Scaling units via the canonical correlation analysis in the DEA context

Friedman, Lea; Sinuany‐Stern, Zilla

doi:10.1016/s0377-2217(97)84108-2

Cited by 156 publications

(71 citation statements)

References 19 publications

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“…There is a long tradition of using DEA to measure the technical efficiency of Two alternative approaches have been taken in a small number of empirical studies: to evaluate the performance of all departments within one university (Arcelus & Coleman 1997;Friedman & Sinuany -Stern 1997), and to analyse the performance of higher education sectors across states or countries (Breu & Rabb 1994;Kocher et al 2001). The validity of these approaches seems particularly questionable on the grounds that the DMUs in each case are clearly not a homogenous set of producing units.…”

Section: Methodsmentioning

confidence: 99%

“…Its popularity in these contexts derives from the fact that it is based on a distance function approach and hence can handle multiple outputs and multiple inputs; it does not assume any specific behavioural assumptions of the firm (eg cost minimisation or profit maximisation); it makes no assumption regarding the distribution of efficiencies; and it requires no a priori information regarding the prices of either the inputs or the outputs. Despite there being a plethora of studies which examine the efficiency of the higher education sectors of various countries such as the UK, the USA, Canada, Finland, Israel and Australia (Athanassopoulos & Shale 1997;Johnes 2006a;Ahn et al 1989;Breu & Raab 1994;Haksever & Muragishi 1998;Arecelus & Coleman 1997;El Mahgary & Lahdelma 1995;Friedman & Sinuany-Stern 1997;Coelli et al 1998;Avkiran 2001;Abbott & Doucouliagos 2003), little work has been done on measuring the efficiency of higher education institutions (HEIs) in China.…”

Section: Introductionmentioning

confidence: 99%

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Measuring the research performance of Chinese higher education institutions using data envelopment analysis

Johnes

2008

China Economic Review

238

110

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This study uses data envelopment analysis (DEA) to examine the relative efficiency of over 100 selected Chinese regular universities. Various models are developed to measure the research efficiency of these higher education institutions (HEIs) using data for 2003 and 2004. The findings show that the level of efficiency depends on whether or not a subjective measure of research output (based on experts' opinions of the HEIs) is included as an output in the model. Mean efficiency is higher when the reputation variable is included (around 90%) than when it is not (mean efficiency is around 55% in this case). However, the rankings of the universities are remarkably insensitive to whether or not this variable is included. Bootstrapping procedures are used to find the 95% confidence intervals for the efficiencies, and indicate that the best and worst performing institutions are significantly different from each other; only the middle-performing 30% of HEIs cannot be distinguished from each other in terms of their performance. Further investigation suggests that regional location, source of funding and whether the university is comprehensive or specialist may all contribute to the observed differences in performance. The regional differences are consistent but not significant at conventional levels of significance; the efficiencies differ significantly by administrative type when the subjective measure of research output is excluded from the analysis; comprehensive universities consistently and significantly outperform specialist institutions. The possibility of regional differences in performance is particularly worrying since the already economically disadvantaged Western region may suffer a continued lag in development if its HEIs are less efficient than those in the better developed Central and coastal regions. JEL classification:I21, I23, C14

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring the research performance of Chinese higher education institutions using data envelopment analysis

Johnes

2008

China Economic Review

238

110

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“…If the value is constant, the variable will be incapable of distinguishing one unit from another, whereas a pronounced variation indicates an important influence. Jenkins and Anderson (2003) use partial correlation as a measure of information, instead of a simple correlation matrix (Friedman and Sinuany-Stern (1997)). However, partial correlation is based on the assumptions that the data is drawn from an approximately normal distribution and the conditional variance is homoscedastic.…”

Section: Multivariate Statistical Approach To Variable Reductionmentioning

confidence: 99%

Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction

Adler

Yazhemsky

2010

European Journal of Operational Research

192

117

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“…Andersen and Petersen [5] modify the CCR model to allow for a ranking of the efficient units. A procedure using DEA to rank DMUs of interests is proposed by combining multivariate statistic approaches such as discriminant analysis of ratios [6] and canonical correlation analysis [7]. Wang and Luo [8] proposed a ranking approach by incorporating DEA into the technique for order preference by similarity to ideal solution (TOPSIS, 1981).…”

Section: Introductionmentioning

confidence: 99%

A note on ranking efficient DMUs based on a single virtual inefficient DMU in DEA

Balf¹

2017

Data Envelopment Analysis and Decision Science

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In this paper we give a comment on paper by Shetty and Pakkala [U. Shetty, T.P.M. Pakkala, (2010), Ranking efficient DMUs based on single virtual inefficient DMU in DEA. OPSEARCH, 47 (1):50-72]. They proposed an approach to rank the efficient decision making units based on a virtual DMU in the constant return to scale DEA model. The input and output levels of this virtual DMU are the average of input and outputs of all DMUs. We obtain another method for select virtual DMU as negative ideal DMU. Our approach doesn't need to existence at least an inefficient DMU in the set DMUs. In addition, it can be used in constant and variable return to scale DEA models. This brief comment provides an alternative approach for their work.

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Scaling units via the canonical correlation analysis in the DEA context

Cited by 156 publications

References 19 publications

Measuring the research performance of Chinese higher education institutions using data envelopment analysis

Measuring the research performance of Chinese higher education institutions using data envelopment analysis

Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction

A note on ranking efficient DMUs based on a single virtual inefficient DMU in DEA

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