Recruitment for qualitative research

Recuriting qualitative participants


You’ll find a lot of information and debate about sampling issues in qualitative research: discussions over ‘random’ or ‘purposeful’ sampling, the merits and pitfalls of ubiquitous ‘snowball’ sampling, and unending questions about sample size and saturation. I’m actually going to address most of these in the next blog post, but wanted to paradoxically start by looking at recruitment. What’s the difference, and why think about recruitment strategies before sampling?


Well, I’d argue that the two have to be considered together, but recruitment tends to be a bit of an afterthought and is so rarely detailed in journal articles (Arcury and Quandt 1999) I feel it merits its own post. In fact, there is a great ONS document about sampling, but it only has one sentence on advice for respondent recruitment: “The method of respondent recruitment and its effectiveness is also an important part of the sampling strategy”. Indeed!


When we talk about recruitment, we are considering the way we actually go out and ask people to take part in a research study. The sample frame is how we choose what groups of people and how many to approach, but there are huge practical problems in implementing our chosen sampling method that can be dealt with by writing a comprehensive recruitment strategy.


This might sound a bit dull, but it’s actually kind of fun – and the creation of such a strategy for your qualitative research project is a really good thought exercise, helping you plan and later acknowledge shortcomings in what actually happened. Essentially, think of this process as how you will market and advertise your research project to potential participants.


Sometimes there is a shifting dynamic between sampling and recruitment. Say we are doing random sampling from numbers in a phone book, a classic ‘random’ technique. The sampling process is the selection of x number of phone numbers to call. The recruitment is the actually calling and asking someone to take part in the research. Now, obviously not everyone is going to answer the phone, or want to answer any questions. So you then have a list of recruited people, which you might actually want to sample from again to make a representative sample. If you found out everyone that answered the phone was retired and over 60, but you wanted a wider age profile, you will need to refactor from your recruited sample.


But let’s think about this again. Why could it be that everyone who consented to take part in our study was retired? Well, we used numbers from the phone book, and called during the day. What effect might this have? Numbers in the phone book tend to be people who have been resident in one place for a long time, many students and young people just have mobiles, and if we call during the day, we will not get answers from most people who work. This illustrates the importance of carefully considering the recruitment strategy: although we chose a good random sampling technique, our strategy of making phone calls during the day has already scuppered our plans.


How about another example: recruitment through a poster advertising the study. Many qualitative studies aren’t looking for very large number of respondents, but are targeting a very specific sample. In this example, maybe it’s people who have visited their doctor in the last 6 months. Sounds like a poster in the waiting room of the local GP surgery would work well. What are the obvious limitations here?


simple qualitative analysis software from quirkos


First of all, people who see the poster will probably have visited the GP (since they are in that location), however, it actually only would recruit people who are currently receiving treatment. People who had been in the previous 6 months but didn’t need to go back again, or had such a horrible experience they never returned, will not see our poster and don’t have a chance to be recruited. Both of these will skew the sample of respondents in different ways.


In some ways this is inevitable. Whichever sampling technique and recruitment strategy we adopt, some people will not hear about the study or want to take part. However, it is important to be conscious of not just who is being sampled, but who is left out, and the likely effect this has on our sample and consequently our findings. For example our approach here probably means we oversample people who have chronic conditions requiring frequent treatment, and undersample people who hate their doctor. It’s not necessarily a disaster, but just like making a reflexivity statement about our own biases, we must be forthright about the sampling limitations and consider them when analysing and writing conclusions.


For these reasons, it’s often desirable to have multiple and complementary recruitment strategies, so that one makes up for deficiencies in the other. So a poster in the waiting room is great, but maybe we can get a list of everyone registered at the surgery, so we can also contact people not currently seeking treatment. This would be wonderful, but in the real world, we might hit problems with the surgery not being interested in the study, not able to release that information for confidentiality reasons, and the huge extra time such a process would require.


