In the last blog post I referenced a workshop session at the International Conference of Qualitative Inquiry entitled the ‘Archaeology of Coding’. Personally I interpreted archaeology of qualitative analysis as being a process of revisiting and examining an older project. Much of the interpretation in the conference panel was around revisiting and iterating coding within a single analytical attempt, and this is very important.

In qualitative analysis it is rarely sufficient to only read through and code your data once. An iterative and cyclical process is preferable, often building on and reconsidering previous rounds of coding to get to higher levels of interpretation. This is one of the ways to interpret an ‘archaeology’ of coding – like Jerusalem, the foundations of each successive city is built on the groundwork of the old. And it does not necessarily involve demolishing the old coding cycle to create a new one – some codes and themes (just like significant buildings in a restructured city) may survive into the new interpretation.

But perhaps there is also a way in which coding archaeology can be more like a dig site: going back down through older layers to uncover something revealing. I allude to this more in the blog post on ‘Top down or bottom up’ coding approaches, because of course you can start your qualitative analysis by identifying large common themes, and then breaking these up into more specific and nuanced insight into the data.

But both these iterative techniques are envisaged as part of a single (if long) process of coding. But what about revisiting older research projects? If you get the opportunity to go back and re-examine old qualitative data and analysis?

Secondary data analysis can be very useful, especially when you have additional questions to ask of the data, such as in this example by Notz (2005). But it can also be useful to revisit the same data, question or topic when the context around them changes, for example due to a major event or change in policy.

A good example is our teaching dataset conducted after the referendum for Scottish Independence a few years ago. This looked to see how the debate had influenced voters interpretations of the different political parties and how they would vote in the general election that year. Since then, there has been a referendum on the UK leaving the EU, and another general election. Revisiting this data would be very interesting in retrospect of these events. It is easy to see from the qualitative interviews that voters in Scotland would overwhelmingly vote to stay in the EU. However, it would not be up-to-date enough to show the ‘referendum fatigue’ that was interpreted as a major factor reducing support for the Scottish National Party in the most recent election. Yet examining the historical data in this context can be revealing, and perhaps explain variance in voting patterns in the changing winds of politics and policy in Scotland.

While the research questions and analysis framework devised for the original research project would not answer the new questions we have of the data, creating new or appended analytical categories would be insightful. For example, many of the original codes (or Quirks) identifying political parties people were talking about will still be useful, but how they map to policies might be reinterpreted, or higher level themes such as the extent that people perceive a necessity for referendum, or value of remaining part of the EU (which was a big question if Scotland became independent). Actually, if this sounds interesting to you, feel free to re-examine the data – it is freely available in raw and coded formats.

Of course, it would be even more valuable to complement the existing qualitative data with new interviews, perhaps even from the same participants to see how their opinions and voting intentions have changed. Longitudinal case studies like this can be very insightful and while difficult to design specifically for this purpose (Calman, Brunton, Molassiotis 2013), can be retroactively extended in some situations.

And of course, this is the real power of archaeology: when it connects patterns and behaviours of the old with the new. This is true whether we are talking about the use of historical buildings, or interpretations of qualitative data. So there can be great, and often unexpected value in revisiting some of your old data. For many people it’s something that the pressures of marking, research grants and the like push to the back burner. But if you get a chance this summer, why not download some quick to learn qualitative analysis software like Quirkos and do a bit of data archaeology of your own?

I was recently privileged to chair a session at ICQI 2017 entitled “The Archaeology of Coding”. It had a fantastic panel of speakers, including Charles Vanover, Paul Mihas, Kathy Charmaz and Johnny Saldaña all giving their own take on this topic. I’m going to write about my own interpretation of qualitative coding archaeologies in the next blog post, but for now I wanted to cover an important common issue that all the presenters raised in their presentations: coding is never finished.

In my summary I described this as being like the river in the novel Siddhartha by Herman Hesse: ‘coding is never still’. It should constantly change and evolve, and recoil from attempts to label it as ‘done’ or ‘finished’. Heraclitus said the same thing, “You cannot step twice into the same rivers” for they constantly change and shift (as do we). When we come back to revisit our coding, and even during the process of coding, change is part of the process.

I keep coming back to the image of butterflies in a museum display case: dead, pinned to the board with a neatly assigned label of the genus. It’s tempting to approach qualitative coding with this entomologist’s approach: creating seemingly definitive and static codes that describe one characteristic of the data.

Yet this taxonomy can create a tension, lulling you into feeling that some codes (and frameworks) are still, complete, and don’t need revision and amendment. This might be true, but it usually isn’t! If you are using some type of open-ended coding or grounded theory approach, creating a static code can be beguiling, and interpreted as showing progress. But instead, try and see every code as a place-holder for a better category or description – try not to loose the ability for the data to surprise you, and the temptation to force quotes into narrow categories. Assume that you are never finished with coding.

Unless you are using a very strict interpretation of framework analysis, your first attempt at coding will probably change, evolve as you go through different sources, and take you to a place where you want to try another approach. And your attempts at creating a qualitative classification and coding system might just end up being wrong.

Even in biology, classification attempts are complicated. While the public are still familiar with the different ‘animal kingdom’ groupings, attempts to create a taxonomy in the ‘tree of life’ common descent model are now succeeded by the modern ‘cladistic’ approach, based around common history and derived characteristics of a species. And these approaches also have limitations, since they are so complex and subjective (just like qualitative analysis!).

