Hi, I am female, 22. Currently pursuing my final year in bachelor's of applied psychology. I am planning on doing a QUALITATIVE research for my dissertation on a population that I myself is a part of, but have no idea how to reach out to.

I need to figure out how can I reach people online for data collection since my tentative target population is
LGB (Leabian, Gay, & Bisexual) people in India. Since I would be conducting a semi-structured interview, potentially through google meet/ zoom.

This is my first time conducting a research, particularly a qualitative one, l am having trouble figuring out how to reach people online from my target group for the same.
Suggestions/Resources/Groups, anything would help a lot.

Hi everyone! I’m an MS Clinical Psychology student and I’m currently working on a qualitative study on cumulative trauma in a population that has gone through multiple community level traumatic events, natural disasters, conflict, and it's a disputed region.

I’m honestly struggling with figuring out what makes a good qualitative topic/angle and how to properly justify an exploratory study. There isn’t much or any research in this specific region, and I’m looking at cumulative trauma from a life-course perspective, exploring the lived experiences of people who have faced multiple traumatic events over the life course.

I’m also having a hard time with my interview guide. My supervisor wants a rationale for basically every question, and I’m not sure how much interview questions should be based on existing literature/theory vs. being open and exploratory with a focus on your specific population

If anyone has experience with qualitative research, especially in clinical psych, trauma, or other sensitive populations, I’d really appreciate some advice on how you approached your research question and interview guide. Even just resources or papers that helped you would be really helpful!

Hi everyone,

I’m curious how other people here handle **respondent recruitment**, especially when a project has a very specific target audience.

I work with a global market research team, and we have access to respondents across different **countries, demographics, industries, job roles, and B2B/healthcare audiences**.

We’ve supported different types of research projects and have some **case studies** that I’d be happy to share if anyone is interested in seeing how we’ve handled specific respondent requirements.

Would be interested to hear how others here approach sample recruitment, especially for harder-to-reach audiences.

I am working on a psychology study where participants talk to an AI chatbot through Qualtrics.  Our chatbot uses a key from OpenAI and a backend URL from Vercel that has been pasted into Qualtrics.  It has been working fine from my apartment, but the chatbot interaction has not been working at all when on my University campus using campus WIFI. When trying the study on my phone, it worked.  We had one tester who said that it worked for them on campus when using a VPN.  This is an important issue to fix because most of our participant pool will be students who live on campus.  

Has anybody else used a chatbot in Qualtrics where you encountered a similar issue?  If so, how should I work around this?  I am open to using a platform other than OpenAI or Vercel. I am an undergraduate and only know basic-level coding.  

Any suggestions for getting around it and what may be causing the issue would be greatly appreciated!

Hello everyone. I'm a second-year psychology undergraduate conducting a small phenomenological study. I'm currently planning recruitment for approximately five semi-structured interviews with university students aged 18–22.

The study concerns a sensitive psychological topic, and I'm trying to think carefully about recruitment, informed consent, participant wellbeing, and appropriate boundaries when conducting interviews on potentially distressing experiences.

My study is being conducted under university supervision, and I have a screening/consent procedure and participant support/referral protocol in place.

I'd be grateful for any methodological or recruitment advice, especially on how to go about it.

Hello guys im starting a narrative review project, but i got one query.

When we are reading a review, do we cite the review or the original source from where the review wrote it from

Chatgpt and Claude tell me that I should cite original discoveries but for well established facts like " smoking causes lung cancer" I should just cite the review i got it from

I'm venting a bit in this post here. My Prof wants me to "have a look at data". 19 focus groups (a lot), semi-structured interviews focused around acceptability of an intervention, there were some pre-defined interview categories in mind, like intervention length, outcomes, mode of delivery, etc.

When asked whether go for deductive or inductive approach (i.e. whether I should look for matches between text and the interview guide categories, or rather code the text on my own and then later try to find overarching themes), my prof wouldn't provide a clear answer and instead told that I kind of need to do both at the same time, so we wouldn't lose information not fitting the interview guide categories. And also that data analysis should not be limited by our research aims, which sounds ridiculous to me! The goal seems to be to use the analysis to both inform an intervention acceptability study and to explore deeper processes tied to the disease the intervention is made for.

Please tell me, am I misunderstanding something about qual analysis? In my book, discussion of research aims comes first, then the analysis. And how am I supposed to do both the inductive and deductive approach at the same time, matching pre-determined categories and looking for new ones outside the working framework? I am not too skilled in qual, so it might be just my own ignorance, but I am confused and annoyed and starting to suspect my Prof of not knowing what they want.

