Ever since ChatGPT 3 was released for public use, several people assumed that AI is already the new way to get almost everything done! User research analysis almost fell to that assumption. However, Hold your horses! That transition is not happening anytime soon.
Most of the AI analysis software, whether hurriedly built on user research platforms, video calling software like MS-teams or several meeting softwares like Grain AI recorder, Otter.ai, Read.ai, etc. fall well short of delivering sensible insights from qualitative data.
What is Qualitative Research Data Analysis
Qualitative research data analysis is the systematic process of examining non-numerical data, such as text, audio, or video, to discover patterns, themes, and insights that reveal deeper meanings and understanding of human experiences.
This involves organizing the raw data, creating a coding system to categorize information, and identifying overarching themes to develop a comprehensive narrative that answers the research questions. Key steps include data preparation, coding, theme identification, and interpretation to generate actionable findings.
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Qualitative Data Analysis methods
Thematic Analysis:A flexible method for identifying, analyzing, and reporting patterns (themes) within qualitative data.
Content Analysis:A systematic method for analyzing specific words, themes, or concepts in large volumes of text by examining the frequency and context of their appearance.
Discourse Analysis:Focuses on how language is used in social contexts to understand the meaning behind conversations and texts.
Narrative Analysis:Examines the “stories” participants tell to understand how individuals construct meaning and experience their lives.
Key Steps in Qualitative Data Analysis
Broadly, two main approaches are used most often.
1) Inductive
This is a bottom-up approach of identifying themes from the existing data. The name of the themes are not known from the outset, they emerge as the analysis proceeds forward and new themes are found. This approach is most often used in exploratory research topics, when not much is known about user behavior. Using an Inductive approach helps the researchers to develop new theories and gather new insights from the qualitative data.
2) Deductive
Opposite of Inductive, this approach is top-down. Themes are known before the thematic analysis is started. Themes might be known due to a preceding research or with clear objectives of research e.g. in case of usability studies with clear problem definition. Researchers look for evidence of the known themes in the data and then Code the data.
Importance of each step in thematic analysis
- Familiarization with data
Researchers read over the transcripts and refine those by watching the video recordings. This process must be repeated at least a couple of times to familiarise themselves with the data. This initial step immensely helps researchers to identify potential codes that must be used to process this data set for analysis. - Initial coding of content
The entire qualitative data is read through and potential patterns are identified. Codes are created and assigned to each of the likely patterns. Pre-existing codes (aka Tags) in Analysis Space of DeepDive can be used as an inventory of codes applicable throughout the account. - Clustering codes into themes
The entire qualitative data is sifted through and specific pieces of interesting text information are highlighted. Codes are assigned according to pre-identified codes. If required, new codes can be created at this step. Softwares that support Thematic analysis e.g. DeepDive will automatically create clusters of coded text whether they are coming from voice to text transcripts, translations, video clips or notes. - Review codes, merge repetitions to finalise themes
Once the clusters of various excerpts of text information have been made, review to ensure that right information is categorised under the most suitable clusters. In this step, revisions can be made and tagged information moved among clusters if necessary. Some clusters can also be merged to eliminate duplication of similar patterns.
In the past few months, AI based software have made several claims about automatic analysis of qualitative data.
However, most of those software have fallen well short of the expectations of researchers. The analysis from those AI softwares that we evaluated at UXArmy have been awfully inaccurate and sometimes even ridiculous.
With due regards to creators of those interview summary softwares, we reckon that the technology is not mature enough yet to be useful. UXArmy research team tried ChatGPT to process transcripts from a project with 8 interview transcripts.
We requested ChatGPT to generate codes, quotes and interview summaries. For each of the interview scripts, several different codes were generated. Some of the quotes were totally meaningless, requiring our research team to do the analysis on DeepDive Analysis Space.
As Nielsen Norman quoted in their article written in July, 2023 “Be skeptical of the marketing claims being made by AI tools designed for UX researchers. Many of these systems are not able to do everything they claim.”
The skill to analyse Qualitative data is quintessential for user reseachers. While doing the analysis is not difficult, it is time consuming and honestly, not all that fun 🙂 A thematic analysis approach produces useful insights, provided it is meticulously done and researchers’ biases eliminated as much as possible. Software used for qualitative analysis like DeepDive, are efficient to work with, offering flexibilty and collaboration.
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Frequently asked questions
What are the 5 methods to analyze qualitative data?
Five popular methods for analyzing qualitative data are Thematic Analysis, Content Analysis, Narrative Analysis, Discourse Analysis, and Grounded Theory. The choice of method depends on your research objectives, with thematic analysis focusing on identifying patterns and themes, content analysis categorizing data, narrative analysis exploring stories, discourse analysis examining language use, and grounded theory developing new theories from the data itself.
Can AI handle qualitative user research effectively?
Not yet—AI tools often miss context, nuance, and biases; human analysis remains crucial for meaningful insights.
What are the 6 steps of data analysis in qualitative research?
Clarke and Braun’s (2013) Six Step Data Analysis Process
Familiarization of data.
Generation of codes.
Combining codes into themes.
Reviewing themes.
Determine significance of themes.
Reporting of findings.
What are the core steps of thematic analysis?
Gather data → Familiarize → Code → Develop themes → Pause and reflect → Review themes for relevance and coherence.
Why do qualitative AI tools fall short of human analysis?
AI lacks deep context and cultural nuance—often mishandling tone, sarcasm, or underlying motivations, leading to superficial insights.
Should AI ever replace researchers in UX analysis?
No AI can support tasks like summarizing or tagging, but researchers must lead synthesis and bias checking. Human oversight remains indispensable.
What limitations should researchers be aware of when using AI tools?
AI often produces inconsistent results, lacks context, and may introduce algorithmic bias based on training data limitations.
