The LEAF-7 questionnaire has seven questions each with four levels of response.
As such we can capture results and count responses in a simple 7x4 grid that shows the frequency with which ratings were given. We double up that grid when we want to compare responses at two time points. A typical way of doing this is shown here:
This illustrates the improvements in all domains that were achieved on this service, and most strikingly in people being able to do things that are important and enjoyable for them. At Age UK Wakefield District we use the Wilcoxon Signed Rank statistical test to check whether our results are significant, and the effect size formula for Wilcoxon/Mann Whitney to gauge the effect sizes.
Because the factor analysis of LEAF scores suggests one underlying factor with all domains significantly correlating with this factor we are able to total people’s scores and look at means (averages) for groups of people. Purely as an example, we can use the BASS Q3 figures above to show how this can work. We will say that a ‘hardly’ rating = 1, ‘a little’ = 2, ‘mostly’ = 3 and ‘completely able’ = 4. Therefore someone who rates themselves in all seven questions as mostly able to manage, do and have the life that they want will score 21. With the BASS sample we find that at the initial assessment the mean total client rating score was 16.5 and at the exit assessment it had risen to 21.5. In due course we will have a sufficient bank of records to be able to reference or benchmark people or groups against typical total scores. This will develop over time.
All of this information on ratings can be collected independently of any Client Management System - it might just be input on a spreadsheet. But it can be a lot more insightful if the LEAF data is attached to clients’ records within a database.
At AUKWD we record LEAF scores on the Charitylog system in an extension database that can be matched with other client attributes, such as age, gender, living arrangements, health conditions, address location etc. This allows us to describe the characteristics of the groups of people we are working with. When matched with LEAF ratings, this can provide insight into the ways the support services are working and who they are more and less effective with.