For example, if you use the NCBI Taxonomy browser you will see dozens of entries in square brackets. These are the misclassified organisms which have been currently recognised, species placed in the wrong genus. These problems don’t even include the cases when one species is found to be many unique but significantly separate species on closer study. This has even been found to be the case for the common ‘medicinal’ leech!

Trying to turn the endless forms most beautiful of the animal ‘kingdoms’ into neat categories is complex, even when just looking at appearance. And these taxonomic groupings tell us little of the diverse range of behaviour and life behind the dead pinned insects.

In a similar way, when we code and analyse qualitative data, we are attempting to listen to the voices of our respondents, and change the rich multitude of lives and experiences into a few key categories that rise up to us. We often need to recognise the reductive nature of this practice, and keep coming back to the detailed rich data behind it. In a way, this is like the difference between knowing the Latin name for a species of butterfly, and knowing how it flies, it’s favourite flowers, and all the details that actually make them unique, not just a name or number.

In Siddhartha, the central character finds nirvana listening to the chaotic, blended sound of a river, representing the lives and goals of all the people in his life and the world.

“The river, which consisted of him and his loved ones and of all people, he had ever seen, all of these waves and waters were hurrying, suffering, towards goals, many goals, the waterfall, the lake, the rapids, the sea, and all goals were reached, and every goal was followed by a new one, and the water turned into vapour and rose to the sky, turned into rain and poured down from the sky, turned into a source, a stream, a river, headed forward once again”

Like the river, qualitative analysis can be a circle, with each iteration and reading different from the last, building on the previous work, but always listening to the data, not being quick to judge or categorise. Until we have reached this analytical nirvana, it is difficult to let go of our data, and feel that it is complete. This complex, turbulent flow of information defies our attempts to neatly categorise and label it, and the researcher’s quest for neatness and uncovering the truth under our subjectivity demands a single answer and categorisation scheme. But, just like taxonomy, there may never be a state when categorisation is complete, in a single or multiple interpretation. New discoveries, or new context can change it all.

We, the researcher, are a dynamic and fallible part of that process – we interpret, we miscategorise, we impose bias, we get tired and loose concentration. When we are lazy and quick, we take the comfort of labels and boxes, lulled into conformity by the seductive ease of software and coloured markers. But when we become good qualitative researchers: when we are self-critical and self-reflexive, finally learning to fully listen, then we achieve research nirvana:
 

“Siddhartha listened. He was now nothing but a listener, completely concentrated on listening, completely empty, he felt, that he had now finished learning to listen. Often before, he had heard all this, these many voices in the river, today it sounded new. Already, he could no longer tell the many voices apart, not the happy ones from the weeping ones, not the ones of children from those of men, they all belonged together”

Download a free trial of Quirkos today and challenge your qualitative coding!

I’m often asked ‘How does Quirkos compare to Nvivo?’. Nvivo is by far the largest player in the qualitative software field, and is the product most researchers are familiar with. So when looking at the alternatives like Quirkos (but also Dedoose, ATLAS.ti, MAXQDA, Transana and many others) people want to know what’s different!

In a nutshell, Quirkos has far fewer features than Nvivo, but wraps them up in an easier to use package. So Quirkos does not have support for integrated multimedia, Twitter analysis, quantitative analysis, memos, or hypothesis mapping and a dozen other features. For large projects with thousands of sources, those using multimedia data or requiring powerful statistical analysis, the Pro and Plus versions of Nvivo will be much more suitable.

Our focus with Quirkos has been on providing simple tools for exploring qualitative data that are flexible and easier to use. This means that people can get up and running quicker in Quirkos, and we hear that a lot of people who are turned off by the intimidating interface in Nvivo find Quirkos easer to understand. But the basics of coding and analysing qualitative data are the same.

In Quirkos, you can create and group themes (called Nodes in Nvivo), and use drag and drop to attach sections of text to them. You can perform code and retrieve functions by double clicking on the theme to see text coded to that node. And you can also generate reports of your coded data, with lots of details about your project.

Like Nvivo, we can also handle all the common text formats, such as PDFs, Word files, plain text files, and the ability to copy and paste from any other source like web pages. Quirkos also has tools to import survey data, which is not something supported in the basic version of Nvivo.

While Quirkos doesn’t have ‘matrix coding’ in the same way as Nvivo, we do have side-by-side comparison views, where you can use any demographic or quantitative data about your sources to do powerful sub-set analysis. A lot of people find this more interactive, and we try and minimise the steps and clicks between you and your data.

Although Quirkos doesn’t really have any dedicated tools for quantitative analysis, our spreadsheet export allows you to bring data into Excel, SPSS or R where you have much more control over the statistical models you can run than Nvivo or other mixed-methods tools allow.

However, there are also features in Quirkos that Nvivo doesn’t have at the moment. The most popular of these is the Word export function. This creates a standard Word file of your complete transcripts, with your coding shown as color coded highlights. It’s just like using pen and highlighter, but you can print, edit and share with anyone who can open a Word file.

Quirkos also has a constant save feature, unlike Nvivo which has to be set up to save over a certain time period. This means that even in a crash you don’t loose any work, something I know people have had problems with in Nvivo.

Another important differential for some people is that that Quirkos is the same on Windows and Mac. With Nvivo, the Windows and Mac versions have different interfaces, features and file formats. This makes it very difficult to switch between the versions, or collaborate with people on a different platform. We also never charge for our training sessions, and all our online support materials are free to download on our website. 

