How to Interpret the Report
Most outcomes are presented as average (mean) values for your group. For example, if you look at the sample result below, you will see that the average value for the “How interconnected you are with others?” item was 73.4 before the workshop and was 90 after the workshop.
You can look at the average values and see if they went up or down after your workshop. For example, if the pain scores went from 5.0 before to 2.0 after, you would understand that participants had pain symptoms decreased from the workshop. In our interconnectedness example above we see that the score increased by 16.6 points towards greater interconnectedness with others, which is in the direction that we would like.
If the measure was completed before and after your event, you will see a p-value calculating the statistical difference between the pre- and post-values. You want to see this value be below 0.05 which will be marked with an asterisk*. This means that statistically speaking there is a 95% likelihood that the difference you are seeing actually does exist and is not due to chance. The p = 0.05 is the common cut-off used for scientific studies. The lower the p-value, the more confident you can be in the change. In the self-transcendence example above, we see a p-value of 0.01. This means that we are 99% sure that the difference between the pre- and post-values are not due to chance.
What if none of my measures have a p-value less than 0.05?
If the values are moving in the right direction but the p-value is greater than 0.05 that may mean that you did not have enough participants for statistical power to detect a change in the pre-post values. The more participants you have taking the measures, the more confident you can feel of the results you are seeing. It may also mean that your workshop does not affect that factor in the way we are measuring it. This information can help you refine your workshop so that you are achieving the outcomes you are intending with your participants.