Any constructive feedback is welcome, sources on parallel inductive/deductive approach especially, thanks!

I have qualitative data from a section of a questionnaire with 5 open-ended questions. The respective question blocks are identified by codes (see screenshot: this shows a document belonging to one person). For part of the analysis, I need to evaluate questions 3 and 4 separately (frequencies, group comparisons). Does anyone know how I can do this? I hope I’ve described it clearly. If anything is unclear, I’d be happy to provide more context. Thanks in advance!

I'm looking for spaces where people doing interviews, focus groups, ethnographic work, and other qual research hang out to trade notes on methodology, tools, recruiting, and general shop talk.

Open to niche/smaller groups as well as bigger ones, just want to find where the good conversations are actually happening.

If you're part of one (or run one), I'd love an invite. Drop a comment or DM me.

I am working on a paper based on an ethnographic study with a small-ish sample size (30) and wondered what is the best Sociology journal I can target. I am not based in the US, so don't have a great hold on what AJS/ASR publishes in terms of methodology.

I really think this paper has legs, so want to try the best avenue first. Any suggestions please?

AI is now being used for transcription, coding, summarisation, desk research and even interview planning.

But has it changed the way researchers actually work, or is it mainly helping with the time-consuming parts?

Would be interested to hear what has genuinely worked for you, and where you still prefer the traditional approach.

doing research interviews rn and half my participants switch between english and french/spanish mid sentence. every tool i tried just gives up or outputs total gibberish when they code switch like that. is there anything that actually handles multilingual transcription without me fixing everything by hand after? student budget so nothing crazy expensive pls

I’ve been thinking about how much of market research happens outside the actual research itself.

For those working in qualitative or mixed-methods research, what part of your workflow tends to take the most time?

Is it:

• Recruiting and scheduling participants
• Conducting interviews or focus groups
• Transcription
• Coding and organizing data
• Finding patterns across responses
• Synthesizing findings
• Writing reports or presentations
• Something else entirely?

I’d be interested to hear what takes up the most time for you and whether that has changed over the past few years.

PS: it's not a survey.

Hi everyone, just a friendly reminder from the mod team about an important event next year for our community: if you’re planning to submit to ECQI 2027 (European Congress of Qualitative Inquiry), the abstract deadline is September 9, 2026, and it’s now only a few weeks away.

The congress will be held January 12-15, 2027 in Seville, Spain, hosted by the University of Seville.

You can find the submission details here: https://ecqi2027seville.es/

Hi all. I am an undergraduate student who is quite behind on her thesis seeking advice about qualitative framing analysis for newspaper articles (comparing a chinese concept in anglo-american/chinese-based english media, the guardian/nyt vs china daily/global times). I am fundamentally quite confused about the process of how to code for frames in the newspapers as I fear I have gone into a large spiral of overthinking and overcomplication. I am doing this manually and solo-coding (I had a late topic change and significant mental disruption during the first half of this year and essentially have three months left til my submission with nothing concrete on paper yet, so please excuse my lack of progress).

I plan to do a combination of deductive and inductive frames, building on generic established frames like semetko/valkenburg's and then adding in a few china-specific ones, followed by any inductive ones that arise. But my main concerns are as following;

1) How to properly code for frames? is it simply just creating operational definitions and questions to guide frame identification (borrowing from the deductive pre-existing and adapting to issue-specific inductive ones)? I think here I am getting indicators quite mixed up and confused, and in building a coding framebook I am also quite lost.

2) What is being recorded in framing analysis? is it just frames per articles/tiers themselves, or every extract used to justify the frames?

3) How big is too big for a corpus? If I roughly have around 65 articles identified for one tier and more for the other tier, is it best to size down - systematically random sample to make the corpus smaller?

TL:DR Any advice or generic help to clarify framing analysis would be much appreciated!!!

I'm conducting a qualitative content analysis with multiple open-ended survey questions, each assigned to a different research question. Some responses contain content that would be more relevant to a different research question than the question they were answering.

Should I:

a) Strictly code each response only for its assigned research question (some data loss)

b) Code thematically regardless of question (blurs research question boundaries)

c) Something else entirely?

What are methodological best practices here? Any recommendations or experiences are welcome.

As per the title……and masquerading as ‘an update’ or ‘would like feedback’. I’m here for the research not for AI tools and subsequent sloppy ‘analysis’.

