To learn more from a set of data, you may want to calculate additional variables. Here is an example from a client satisfaction survey.
If you are a general counsel and you ask your clients to assess your department, ask them not only to evaluate your group’s performance on a set of attributes but also to rank those attributes by importance. The more important the attribute – such as timeliness, understanding of the law, responsiveness – the more your clients should expect good performance from the law department. You want to focus on what your clients value.
From the survey data, create an “index of client satisfaction” which divides the reality (performance ratings) by the expectations of clients (importance ratings) on each attribute. In short, reality divided by expectations, which is client satisfaction. Then you can calculate averages, medians, etc.
With 1.0 being the absolute best, where the delivered performance fully met the expectations of all your clients, your index will decline to the degree the performance of the law department fell short of what clients felt was important and expected. By the way, low expectations (importance) fully met shows up in the index as high satisfaction. Focus on the gap between the highest ranking attributes and their evaluation ratings.