And we didn’t mention the thing people love most about Quirkos – the clear visual interface! With your themes represented as colourful, dynamic bubbles, you are always hooked into your data, and have the flexibility to play, explore and drill down into the data.

Of course, it’s best to get some impartial comparisons as well, so you can get reviews from the University of Surrey CAQDAS network here: https://www.surrey.ac.uk/sociology/research/researchcentres/caqdas/support/choosing/

But the best way to decide is for yourself, since your style of working and learning, and what you want to do with the software will always be different. Quirkos won’t always be the best fit for you, and for a lot of people sticking with Nvivo will provide an easier path. And for new users, learning the basics of qualitative analysis in Quirkos will be a great first step, and make transitioning to a more complex package like Nvivo easier in the future. But download our free trial (ours lasts for a whole month, not just 14 days!) and let us know if you have any questions!

This blog now has nearly 120 posts about all different kinds of qualitative methods, and has grown to hosting thousands of visitors a month. There are lots of other great qualitative blogs around, including Margaret Roller’s Research Design Review and the Digital Tools for Qualitative Research group and the newly relaunched Qual Page.

But these are only one part of the online qualitative landscape, and there are an increasing number of people engaged in teaching, commenting and exploring qualitative methods and analysis on social media. By this I mean popular platforms like Twitter, Facebook, Linkedin, Academia.net, Researchgate and even Instagram and Snapchat. And yes, people are even using Instagram to share pictures and engage with others doing qualitative research.

So the call for a talk at the International Conference of Qualitative Inquiry (ICQI 2017) asked: How can educators reach out and effectively use social media as a way to teach and engage students with qualitative methodologies?

Well, a frequent concern of teachers is how you teach the richness and complexity of qualitative methods in something like a Tweet which has a 140 character limit? Even the previous sentence would be too long for a Tweet! While other platforms such as comments on Facebook don’t have such tight limits, they are still geared towards short statements. Obviously, detailing the nuances of grounded theory in this way is not realistic. But it can be a great way to start a conversation or debate, to link and draw attention to other longer sources of media.

For example the very popular ‘Write That PhD’ Twitter feed by Dr Melanie Haines of the University of Canberra has nearly 20 thousand followers. The feed offers advice on writing and designing a PhD and often posts or retweets pictures which contain a lot more detailed tips on writing a thesis. This is a good way of getting around the character limit, and pictures, especially when not just of a long block of text are a good way to draw the eye. Social media accounts can also be used to link to other places (such as a blog) where you can write much longer materials – and this is an approach we use a lot.

But to use social media effectively for outreach and engagement, it is also important to understand the different audiences which each platform has, and the subsets within each site. For example, Snapchat has a much younger audience than Facebook, and academic focused platforms might be a good place to network with other academics, but doesn’t tend to have many active undergraduates.

It’s also important to think how students will be looking and searching for information, and how to get into the feeds that they look at on a daily basis. On Facebook and especially Twitter, hashtags are a big part of this, and it’s worth researching the popular terms that people are searching for which are relevant to your research or teaching. For example the #phdlife and #phdchat tags are two of the most popular ones, #profchat and #research have their own niches and audiences too. While it can seem like a good idea to start a new hashtag for yourself like #lovequalitiative, it takes a lot of work and influential followers to get them off the ground.

Don’t forget that hashtags and keywords are just one way to target different audiences. Twitter also has ‘lists’ of users with particular interests, and Linkedin and Facebook have groups and pages with followers which it can be worth joining and contributing to. On Researchgate and Academia.net the question forums are very active, and there are great discussions about all aspects of qualitative research.

But the most exciting part of social media for teaching qualitative research is the conversations and discussions that you can have. Since there are so many pluralities of theory and method, online conversations can challenge and promote the diversity of qualitative approaches. This is a challenge as well, as it requires a lot of time, ideally over a long period of time, to keep replying to comments and questions that pop up. However, the beauty of all these platforms is that they effectively create archives for you, so if there was a discussion about qualitative diary methodologies on a Facebook group a year ago, it will still be there, and others can read and learn from it. Conversely, new discussions can pop up at any time (and on any of the different social media sites) so keeping on top of them all can be time consuming.

In short, there is a key rule for digital engagement, be it for teaching or promoting a piece of research: write once, promote often. Get a digital presence on a blog or long form platform (like Medium) and then promote what you’ve written on as many social media platforms as you can. The more you promote, the more visible and the higher rated your content will become, and the greater audience you can engage with. And the best part of all is how measurable it is. You can record the hits, follows and likes of your teaching or research and show your REF committee or department the extent of your outreach. So social media can be a great feather to add to your teaching cap!

We’ve actually talked about communicating qualitative research and data to the public before, but never covered writing journal articles based on qualitative research. This can often seem daunting, as the prospect of converting dense, information rich studies into a fairly brief and tightly structured paper takes a lot of work and refinement. However, we’ve got some tips below that should help demystify the process, and let you break it down into manageable steps.

Choose your journal

The first thing to do is often what left till last: choose the journal you want to submit your article to. Since each journal will have different style guidelines, types of research they publish and acceptable lengths, you should actually have a list of a few journals you want to publish with BEFORE you start writing.

To make this choice, there are a few classic pointers. First, make sure your journal will publish qualitative research. Many are not interested in qualitative methodologies, see debates about the BMJ recently to see how contested this continues to be. It’s a good idea to choose a journal that has other articles you have referenced, or are on a similar topic. This is a good sign that the editors (and reviewers) are interested in, and understand this area.

