I was tasked within the group of analysing the Non-GoBus Users data from the surveys. The analysis of survey data should strive for accuracy and immediate utility. The two common analysis techniques can be summarised simply as counting and comparing, Goodman E., Kuniavsky M., Moed A., 2012.
Using the survey data collected into Google Sheets I started by breaking it down question-by-question. I produced graphs that visually represented the qualitative data for the most significant finding. By imputing the question into Google Sheets “What is the distribution for question 1?” for each question in the survey I was able to get a breakdown of how the 68 people answered each question in the survey. Using the highest number/highest result, I then worked out what the corresponding percentage was for that number within the group.
Once I had the number of people count and the corresponding percentage I gave a brief conclusion on each question. When all the questions were laid out like this I looked at all the conclusions and but together a basic persona profile of a Non-User/Potential User. The profile was a useful way of communicating with the team and summarising my finding as a qualitative result based upon the quantitative data produced by the survey.
68 people out of 121
68 = 100%
1% = 1.47
I then applied this same method to the 53 users who completed the survey. The results corresponded to Fergal’s finding who was analysing the 53 GoBus users from the survey.