Qualitative evidence for SANDS Lothians

qualitative charity research - image by cchana

Charities and third sector organisations are often sitting on lots of very useful qualitative evidence, and I have already written a short blot post article on some common sources of data that can support funding applications, evaluations and impact assessments. We wanted to do a ‘qualitative case study’: to work with one local charity to explore what qualitative evidence they already had, what they could collect, and use Quirkos to help create some reports and impact assessments.

 

SANDS Lothians is an Edinburgh based charity that provides long-term counselling and support for families who have experienced bereavement through the loss of a child near-birth. They approached us after seeing advertisements for one of our local qualitative training workshops.


Director Nicola Welsh takes up the story. “During my first six months in post, I could see there was much evidence to highlight the value of our work but was struggling to pull this together in some order which was presentable to others. Through working with Daniel and Kristin we were able to start to structure what we were looking to highlight and with their help begin to organise our information so it was available to share with others. Quirkos allowed us to pull information from service users, stats and studies to present this in a professional document. They gave us the confidence to ask our users about their experiences and encouraged us to record all the services we offered to allow others at a glance to get a feel for what we provide.”

 

First of all, we discussed what would be most useful to the organisation. Since they were in discussion with major partners about possible funding, an impact assessment would be valuable in this process.

 

They also identified concerns from their users about a specific issue, prescriptions for anti-depressants, and wanted to investigate this further. It was important to identify the audience that SANDS Lothians wanted to reach with this information, in this case, GPs and other health professionals. This set the format of a possible output: a short briefing paper on different types of support that parents experiencing bereavement could be referred to.

 

We started by doing an ‘evidence assessment’ (or evidence audit as this previous blog post article notes), looking for evidence on impact that SANDS Lothians already had. Some of this was quantitative, such as the number of phone calls received on a monthly basis. As they had recently started counting these calls, it was valuable evidence of people using their support and guidance services. In the future they will be able to see trends in the data, such as an increase in demand or seasonal variation that will help them plan better.

 

They already had national reports from NHS Scotland on Infant Mortality, and some data from the local health board. But we quickly identified a need for supportive scientific literature that would help them make a better case for extending their counselling services. One partner had expressed concerns that counselling was ineffective, but we found a number of studies that showed counselling to be beneficial for this kind of bereavement. Finding these journal articles for them helped provide legitimacy to the approach detailed in the impact assessment.

 

In fact, a simple step was to create a list of all the different services that SANDS Lothians provides. This had not been done before, but quickly showed how many different kinds of support were offered, and the diversity of their work. This is also powerful information for potential funders or partners, and useful to be able to present quickly.

 

Finally, we did a mini qualitative research project!

 

A post on their Facebook page asking for people to share experiences about being prescribed antidepressants after bereavement got more than 20 responses. While most of these were very short, they did give us valuable and interesting information: for example, not all people who had been suggested anti-depressants by their GP saw this as negative, and some talked about how these had helped them at a difficult time.

 

SANDS Lothians already had amazing and detailed written testimonials and stories from service users, so I was able to combine the responses from testimonials and comments from the Facebook feed into one Quirkos project, and draw across them all as needed.

 

Using Quirkos to pull out the different responses to anti-depressants showed that there were similar numbers of positive and negative responses, and also highlighted parent’s worries we had not considered, such as the effect of medication if trying to conceive again. This is the power of an qualitative approach: by asking open questions, we got a responses about issues we wouldn’t have asked about in a direct survey.

 

quirkos bubble cluster view

 

When writing up the report, Quirkos made it quick and easy to pull out supportive quotes. As I had previously gone through and coded the text, I could click on the counselling bubble, immediately see relevant comments, and copy and paste them into the report. Now SANDS Lothians also has an organised database of comments on how their counselling services helped clients, which they can draw on at any time.

 

Nicola explains how they have used the research outputs. “The impact assessment and white paper has been extremely valuable to our work. This has been shared with senior NHS Lothian staff regarding possible future partnership working.  I have also shared this information with the Scottish Government following the Bonomy recommendations. The recommendations highlight the need for clear pathways with outside charities who are able to assist bereaved parents. I was able to forward our papers to show our current support and illustrate the position Lothians are in regarding the opportunity to have excellent bereavement care following the loss of a baby. It strengthened the work we do and the testimonials give real evidence of the need for this care. 

 

I have also given our papers out at recent talks with community midwives and charge midwives in West Lothian and Royal Infirmary Edinburgh. Cecilia has attached the papers to grant applications which again strengthens our applications and validates our work.”

 

Most importantly, SANDS Lothians now have a framework to keep collecting data, “We will continue to record all data and update our papers for 2016.  Following our work with Quirkos, we will start to collate case studies which gives real evidence for our work and the experiences of parents.  Our next step would be to look specifically at our counselling service and its value.” 

 

“The work with Quirkos was extremely helpful. In very small charities, it is difficult to always have the skills to be an expert in all areas and find the time to train. We are extremely grateful to Daniel and Kristin who generously volunteered their time to assist us to produce this work. I would highly recommend them to any business or third sector organisation who need assistance in producing qualitative research.  We have gained confidence as a charity from our journey with Quirkos and would most definitely consider working with them again in the future.”