Secondly, there are some practical considerations. For those looking for tenure or to one day be part of schemes that assess the quality of academic institutions by their published work such as the REF (in the UK) or PBRF (in New Zealand) you should consider ‘high impact’ or ‘high tier’ journals. These are considered to be the most popular journals in certain areas, but will also be the most competitive to get into.

Before you start writing, you should also read the guidance for authors from the journal, which will give you information about length, required sections, how they want the summary and keywords formatted, and the type of referencing. Many are based on the APA style guidelines, so it is a good idea to get familiar with these.

Describing your methodology, literature review, theoretical underpinnings

When I am reviewing qualitative articles, the best ones describe why the research is important, and how it fits in with the existing literature. They then make it clear how the researcher(s) chose their methods, who they spoke to and why they were chosen. It’s then clear throughout the paper which insights came from respondent data, and when claims are made how common they were across respondents.

To make sure you do this, make sure you have a separate section to detail your methods, recruitment aims and detail the people you spoke to – not just how many, but what their background was, how they were chosen, as well as eventually noting any gaps and what impact that could have on your conclusion. Just because this is a qualitative paper doesn’t mean you don’t have to say the number of people you spoke to, but there is no shame in that number being as low as one for a case study or autoethnography!

Secondly, you must situate your paper in the existing literature. Read what has come before, critique it, and make it clear how your article contributes to the debate. This is the thing that editors are looking for most – make the significance of your research and paper clear, and why other people will want to read it.

Finally, it’s very important in qualitative research papers to clearly state your theoretical background and assumptions. So you need to reference literature that describes your approach to understanding the world, and be specific about the interpretation you have taken. Just saying ‘grounded theory’ for example is not enough – there are a dozen different conceptualisations of this one approach.
 

Reflexivity

It’s not something that all journals ask for, but if you are adopting many qualitative epistemologies, you are usually taking a stance on positivism, impartibility, and the impact of the researcher on the collection and interpretation of the data. This sometimes leads to the need for the person(s) who conducted the research to describe themselves and their backgrounds to the reader, so they can understand the world view, experience and privilege that might influence how the data was interpreted. There is a lot more on reflexivity in this blog post.

How to use quotations

Including quotations and extracts from your qualitative data is a great feature, and a common way to make sure that you back up your description of the data with quotes that support your findings. However, it’s important not to make the text too dense with quotations. Try and keep to just a few per section, and integrate them into your prose as much as possible rather than starting every one with ‘participant x said’. I also like to try and show divergence in the respondents, so have a couple of quotes that show alternative view points.

On a practical note, make sure any quotations are formatted according to the journal’s specifications. However, if they don’t have specific guidelines, try and make them clear by always giving them their own indented paragraph (if more than a sentence) and clearly label them with a participant identifier, or significant anonymised characteristic (for example School Administrator or Business Leader). Don’t be afraid to shorten the quotation to keep it relevant to the point you are trying to make, while keeping it an accurate reflection of the participant’s contribution. Use ellipsis (…) to show where you have removed a section, and insert square brackets to clarify what the respondent is talking about if they refer to ‘it’ or ‘they’, for example [the school] or [Angela Merkel].

Don’t forget visualisations

If you are using qualitative analysis software, make sure you don’t just use it as a quotation finder. The software will also help you do visualisations and sub-set analysis, and these can be useful and enlightening to include in the paper. I see a lot of people use an image of their coding structure from Quirkos, as this quickly shows the relative importance of each code in the size of the bubble, as well as the relationships between quotes. Visual outputs like this can get across messages quickly, and really help to break up text heavy qualitative papers!

Describe your software process!

No, it’s not enough to just say ‘We used Nvivo’. There are a huge number of ways you could have used qualitative analysis software, and you need to be more specific about what you used the software for, how you did the analysis (for example framework / emergent) and how you got outputs from the software. If you did coding with other people, how did this work? Did you sit together and code at one time? Did you each code different sources or go over the same ones? Did you do some form of inter-rater reliability, even if it was not a quantitative assessment? Finally, make sure you include your software in the references – see the APA guides for how to format this. For Quirkos this would look something like:

Quirkos Software (2017). Quirkos version 1.4.1 [Computer software]. Edinburgh: Quirkos Limited.

Be persistent!

Journal publication is a slow process. Unless you get a ‘desk rejection’, where the editor immediately decides that the article is not the right fit for the journal, hearing back from the reviewers could take months or even a year. Ask colleagues and look at the journal information to get an idea of how long the review process takes for each journal. Finally, when you get some feedback it might be negative (a rejection) or unhelpful (when the reviewers don’t give constructive feedback). This can be frustrating, especially when it is not clear how the article can be made better. However, there are excellent journals such as The Qualitative Report that take a collaborative rather than combatitative approach to reviewing articles. This can be really helpful for new authors.

Remember that a majority of articles are rejected at any paper, and some top-tier journals have acceptance rates of 10% or less. Don’t be disheartened; try and read the comments, keep on a cycle of quickly improving your paper based on the feedback you can get, and either send it back to the journal or find a more appropriate home for it.

Good luck, and don’t forget to try out Quirkos for your qualitative analysis. Our software is easy to use, and makes it really easy to get quotes into Word or other software for writing up your research. Learn more about the features, and download a free, no-obligation trial.

