offer rates and attracting and retaining those prospective students who are most suited to course completion. The purpose of this paper is to demonstrate the influence of demographic and psychological constructs on students' preferences when choosing to study in a particular faculty through the application and testing of a student choice logit model based on data collected from a survey of existing students. Design/methodology/approach: Logistic regression techniques were used to estimate the probability of undergraduate prospective students' choices with reference to a set of variables that allows for the prediction and classification of students ( n =304) at an Australian university. Using the estimated coefficients of both student characteristics and psychological variables, probability outputs were constructed to compute the faculty membership for student groups. Outputs were also illustrated via a set of simulation analyses. Findings: The results of the student choice logit model are highly significant suggesting demographic, socioeconomic and psychological variables play a role in the prediction of faculty membership of undergraduate students. Practical implications: These findings have implications for researchers, educational policy makers and career planners. The study also suggests that these policies should take into account the complexities of multi-attribute influences on students' decision-making choices. Originality/value: This research offers an innovative marketing use of logistics regression techniques with application of the student choice logit model through predicting the likelihood of faculty membership in an education context.