 

It was an incredible and emotional experience to work with Nicola and Cecilia at SANDS Lothians on this small project, and I am so grateful to for them for inviting us in to help them, and sharing so much. If you want any more information about the services they offer, or need to speak to someone about losing a baby through stillbirth, miscarriage or soon after birth, all their contact details are available on their website: http://www.sands-lothians.org.uk .

 

If you want any more information about Quirkos and a qualitative approach, feel free to contact us directly, or there is much more information on our website. Download a free trial, or read more about adopting a qualitative approach.

 

 

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: twitter.com/quirkossoftware.

 

 

Designing a semi-structured interview guide for qualitative interviews

clipboard by wikidave https://www.flickr.com/photos/wikidave/7386337594

 

Interviews are a frequently used research method in qualitative studies. You will see dozens of papers that state something like “We conducted n in-depth semi-structured interviews with key informants”. But what exactly does this mean? What exactly counts as in-depth? How structured are semi-structured interviews?

 

This post is hosted by Quirkos, simple and affordable software for qualitative analysis.
Download a 1 month free trial!

 

The term “in-depth” is defined fairly vaguely in the literature: it generally means a one-to-one interview on one general topic, which is covered in detail. Usually these qualitative interviews last about an hour, although sometimes much longer. It sounds like two people having a discussion, but there are differences in the power dynamics, and end goal: for the classic sociologist Burgess (2002) these are “conversations with a purpose”.

 

Qualitative interviews generally differ from quantitative survey based questions in that they are looking for a more detailed and nuanced response. They also acknowledge there is no ‘one-size fits all’, especially when asking someone to recall a personal narrative about their experiences. Instead of a fixed “research protocol” that asks the same question to each respondent, most interviewees adopt a more flexible approach. However there is still a need “...to ensure that the same general areas of information are collected from each interviewee; this provides more focus than the conversational approach, but still allows a degree of freedom and adaptability in getting information from the interviewee” –MacNamara (2009).

 

Turner (2010) (who coincidentally shares the same name as me) describes three different types of qualitative interview; Informal Conversation, General Interview Guide, and Standardised Open-Ended. These can be seen as a scale from least to most structured, and we are going to focus on the ‘interview guide’ approach, which takes a middle ground.

 

An interview guide is like a cheat-sheet for the interviewer – it contains a list of questions and topic areas that should be covered in the interview. However, these are not to be read verbatim and in order, in fact they are more like an aide-mémoire. “Usually the interviewer will have a prepared set of questions but these are only used as a guide, and departures from the guidelines are not seen as a problem but are often encouraged” – Silverman (2013). That way, the interviewer can add extra questions about an unexpected but relevant area that emerges, and sections that don’t apply to the participant can be negated.

 

So what do these look like, and how does one go about writing a suitable semi-structured interview guide? Unfortunately, it is rare in journal articles for researchers to share the interview guide, and it’s difficult to find good examples on the internet. Basically they look like a list of short questions and follow-on prompts, grouped by topic. There will generally be about a dozen. I’ve written my fair share of interview guides for qualitative research projects over the years, either on my own or with the collaboration of colleagues, so I’m happy to share some tips.

 


Questions should answer your research questions
Your research project should have one or several main research questions, and these should be used to guide the topics covered in the interviews, and hopefully answer the research questions. However, you can’t just ask your respondents “Can the experience of male My Little Pony fans be described through the lens of Derridean deconstruction?”. You will need to break down your research into questions that have meaning for the participant and that they can engage with. The questions should be fairly informal and jargon free (unless that person is an expert in that field of jargon), open ended - so they can’t be easily answered with a yes or no, and non-leading so that respondents aren’t pushed down a certain interpretation.

 

 

Link to your proposed analytical approach
The questions on your guide should also be constructed in such a way that they will work well for your proposed method of analysis – which again you should already have decided. If you are doing narrative analysis, questions should be encouraging respondents to tell their story and history. In Interpretative Phenomenological Analysis you may want to ask more detail about people’s interpretations of their experiences. Think how you will want to analyse, compare and write up your research, and make sure that the questioning style fits your own approach.

 

 

Specific ‘Why’ and prompt questions
It is very rare in semi-structured interviews that you will ask one question, get a response, and then move on to the next topic. Firstly you will need to provide some structure for the participant, so they are not expected (or encouraged) to recite their whole life story. But on the other level, you will usually want to probe more about specific issues or conditions. That is where the flexible approach comes in. Someone might reveal something that you are interested in, and is relevant to the research project. So ask more! It’s often useful in the guide to list a series of prompt words that remind you of more areas of detail that might be covered. For example, the question “When did you first visit the doctor?” might be annotated with optional prompts such as “Why did you go then?”, “Were you afraid?” or “Did anyone go with you?”. Prompt words might reduce this to ‘Why THEN / afraid / with someone’.