That’s why I see a recruitment strategy as a practical battle plan that tries to consider the limitations and realities of engaging with the real world. You can also start considering seemingly small things that can have a huge impact on successful recruitment:

• The design of the poster
• The wording of invitation letters
• The time of day you make contact (not just by phone, but don’t e-mail first thing on a Monday morning!)
• Any incentives, and how appropriate they are
• Data protection issues
• Winning the support of ‘gatekeepers’ who control access to your sample
• Timescales
• Cost (especially if you are printing hundreds of letters of flyers)
• Time and effort required to find each respondent
• And many more…

For a more detailed discussion, there’s a great article by Newington and Metcalfe (2014) specifically on influencing factors for recruitment in qualitative research.


Finally, I want to reiterate the importance of trying to record who has not been recruited and why. If you are directly contacting a few dozen respondents by phone or e-mail, this is easy to keep track of: you know exactly who has declined or not responded, likely reasons why and probably some demographic details.


However, think about the poster example. Here, we will be lucky if 1% of people that come through the surgery contact us to take part in the study. Think through these classic marketing stages: they have to see the poster, think it’s relevant to them, want to engage, and then reach out to contact you. There will be huge losses at each of those stages, and you don’t know who these people are or why they didn’t take part. This makes it very difficult in this kind of study to know the bias of your final sample: we can guess (busy people, those who aren’t interested in research) but we don’t know for sure.


Response rates vary greatly by method: by post 25% is really good, direct contact much higher, posters and flyers below 10%. However, you can improve these rates with careful planning, by considering carefully who will engage and why, and making it a good prospect to take part: describe the aims of the research, compensate time, and explain the proposed benefits. But you also need to take an ethical approach, don’t coerce, and make promises you can’t keep. Check out the recruitment guidelines drawn up by the Association for Qualitative Research.


My personal experience tells me that most people who engage with qualitative research are lovely! They want to help if they can, and love an opportunity to talk about themselves and have their voice heard. Just be aware of what kinds of people end up being your respondents, and make sure you acknowledge the possibility of hidden voices from people who don’t engage for their own reasons.


Once you get to your analysis, don't forget to try Quirkos for free, and see how our easy-to-use software can make a real qualitative difference to your research project! To keep up to date with new blog articles on this, and other qualitative research topics, follow our Twitter feed:



Starting out in Qualitative Analysis

Qualitative analysis 101


When people are doing their first qualitative analysis project using software, it’s difficult to know where to begin. I get a lot of e-mails from people who want some advice in planning out what they will actually DO in the software, and how that will help them. I am happy to help out individually, because everyone’s project is different. However, here are a few pointers which cover the basics and can help demystify the process. These should actually apply to any software, not just Quirkos!


First off: what are you going to be able to do? In a nutshell, you will read through the sources, and for each section that is interesting to you and about a certain topic, you will ‘code’ or ‘tag’ that section of text to that topic. By doing this, the software lets you quickly see all the sections of text, the ‘quotes’ about that topic, across all of your sources. So you can see everything everyone said about ‘Politics’ or ‘Negative’ – or both.


You can then look for trends or outliers in the project, by looking at just responses with a particular characteristic like gender. You’ll also be able to search for a keyword, and generate a report with all your coded sections brought together. When you come to write up your qualitative project, the software can help you find quotes on a particular topic, visualise the data or show sub-section analysis.  


So here are the basic steps:


1.       Bring in your sources.
I’m assuming at this stage that you have the qualitative data you want to work with already. This could be any source of text on your computer. If you can copy and paste it, you can bring it into Quirkos. For this example let’s assume that you have transcripts from interviews: this means that you have already done a series of interviews, transcribed them, and have them in a file (say a Word document or raw text file). I’d suggest that before you bring them in, just have a quick look through and correct them in a Word Processor for typos and misheard words. While you can edit the text in Quirkos later, while using a Word or equivalent you have the advantage of spell checkers and grammar checkers.