In the last couple of weeks there has been a really interesting discussion on the Qualrs-L UGA e-mail discussion group about the use of software in qualitative analysis. Part of this was the question of whether qualitative software leads to the ‘homoginisation’ of qualitative research and analysis. As I understand it, this is the notion that the qualitative sphere is contracting from diverse beginnings, narrowing to a series of commonly used and accepted methods of collection and interpretation. For example, the most popular are probably semi-structured interview transcripts coupled with some type of framework based interpretation. Are more and more researchers using qualitative research churning out work using the same research? Is modern qualitative technology leading to a unified outputs like the introduction of the printing press, or helping increasing the accessibility of the discipline?

While I do see some evidence of trends emerging in the literature and research articles, I do not see them as inevitable, or feel that alternative approaches have been relegated, or that software need be a force for homogenisation.

Actually, I see a lot of similarities in this debate with a keynote talk on conformity in qualitative research by Professor Maggie MacLure at the ICQI conference last year. Referencing Deleuze, Nietzsche and the Greek Myths, she described the need to balance the dichotomy of two of the sons of Zeus in Greek legend: Apollo and Dionysus. Dionysus represents, chaos, emotion (and excess drinking of wine) while Apollo masters truth, rational thinking and prophecy. One can argue that following Apollo can lead to homogenisation, while too much Dionysus in your research can lead to chaos and a difficulty in drawing meaningful conclusions (especially with the wine drinking, although many researchers I know would disagree on this important point when writing up research).

However, a little creativity is important, especially at the point of choosing your methodology. In qualitative research, you can use arts-based research, using participant creation of drawings, games or even pottery as data. There are real challenges in keeping the richness of these creative methods alive through the analysis process: how do you analyse a drawing by a participant? Yet it’s rarely enough to just look at transcripts of respondents talking about their creations, and ignoring the art work itself. So take a pinch of the creative to ward against homogenisation: the excellent overview on Creative Research Methods by Helen Kara is a great place to start.

But what about the analysis and qualitative software? Can this be creative and unique as well?

I would argue that it can – especially with certain tools. I think there is a tendency for software to ‘lead’ users into particular behaviours and approaches, which is why users should look at the Five Level QDA approach advocated by Woolf and Silver and decide how they want to analyse their data before choosing a software package. But most software is very flexible. Even tools like Atlas.ti that was originally designed for grounded theory can be used for other theoretical approaches (Friese 2014). However you can still see this legacy in the design, for example the difficulty in creating a hierarchical coding structure in Atlas.ti remains today.

The design methodology for Quirkos was to create a very simple qualitative software tool that allowed people to use it in anyway they wanted. And in my experience from 3 years in running a qualitative software company, I can assure you that there is little risk of homogenisation in software users! Users occasionally share their projects with me to get advice on a problem, and I can see people using the features in ways we never envisaged! I also get lots of emails in my Inbox with suggestions on how we can make small tweaks to allow people to use Quirkos in different ways. The demand from the users is not to adopting the same approach over and over again, but being able to customise the software to their own needs and ways of working. And again, I can assure you these approaches are more diverse than I ever imagined.

And what about the argument that software creates mechanical and thought-less analysis? Well, I think this is a risk, and I’ve written about the discipline that users need to avoid this. But I think that any reductive analytical process risks becoming automatic, and thus removing the richness of the qualitative data. Even a pen and highlighters approach to analysis can become automatic and brainless if not done with care, and when re-reading data the eye can skip to the brightly coloured sections, sometimes missing vital context.

Ironically there is also some homogenisation in the software industry itself. Many scholars including Fielding and Lee (1998) have talked about ‘Creeping featurism’ and a trend of software packages to become more similar and (complex) as they add tools and functionality from each other. They tend to have similar interfaces, and function in ways that often seem very similar to the new user. Now, a fan of any one qualitative software package will quickly let you know how superior X is to Y because of a subtle aspect of the layout, and how easy it is to work in a particular way. Again this seems to evidence that software itself does not lead to homogenisation of approaches.

There are more than a dozen qualitative software packages actively developed at the moment, and between them they offer a fantastic variety of conceptual and practical approaches to data coding and management. For most people I speak to, the choice of software is bewildering, just like the variety of methods that can be used in qualitative research. I hope that new students are led so that, rather than being shoehorned into a particular approach, they are excited by the dizzying heights of possibility in qualitative research.

If you would like to give the unique Quirkos experience a try, we have a free trial you can download so you can see if the simple, visual and colourful approach is right for your qualitative research. And as ever, if you have any questions, feel free to get in touch with us at support@quirkos.com.

A little later than our Windows and Mac version, we are happy to announce that we have just released Quirkos 1.4.1 for Linux. There are some major changes to the way we release and package our Linux version, so we want to provide some technical details of these, and installation instructions.

Previously our releases had a binary-based and distro independent installer. However, this was based on 32 bit libraries to provide backwards compatibility, and required a long list of dependencies to work on many systems.

From this release forward, we are releasing Quirkos as an AppImage – a single file which contains a complete image of the software. This should improve compatibility across different distros, and also remove some of the dependency hell involved in the previous installer.

Once you download the .AppImage file, you will need to give the file executable permissions (a standard procedure when downloading binaries). You can do this at the command-line just by typing ‘chmod +x Quirkos-1.4.1-x86_64.AppImage’. This step can also be done with a File Manager GUI like Nautilus (the default in Gnome and Ubuntu) by right clicking on the downloaded file, selecting the Permissions tab, and ticking the ‘Allow executing file as program’ box. Then you can start Quirkos from the command-line, or by double clicking on the file.

Since an AppImage is essentially a ‘live’ filesystem contained in a single file, there is no installation needed, and if you want to create a Desktop shortcut to the software stored in a different location, you will have to create one yourself.
 

