About This Course

Most teams are under pressure to pull insights from research data. When it comes to research data as transcripts, the pressure becomes tenfold. Managers assume the hard work is already done now that the interviews are over. “Give me the insights already,” they ask. Often, teams are not trained nor given support in handling data. They are told to just “find the affinities.” So, they jump to that.

• Section 1 – Recognizing Concepts in Transcripts
• Section 2 – Clarifying the Concept
• Section 3 – The Format of a Summary
• Section 4 – Clarifying the Summary

Research teams typically end up doing two steps in one: identify concepts at the same time as seeing what patterns form across transcripts. They use sticky notes to identify 10 or 15 concepts in one transcript and quickly move to the next transcript to see what is familiar. However, in both analysis and in synthesis there are two steps. But teams are expected to do what the manager says, and the manager has a hunger for quick insights. Managers erroneously give the team the imperative to jump to the second pattern-finding step. This mistake reduces good transcript data to ineffectual, “we already know that” takeaways.

Some of the video topics:

• How people rarely communicate concepts one at a time in a linear fashion
• Collecting bits of a concept as it is mentioned in different places of the transcript
• Untangling concepts that are braided together
• Deciding whether an implied concepts is worth keeping in the data set
• Summarizing each concept in a manner that makes it easily recalled, and easily compared across transcripts to see patterns
• (Finding these patterns is the second course of this pair)
• Recognizing that “insights” must wait until the solutions already in place are compared to the patterns that arise from the data

If the research team does follow the two steps, it becomes an overwhelming task. There is not enough time to carefully identify the 40-120 concepts that appear in each transcript. Their initial thoroughness quickly becomes a little more high-level, a little speedier. This is when assumptions creep in. It takes a well-trained mind not to fall prey to cognitive bias.

When you come at data from a top-down approach, the concepts that you’ve already become interested in get emphasized. Bottom-up synthesis is a method that helps you avoid cognitive bias and let people’s different perspectives get represented their way. It requires keeping a neutral mindset and looking at the transcripts for types of concepts, not concept meaning. The meaning will bias you toward the things you are already interested in. Types of concepts, enables a bit of neutrality. Once you pull those types out and summarize them, you will have a true set of concepts that were actually expressed.

Course Features (for new format in bundle):

• Videos that don’t require you to look at the screen
• Exercises with answer sheets
• Downloadable diagrams
• Closed captions for each video, along with descriptive transcripts
• Accessible PDFs
• Downloadable transcripts
• Add on Live Group Meetings to do the exercises with Indi
• Live Group Meetings count toward Certification in Problem Space Research
• Membership in Indi’s Slack workspace (no time limit)

If you want to bring rock-solid, reliable information to your organization, you need to know how. You need to know how to push back on requests like, “What are the highlights?” “Trends?” “Pain points?” You need to know how to let the data show the variety of people’s real interior cognition. These two courses in data synthesis will give you these skills.