Now, create a new, unstructured project in Quirkos, and save it somewhere locally on your computer. We don’t recommend you save directly to a network location, or USB stick, as if either of these go down, you will have a problem! Next, bring in the sources using the (+) Add Source button on the bottom right. You can bring in each file one at a time, or a whole folder of files in one go, in which case the file name will become the default source name. Don’t forget, you can always add more sources later, there is no need to bring in everything before you start coding. Now your project file (a little .qrk file you named) will contain all the text sources in one place. With Quirkos files, just backing up and copying this file saves the whole project.


2.       Describe your sources
It’s usually a good idea to describe some characteristics of your qualitative sources that you might use later to look for differences or similarities in the data. Often these are basic demographic characteristics like age or gender, but can also be things about the interview, such as the location, or your own notes.


To do this in Quirkos, click on the little grid button on the top right of the screen, and use the source properties. The first thing you can do here is change the name of the sources from the default (either a sequential number like ‘Source 7’ or the file name. You can create a property with the square [+] ‘Quickly add a new property’ button. The property (eg Gender) and a single value (eg Male) can be added here. The drop down arrow next to that property can be used later to add extra values.


The reason for doing this is that you can later run ‘queries’ which show results from just certain sources that have properties you defined. So you can do a side-by-side comparison of coded responses from men next to women. Don’t forget, you can add properties at any time, so you can even create a variable for ‘these people don’t fit the theory’ after you’ve coded, and try and see what they are saying that makes them different.



3.       Create your themes
Whatever you call them: themes, nodes, bubbles, topics or Quirks, these are the categories of interest you want to collect quotes about from the text. There are two approaches here, you can try and create all the categories you will use before you start reading and coding the text (this is sometimes called a framework approach), or you can add themes as you go (grounded theory). (For much much more on these approaches, look here and here.)


In Quirkos, you create themes as coloured bubbles, which grow in size the more text is added. Just click on the grey (+) button on the top right of the canvas view to add a new theme. You can also change the name, colour, level in this dialogue, or right click on the bubble and select ‘Quirk Properties’ at any time. To group, just drag and drop bubbles on top of each other.



4.       Do your coding
Essentially, the coding process involves finding every time someone said something about ‘Dieting’ and adding that sentence or paragraph to the ‘Dieting’ bubble or node. This is what is going to take the most time in your analysis (days or weeks) and is still a manual process. It’s best to read through each source in turn, and code it as you go.


However, you can also use the keyword search to look for words like ‘Diet’ or ‘eating’ and code from the results. This makes it quicker, but there is the risk of missing segments that use a keyword you didn’t think to search for like ‘cut-down’. The keywords search can help when you (inevitably) decide to add a new topic halfway through, and the first few interviews haven’t been coded for the new themes. You can use the search to look for related terms and find those new segments without having to go over the whole text again.



5.       Be iterative
Even if you are not using a grounded theory approach, going back over the data a second time, and rethinking codes and how you have categorised things can be really useful. Trust me: even if you know the data pretty well, after reading it all again, you will see some topics in a slightly different light, or will find interesting things you never thought would be there.


You may also want to rearrange your codes, especially if you have grouped them. Maybe the name you gave a theme isn’t quite right now: it’s grown or got more specific. Some vague codes like ‘Angry’ might need to be split out into ‘Irate’ and ‘Annoyed’. Depending on your approach, you  will probably constantly tweak and adjust the themes and coding so they best represent the intersection of your research questions and data.



6.       Explore the data.
Once your qualitative data is all coded, the big advantages of using CAQDAS software come into play. Using the database of your tagged text, you can choose to look at it in anyway: using any of the source properties, who did the coding or when, or whether a result comes from any particular group of codes. This is done using the 'Query' views in Quirkos.


In Quirkos there are also a lot of visualisation options that can show you the overall shape and structure of the project, the amount of coding, and connections that are emerging between the sources. You can then use these to help write your outputs, be they journal articles, evaluations or a thesis. Software will generate reports that let you share summaries of the coded data, and include key statistics and overviews of the project.

While it does seem like a lot of work to get to this stage, it can save so much time at the final stages of writing up your project, when you can call up a useful quote quickly. It also can help in the future to have this structured repository of qualitative data, so that secondary analysis or adding to the dataset does not involve re-inventing the wheel!


