Once the data is collected and the weightings are decided upon, the next thing to do is to calculate standard scores for each column of data so that they are compatible with each other and allow us to combine the data reliably and apply the weightings fairly in the calculation of the overall score. Before 2007, the approach taken here was an over-simplistic one – find the top scoring institution, award them 100 notional points and scale the remaining entries proportionally to that top performer. This approach had some disadvantages:

• Anomalous application of weightings
• Lack of control for “outliers”
• The smallest of errors in the assessment of the top performing institution in any indicator could have dramatic effects

From 2007, a more complicated, but widely used standardization or normalization method has been adopted involving z-scores. There are numerous online sources explaining how this works: -

Wikipedia – http://en.wikipedia.org/wiki/Standard_score

UCLA – http://www.gseis.ucla.edu/courses/ed230a2/notes/z1.html

There are even a range of video podcasts available on YouTube: -

Z Scores Explained – http://uk.youtube.com/watch?v=1xhCL5m4nI0

Calculating Z Scores – http://uk.youtube.com/watch?v=s0lLBcARxL4

In order to calculate z-scores the mean and the standard deviation of the sample are required. These are as follows…

Standard Deviations and Means for Individual Indicators in 2011

Figure 13: Standard Deviations and Means for Individual Indicators in 2011

Once the scores are calculated, their position on the normal curve is plotted resulting in their score for each indicator. The resulting scores are finally scaled between 1 and 100 for each indicator to result in a set of results compatible with those for the other indicators