![]() Simply divide 5 (participants who reported a technical issue) by 100 (the total number of participants in that segment) to get a figure of 5%. Let’s say 5 of the 100 participants who completed a task on ‘Amazing airlines’ reported technical issues. The next step is to turn your counts in to percentages. So how would you summarise the data? Summarising thematic analysis data ![]() At this point you may be tempted to just summarise these issues in a paragraph, however it is vital that you quantify how often each issue was encountered.įor example, if 25 participants encountered technical issues and 1 struggled to locate the search functionality, it would be misleading to list these two issues together in a manner that implies they occur with equal frequency. When complete, the column headings of your thematic analysis will show you the full range of usability issues that were encountered. ![]() In the example below we can see that the first user simply reports a technical issue, whilst the second user reported both a technical issue and stated they were unable to enter their details in to the booking panel. This is where each open comment is coded into a theme. The standard method for analysing qualitative comments is to perform a ‘thematic analysis’. The challenges of traditional thematic analysis ![]() So let me recommend an easier way to deliver UX insights – qualitative heatmaps!īut first let’s take a look at your other options, and why they present a challenge. Quantitative metrics from multiple sites from these benchmarks (such as ‘time-on-task’) can be quickly and effectively visualised in a single bar graph. However, condensing qualitative data into one slide presents much more of a challenge. In the last year I have undertaken several large scale benchmark studies, typically comparing four or more sites at a time. ![]()
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