Finally, there is no one-size-fits-all approach, and it's important to find a strategy that fits with your way of working. Before you set out, talk to peers and supervisors, read guides and textbooks, and even go on training courses. While the software can help, it's not a replacement for considered thinking, and you should always have a good idea about what you want to do with the data in the end.



Tips and advice from one year of Quirkos

birthday cake CC by theresathompson


This week marks the one-year anniversary of Quirkos being released to the market! On 6th October 2014, a group of qualitative researchers, academics and business mentors met in a bar in Edinburgh, and at 8pm, version 1.0 of Quirkos was launched to the world. We then drank the bar dry of Prosecco (Champagne being much too expensive). Now Quirkos is being used in more than 30 universities across the world, and it's so exciting to see how people have used it for their PhDs, or in major research projects.


Obviously, the story didn't begin on that October night. It was the cumulation of nearly 2 years of planning, testing and development, not just of software, but of the skills and networks of many people behind the scenes. This blog post is mostly intended to share some of the things that went wrong, what went right, and to provide encouragement to those starting down the road to their own business for the first time.


There are frightening statistics about how many start-ups fail in the first few years. Some say 20% in the first year, others as high as 50% in the first two years. Whomever you believe, the rates are high, and this has to be expected. It's a competitive world out there and the cost of starting a business is high, nearly always higher than people anticipate (Quirkos included in this).


Now, 3 years after I quit my job to work full time on Quirkos, it feels like we have beaten the odds (so far). I also know many start-ups that didn't make it, many colleagues in Edinburgh who were embarking on their own adventures, who saw their business fade during that time. But it's interesting that all those people have still done well individually. Whatever gave them that entrepreneurial desire has led them all to new and different things, just maybe not what they originally planned!


At the risk of adding to the 3752 (est) other lists of start-up advice on the internet, here are some numbered pointers:

1. Don't develop qualitative research software
This is annoyingly specific advice, but has become a bit of a running joke for me. If you want to get poor quickly, qualitative analysis software is ideal for you, there is not a lot of money in it. It's a niche, and a very un-sexy one at the moment too. People always suggest that Quirkos should branch out into 'big-data' or add more quantitative features, but this is not really what I want to do.


Qualitative research is what I am passionate about, and what I know best. I didn't start this project to make a fortune, but because I felt software was holding people back from better understanding the world, and that was a gap in the market (my pain point).


2. Advice is free, but guidance is invaluable
Fortunately, it's really easy to get advice and support. Government initiatives (at least in Scotland and the UK) provide lots of basic training workshops and materials for free. We've benefited from advice from Business Gateway and Scottish Enterprise and their partners on strategy, funding, IP, you name it. However, these people won't tell you what to do, and obviously don't have very specific knowledge for your industry.


That is where our great mentors have come in, with knowledge from our specific area (software) and in working with our main markets (public sector and academic). This allowed us to plan a lot better, and make much more realistic projections about things like conversion rates, lead times, and even cultural differences selling abroad.



3. Awareness is everything
Insulting though it may be, people don't go out looking for your product. They are looking for a solution to that problem, and at first they don't know your name is Quirkos. They search for 'qualitative analysis software' in Google, go to qualitative research conferences, and read journals on all manner of related disciplines. It is never enough to 'build it and they will come' – you have to go to where your potential users will be.


Awareness is just the first step, then you need people to believe that you can help them. That can only be done by yourself to a certain extent, word of mouth and recommendations are much more important than corporate-sponsored hearsay. That's why I think that quality and customer happiness are so important, because people don't really believe what they read in adverts (I know I don't).



4. You ultimately invest in yourself
The last few years have been a roller-coaster, but I have learned so much. I learnt about running a business, accounting, tax, sales, marketing, search engine optimisation, PHP, Javascript, SSL certification, social media, software testing, promotional printing, exhibitions, conferences, planning, strategy, and on and on.


I've always thought at the back of my mind, what if this fails? Is all that time and money lost? Well, yes, but through the experience I have learnt so much, and developed real skills in real situations. I can't say I was worried about finding employment afterwards, since I was always adding so much to my CV.



