Data Envelopment Analysis in University Rankings

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Data Envelopment Analysis in University Rankings 1.0 Introduction Data Envelopment Analysis (DEA) is a means of producing a performance measure where sets of organisational decision making units (DMUs) have multiple inputs and outputs. (Dyson, 2000, OR Insight Vol13 Issue 4). DEA considers each unit in turn through linear programming and selects the most favourable weights for it. In this investigation the DMUs are universities, and the outputs are the different categories, ie ‘Completion Rate’ 1.1 Data Set Chosen / Assumptions From the Times Top 100 universities a list of 12 was made from what was considered to be the top “Computer Science” universities listed on the Times Website (Appendix d). “Computer Science” was chosen solely as a means of selecting 12 Universities. These 12 universities were then investigated using Data Envelopment Analysis (DEA) in EXPRESS-IVE. In the appendices the rankings were altered so that the selected universities were also ranked 1 to 12 in the order they appeared in the Times league table. This facilitated comparisons. Weightings of variables were changed and are therefore subjective as to how I perceived their importance. 4 decimal places were used as a minimum to ensure as greater accuracy as possible. 2.0 Calculating efficiency scores for each university & comparison with Times listing (Appendix a) The criteria used in the Times Top 100 University DEA model were all assigned a minimum weighting (0.0001). The item number (representing where the university placed in the league table) was then altered to the required university before compiling and then run/re-... ... middle of paper ... ...arios, Oxford appears to be the best option as it deviates from 1st only once (when compared to The Times rankings) where it ends up 2nd in the table (when looking at the “good times at University and good job afterwards” scenario). Cambridge and Imperial appear to consistently perform well. However even these change in the final scenario. People cannot be put into boxes, and in a sense DEA could be considered a victim of its own creation; by eliminating so much of the subjectivity associated with it, it creates an initial stage of subjectivity; that of deciding the weightings. Individuals have different needs and goals and therefore a model such as this needs to be used cautiously as evidently there are various other factors that need to be taken into consideration, many of which are unquantifiable without bias.

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