 

 

Be flexible with order
Generally, an interview guide will be grouped into several topics, each with a few questions. One of the most difficult skills is how to segue from one topic or question to the next, while still seeming like a normal conversation. The best way to manage this is to make sure that you are always listening to the interviewee, and thinking at the same time about how what they are saying links to other discussion topics. If someone starts talking about how they felt isolated visiting the doctor, and one of your topics is about their experience with their doctor, you can ask ‘Did you doctor make you feel less isolated?’. You might then be asking about topic 4, when you are only on topic 1, but you now have a logical link to ask the more general written question ‘Did you feel the doctor supported you?’. The ability to flow from topic to topic as the conversation evolves (while still covering everything on the interview guide) is tricky, and requires you to:

 

 

Know your guide backwards - literally
I almost never went into an interview without a printed copy of the interview guide in front of me, but it was kind of like Dumbo’s magic feather: it made me feel safe, but I didn’t really need it. You should know everything on your interview guide off by heart, and in any sequence. Since things will crop up in unpredictable ways, you should be comfortable asking questions in different orders to help the conversational flow. Still, it’s always good to have the interview guide in front of you; it lets you tick off questions as they are asked (so you can see what hasn’t been covered), is space to write notes, and also can be less intimidating for the interviewee, as you can look at your notes occasionally rather than staring them in the eye all the time.

 


Try for natural conversation
Legard, Keegan and Ward (2003) note that “Although a good in-depth interview will appear naturalistic, it will bear little resemblance to an everyday conversation”. You will usually find that the most honest and rich responses come from relaxed, non-combative discussions. Make the first question easy, to ease the participant into the interview, and get them used to the question-answer format. But don’t let it feel like a tennis match, where you are always asking the questions. If they ask something of you, reply! Don’t sit in silence: nod, say ‘Yes’, or ‘Of course’ every now and then, to show you are listening and empathising like a normal human being. Yet do be careful about sharing your own potentially leading opinions, and making the discussion about yourself.

 

 

Discuss with your research team / supervisors
You should take the time to get feedback and suggestions from peers, be they other people on your research project, or your PhD supervisors. This means preparing the interview guide well in advance of your first interview, leaving time for discussion and revisions. Seasoned interviewers will have tips about wording and structuring questions, and even the most experienced researcher can benefit from a second opinion. Getting it right at this stage is very important, it’s no good discovering after you’ve done all your interviews that you didn’t ask about something important.

 

 

Adapting the guide
While these are semi-structured interviews, in general you will usually want to cover the same general areas every time you do an interview, no least so that there is some point of comparison. It’s also common to do a first few interviews and realise that you are not asking about a critical area, or that some new potential insight is emerging (especially if you are taking a grounded theory approach). In qualitative research, this need not be a disaster (if this flexibility is methodologically appropriate), and it is possible to revise your interview guide. However, if you do end up making significant revisions, make sure you keep both versions, and a note of which respondents were interviewed with each version of the guide.

 

 

Test the timing
Inevitably, you will not have exactly the same amount of time for each interview, and respondents will differ in how fast they talk and how often they go off-topic! Make sure you have enough questions to get the detail you need, but also have ‘lower priority’ questions you can drop if things are taking too long. Test the timing of your interview guide with a few participants, or even friends before you settle on it, and revise as necessary. Try and get your interview guide down to one side of paper at the most: it is a prompt, not an encyclopaedia!

 


Hopefully these points will help demystify qualitative interview guides, and help you craft a useful tool to shape your semi-structured interviews. I’d also caution that semi-structured interviewing is a very difficult process, and benefits majorly from practice. I have been with many new researchers who tend to fall back on the interview guide too much, and read it verbatim. This generally leads to closed-off responses, and missed opportunities to further explore interesting revelations. Treat your interview guide as a guide, not a gospel, and be flexible. It’s extra hard, because you have to juggle asking questions, listening, choosing the next question, keeping the research topic in your head and making sure everything is covered – but when you do it right, you’ll get rich research data that you will actually be excited to go home and analyse.

 

 

Don’t forget to check out some of the references above, as well as the myriad of excellent articles and textbooks on qualitative interviews. There’s also Quirkos itself, software to help you make the research process engaging and visual, with a free trial to download of this innovative tool. We also have a rapidly growing series of blog post articles on qualitative interviews. These now include 10 tips for qualitative interviewing, transcribing qualitative interviews and focus groups, and how to make sure you get good recordings. Our blog is updated with articles like this every week, and you can hear about it first by following our Twitter feed @quirkossoftware.

 

 

An early spring update on Quirkos for 2016

spring snowdrops

 

About this time last year, I posted an update on Quirkos development for the next year. Even though February continues to be cold and largely snow-drop free in Scotland, why not make it a tradition?!

 

It’s really amazing how much Quirkos has grown over the last 18 months since our first release. We now have hundreds of users in more than 50 universities across the world. The best part of this is that we now get much more feedback and suggestions from qualitative researchers who are using Quirkos for different projects. Although we have always had a ‘road-map’ for developing new features for Quirkos, it’s been an aim to keep that flexible so we adapt to people’s needs.

 

We are planning a new update for Quirkos (free of course) for the end of March 2016. This version (1.4) will be a fairly major upgrade, but as ever will be released at the same time for Windows, Mac and Linux, with identical features and compatibility across all three.

 

The most significant improvement will be speed. Although v1.3 did improve this a little, it was not enough. The underlying ‘engine’ for coding and highlights was laggy and slow with large projects, and required complete rewriting from scratch. It has justifiably been the biggest source of criticism so far about Quirkos, but we hope this will now remove the last thing holding many users back. This has taken months, which is why this release is a little later than our typical quarterly updates. However, the difference so far is amazing: a near 10 fold increase in speed when loading, coding and editing sources. Although the interface will still look the same, everyone will notice the under-the-hood difference in small and large projects alike.