Secondly, we have also moved to a 64 bit release for this version of Quirkos. While we initially wanted to provide maximum compatibility with older computers, this actually creates a headache for the vast majority of Linux users with 64 bit installations. They were required to install 32 bit libraries for many common packages (if they did not have them already), creating duplication and huge install requirements. Now Quirkos should run out-of-the-box for a vast majority of users.

Should you prefer the older 32 bit installer package, you can still download the old version from here:
https://www.quirkos.com/quirkos-1.4-linux-installer.run

Supporting Linux is really important to us, and we are proud to be the only major commercial qualitative software company creating a Linux version, let alone one that is fully feature and project compatible with the Windows and Mac builds. While there are great projects like RQDA which are still supported, TAMS Analyzer and Weft QDA have not been updated for Linux in many years, and are pretty much impossible to build these days. Dedoose is an option in Linux since it is browser based, but sometimes requires some tweaking to get Flash running properly. Adobe AIR for Linux is now no longer supported, so the Dedoose desktop App is sadly no longer an option.
 

But Quirkos will keep supporting Linux, and provide a real option for qualitative researchers wanting to use free and open platforms.

We REALLY would love to have your feedback on our new Linux release, positive, negative or neutral! We still have a relatively small number of users on Linux, so your experiences are extra important to us. Is the AppImage more convenient? Have you had any dependency problems? Would you prefer we kept providing 32bit packages? E-mail us at support@quirkos.com and let us know!

Since Quirkos version 1.4 came out last year, we have been gathering feedback from dozens of users who have given us suggestions, or reported problems and bugs. This month we are releasing a small update for Quirkos, which will improve more than a dozen aspects of the software:

• MacOS – Since our last version, a new version of Mac OS X (now called macOS) has been released. This actually caused a few minor glitches in Quirkos, we hope we have fixed them all!
   
• Tree view – Deleting top-level Quirkos in Tree View no longer causes crashing on Mac.
   
• Canvas View – In the main canvas view, rearranging Quirks sometimes caused bubbles to become stuck – this has now been addressed.
   
• Disappearing text fix – On some systems, an occasional glitch would cause the top line of text highlighted in the source column to become invisible (although it was still there, and coded correctly).
   
• Percentage coding figures – In some circumstances, the 'Source Text Coverage' figures displayed in the bottom right status area were wildly inaccurate, sometimes showing figures over 100%. This has been fixed, figures displayed in the source browser were not affected.
   
• Percentage coding updates – The 'Source Text Coverage' is now updated quicker when removing a Quirk or performing Undo operations to give a more accurate live picture of how much of the project has been coded.
   
• Incorrectly closed files – If Quirkos or the computer crashes, no data is lost as Quirkos saves your project after each action. However, when the file is not closed, a message was displayed stating that “The selected file seems to be already opened in another session. Opening file in multiple sessions may result in data inconsitency. Do you still want to open this file?”. This message is intended to make sure that the file is not being used by two users at once, which could cause problems! However, this situation was rare, and the message was causing anxiety in users who feared problems in their projects (when it was safe to keep working). We have improved the wording of the message to “Please check that the project file is not open in another window. If this is not the case, it is safe to continue.”
   
• Merge – Some Quirk merge operations would remove the highlighting from the last coded section of text in the source. This has been fixed. Please note that if text is coded the same way in two merging Quirks, or the text between two coded sections overlaps, they will become one section of highlighted text in the new merged code. This means that sometimes the number of coded segments in a merged Quirk will be lower than in the two Quirks separately, but does not mean sections got ignored!
   
• Report generation – We have improved the system used to display reports generated in HTML. This means they now load and display quicker.
   
• Improved PDF export – The PDF export of the reports is also updated, this should now be quicker, and produce smaller file sizes. Where the old reports had large amounts of text as uneditable images, these are now displayed as text, which can be selected and copied.
   
• PDF characters – some PDF files contained non-standard formatting characters, which were incorrectly interpreted when imported into Quirkos. Although these were not notable, these sometimes caused CSV exports to have many unnecessary line breaks. This has now been fixed.
   
• Faster start-up – on most systems Quirkos should now start faster

Note on printing long reports – On Windows we have noted a new issue with this release: trying to print very large reports can create a crash on some systems. Unfortunately, this problem is due to the printing system we use in Windows, and we cannot fix this ourselves! However, printing from a PDF file works fine, so a simple workaround is to save the report as a PDF file, and then print from there. This also gives you more flexibility on which pages to print, custom formatting options and the ability to see a preview. We hope this issue will be fixed for the next release...

The new version is available to download now for Mac and Windows, and you can just install over the old version. There is no problem with compatibility, so once again all your projects will work in the new version. Anyone using older versions will not see any difference, but we recommend that people update as soon as possible to get the benefits above! The Linux version will be relased shortly, as requested we are moving to a proper .deb packaging release, which should ease dependency issues some people had. We are changing to a 64bit Linux release, which for most people will require less lib32 compatibility libraries to be installed.

We don't charge for updates, and thus they are available for all our users, and even those on the free trial! We think this is the fairest way to do software: I never want to have users stuck using old outdated versions of Quirkos because they (or their department) can't afford to upgrade. We continue our promise to protect forward, backwards and cross-platform compatibility for Quirkos projects so that people never loose access to their own or other's data.

We have already started work on the next major release of Quirkos, which will be version 1.5. This is going to include two major and highly requested new features. First will be the ability to merge project files.

