Student wastage at Edinburgh university. I. Factors related to failure and dropout

Kapur, R. L.

doi:10.1111/j.1468-2273.1972.tb01912.x

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…There is mixed evidence concerning the effect of a student's age on his performance in higher education. The negative relationship between maturity and degree results of university graduates found in a number of previous studies (Barnett and Lewis, 1963;Barnett et al, 1968;Kapur, 1972;Entwistle and Wilson, 1977) may be explained by the fact that mature students have spent time away from academia. Any disadvantage caused by this time out may be reinforced by the additional commitments outside higher education which a mature student is likely to have (i.e.…”

Section: Socio-economic and Personal Characteristicsmentioning

confidence: 91%

The Potential Effects of Wider Access to Higher Education on Degree Quality

Johnes

1992

Higher Education Quarterly

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Section: Socio-economic and Personal Characteristicsmentioning

confidence: 91%

The Potential Effects of Wider Access to Higher Education on Degree Quality

Johnes

1992

Higher Education Quarterly

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“…Note that duplicate subjects are not counted Freeman, 1970;Kapur, 1972;Tarsh, 1982;Crum and Parikh, 1983;Sear, 1983;Rudd, 1984;Montague and Odds, 1990;Johnes, 1992;Chapman, 1994;Rodgers and Ghosh, 2001;Smith and Naylor, 2001a;Bratti, 2002 Bee andDolton, 1985;Connolly and Smith, 1986 Age Age in 1993 Walker, 1975;Eaton and West, 1980;Smith and Naylor, 2001a Barnett and Lewis, 1963; Barnett et al, 1968;Kapur, 1972;Entwistle and Wilson, 1977;Bee andDolton, 1985 Nisbett andWelsh, 1972;Smith, 1990 Categorical explanatory variables: Rabash et al, 2004) in the MLM analysis.…”

Section: Bulletin Of Economic Researchmentioning

confidence: 99%

Measuring Efficiency: A Comparison of Multilevel Modelling and Data Envelopment Analysis in the Context of Higher Education

Johnes

2006

Bulletin of Econ Res

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Data envelopment analysis (DEA) and multilevel modelling (MLM) are applied to a data set of 54,564 graduates from UK universities in 1993 to assess whether the choice of technique affects the measurement of universities' performance. A methodology developed by Thanassoulis and Portela (2002; Education Economics, 10(2), pp. 183-207) allows each individual's DEA efficiency score to be decomposed into two components: one attributable to the university at which the student studied and the other attributable to the individual student. From the former component, a measure of each institution's teaching efficiency is derived and compared to the university effects from various multilevel models. The comparisons are made within four broad subjects: pure science, applied science, social science and arts. The results show that the rankings of universities derived from the DEA efficiencies which measure the universities' own performance (i.e., having excluded the efforts of the individuals) are not strongly correlated with the university rankings derived from the university effects of the multilevel models. The data were also used to perform a university-level DEA. The university efficiency scores derived from these DEAs are largely unrelated to the scores 75 from the individual-level DEAs, confirming a result from a smaller data set (Johnes, 2006a; European Journal of Operational Research, forthcoming). However, the university-level DEAs provide efficiency scores which are generally strongly related to the university effects of the multilevel models.

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“…The various factors in the two questionnaires related to the academic performance have been reported in a previous paper (Kapur, 1972). This paper reports the attempts at preparing a predictive scale, and testing its validity.…”

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confidence: 92%