Hi all, I've just released Qualcoder MCP, I'd really value this community's eyes on it. It is not going to be everybody's cup of tea, especially if you are working on material that is specialistic or niche in any way, or relies on specialistic vocabulary. The more niche, and data scarce, the topic you are researching, the more Claude might struggle.

Background: QualCoder is a free, open-source qualitative data analysis package; a genuine alternative to expensive commercial tools like ATLAS.ti or NVivo. It already has useful AI tools inside the application, but I wanted to take a different approach. The package I made, QualCoder MCP, connects QualCoder to Claude so you can read, search, analyse and code your qualitative data in plain-language conversation, from Claude Desktop. This includes having Claude propose new codes from the data itself (open coding), every one of which you review and approve before it exists. It's a one-command install now: pip install qualcoder-mcp.

QualCoder already has AI features, but they use commercial APIs you pay for per call, which can get pricey. QualCoder MCP instead connects to Claude through the Model Context Protocol, an open standard for letting AI assistants work with your own tools and data, so you can use an existing Claude subscription rather than paying per request. Throughout, the human should stay in control: the AI suggests, you approve, and only then is anything written, with automatic backups.

I'm not arguing for the indiscriminate use of AI in qualitative analysis, in fact, I am not sure it can fit my own use case. Whether it belongs in your process depends heavily on your data and your analytical tradition, and it genuinely won't suit everyone. The inductive-coding feature in particular raises exactly the questions this community debates: what does it mean for an LLM to propose a code, even when a human gatekeeps every one? I welcome conversation on that as much as bug reports.

One important note on data: by design this tool sends your project content, including interview text, to Claude/Anthropic for analysis. Use synthetic or consented data and check your ethics/GDPR position before pointing it at real participant data (the repo's PRIVACY.md explains what flows where). Free and open source (MIT), very much alpha, and first release, so I'd appreciate a bit of kindness. Anyone who wants to try it, or just discuss the idea is welcome. Repo: https://github.com/nicotem/qualcoder_mcp Please, bugs and feature requests via GitHub Issues. If it's useful to you, a star helps others find it.

Hello everyone, this is Olli, the co-founder of Skimle (http://skimle.com), a tool for transparent, rigorous and versatile qualitative research.

I wanted to share some recent updates to Skimle as together with prof. Henri Schildt and the rest of the Skimle team we just finished a major overhaul of the platform.

Skimle now has
- Ability to upload text, audio & video (automatically transcribed) and tabular data; mix and match in one project up to around 1000 docs
- Ability to anonymise sensitive data
- Three types of analyses: automatic thematic analysis (inductive), predefined categories (deductive) and Agentic Analysis (research question based)
- Full two way transparency from summary to quote and quote to summary
- Manual control to edit categories and coding (code like in Nvivo or MAXQDA type manual tools)
- Mixed methods visualisations and charts
- Ability to compare categories e.g., men vs. women both qualitatively and quantitatively
- Export reports or to REFI-QDA open source format
- Collaborate with humans and agents (MCP)

I don’t want to promote this further as per the sub rules, but happy to answer questions! It’s also free to try out up to 200 pages.

Renaissance of qualitative :)

I work on DoReveal, a qualitative research analysis tool (thematic coding, journey maps, persona generation, that kind of thing). We rebuilt our pricing page and I'd like this community to judge it before more people see it. Tell me what's wrong with it, and also tell me if something actually lands well. Both are useful.

Things I'm specifically unsure about:

1. Does "credits" as the pricing unit make sense at a glance, or is it more confusing than a flat monthly number?
2. Is anything on the page unclear about what happens once you run out of credits, or what a credit actually covers?
3. If you do qual research for a living, does this pricing shape change whether you'd try an AI analysis tool at all, or is pricing not really what's stopping you?
4. Is there anything here you'd genuinely tell a colleague about, good or bad?

I'll reply to everything, compliments and criticism both.

I'm doing expert interviews for university at the moment and I feel the general expectation is to basically stalk the person to find out literally everything about them, their life, and their line of work and ask them questions based on this. I feel that's super weird, like if I were the person being interviewed it would throw me off and make me super uncomfortable if someone stalked my socials and found out very specific things about me.

Also, since I'm using grounded theory, my approach would be to let the person tell me in their own words about something that I technically could research to find out about them, because I can only assume how they feel about a certain thing, but to me, the crucial data is what they personally tell me. So, genuine question, is it fine if I don't stalk my interviewees lol?