Studies and surveys seem to show that the amount of qualitative research is growing, and that more and more people are using software to help with their qualitative analysis (Woods et al. 2015). However, these studies also highlight that users report problems with learning qualitative software, and that universities sometimes struggle to provide enough expertise to teach and troubleshoot them (Gibbs 2014).

Quirkos was specifically designed to address many of these issues, and our main aim is to be a champion for the field of qualitative research by making analysis software more accessible. But what does accessibility mean in this context, and what problems still need to be overcome?

Limitations of paper

The old paper and highlighters method is a very easy and accessible approach to qualitative analysis. Indeed, it’s common for some stages in the analysis exploration to be done on paper (such as reviewing), even if most of it is done in software. However, when projects get above a certain size or complexity, it can be difficult to keep track of all the different sources and themes. Should you have dozens of topics you are looking for in the project, you can quickly run out of different colours for your highlighters or Post-it notes (6 colours seems to be the most you can easily find) and I’ve seen very creative but laborious use of alternating coloured stripes and other techniques!

In these situations, qualitative analysis software can actually be more accessible, and make the process a lot easier. The big advantage to computers is that they have huge memories, and think nothing of working with hundreds of sources, and hundreds of coding topics. There are some people that are able to keep hundreds of topics in their head at once, (my former boss Dr Sarah Salway was one of these) but for us mortals, software can really help. However, software needs to try and be as easy to use as paper, and make sure that it doesn’t start making the data more difficult to see, or makes the coding process seem more important than deep reading and comprehension.

Learning curve

Secondly, if the software is going to be accessible, it has to be easy to learn and understand. While the best way to learn is often with face-to-face teaching, not everyone has the luxury of access to this, and it can be expensive. So there needs to be good, and freely available training materials. Ideally the software would be so simple that it didn’t need tuition at all, but inevitably people will get stuck, and good video guides and manuals should be easily available.

The software has to tread a fine line between being clear and non-patronising. I did have a discussion with one trainer in qualitative analysis about introducing an animated guide like Clippy to QDA software, to guide users through the process. Can you imagine what this would be like? A little character that pops up and says things like “Hi! It looks like you are doing grounded theory! Would you like some help with that?”. But most users I talk to want the software to be as invisible as possible. If it gets in the way frequently it is hindering, not helping the analysis process.

Flexibility

Software also needs to be as flexible as possible, it’s no good if it doesn’t fit your approach or the way you need to work. So it has to allow you to work with the type of data you have, without having to spend ages reformatting it.
It should be neutral to your approach as well, making sure that whatever the methodological and theoretical approach the user is taking, the software will allow researchers to work their own way. A lot of flexibility requirements comes when working with others too, getting data both in and out should be painless, and fit the rest of a researcher’s workflow.

Sharing with others

Most qualitative researchers like to work with others, either as part of a research team, or just as a resource to bounce ideas off. Sending project files from qualitative analysis software to another research is easy enough, but often only if they are using the same software on the same operating system. Cross platform working is really important, and it is frustrating at the moment how difficult it is to get coded data from one software package to another. We are having discussions with other developers of qualitative software about making sure that there is interoperability, but it is going to be a long journey.

It’s also important for software to create exports of the data in more common formats, such as PDF, Word files or the like, so people without specialist CAQDAS software can still engage and see the data.

Visual impairment

At the moment Quirkos is a very visual piece of software, and not well suited to those with visual or physical limitations. We have tried to choose options that make the software easier for those with vision impairment, such as high contrast text and large font sizes, but there is still a long way to go. At the moment, although shortcut keys can make using Quirkos a lot easier, navigating and selecting text without a mouse is not possible. We want to add the ability to run all the main operations from the keyboard or a specialist controller so that there are fewer barriers for those with reduced mobility.

We’ve even had serious discussions with blind qualitative researchers about how Quirkos could meet their needs! The main problem here seems to be the wide range of specialist computers and equipment – although there are fantastic tools out there for people with total or near-total visual impairment, they are all very different, and getting software that would work with them all is a huge challenge.

Affordability

However, there is another barrier to access for many: the price of software licences. In many countries, relative low wages mean that qualitative analysis software is prohibitively expensive. This is not just in Latin America, Africa and many parts of Asia: even in Eastern Europe, a single CAQDAS licence can cost as much as many earn in a month. (Haratyk and Kordasiewicz 2014).

