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Collect, score, analyse and interpret WEMWBS


The WEMWBS scales have been designed to be self-completed. Both digital and paper versions work well. If there is a need to help participants complete the scale by reading out items or filling in the questionnaire for them there is a risk that participants will respond more positively than if they were completing the questionnaire on their own. This could introduce bias into the study.


The 14-item scale WEMWBS is very simple to score. The total score is obtained by summing the score for each of the 14 items. The scoring range for each item is from 1 – 5 and the total score is from 14-70. For more help with scoring WEMWBS please refer to the user guides below.

The 7-item scale WEMWBS is more complicated to score and it is important that it is scored correctly in order for comparisons to be made across different studies. The 7-item scale WEMWBS is a shortened version of WEMWBS which is Rasch compatible. This means the seven items have superior scaling properties to the 14 items, but in order to take advantage of this and to compare results with those of other studies using the 7-item scale WEMWBS, raw scores need to be transformed. Transformation just means that the total raw scores need to be converted as in this conversion table.


The 7-item scale WEMWBS scores approximate to a normal distribution, permitting parametric analysis. So the most statistically efficient approach to analysing data is to calculate means and standard deviations and compare results using Students T-test. The UK Population norms have been published and can be used as comparators for your scores (WEMWBS and SWEMBS population norms Health Survey for England 2011).

Some investigators prefer to analyse their data using categorical approaches. Scores can be divided into high. average and low mental wellbeing using cut points. Several different cut-points have been used. One statistical approach is to put the cut points at plus or minus one standard deviation. This approach puts approximately 15% of the participants into high and 15% into low categories. In UK population samples, the top 15% of scores range from 60-70 and the bottom 15% 14-42.


If you have used a parametric approach to analysis (students t-test), you need to compare your scores with population norms. Ideally, results should be adjusted for differences in age and sex distribution.

If you are using a categorical approach, it is possible to find a point between low and average mental wellbeing which corresponds to the cut points on validated scales of mental illness such as the CES-D measure of depression (Donatella Bianca report). In the latter study, a score of 40 and below corresponded to probable depression and a score of 44 and below to possible depression. NHS direct have used this cut point of 40 and below as the cut point for low mental wellbeing in their self-assessment scale.

As there is no gold standard for measuring high mental wellbeing all cut points are by definition arbitrary. NHS Direct has chosen to use a cut-point of 59 and above as high mental wellbeing.

How you analyse your information further will depend on your study design. As a rule of thumb, studies need to include at least 50 people with evaluation data at two points in time, or 50 people in each group if two groups are going to be compared. Further information on sample size is available in the guides below.

Individual-level differences: Neither of the measures was developed for monitoring change at the individual level or in clinical settings, but both the 14-item scale and 7-item scale WEMWBS have been shown to be responsive to change at the individual level and some practitioners are using them to help clients and patients think about ways in which their mental health is changing. Different statistical approaches give different results with regard to minimally important levels of change. For the 14-item scale WEMWBS the methods give a minimum of 3 points and a maximum of 8 points; for the 7-item scale WEMWBS, a minimum of 1 point and a maximum of 3 points.