 

There will also be a few minor bug fixes in this release. We had reports that when moving encrypted projects between Windows and Mac, passwords were not accepted. We’ve fixed this issue, and a few others that people have reported. There are also several small improvements suggested by users that should make exploring the data easier. So please always e-mail us with bugs or suggestions, everything reported gets investigated, and we try and fix issues as soon as we can!

 

We will be sending the new version out to an international group of beta-testers at the end of February, so we are confident that everything works as intended before we make it publicly available. The best way to keep abreast of updates is to follow our Twitter feed: twitter.com/quirkossoftware which is usually updated every day.

 

Looking forward, the next release (v1.5) is due for the summer, and will add some exciting new features, probably including the second most frequently requested addition: memos! Proper note taking functionality is top of many people’s request lists, and will make it much easier to record researcher’s ponderings during the analysis process. For the meantime, check out our blog post article on how to record and code your notes in Quirkos. We also hope to add a lot more tools to help look at word-frequency in their qualitative data sets, including the ever popular word clouds!

 

In addition to all this, we will have a major new collaboration to announce in the next few months. This is going to represent a major leap forward in functionality for Quirkos, bringing some top minds into the fray to work on the next generation of qualitative analysis software.

 

So far, we have reinvested all our sales income into development, to make sure that we keep making the software better, and keep current and future users happy. Since all our updates are free, the best way to support further development is to buy a licence, and you will always benefit from work we do in the future to add new capabilities, and be able to suggest the features that will make your qualitative research easier and more fun.

 

 

Recording good audio for qualitative interviews and focus groups

 

Last week’s blog post looked at the transcription process, and what’s involved in getting qualitative interview or focus-group data transcribed. This week, we are going to step back, and share a few tips from researchers into what makes for good quality audio that will be easy to hear and transcribe.

 

1. Phones aren’t good enough
While many smartphones can now be used in a ‘voice memo’ mode to record audio, you will quickly find the quality is poor. Consider how tiny the microphone is in a phone (the size of a pin head) and that it is designed only to pick up your voice when right next to your face. Using a proper Dictaphone or voice recorder is pretty much essential to pick up the voice of interviewer and respondent(s) clearly.

 

2. Choosing a Dictaphone
Even if you want to buy one, a cheap £20 ($30) voice recorder will be a vast improvement over a phone. Most researchers won’t need one with a lot of memory: just 2GB of storage will usually record for more than 30 hours at the highest setting. There is usually little benefit in spending a lot more money, unless your ethics review board states that you need one that will securely encrypt your data as it records. These might cost closer to £250 ($400). However, you can often borrow one from your library or department.


A recorder should always be digital. There is no real advantage to a tape one – they are expensive, have less capacity, use batteries faster, are larger, and are much more prone to losing your data with erased, overwritten or mangled tape. This is one part where the advanced technology wins hands down! The format they record in doesn’t really matter, as long as your computer and transcriber can play it back. MP3 is the most compatible, note that some of the older Olympus ones use their own DSS format which is a pain to convert or play back on a computers. Digital recorders will have various settings for recording quality, you will usually want to choose the high or highest setting for clear audio. Test before you do a full interview!

 

2. Carry spare batteries!
I’ve definitely got caught out here before, make sure you have a fresh (or recharged) pair of batteries in the Dictaphone, and a spare set in your bag! Every few minutes during the interview, have a quick look to make sure the recording is still running, and before you start, check you have enough time left on the device.

 

3. Choose a quiet location if possible
While cafés can be convenient, relaxed and neutral places to meet respondents for a one-on-one, they tend to be noisy. You will pick up background music, other conversations, clattering plates and especially noisy coffee machines that make the audio difficult to transcribe. A quiet office location works much better, but if you do need to meet at a café, try and do a bit of reconnaissance first: choose one that is quiet, don’t go at lunchtime or other busy times, choose a part of the café away from the kitchen and coffee grinders, and ask them to turn off any music.

 

4. Position the Dictaphone
Usually you will want the Dictaphone to point towards the respondent, since they will be doing most of the talking. But don’t put the Dictaphone directly on a table, especially if you are having tea/coffee. You will pick up loud THUD noises every time someone puts down their mug, or taps the table with their hand. Just putting the recorder on a napkin or coaster will help isolate the sound.

 

5. Prevent stage fright!
Some people will get nervous as soon as the recording starts, and the conversation will dry up. To prevent this, you can cover the scary red recording light with a bit of tape or Blu-Tack. However, it can also help to start the recorder half-way through the casual introductions, so there isn’t a sudden ‘We’re Live!’ moment. You don’t need to transcribe all the initial banter, but it helps the conversation seamlessly shift into the research questions. Also, try and ignore the Dictaphone as much as possible, so that you both forget about it and have a natural discussion.