5. Getting funding costs money and time
However you want money: grants, competitions, loans, equity investment, all these things are very expensive in terms of time and paperwork. We spent a long time going to the final (term-sheet) stages of angel investment, before deciding that the timing wasn't right, and the costs of the transaction were going to be too high.


So you need to pick your battles carefully, but plan for redundancy. Assume that only 1 out of 3 sources of funding will come through, so always have a back-up ready to pursue. For Quirkos, a friends and family round worked really well for our first funding cycle, and allows us flexibility in the future.


6. Critical Path Analysis
I'm a little obsessed by this, but I have to admit, I actually learnt it from a children's book decades ago. It was 'Truckers' by Terry Pratchett, and it describes it thus: “It’s something called critical path analysis. It means there’s always something you should have done first. For example, if you want to build a house you need to know how to make bricks, and before you can make bricks you need to know what kind of clay to use. And so on”.


I actually do this in my head all the time now, whether I am doing a marketing strategy, releasing software or even making dinner. It just means working backwards from what you want to achieve, and working out the things that will hold you back if you don't get them done first. For example, if I am having flyers handed out at a conference, they need them 3 days before the conference. To get there, they have to be in the post 5 days before that. They take 3 days to come back from the printer, take me 1 day to design, and my colleague who has agreed to proof read them is on holiday next week. So quickly I can see that the last day I can do the first draft of the flyer is 17 days before the conference!


In a small business you end up doing everything, so being able to plan your time like this is essential. With experience, you also learn that the uncertain part in the chain is always when you have to rely on other people (who can be late, sick, forgetful) so you always factor in more time for the post, printers, and proof readers. Not that you won't be late, sick or forgetful yourself sometimes, but generally you know when this is happening. It's no coincidence that most of the 'Truckers' book is actually about managing people (seriously, it's the best management book I've ever read).


7. Network, network, network
Actually, I hate netwo rking, or at least that kind of endless socialising in large groups without direct purpose . But very targeted networking is essential to getting the word out, and cultivating positive relationships with key people and organisations is essential. A case-study or positive review is always more valuable than just a quick sale, and there are always influential people in any industry who have a large audience. Engaging with these networks is essential.



8. Love, love, love
I couldn't have done this on my own. Over the years so many people have given time, support, money and advice, and I can't thank them all here. If I was thinking in terms of social capital (Putnam style) I would have used up a lifetime of favours and goodwill. To be honest, I don't think I could have got this far without them, and the  love and belief from friends, family, colleagues and spouse. So I'm going to finish on a song, and say “Thank You!”


  “When you were giving me advice, that I seldom ever took
  But your head never shook - That's love


  Both knowing you were right, never shook it left and right
  Just gave me that look - That's love


  When I had to learn the hard way, and you would let me fall
  But never did it out of spite - That's love


  You told me never burn a bridge
  If you build it, then you need it
  Whether a river or a brook”


That's Love – Oddisee (from the album The Good Fight)
Performed live here from the awesome NPR Tiny Desk series!



6 meta-categories for qualitative coding and analysis

rating for qualitative codes

When doing analysis and coding in a qualitative research project, it is easy to become completely focused on the thematic framework, and deciding what a section of text is about. However, qualitative analysis software is a useful tool for organising more than just the topics in the text, they can also be used for deeper contextual and meta-level analysis of the coding and data.

Because you can pretty much record and categorise anything you can think of, and assign multiple codes to one section of text, it often helps to have codes about the analysis that help with managing quotes later, and assisting in deeper conceptual issues. So some coders use some sort of ranking system so they can find the best quotes quickly. Or you can have a category for quotes that challenge your research questions, or seem to contradict other sources or findings. Here are 6 suggestions for these meta-level codes you could create in your qualitative project (be it Quirkos, Nvivo, Atlas-ti or anything!):



I always have a node I call ‘Key Quotes’ where I keep track of the best verbatim snippets from the text or interview. It’s for the excited feeling you get when someone you interviewed sums up a problem or your research question in exactly the right way, and you know that you are going to end up using that quote in an article. Or even for the title of the article!

