Tools and software
Talk based methods
There are many tools available to support qualitative work, from transcribing recordings to organising and analysing data. This chapter gives a brief overview of the main options and some guidance on choosing what is right for your project.
Recording and transcription software
Many experienced qualitative researchers find that transcribing their own interviews gives them a head start on analysis, as patterns and ideas often begin to emerge during the transcription process. However, large datasets, long interviews, or focus groups with many participants can be more time-consuming to transcribe manually, so you might opt for automatic transcription.
Online interviews and focus groups can be recorded and transcribed using in-built functions in Microsoft Teams. If you are carrying out an interview or focus group in-person, you can use the Sound Recorder app on your Camden laptop. Do not use any personal devices (e.g., Voice Memos on a personal iPhone).
Automated transcripts are rarely perfect and will always require checking and correcting against the original recording. Always double-check names, technical terms, or any sections where audio quality is poor.
Tools for analysis
There are various tools which can help you organise, code, and work with your data. The right tool depends on the scale and complexity of your project, your familiarity with different software, and what is available to you.
Specialist qualitative data analysis software
Software such as NVivo, ATLAS.ti, and MAXQDA are purpose-built for qualitative analysis. These tools allow you to import transcripts, apply and manage codes (see Coding your data), and visualise patterns and relationships in your data.
The main benefits of specialist software are that it keeps everything in one place and makes it easier to manage larger datasets. However, these tools require some training, and for smaller or less complex projects the time invested in learning the software may outweigh the benefits of using it.
NVivo is one of the most commonly used platform in public health and local authority settings. If you are considering using it and have not done so before, speak to PHI about whether it is the right choice for your project and what support is available.
You can request a license to specialist programmes through Camden’s IT Service Desk
Microsoft Excel
Excel is a practical and accessible option particularly for more structured qualitative analysis approaches (e.g., framework analysis) where you are working with a defined set of questions or themes and want to compare responses systematically across participants.
A simple matrix can be set up using rows (e.g., participants, data collection events) and columns (e.g., themes or categories), with summarised or verbatim data entered into each cell.
There are more complex ways of setting up your analysis using Excel, to support structured and systematic analysis1. For example, you can:
Set up dropdown lists (using data validation) that allow you to click and apply codes to sections of data, rather than adding them manually.
Add filter functions to columns containing participant variables (such as role, age group, or service area) allows you to quickly sort and compare responses across different subgroups.
Use conditional formatting to colour-code coded sections visually, making patterns easier to spot.
Create pivot tables to help you see how codes or categories are distributed across your sample.
These additional features take some time to set up at the start of a project but can make the analysis process more efficient and consistent, especially when more than one person is working with the data.
Speak to PHI if you would like support setting up an Excel framework for your project.
Microsoft Word, Adobe Acrobat Reader and manual approaches
Many researchers work directly in Microsoft Word or equivalent (e.g., Adobe Acrobat Reader) using highlighting, comments, and colour-coding to identify and organise coded sections of text. If you are using Microsoft Word, you can export all comments (e.g., these might be your codes) into a separate document to create an organised list.
This is a perfectly legitimate approach and can work well if you have a relatively small dataset (e.g., 5 to 10 interviews). It requires discipline to stay organised but does not require use of (or training in) any additional software.
Some researchers also prefer to work with printed transcripts and annotate by hand, particularly in the early stages of analysis. Close reading on paper can support a level of engagement with the data that screen-based work sometimes makes harder.
When you do and don’t need specialist software
The honest answer is often you do not need any specialist software to do good qualitative analysis. The quality of your analysis depends on the quality of your thinking, not how sophisticated the tool you use is.
Specialist software supports analysis, but it does not do it for you.
As a rough guide:
For small projects (e.g., fewer than around 10–15 interviews, a single focus group, a clearly defined analytical framework) Word, Excel, or manual approaches are most likely sufficient.
For medium projects (e.g., 15 to 30 interviews, multiple focus groups, more exploratory analysis) Excel-based frameworks or NVivo may be helpful, depending on your analytical approach.
For large or complex projects (e.g., 30+ interviews, multiple data sources, longitudinal data, team-based analysis) specialist software is likely to make the process more manageable and help you to keep a clear audit trail.
If you are unsure about whether to use specialist software for your analysis, speak to PHI before investing time in learning a new software.
Using AI for qualitative analysis
AI tools, including generative AI tools like ChatGPT or CoPilot, dedicated analysis platforms, and features built into transcription software, are increasingly being marketed as ways to ‘automate’ qualitative analysis. It is important to approach how we use these tools carefully.
AI cannot replace genuine qualitative analysis. Qualitative analysis is an interpretive process that requires human judgement. Understanding the context, sitting with ambiguity, making reasoned decisions about meaning, and being accountable for those decisions. AI tools can identify keywords, produce summaries, or suggest possible themes based on patterns in text, but this is not the same as analysis. Outputs generated by AI tools may appear plausible but can miss nuance, overlook complexity, or reflect the biases2 embedded in the tool rather than what your data is actually telling you.
Inputting personal data into AI tools carries significant risks. Interview and focus group transcripts typically contain personal and potentially sensitive information about participants. Uploading this data to open AI tools (e.g., a free and unauthorised version of ChatGPT) is likely to have significant data protection implications.
A few questions worth asking before using an AI tool at any stage of your qualitative work:
Have I checked whether this tool is approved for use with personal data? Some tools (e.g., Copilot) may allow you to upload raw data securely, check this before you share any potentially sensitive or personal data.
Am I using this tool because it genuinely improves the work, or because it is faster? Speed is not necessarily a good reason to use AI tools for qualitative analysis, particularly where the quality of analysis has real consequences for the communities or services involved.
Could I explain to a participant exactly what the tool did with their data, and would they be comfortable with that? Participants consent to their data being used for specific reasons, in a specific way. If your data management or consent documentation did not anticipate the use of AI tools, then using them might fall outside what participants agreed to.
For more information on using AI tools in your work at Camden see Generative AI (GenAI): Using it at Work
Footnotes
Ho JQ, Hartanto A, Koh A, Majeed NM. Gender biases within artificial intelligence and ChatGPT: evidence, sources of biases and solutions. Computers in Human Behavior: Artificial Humans. 2025 May 1;4:100145.
Davison RM, Chughtai H, Nielsen P, Marabelli M, Iannacci F, van Offenbeek M, Tarafdar M, Trenz M, Techatassanasoontorn AA, Andrade AD, Panteli N. The ethics of using generative AI for qualitative data analysis. Information Systems Journal. 2024 Sep;34(5):1433-9.
Rickman S. Evaluating gender bias in large language models in long-term care. BMC Medical Informatics and Decision Making. 2025 Aug 11;25(1):274.↩︎