I know a lot of people work as a team, especially in multiple locations, and sharing one file back and forwards has been a pain at the moment. We have quite an exciting solution being tested for this, which will allow projects to be merged together from multiple coders, different frameworks, and on different sources. We are confident that this is going to be the most powerful, but also easiest to use project merge function in any qualitative software package.

The second addition will be memos! The ability to comment and write memos and reflexive text during the analysis is a fundamental part of creating strong and transparent qualitative analysis, and previously users have had to use Source Properties and write in dedicated Memo Sources to achieve this in Quirkos. However, the next release will create dedicated functionality to allow many different types of commenting, and greatly improve collaborative and reflexive practice in your analysis.

Quirkos v1.5 should be released in the next 6 months, and will include the usual number of small tweaks to operation and work-flow that get requested, so if you have any ideas or things that are bugging you, let us know! More than half of the improvements above were requested by users, so e-mail support@quirkos.com and let us know how we can make the best software for qualitative research!

So you spend weeks or months coding all your qualitative data. Maybe you even did it multiple times, using different frameworks and research paradigms. You've followed our introduction guides and everything is neatly (or fairly neatly) organised and inter-related, and you can generate huge reports of all your coding work. Good job! But what happens now?

It's a question asked by lot of qualitative researchers: after all this bruising manual and intellectual labour, you hit a brick wall. After doing the coding, what is the next step? How to move the analysis forward?

The important thing to remember is that coding is not really analysis. Coding is often a precursor to analysis, in the same way that a good filing system is a good start for doing your accounts: if everything is in the right place, the final product will come together much easier. But coding is usually a reductive and low-level action, and it doesn't always bring you to the big picture. That's what the analysis has to do: match up your data to the research questions and allow you to bring everything together. In the words of Zhang and Wildemuth you need to look for “meanings, themes and patterns”

Match up your coding to your research questions

Now is a good time to revisit the research question(s) you originally had when you started your analysis. It's easy during the coding process to get excited by unexpected but fascinating insights coming from the data. However, you usually need to reel yourself in at this stage, and explore how the coded data is illuminating the quandaries you set out to explore at the start of the project.

Look at the coded framework, and see which nodes or topics are going to help you answer each research question. Then you can either group these together, or start reading through the coded text by theme, probably more than once with an eye for one research question each time. Don't forget, you can still tag and code at this stage, so you can have a category for 'Answers research question 1' and tag useful quotes there.

One way to do this in Quirkos is the 'Levels' function, which allows you to assign codes/themes to more than one grouping. You might have some coded categories which would be helpful in answering more than one research question: you can have a level for each research question, and  Quirks/categories can belong to multiple appropriate levels. That way, you can quickly bring up all responses relevant to each research question, without your grouping being non-exclusive. 

Analyse your coding structure!

It seems strange to effectively be analysing your analysis, but looking at the coding framework itself gets you to a higher meta-level of analysis. You can grouping themes together to identify larger themes and coding. It might also be useful to match your themes with theory, or recode them again into higher level insights. How you have coded (especially when using grounded theory or emergent coding) can reveal a lot about the data, and your clusterings and groupings, even if chosen for practical purposes, might illuminate important patterns in the data.

In Quirkos, you can also use the overlap view to show relationships between themes. This illustrates in a graphical chart how many times sections of text 'overlap' - in that a piece of text has been coded with both themes. So if you have simple codes like 'happy' or 'disappointed' you can what themes have been most coded with disappointment. This can sometimes quickly show surprises in the correlations, and lets you quickly explore possible significant relationships between all of your codes. However, remember that all these metrics are quantitative, so are dependent on the number of times a particular theme has been coded. You need to keep reading the qualitative text to get the right context and weight, which is why Quirkos shows you all the correlating text on the right of the screen in this view.

Compare and contrast

Another good way to make your explorations more analytical is to try and identify and explain differences: in how people describe key words or experiences, what language they use, or how their opinions are converging or diverging from other respondents. Look back at each of the themes, and see how different people are responding, and most importantly, if you can explain the difference through demographics or difference life experiences.

In Quirkos this process can be assisted with the query view, which allows you to see responses from particular groups of sources. So you might want to look at differences between the responses of men and women, as shown below. Quirkos provides a side-by-side view to let you read through the quotes, comparing the different responses. This is possible in other software too, but requires a little more time to get different windows set up for comparison.

Match and re-explore the literature

It's also a good time to revisit the literature. Look back at the key articles you are drawing from, and see how well your data is supporting or contradicting their theory or assumptions. It's a really good idea to do this (not just at the end) because situating your finding in the literature is the hallmark of a well written article or thesis, and will make clear the contribution your study has made to the field. But always be looking for an extra level of analysis, try and grow a hypothesis of why your research differs or comes to the same conclusions – is there something in the focus or methodology that would explain the patterns?

Keep asking 'Why'

Just like an inquisitive 6 year old, keep asking 'Why?'! You should have multiple levels of Why, with explanations in qualitative focus usually explaining individual, then group, and all the way up to societal levels of causation. Think of the maxim 'Who said What, and Why?'. The coding shows the 'What', exploring the detail and experiences of the respondents is the 'Who', the Why needs to explore not just their own reasoning, but how this connects to other actors in the system. Sometimes this causation is obvious to the respondent, especially if articulated because they were always asked 'why' in the interview! However analysis sometimes requires a deeper detective type reading, getting to the motivations as well as actions of the participants.

Don't panic!