So also am proud to announce from today, that we will offer a 25% discount for researchers based in ‘developing’ or ‘emerging’ nations. I don’t like these terms, so for clarity I am taking this to mean any country with a GDP Per-Capita below US$2600 PPP, or a monthly average salary below 1000EUR. This is on top of our existing discounts for students, education, charity and education sectors. As far as I can see, we are the first qualitative analysis software company to offer a discount of this type. To check if your country qualifies, and to place an order with this discount, just send an e-mail to sales@quirkos.com and we will be happy to help.

Quirkos is already around half the price of the other major CAQDAS software packages, but from now we are able to provide an extra discount to researchers in 150 countries, representing nearly 80% of the world population. We hope this will help qualitative researchers in these countries to use qualitative research to explore and answer difficult questions in health, development, transparency and increasing global happiness.

The terms ‘unstructured data’ and ‘qualitative data’ are often used interchangeably, but unstructured data is becoming more commonly associated with data mining and big data approaches to text analytics. Here the comparison is drawn with databases of data where we have a defined field and known value and the loosely structured (especially to a computer) world of language, discussion and comment. A qualitative researcher lives in a realm of unstructured data, the person they might be interviewing doesn’t have a happy/sad sign above their head, the researcher (or friend) must listen and interpret their interactions and speech to make a categorisation based on the available evidence.

At their core, all qualitative analysis software systems are based around defining and coding: selecting a piece of text, and assigning it to a category (or categories). However, it is easy to see this process as being ‘reductionist’: essentially removing a piece of data from it’s context, and defining it as a one-dimensional attribute. This text is about freedom. This text is about liberty. Regardless of the analytical insight of the researcher in deciding what relevant themes should be, and then filtering a sentence into that category, the final product appears to be a series of lists of sections of text.

This process leads to difficult questions such as, is this approach still qualitative? Without the nuanced connections between complicated topics and lived experiences, can we still call something that has been reduced to a binary yes/no association qualitative? Does this remove or abstract researchers from the data? Isn't this a way of quantifying qualitative data?

While such debates are similarly multifaceted, I would usually argue that this process of structuring qualitative data does begin to categorise and quantify it, and it does remove researchers from their data. But I also think that for most analytical tasks, this is OK, if not essential! Lee and Fielding (1996) say that “coding, like linking in hypertext, is a form of data reduction, and for many qualitative researchers is an important strategy which they would use irrespective of the availability of software”. When a researcher turns a life into 1 year ethnography, or a 1 hour interview, that is a form of data reduction. So is turning an audio transcript into text, and so is skim reading and highlighted printed versions of that text.

It’s important to keep an eye on the end game for most researchers: producing a well evidenced, accurate summary of a complex issue. Most research, as a formula to predict the world or a journal article describing it, is a communication exercise that (purely by the laws of entropy if not practicality) must be briefer than the sum of it’s parts. Yet we should also be much more aware that we are doing this, and together with our personal reflexivity think about the methodological reflexivity, and acknowledge what is being lost or given prominence in our chosen process.

Our brains are extremely good at comprehending the complex web of qualitative connections that make everyday life, and even for experienced researchers our intuitive insight into these processes often seem to go beyond any attempt to rationalise them. A structuralist approach to qualitative data can not only help as an aide-mémoir, but also to demonstrate our process to others, and challenge our own assumptions.

In general I would agree with Kelle (1997) that “the danger of methodological biases and distortion arising from the use of certain software packages is overemphasized in current discussions”. It’s not the tool, it’s how you use it!

Having been round many market research firms in the last few months, the most striking things is the piles of paper, or at least in the neater offices - shelves of paper!

When we talk to small market research firms about their analysis process, many are doing most of their research by printing out data and transcripts, and coding them with coloured highlighters. Some are adamant that this is the way that works best for them, but others are a little embarrassed at the way they are still using so much time and paper with physical methods.

The challenge is clear – the short turn-around time demanded by clients doesn't leave much time for experimenting with new ways of working, and the few we had talked to who had tried qualitative analysis software quickly felt this wasn't something they were able to pick up quickly.

So, most of the small Market Research agencies with less than 5 associates (as many as 75% of firms in the UK) are still relying on work-flows that are difficult to share, don't allow for searching across work, and don't have an undo button! Not to mention the ecological impact of all that printing, and the risk to deadlines from an ill placed mug of coffee.