 

6. Watch your confirmation noises!
Speaking of natural conversation, it is rare while listening for the interviewer not to make ‘confirmation sounds’ like ‘Yes’, ‘Uh-ha’, ‘Mmm’ etc. Yet these are a pain for qualitative transcription (as most people will want to keep the researchers comments, especially for discourse analysis) and it also breaks up the flow of the transcript. Obviously, just staring silently at your participant while they talk can be disconcerting to say the least! It takes a little practice, but you can communicate and encourage the flow of the conversation just with periodic eye contact, nodding and positive body language. If someone makes a request for confirmation such as: ‘So of course that’s what I did, right?’ Rather than actually verbally responding, you can nod, turn your palms up and shrug, and roll your eyes. This way, it shows you are listening and engaging with the conversation, without constantly interrupting the flow of the narrative.

 

7. Use a boundary mic for group discussions
For focus groups or table discussions, use a cheap ‘boundary’ microphone so that it will pick up all the voices: ideally stereo ones that give some sense of direction to help identify who-said-what during transcription. Again, these don’t need to be expensive: I’ve used a cheap £20 ($30) button-battery powered one with great results. It’s something you can spend a lot of money on for high-end equipment, so again look for opportunities to borrow. 

 

8. Get a group to introduce themselves
For qualitative group sessions, you will almost always want to be able to assign contributions to individual participants. If you are doing the transcription and know the people very well, this can be easy. However, it is surprisingly difficult to differentiate a group of voices that you don’t know just with a recording. For voices to be identified, make sure you start the recording by getting everyone to go round the table and introduce themselves with a few sentences for context (not just their name).

 

9. Backup immediately!
Got your recording? Great! Now back it up! All the time it exists only on your Dictaphone, it can be lost, stolen or dropped in a puddle, losing your data for ever. As soon as you can, get it back to a computer or laptop and copy it to another location. Make sure that your data storage procedure matches your data protection and ethics requirements, and try not to carry around your interview recordings longer than you need to.

 

10. Finally, listen and engage!
Try not to worry about the technical aspects during the interview, shift into researcher and facilitator mode. Take notes if you feel comfortable doing so: even though you are getting a recording, some brief notes can make a good summary and helps concentration. Tick off your qualitative research/interview questions as you go, and write a few notes about how the interview went and the key points immediately afterwards.

 

If you need more advice, you can also read our top 10 tips for qualitative interviews, to make sure things go smoothly on the day. Hopefully, following these steps will help you get great audio recordings for your research project, that will make transcription and listening to your data easier.

 

 

Once you’ve got it transcribed, you'll find that Quirkos is the most intuitative and visual software for qualitative analysis of text data. You can download a free trial for a month, and see an overview of the features here…

 

 

Transcription for qualitative interviews and focus-groups

transcription and a dictaphone

 

Audio and video give you a level of depth into your data that can’t be conveyed by words alone, letting you hear hesitations, sarcasm, and nuances in delivery that can change your interpretations of what your participants say. Yet most researchers and students will want to have typed transcripts of their qualitative interviews.

 

Text gives many advantages during the qualitative analysis process. You can read or skim read text much faster than you can listen to audio, and your eyes are good at quickly picking out keywords. Transcribed text can also be searched for keywords or synonyms, taking you directly to that point in the text.

 

It’s also easier to code text than multimedia data, and since most research outputs (especially journal articles, a thesis or book) tend to remain stubbornly text-based, including quotes is a standard way to embed qualitative data. Text can also be analysed in other ways, including statistical analysis and automated sentiment analysis.

 

Even when working primarily with the video or audio of qualitative interviews, most academic researchers and students will still generate a transcript for these reasons. But at the moment, there is no software that can reliably understand untrained interview audio, find words or create automatic transcriptions. Either the researcher themselves, or often an experienced transcriber will have to listen to the audio and type it up word for word.

 

Thus BEFORE starting interviews, it is worth considering a few ways to make sure that transcription goes smoothly, and cheaply. Finding a transcriber or transcription service is a key part of most qualitative research. But how much will it cost?

 

Well, this depends on the level of detail required. Verbatim transcriptions, especially when there is a need to capture the nuance of the conversation, are very time consuming to produce. These will capture not just the conversation word for word, but also every um, er, pause and hesitation, and sometimes even infliction. When there are gaps or pauses these will be detailed (such as [pause 5 sec]). This level of detail is illuminating, especially for discourse analysis, but expensive. Often researchers would like regular timestamps included (say at the top of each page) so that it is easy to find the position of the text in the audio. This also increases cost.

 

Often you will hear the phrase ‘Intelligent Verbatim’ used by transcription services, which denotes a middle ground where the transcriber chooses which pauses and detail are relevant, but is careful to make sure that the exact wording of the dialogue is recorded. This is what most qualitative research projects use, unless there is a methodological need for more detail.

 

This is still more detailed than what you would get from a standard typing service used in business, where phrases and words may be approximated. These services, sometimes called a ‘clean transcript’ are cheaper and easier to read (since they don’t have breaks or interjections, they are much more like reading dialogue from a novel), but generally lack the rigour and specificity for qualitative analysis. If someone said ‘afraid’ or ‘anxious’ it might represent a difference in your interpretation, so the exact words uttered must usually be noted. For more discussion, there is an interesting paper by Halcomb and Davidon (2006).

