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Inter-university variations in undergraduate non-completion rates: A statistical analysis by subject of study

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  • Jill Johnes

Abstract

Non-completion of higher education degree courses is a considerable problem, incurring costs on the taxpayer, higher education institutions and the students who fail to complete. Closer examination of the data reveals that non-completion rates in higher education vary substantially across institutions and by subject of degree. The purpose of this paper is to investigate, within each of 13 broad subject categories, the potential determinants of inter-university variations in non-completion rates. Published data are used to compute university non-completion rates over four time periods and to construct corresponding explanatory variables which could potentially be related to non-completion rates. The explanatory variables measure the characteristics (both academic and socioeconomic) of students recruited by universities and the characteristics of the institutions themselves. The significance of the relationship between the possible explanatory variables and non-completion rates within each given subject is assessed using both weighted leastsquares and weighted logit analysis. The conclusions drawn from the results of each technique are identical, and, therefore, for interpretation reasons, only the results of the weighted least-squares analysis are reported. As expected, the academic quality of student entrants is an important determinant of non-completion rates in the majority of subjects, although the magnitude of the effect varies according to subject. Variables reflecting the age and gender mix of university entrants are generally not significantly related to noncompletion rates. The characteristics of institutions which are significantly related to non-completion rates in specific subjects include the staff student ratio and the length of the degree course

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  • Jill Johnes, 1997. "Inter-university variations in undergraduate non-completion rates: A statistical analysis by subject of study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(3), pages 343-362.
    • Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:343-362
    DOI: 10.1080/02664769723738
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    Cited by:

    1. Xavier Triadó-Ivern & Pilar Aparicio-Chueca & Joan Guàrdia-Olmos & Maribel Peró-Cebollero & Natalia Jaría-Chacón, 2013. "Empirical approach to the analysis of university student absenteeism: proposal of a questionnaire for students to evaluate the possible causes," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2281-2288, June.
    2. Tim Maloney & Kamakshi Singh, 2017. "Using Validated Measures of High School Academic Achievement to Predict University Success," Working Papers 2017-10, Auckland University of Technology, Department of Economics.
    3. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2022. "The determinants of university dropout: A review of the socio-economic literature," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    4. Kerkvliet, Joe & Nowell, Clifford, 2005. "Does one size fit all? University differences in the influence of wages, financial aid, and integration on student retention," Economics of Education Review, Elsevier, vol. 24(1), pages 85-95, February.

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