However, another way you can manage quotes is to give them a ranking scheme. This was suggested to me by a PhD student at Edinburgh, who gives quotes a ranking from 1-5, with each ‘star-rating’ as a separate code. That way, it’s easy to cross reference, and find all the best quotes on a particular topic. If there aren’t any 5* quotes, you can work down to look at the 4 star, or 3 star quotes. It’s a quick way to find the ‘best’ content, or show who is saying the best stuff. Obviously, you can do this with as little or much detail as you like, ranking from 1-10 or just having ‘Good’ and ‘Bad’ quotes.

Now, this might sound like a laborious process, effectively adding another layer of coding. However, once you are in the habit, it really takes very little extra time and can make writing up a lot quicker (especially with large projects). By using the keyboard shortcuts in Quirkos, it will only take a second more. Just assign the keyboard numbers 1-5 to the appropriate ranking code, and because Quirkos keeps the highlighted section of text active after coding, you can quickly add to multiple categories. Drag and drop onto your themes, and hit a number on the keyboard to rank it. Done!



It is sometimes useful to record in one place the contradictions in the project – this might be within the source, where one person contradicts themselves, or if a statement contradicts something said by another respondent. You could even have a separate code for each type of contradiction. Keeping track of these can not only help you see difficult sections of data you might want to review again, but also show when people are being unsure or even deceptive in their answers on a difficult subject. The overlap view in Quirkos could quickly show you what topics people were contradicting themselves about – maybe a particular event, or difficult subject, and the query views can show you if particular people were contradicting themselves more than others.



In qualitative interview data where people are talking in an informal way about their stories and lives, people often say things where the meaning isn’t clear – especially to an external party. By collating ambiguous statements, the researcher has the ability to go back at the end of the source and see if each meaning is any clearer, or just flag quotes that might be useful, but might be at risk of being misinterpreted by the coder.



Slightly different from ambiguities: these are occasions when the meaning is clear enough, but the coder is not 100% sure that it belongs in a particular category. This often happens during a grounded theory process where one category might be too vague and needs to be split into multiple codes, or when a code could be about two different things.

Having a not-sure category can really help the speed of the coding process. Rather than worrying about how to define a section of text, and then having sleepless nights about the accuracy of your coding, tag it as ‘Not sure’ and come back to it at the end. You might have a better idea where they all belong after you have coded some more sources, and you’ll have a record of which topics are unclear. If you are not sure about a large number of quotes assigned to the ‘feelings’ Quirk (again, shown by clustering in the overlap view in Quirkos), you might want to consider breaking them out into an ‘emotions’ and ‘opinions’ category later!



I know how tempting it can be to go through qualitative analysis as if it were a tick-box exercise, trying to find quotes that back up the research hypothesis. We’ve talked about reflexivity before in this blog, but it is easy to go through large amounts of data and pick out the bits that fit what you believe or are looking for. I think that a good defence against this tendency is to specifically look for quotes that challenge you, your assumptions or the research questions. Having a Quirk or node that logs all of these challenges lets you make sure you are catching them (and not glossing over them) and secondly provides a way to do a validity assessment at the end of coding: Do these quotes suggest your hypothesis is wrong? Can you find a reason that these quotes or individuals don’t fit your theory? Usually these are the most revealing parts of qualitative research.


Actually, I don’t know a neat way to capture the essence of something that isn’t in the data, but I think it’s an important consideration in the analysis process. With sensitive topics, it is sometimes clear to the researcher that an important issue is being actively avoided, especially if an answer seems to evade the question. These can be at least coded as absences at the interviewer’s question. However, if people are not discussing something that was expected as part of the research question, or was an issue for some people but not others, it is important to record and acknowledge this. Absence of relevant themes is usually best recorded in memos for that source, rather than trying to code non-existent text!



These are just a few suggestions, if you have any other tips you’d like to share, do send them to or start a discussion in the forum. As always, good luck with your coding!