 

If you are conducting focus groups, this can also increase cost and difficulty because of the need to identify the different voices in the room. Typically this can add 20% to the transcription costs, some services will charge for each additional participant. Many transcribers will justifiably add an additional 20% or more for bad audio. We are going to look in the next blog post article about how to make sure this doesn’t happen, but noisy environments and bad recordings make the process much more time consuming, as it is necessary to keep going back and forward to correctly hear muffled words.

 

In general, you should expect to pay between £1 (often $1 in the States) at the absolute minimum and £3 ($2.70) per audio minute for transcription. This means a one-hour interview will cost around £80 ($60) to be transcribed, depending on the quality of the service and number of people speaking. For fast turnaround (ie within 24 or 48hrs) expect to pay a premium. After salaries, it is often the most expensive part of a qualitative research project. So if you have 20 interviews, you will need to budget £1600 ($1200). This is why many students end up doing transcription themselves, and while this is good for keeping close to your data, it is not easy, and can be a false economy.

 

As someone who has also done transcription before, it is vital to stress what a difficult and specialist job this is. Almost no-one can type at the same speed that people speak, and so the work takes much longer than the length of the interview. You are not paying someone £60+ an hour, they will work two or three times that long to get everything typed and corrected. It is also exhausting, and mentally draining. I’ve tried automated software for transcription, like Dragon Dictate or Microsoft’s Project Oxford, but these are not yet geared up for this type of work. They struggle with words that run together, require perfect audio recordings with no extra noises, and can’t identify different voices. I know some transcribers that use trained dictation software in their work: the computer recognises their own voice, so they have to listen with headphones to the audio and clearly repeat every word, one by one.

 

There are many online services offering transcription services, easily found with a quick Google search. I don’t have any specific ones to recommend at the moment, but if you want to use a company I would suggest you choose one that specifically works with research interviews, and offers the options above. It is also a good idea to choose one that works in your native dialect! If you are not used to hearing British, Scottish or Indian accents as an American transcriber (or vice versa), there can be odd misunderstandings and discrepancies that arise.

 

A transcriber that is used to working in your field of study is also useful: they will spot commonly used terminology and abbreviations. My favourite transcriber had worked in a medical field before, so was used to most of the NHS acronyms, and if there were terms or phrases he hadn’t come across before, he would Google them to make sure they were right. Good people like this are hard to come by!

 

Personally, I have always used a few freelance transcribers who work exclusively with universities. Ask your department or colleagues for local recommendations, and if part of a research project, one who is already on the university payroll system can save major headaches and delays. Don’t be afraid to give a new transcriber just one transcript to see how they do, before you commit yourself to giving them all the work. It’s also not a bad idea to have a back-up, especially if a transcriber gets sick, or you need a large batch of transcripts in a hurry.

 

Finally, there will always be errors and uncertainties. You still need to have at least a cursory read through of the transcript to make sure it makes sense and there aren’t typos. A feedback loop is a valuable thing to set up with a good transcriber, so they can learn about common phrases and terms they are mishearing, and the accuracy will improve. Words misheard will usually be marked with [inaudible] and you will need to go through and fix these. Often, it will be obvious to you as the researcher who was there in the room, but not for someone else, especially when it occurs just as the noisy expresso machine turns on!

 

I hope this is illuminating, it’s one of those things that is difficult to find much written advice on. Very few articles discuss this essential part of the research process – Davidson 2009 is a notable exception. Check out some of our other blog post articles for more on this stage, including how to get good quality recordings, and 10 tips for qualitative interviewing, and let us know if you have any suggestions or tips of your own!

 

 

Once you’ve got a transcript, you will be ready to start qualitative coding your text data, and Quirkos is an ideal software tool to bring your interview and focus-group data to life, with a visual and intuitive interface. Download a free trial, or watch a video overview showing you how to start a new analysis project in just 20 minutes.

 

 

Building queries to explore qualitative data

qualitative analysis with queries in Quirkos

 

So, you’ve spent days, weeks, or even months coding your qualitative data. Now what?

 

Hopefully, just the process of being forced to read through the data, and thinking about the underlining themes has revealed a few likely points of interest. Now is a good time to step back, put your research questions in front of you, and think about what the data is telling you about the main topics, and how you can work this into a convincing argument.

 

But it’s also a good time to try something different: to challenge your assumptions and come at the data sideways.

 

If you have been using a qualitative software package like Quirkos, you may already be able to see some trends and connections in the data. For example, what are the themes or nodes which you have coded most to? Are these surprising? Step back a little more: what has been coded more or less than you thought? Don’t forget to look beyond the numbers as well, click on major themes and see what is actually being said.

 

Next, it’s a good idea to try and look at connections between your coded topics. Most CAQDAS software will let you do this: in Quirkos the ‘overlap’ view will visualise which topics were coded together for any of the Quirks in your project. You might be intrigued to see that quotations coded as being ‘Negative’ were particularly correlated with statements about ‘Diet’ or ‘Experience’. Again, make sure you look at the actual quotes, so you can understand the substantive reasons for these associations.

 

Contradictions are also a good thing to look out for. When are people making comments that contrast from the general view? Are there particular contentious issues?  Hopefully, by looking at the quotes in context, and thinking about the narrative or demographics of the person, there will be an obvious reason for this. But don’t bet on it! It’s always OK to flag things that aren’t understood, either for further discussion with colleagues, revisiting the literature, or the fabled ‘more research is needed’.

