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Research Synthesis Step 2: Determining patterns #13214

sarahmonster opened this Issue Jan 5, 2019 · 0 comments


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sarahmonster commented Jan 5, 2019

Note: this is to discuss and reference methodology for determining patterns in the data pulled from our sitebuilding study. This isn't 100% set in stone just yet and will be tweaked as we finalise details.

Want to get involved? No prior research experience is necessary, and we'd ❤️ your help!


We don't have a clear idea in terms of segments of the WordPress userbase, but I've made some preliminary guesses (in Airtable) and have coded the sessions accordingly.

Keep an eye on any clear patterns in terms of segments, but I don’t think we’ve talked to enough people to get a very good sense of that yet, especially when the landscape is so huge and varied.

Identifying & exploring themes

Look at all the codes and try to identify patterns and larger themes in the data. This will be done by an affinity mapping exercise followed by a discussion to review themes identified.

Then, look through all the extracts with the codes relating to your theme—are there any contradictions? Are there repeated patterns?

If there are many contradictions within a theme or it becomes too broad, you should consider splitting the theme into separate themes or moving some of the codes/extracts into an existing theme where they fit better.

This is an iterative process, where you go back and forth between themes, codes, and extracts until you feel that you have coded all the relevant data and you have the right number of coherent themes to represent your data accurately.

Affinity mapping

Affinity mapping will take place over two days, in an asynchronous manner, and will be used to further explore themes and portions of the dataset.

Insights and pain points, for instance, might be easier to group in a remote-stickies affinity map, but we can copy easily from a spreadsheet format and then use a stickies app for grouping and pattern identification.

This will be a collaborative exercise that anyone can join. We'll also discuss in Slack as groupings become apparent, and take notes that will then be used in the final report.

🚨 Be careful!

Watch out for confirmation bias! Is this supporting an existing assumption you had? Perhaps it's time to confer with others. As a way of avoiding confirmation bias, make a list of your existing assumptions about the results before you dive into analysis. Try to look for data that contradicts your pre-existing assumptions

Also make a note of extreme results from extreme people: sometimes one person will have a very strongly held view that isn't held by others. Be careful if you're basing an insight on something we've only observed in one individual.

@sarahmonster sarahmonster created this issue from a note in Phase 2 design (In progress) Jan 5, 2019

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