Qualitative Analysis & Reporting: Free Digital Qual Masterclass (Module 6)

How do you turn hours of interviews, diaries, photos and videos into findings people act on? This free, one-hour course module covers qualitative data analysis and reporting from start to finish: designing your study with the output in mind, organising and interpreting what participants share, deciding where AI should help, and sharing findings in formats that fit each audience.

Module 6 is the final part of the six-part Digital Qual Masterclass Series. It is taught by Laura Pulito, Vice President of Research at Recollective, and is free to watch on demand thanks to the support of Recollective. It’s practical guidance for anyone who analyses qualitative research or has to turn it into a report, brief or presentation.

What you’ll learn

By the end of this module, you’ll be able to:

• Design with the output in mind, working backwards from what stakeholders need to understand and decide to what participants need to share
• Treat analysis as continuous, with clear tasks before, during and after fieldwork rather than leaving it all to the end
• Organise qualitative responses by the research (objective, activity, phase), by the participant (segment, experience, occasion) and by what you notice (topics, behaviours, things to revisit)
• Choose comparisons that matter, such as different audiences, expectations versus actual experience, first versus repeated use, and what participants say versus what they show
• Interpret what participants are really telling you, including who is saying it, why it mattered to them, and whether it was prompted or unprompted
• Decide where AI belongs in qualitative analysis using three categories: AI can do it for me, AI can do it with me, and I should do this myself
• Ask AI better questions and check its answers, including whether it is counting people, entries or mentions, and what it may have left out
• Build a report around decisions, avoiding the common problems that make qualitative reports hard to follow, and prepare for questions and pushback
• Match the format to the audience, from briefs and video highlights to podcasts, interactive reports, working sessions and searchable research repositories

Who this course is for

• Qualitative and market researchers who want a more structured approach to analysis and reporting
• Client-side insights teams who need findings that stakeholders actually use
• UX, product and CX researchers analysing interviews, diaries and usability sessions
• Researchers starting to use AI tools for summaries, themes and clips, and wanting to use them responsibly
• Students and early-career researchers looking for free qualitative research training

What’s covered in Module 6

How qualitative analysis has changed since 2020

In 2020, online qual analysis meant exporting responses, searching transcripts for the right quote and spending hours editing video. Today, AI and platform tools can draft summaries, suggest themes, find relevant moments and compile clips, and research repositories let you search and ask questions across past studies.

Designing qualitative research with the output in mind

Why analysis starts at the planning stage. If a client needs to understand why people struggle with a package, asking whether they like it isn’t enough; you may need to see them open, use and store it over time. A good report makes information easier to understand, but it can’t supply information you never collected.

Qualitative analysis before, during and after fieldwork

What to think about before fieldwork (what you already know, which comparisons matter, who will review responses), what to do while the study is running (follow up, save quotes and clips, compare notes, keep an open mind) and how to look across the full study once fieldwork ends.

Organising, coding and comparing qualitative responses

How to work across written answers, live conversations, photos, video, screen recordings, polls and participant interactions; three ways to organise and tag responses; and how to choose comparisons that help answer the business question rather than running every possible cut.

Interpreting what participants actually mean

Using “convenience” as an example, Laura shows why the same word can mean very different things, why prompted and unprompted mentions carry different weight, and how to work an observation through to a recommendation by asking why, for whom and when it matters.

Using AI in qualitative data analysis

Which analysis tasks to hand to AI, which to do together with AI, and which require your own judgement. Includes how to turn a vague prompt into a more useful one, what to check in an AI-generated summary, and what AI might miss.

Reporting qualitative research to stakeholders

How to tailor findings to the people making decisions, structure the story around what you learned rather than the order of the discussion guide, avoid the most common reasons reports are hard to follow, and be ready to explain your approach, your evidence and how you used AI.

Creative ways to share qualitative research findings

Participant video, audio summaries and podcasts, interactive reports, digital magazines and working sessions, with examples shared by attendees. Plus how AI can help produce outputs, and what must stay the same in every format: participants’ meaning, accurate quotes, context, limitations, clear labels for generated content and permissions.

Research repositories and building on what you learn

Why findings shouldn’t sit in an inbox or project folder, what a searchable research repository should hold, and how to use past research to plan sharper new studies and put new findings in context. The session closes by bringing the whole series together.

About the instructor

Laura Pulito is Vice President of Research at Recollective. She has more than 20 years’ experience in the research industry on both the client and supplier side, and was a Recollective customer before joining the company. She specialises in digital qualitative methods and helps brands, agencies and researchers design innovative online research projects.

About the Digital Qual Masterclass Series

The Digital Qual Masterclass Series is a free, six-module course on modern online qualitative research, hosted by Insight Platforms and supported by Recollective. It refreshes the popular 2020 Fundamentals of Online Qualitative Research course for a world of new platforms, blended methods and AI.

1. Planning & Designing Modern Qualitative Research
2. Live Video Interviews & Groups, including AI-moderated interviews
3. Smartphone Diaries & In-the-Moment Research
4. Asynchronous Qual & Short-Term Communities
5. Long-Term Insight Communities
6. Qualitative Analysis & Reporting (this module)

Frequently asked questions

Is this qualitative data analysis course free? Yes. All six modules of the Digital Qual Masterclass Series are free to watch on demand, supported by Recollective.

Do I need to have watched the earlier modules first? No. Module 6 works on its own for anyone who analyses or reports qualitative research. It does draw on ideas from earlier modules, such as starting with what participants need to do, so watching Module 1 first is useful but not essential.

How long is Module 6? About one hour. You can also read the full edited transcript as a downloadable PDF, along with three one-page handouts.

When should qualitative data analysis start? Before fieldwork begins. Analysis happens throughout a study: before fieldwork you decide what you need to learn and which comparisons matter, during fieldwork you review responses, follow up and save useful quotes and clips, and after fieldwork you compare across the full study and work through what the findings mean. A good report can make information easier to understand, but it can’t give you information you never collected.

How should AI be used in qualitative analysis? The module sorts tasks into three groups. AI can do it for me: first transcripts and translations, draft summaries, finding comments on a topic, suggesting quotes or clips. AI can do it with me: exploring themes, comparing groups, finding responses that don’t fit and connecting study phases. I should do this myself: deciding what matters, understanding responses in context, thinking about who you haven’t heard from and making the final recommendations. Review everything AI produces before you use it.

What should you check in an AI-generated summary of qualitative data? Check that the summary matches the original responses, that quotes are exact and attributed to the right participants, and that it includes different experiences rather than only the common ones. Ask whether it is counting people, entries or mentions, since five mentions from five people mean something different from one person mentioning the same thing five times. And look for what it may have missed, such as an issue that matters to a small group or a participant who changed their mind.

What are some alternatives to a slide deck for reporting qualitative research? Match the format to what people need to do: a concise brief to make a decision, participant video or audio to understand an experience, a short audio summary or podcast for a quick update, an interactive report to explore the detail, a working session to agree what to do next, and a searchable repository so people can revisit the research later. Most studies need only one or a small number of outputs.

Is the course specific to Recollective’s platform? No. The content covers the broader qualitative research landscape and applies regardless of the platform or technology you use. Where platform features are mentioned, capabilities vary by tool.