 

 

A really useful way to explore the themes and trends in coded qualitative data is to do subset analysis. Is there something different about responses from people in difference age ranges? What about gender, or from people who have children? If it’s a literature review, what does one particular author have to say in their later works? Are articles in one journal defending a particular view more than others? A great advantage to using any qualitative software package is the ability to bring up results from just certain sources or responses.  Most CAQDAS will let you do this in some way, but I’m going to go through the process in Quirkos, and how it can illuminate the expected or unexpected.

 

Most of this functionality is built around how you have described your data, also known in Nvivo as ‘Source Classifications’ or the ‘Document Variables’ in MAXQDA. In Quirkos, these are ‘Source Properties’ and can be used to describe anything about the source; be it demographics of the respondent, circumstances of the recruitment, or where and when an article was discovered. See this blog article for more detail on how this can be useful.

 

In Quirkos, you can use the ‘Query View’ to explore your data, by seeing only coded results that meet certain criteria. For example, you can see results just from women, or people in a particular age range. However, you can also see work coded by particular people working on the project, or coding done during a specific period of time. If you’ve created a grouping of Quirks (such as different emotions) you can just see results from these topics.

 

Default query view in Quirkos

 

By default, the first button in the query view shows ‘PR’ for Properties (the source properties you have described). These will automatically be shown the next drop down box to the right, allowing you to select one of any of the properties and values defined in the project. By clicking on the PR button, you can choose from any of these filter options:


PR            Properties                     Which source properties match
HA            Highlight Author           Who coded a segment
QA            Quirk Author                  Who created the Quirk
HD            Highlight Date             When the coding was done
QD            Quirk Date                    When this Quirk was created
QL            Quirk Level                    Which level grouping the Quirk belongs to

You will also see the   =   symbol, by clicking on this, you can change the logic matching to  !=  or ‘not equal to’, so you can get results where Gender was not equal to Male.

 

There are also standard comparison options:


<                      Less than
<=                    Less than or equal to
>                      More than
>=                    More than or equal to


These are useful for date ranges, or numerical source properties like age. So you could get results from all respondents older than 42.

 

But wait, that’s not all!

 

You can also add up to 9 extra criteria to the search, by clicking on the (+) button at the end of the row. This means you can stack search criteria, for example where Gender = Male AND Age > 42. You can even change the AND operand to OR, and thus make your searches wider, rather than more specific. This would give you results from all sources that were Male, as well as respondents (regardless of gender) that were over 42. The example below shows what the response from a search with two criteria might look like:

 

Quirkos qualitative query results

 

Using tools like this, you can explore your qualitative data through many different lenses, and see what interesting things might emerge. You might have a theory, backed up by your own experience or the established literature that certain respondents will behave in certain ways. Or (if methodologically appropriate) you can experiment and try looking at different groups of participants until an interesting pattern emerges. Maybe you will discover that women like toast more than men (as in our example project) or hopefully, something with deeper significance for your research question!

 

Regardless of your discoveries, CAQDAS software like Quirkos can make it easy to filter your coded data, and get an extra level of insight into the underlining intricacies. For further reading, try this chapter from Lewis (2001), and the section on how queries can be used to "test ideas and theories".

 

 

Delivering qualitative market insights with Quirkos

delivering fashion

 

To build a well-designed, well-thought-out, and ultimately useful product, today’s technology companies must gain a deep understanding of the working mentality of people who will use that product. For Melody Truckload, a Los Angeles tech company focused on app-based freight logistics, this means intense market research and a focus on training sales agents as researchers.

 

Kody Kinzie, director of Melody’s special research and operations team, Cythlin Intelligence, was faced with introducing qualitative social research and analysis to people who had never considered themselves researchers before.

 

“Quirkos was the first truly accessible qualitative program I found,” Kinzie said.

 

Quirkos was designed with the philosophy that anyone can become a qualitative researcher. The goal is to allow companies and agencies to adopt unique ways to understand their staff and the wider marketplace. By making qualitative data visual and easy to code, users can see their results emerge and gain quick overviews of complex issues.

 

Companies like Melody are at the forefront of developing the next generation of qualitative insight, and Quirkos is helping to open the door to innovative new methods of business intelligence.

 

Kinzie started training his team members to use Quirkos but said he soon discovered that the simple coding allowed even a novice to develop complex data structures with notable uniqueness. Often, he found that these code structures were well suited to analyzing particular elements the researchers were interested in, and he began documenting the experiment to evaluate the resulting structures.

 

Here’s how Kinzie and his team use Quirkos:
One team member will send a Quirkos database to another team member — a researcher who examines the code structure and walks the requesting team member through an explanation of the thought process that went into creating the code. The data structure’s strengths and weaknesses are then assessed and distilled into a report. The researcher examines Melody’s code construction to discover what kind of information it is most effective at analyzing or categorizing, as well as whether the code tags and organizes information or clusters information into meaningful relationships.

 

This helps researchers understand what kind of questions these information structures should be applied to, and where a particular researcher’s methods might excel. The ability to use Quirkos to build and analyze unique and flexible databases from these structures has given Melody an edge in developing and sharing insights throughout the team.