Your work was not in vain. Even if you end up for some reason scrapping your coding framework and starting again, you will have become so much more engaged with your data by reading it through so closely, and this will be a great help knowing how to take the data forward. Some people even discover that coding data was not the right approach for their project, and use it very little in the final analysis process. Instead they may just be able to pull together important findings in their head, the time taken to code the data having made key findings pop out from the page.

And if things still seem stuck, take a break, step back and print out your data and try and read it from a fresh angle. Wherever possible, discuss with others, as a different perspective can come not just from other people's ideas, but just the process of having to verbally articulate what you are seeing in the data.

Also remember to check out Quirkos, a software tool that helps constantly visualise your qualitative analysis, and thus keep your eye on what is emerging from the data. It's simple to learn, affordably priced, and there is a free trial to download for Windows, Mac and Linux so you can see for yourself if it is the right fit for your qualitative analysis journey. Good luck!

An article was recently posted on the excellent Digital Tools for Qualitative Research blog on how you can use standard spreadsheet software like Excel to do qualitative analysis. There are many other articles describing this kind of approach, for example Susan Eliot or Meyer and Avery (2008). However, it’s also possible to use word processing software as well, see for example this presentation from Jean Scandlyn on the pros and cons of common software for analysing qualitative data.

For a lot of researchers, using Word or Excel seems like a good step up from doing qualitative analysis with paper and highlighters. It’s much easier to keep your data together, and you can easily correct, undo and do text searches. You also get the advantage of being able to quickly copy and paste sections from your analysis into research articles or a thesis. It’s also tempting because nearly everyone has access to either Microsoft Office products or free equivalents like OpenOffice (http://www.libreoffice.org) or Google Docs and knows how to use them. In contrast, qualitative analysis software can be difficult to get hold of: not all institutions have licences for them, and they can have a steep learning curve or high upfront cost.

However, it is very rare that I recommend people use spreadsheets or word processing software for a qualitative research project. Obviously I have a vested interest here, but I would say the same thing even if I didn’t design qualitative analysis software for a living. I just know too many people who have started out without dedicated software and hit a brick wall.

Spreadsheet cells are not very good ways to store text.

If you are going to use Excel or an equivalent, you will need to store your qualitative text data in it somehow. The most common method I have seen is to keep quotes or paragraphs as a separate cell in a column for the text. I’ve done this in a large project, and it fiddly to copy and paste the text in the right way. You will also find yourself struggling with formatting (hint – get familiar with the different wrap text and auto column width options). It also becomes a chore to separate out paragraphs into smaller sections to code them differently, or merge them together. Also, if you have data in other formats (like audio or video) it’s not really possible to do anything meaningful with them in Excel.

You must master Excel to master your analysis

As Excel or other spreadsheets are not really designed for qualitative analysis, you need to use a bit of imagination to sort and categorise themes and sources. With separate columns for source names and your themes, this is possible (although can get a little laborious). However, to be able to find particular quotes, themes and results from sources, you will need to properly understand how to use Pivot Tables and filters. This will allow you some ability to manage and sort your coded data.

It’s also a good idea to get to grips with some of the keyboard shortcuts for your spreadsheet software, as these will help take away some of the repetitive data entry you will need to do when coding extracts. There is no quick drag-and-drop way to assign text to a code, so coding will almost always be slower than using dedicated software.

For these reasons, although it seems like just using software like Excel you already know will be easier, it can quickly become a false economy in terms of the time required to code and learn advanced sorting techniques.

Word makes coding many different themes difficult.

I see a lot of people (mostly students) who start out doing line-by-line coding in Word, using highlight colours to show different topics. It’s very easy to fall into this: while reading through a transcript, you highlight with colours bits that are obviously about one topic or another, and before you know it there is a lot of text sorted and coded into themes and you don’t want to loose your structure. Unfortunately, you have already lost it! There is no way in Word or other word processing software to look at all the text highlighted in one colour, so to review everything on one topic you have to look through the text yourself.

There is also a hard limit of 15 (garish) colours, which limits the number of themes you can code, and it’s not possible to code a section with more than one colour. Comments and shading (in some word-processors) can get around this, but it is still limited: there is no way to create groups or hierarchies of similar themes.

I get a lot of requests from people wanting to bring coded work from a word processor into Quirkos (or other qualitative software) but it is just not possible.

No reports, or other outputs

Once you have your coded data – how do you share it, summarise it or print it out to read through away from the glow of the computer? In Word or Excel this is difficult. Spreadsheets can produce summaries of quantitative data, but have very few tools that deal with text. Even getting something as simple as a word count is a pain without a lot of playing around with macros. So getting a summary of your coding framework, or seeing differences between different sources is hard.

Also, I have done large coding projects in Excel, and printing off huge sheets and long rows and columns is always a struggle. For meetings and team work, you will almost always need to get something out of a spreadsheet to share, and I have not found a way to do this neatly. Suggestions welcome!

I’m not trying to say that using Word or Excel is always a bad option, indeed Quirkos lets you export coded data to Word or spreadsheet format to read, print and share with people who don’t have qualitative software, and to do more quantitative analysis. However, be aware that if you start your analysis in Word or Excel it is very hard to bring your codes into anything else to work on further.

Quirkos tries to make dedicated qualitative software as easy to learn and use as familiar spreadsheet and word processing tools, but with all the dedicated features that make qualitative analysis simple and more enlightening. It’s also one of the most affordable packages on the market, and there is a free trial so you can see for yourself how much you gain by stepping up to real qualitative analysis software!