That's one of the reasons we created Quirkos, and why we are launching our new campaign this week at the Market Research Society annual conference in London. Just go to our new website, www.upgradefrompaper.com and watch our fun, one minute video about drowning in paper, and how Quirkos can help.

Quirkos isn't like other software, it is designed to mimic the physical action of highlighting and coding text on paper with an intuitive interface that you can use to get coding right away. In fact, we bet you can get coding a project before your printer has got the first source out of the tray.

You no longer need days of training to use qualitative analysis software, and Quirkos has all the advantages you'd expect, such as quick searches, full undo-redo capability and lots of flexibility to rearrange your data and framework. But it also has other pleasant surprises: there's no save button, because work is automatically saved after each action. And it creates graphical reports you can share with colleagues or clients.

Finally, you can export your work at any stage to Word, and print it out (if you so wish!) with all your coding and annotations as familiar coloured highlights – ideal to share, or just to help ease the transition to digital. It's always comforting to know you can go back to old habits at any time, and not loose the work you've already done!

It's obviously not just for market research firms; students, academics and charities who have either not tried any qualitative software before, or found the other options too confusing or expensive can reduce their carbon footprint and save on their department's printing costs!

So take the leap, and try it out for a month, completely free, on us. Upgrade from paper to Quirkos, and get a clear picture of your research!

www.upgradefrompaper.com

p.s. All the drawings in our video were done by our very own Kristin Schroeder! Not bad, eh?

I'm really happy to see that the talks from the University of Surrey CAQDAS 2014 are now up online (that's 'Computer Assisted Qualitative Data Analysis Software' to you and me). It was a great conference about the current state of software for qualitative analysis, but for me the most interesting talks were from experienced software trainers, about how people actually were using packages in practice.

There were many important findings being shared, but for me one of the most striking was that people spend most of their time coding, and most of what they are coding is text.

In a small survey of CAQDAS users from a qualitative research network in Poland, Haratyk and Kordasiewicz found that 97% of users were coding text, while only 28% were coding images, and 23% directly coding audio. In many ways, the low numbers of people using images and audio are not surprising, but it is a shame. Text is a lot quicker to skip though to find passages compared to audio, and most people (especially researchers) and read a lot faster than people speak. At the moment, most of the software available for qualitative analysis struggles to match audio with meaning, either by syncing up transcripts, or through automatic transcription to help people understand what someone is saying.

Most qualitative researchers use audio as an intermediary stage, to create a recording of a research event, such as in interview or focus group, and have the text typed up word-for-word to analyse. But with this approach you risk losing all of the nuance that we are attuned to hear in the spoken word, emphasis, emotion, sarcasm – and these can subtly or completely transform the meaning of the text. However, since audio is usually much more laborious to work with, I can understand why 97% of people code with text. Still, I always try to keep the audio of an interview close to hand when coding, so that I can listen to any interesting or ambiguous sections, and make sure I am interpreting them fairly.

Since coding text is what most people spend most of their time doing, we spent a lot of time making the text coding process in Quirkos was as good as it could be. We certainly plan to add audio capabilities in the future, but this needs to be carefully done to make sure it connects closely with the text, but can be coded and retrieved as easily as possible.

But the main focus of the talk was the gaps in users' theoretical knowledge, that the survey revealed. For example, when asked which analytical framework the researchers used, only 23% described their approach as Grounded Theory. However, when the Grounded Theory approach was described in more detail, 61% of respondents recognised this method as being how they worked. You may recall from the previous top-up, bottom-down blog article that Grounded Theory is essentially finding themes from the text as they appear, rather than having a pre-defined list of what a researcher is looking for. An excellent and detailed overview can be found here.

Did a third of people in this sample really not know what analytical approach they were using? Of course it could be simply that they know it by another name, Emergent Coding for example, or as Dey (1999) laments, there may be “as many versions of grounded theory as there were grounded theorists”.

Finally, the study noted users comments on advantages and disadvantages with current software packages. People found that CAQDAS software helped them analyse text faster, and manage lots of different sources. But they also mentioned a difficult learning curve, and licence costs that were more than the monthly salary of a PhD student in Poland. Hopefully Quirkos will be able to help on both of these points...