 

While Melody Truckload’s app currently wraps up beta testing with commercial partners, the Quirkos approach has been put to the test most recently on the Melody team’s latest project, Melody Fashion.

 

“In the complex world of L.A.’s Fashion District, which is the part of town that houses the city’s fashion industry wholesale market, freight consolidation desperately needs to be modernized,” Kinzie said. “The objective of Melody Fashion is to provide a platform for fulfilment and consolidation that takes into account a detailed understanding of a market with many players.”

 

To that end, sales agents were trained to analyze interactions using grounded theory on Quirkos and to aggregate data garnered in their interactions with customers. It led to valuable insights, including a partnership with local shipping experts to bring Melody Fashion’s technology to the district.

 

Melody operations manager Marcus Galamay, who introduced new agents to Quirkos software and guided them through their first qualitative exercises, said, “Quirkos provides an intuitive introduction to qualitative analysis for our sales agents, augmenting their role in a way that’s expanded our insights into our client base. It’s a niche that many might not think to pursue, but it’s already delivered results in terms of better understanding of the data we generate and refining our market strategy based on that.”

 

Thanks to its ease of use and its powerful ability to assist in important social research, Quirkos was instrumental in providing Melody with the insight necessary to build smart and useful technology for a distinct and totally new customer base.

 

 

Using properties to describe your qualitative data sources

Properties and values editor in Quirkos

In Quirkos, the qualitative data you bring into the project is grouped as 'sources'. Each source might be something like an interview transcript, a news article, your own notes and memos, or even journal articles. Since it can be any source of text data, you can have a project that includes a large number of different types of source, which can be useful when putting your research together. This means that you can code things like your research questions, articles on theory, or even grey literature, and keep them in the same place as your research data.


The benefit of this approach is that you can quickly cross-reference your own research together with written articles, coding them on the same themes so you can compare them. However, there will be times that you only want to look at data from some of your sources. Perhaps you only want to look at journal articles written between a certain period, or look at respondent's data from just one city. By using the Source Properties in Quirkos, you can do all this and more: it allows you an essentially unlimited number of ways to describe the data. You can then use the query view to see results that match one or more properties, and even do comparisons. This Properties-Query combo is the best way to examine your coded qualitative data for trends and differences.

 

This article will outline a few different ways that you can use the source properties, and how to get the most use out of your research data and other sources.


When you bring a data source into Quirkos, the computer doesn't know anything about it. It's good practice to describe it, using what is sometimes called 'metadata' or 'data about data'. So for example, respondent data might have values for Age, Gender, Location, Occupation, Purchasing Habits... the list is endless. Research papers and textbooks will have values like Journal Name, Pulbication Year, Volume, Author, Page number etc.

 

Each of these categories in Quirkos are called 'Properties' and the possible data belonging to each property are called 'Values'. So for example, the Age of a respondent is a Property, and the value could be 42. Quirkos lets you have a practically unlimited number of Properties that describe all the sources in a project, and an unlimited number of Values.


The values can also be numerical (like age in years), discrete (like categories for Old, Young or 20-29) or even comments (like 'This person was uncomfortable revealing their age'). Properties can even have a mix of different data types as values.


To create properties and values in your project, click on the small 'grid' button on the top right corner of the screen. This toggles the properties view, and will show you the properties and values for the data source you are currently viewing. To look at a different source, just select it from the tabs at the bottom, or the complete list of sources in the source browser button (bottom left of the source column).


One here, you can create a new property and value with the (+) button at the bottom of the column, or use the 'Properties and Values Editor' to add lots of data at once, or to remove or edit existing values. The Editor also gives you the option of rearranging Properties and Values, and changing a Property to be 'multiple-choice' will let you assign more than one Value to each Property (for example to show that a person has multiple hobbies).


There are also a couple of features that help speed up data entry, for example the Properties Editor also allows you to create Properties that have pre-existing common values, for example 'Yes/No' properties, or common Likert Agree-Disagree scales. To define values for a property, use the orange drop-down arrow next to each Property. When you click on this, you can see all the values that have already been defined, as well as the option to add a new value directly.


I always try and encourage people to also use the properties creatively. You can use them to quickly create groups of your sources, and explore them together. So you may create a property for 'Unusual case', select Yes for those sources, and see what makes them special. There might even be something you didn't collect survey data for, but  is a clear category in the text, such as how someone voted. You can make this a Property too, and easily see who these people are and what they said. They can also be process-based properties: 'Ones I haven't coded Yet' or 'Ones I need to go over again'. Use the properties as a flexible way to manage and sort your data, in anyway you see fit! You can of course create and remove properties and values at any stage of your project, and don't forget to describe the 'type' of source: article, transcript, notes etc.


When you want to explore the data by property, use the Query view. This lets you set up very simple filters that will show you results of coded data that comes from particular sources. You can even run two queries at once, and see the results side-by-side to compare them. While by default the [ = ] option will return sources that match the value, you can also use 'Not equal' [!=] and ranges for numerical or alphabetic values ( < > etc). It's also possible to add many queries together with a simple interface, to create complex filters. So for example you can return results from just people between the ages of 30-35, who are Male, and live in France OR Germany.

 


This was a quick summary of how to describe your qualitative data in Quirkos: as always you can find more information in the video guides, and ask us a question in the forum.

 

 

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.