10 tips for semi-structured qualitative interviewing

Many qualitative researchers spend a lot of time interviewing participants, so here are some quick tips to make interviews go as smooth as possible: before, during and after!


1. Let your participants choose the location

If you want your interviewees to be comfortable in sharing sometimes personal or sensitive information, make sure they can do it in a comfortable location. For some people, this might be their own house, or a neutral territory like a local cafe. Giving them the choice can help build trust, and gives the right impression: that you are accomodating them. However, make sure you make it clear that you need a relatively quiet location free from interruptions: a pub that plays loud music will not only stop you hearing each other, but usually makes recordings unusable!


2. Remember that they are helping you

Be polite and curtious, and be grateful to them for sharing their time and experiences. This always gets interviews off on the right foot. Also, try and think about participants motivations for taking part. Do they want the research to help others? Are they looking for a theraputic discussion? Do they just like a chat? Understanding this will help you guide the interview, and make sure you meet their expectations.


3. A conversation, not an interregation!

Interviews work best when they are a friendly dialogue: don't be afraid to start with some small talk, even when the tape is running. It turns a weird situation into a much more normal human experience, and starting with some easy 'starter for 10' questions helps people open up. Even a chatty "How did you hear about the project?" can gives you useful information.


4. Memorise the topic guide, but keep it to hand

Knowing all the questions in the topic guide can really help, so group them thematically, and memorise them as much as you can. It will really help the flow of information if you can segue seamlessly from one question to another relevant one. However, it's always useful to keep a print-out in front of you, not just for if you forget something, but also to make you seem more human, with a specific role. Joking about remembering all the questions is a great icebreaker, and it gives you something to look at other than the participant, to stop the session turning into a staring match!


5. Use open body language and encouraging cues

Face the participant in a friendly way, and nod or look sympathetic at the right times. Sometimes it's tempting for the interviewer to keep quiet during the responses, and not put in any normal encouraging noises like "Yeah", "Hmm" or "Right" knowing how odd these read in a transcript. But these are important cues that people use to know when to keep talking, so if you are going to drop them, make sure you make positive eye contact, and nod at the right times instead!


Quirkos - simple qualitative analysis software


6. Write notes, even if you don't use them

It always helps me to scribble down some one-word notes on the topic guide when you are doing an interview: first of all it helps focus my thoughts, and remind me about interesting things that the participant mentioned that I want to go back to. But it also helps show you are listening, and makes sure if the recording goes wrong, there is something to fall back on.


7. Write-up the interivew as soon as you finish

Just take 15 minutes after each interview to reflect: the main points that came up, how open the respondent was, any context or distractions that might have impared the flow. This helps you think about things to do better in the next interview, and will help you later to remember each interview.


8. Return to difficult issues

If a particular topic is clearly a difficult question (either emotionally, or just because someone can't remember) don't be afraid to leave the topic and come back to it later, asking in a different way. It can really help recall to have a break talking about something easier, and then approach the issue sideways later on.


9. Ask stupid questions

Don't assume you know anything. In these kinds of interviews, it's usually not about getting the right answer, but getting the respondent's view or opinion. Asking 'What do you mean by family?' is really useful if you discover someone has adopted children, step-sisters and a beloved family dog that all share the house. Don't make any assumptions, let people tell you what they mean. Even if you have to ask something that makes you sound ignorant on a specialist subject, you could discover that someone didn't know the difference between their chemotherapy and radiotherapy.


10. Say thank you

And follow up: send a nice card after the interview, don't be like a date they never hear from again! Also, try and make sure they get a summary of the findings of the study they took part in. It's not just about being nice, but to make sure people have a good experience as a research subject, and will want to be involved in the next project that comes along, which might be yours or mine!


I hope these tips have been hopeful, don't forget Qurikos makes your transcribed interviews easy to analyse, as well as a visual and engaging process. Find out more and download a free trial from our website. Our blog is updated with articles like this every week, and you can hear about it first by following our Twitter feed @quirkossoftware.