Starting a qualitative research thesis, and choosing a CAQDAS package

qualitative thesis software

 

For those about to embark on a qualitative Masters or PhD thesis, we salute you!

 

More and more post-graduate students are using qualitative methods in their research projects, or adopting mixed-method data collection and using a small amount of qualitative data which needs to be combined with quantitative data. So this year, how can students decide the best approach for the analysis of their data, and can CAQDAS or QDA software help their studies?

 

First, as far as possible, don’t chose the software, choose the method. Think about what you are trying to research, the best way to get deep data to answer your research questions. The type and amount of data you have will be an important factor. Next, how much existing literature and theory there is around your research area? This will affect whether you will adopt a grounded theory approach, or will be testing or challenging existing theory.

 

Indeed, you may decide that that you don’t need software for your research project. For small projects, especially case studies, you may be more comfortable using printouts of your data, and while reading mark important sections with highlighters and post-it notes. Read Séror (2005) for a comparison of computer vs paper methods. You could also look at the 5 Level QDA, an approach to planning and learning the use of qualitative software so that you develop strategies and tactics that help you make the most of the QDA software.

 

Unfortunately, if you decide you want to use a particular software solution it’s not always as simple as it should be. You will have to eventually make a practical choice based on what software your university has, what support they provide, and what your peers and supervisors use.

 

However, while you are a student, it’s also a good time to experiment and see what works best for you. Not only do all the major qualitative software packages offer a free trial, student licences are hugely discounted against the full versions. This gives you the option to buy a copy for yourself (for a relatively small amount of money).

 

There’s a lot of variety in the different qualitative data analysis software available. The most common one is Nvivo, which your university or department may already have a licence for. This is a very powerful package, but can be intimidating for first-time users. Common alternatives like MAXQDA or Atlas.ti are more user friendly, but also adopt similar spreadsheet-like interfaces. There are also lots of more niche alternatives, for example Transana is unmatched for video analysis, and Dedoose works entirely in the cloud so you can access it from any computer. For a more comprehensive list, check out the Wikipedia list, or the profiles on textanalysis.info

 

Quirkos does a couple of things differently though. First, our student licences don’t expire, and are some of the cheapest around. This means that it doesn’t matter if your PhD takes 3 or 13 years, you will still be able to access your work and data without paying again. And yes, you can keep using your licence into your professional career. It also aims to be the easiest software package to use, and puts visualisations of the data first and foremost in the interface.

 

So give Quirkos a try, but don’t forget about all the other alternatives out there: between them all you will find something that works in the way you want it to and makes your research a little less painful!

 

 

Tips for managing mixed method and participant data in Quirkos and CAQDAS software

mixed method data

 

Even if you are working with pure qualitative data, like interview transcripts, focus groups, diaries, research diaries or ethnography, you will probably also have some categorical data about your respondents. This might include demographic data, your own reflexive notes, context about the interview or circumstances around the data collection. This discrete or even quantitative data can be very useful in organising your data sources across a qualitative project, but it can also be used to compare groups of respondents.

 


It’s also common to be working with more extensive mixed data in a mixed method research project. This frequently requires triangulating survey data with in-depth interviews for context and deeper understanding. However, much survey data also includes qualitative text data in the form of open-ended questions, comments and long written answers.

 


This blog has looked before at how to bring in survey data from on-line survey platforms like Surveymonkey, Qualtrics and Limesurvey. It’s really easy to do this, whatever you are using, just export as a CSV file, which Quirkos can read and import directly. You’ll get the option to change whether you want each question to be treated as discrete data, a longer qualitative answer, or even the name/identifier for each source.

 


But even if you haven’t collected your data using an online platform, it is quite easy to format it in a spreadsheet. I would recommend this as an option for many studies, it’s simply good data management to be able to look at all your participant data together. I often have a table of respondent’s data (password protected of course) which contains a column for names, contact details, whether I have consent forms, as well as age, occupation and other relevant information. During data collection and recruitment having this information neatly arranged helps me remember who I have contacted about the research project (and when), who has agreed to take part, as well as suggestions from snowball sampling for other people to contact.

 


Finally, a respondent ‘database’ like this can also be used to record my own notes on the respondents and data collection. If there is someone I have tried to contact many times but seems reluctant to take part, this is important to note. It can remind me when I have agreed to interview people, and keep together my own comments on how well this went. I can record which audio and transcript files contain the interview for this respondent, acting as a ‘master key’ of anonymised recordings. 

 


So once you have your long-form qualitative data, how best to integrate this with the rest of the participant data? Again, I’m going to give examples using Quirkos here, but the similar principals will apply to many other CAQDAS/QDA software packages.

 


First, you could import the spreadsheet data as is, and add the transcripts later. To do this, just save your participant database as a CSV file in Excel, Google Docs, LibreOffice or your spreadsheet software of choice. You can bring in the file into a blank Quirkos project using the ‘Import source from CSV’ on the bottom right of the screen. The wizard in the next page will allow you to choose how you want to treat each column in the spreadsheet, and each row of data will become a new source. When you have brought in the data from the spreadsheet, you can individually bring the qualitative data in as the text source for each participant, copy and pasting from wherever you have the transcript data.

 


However, it’s also possible to just put the text into a column in the spreadsheet. It might look unmanageable in Excel when a single cell has pages of text data, but it will make for an easy one step import into Quirkos. Now when you bring in the data to Quirkos, just select the column with the text data as the ‘Question’ and discrete data as the ‘Properties’ (although they should be auto-detected like this).

 


You can also do direct data entry in Quirkos itself, and there are some features to help make this quick and relatively painless. The Properties and Values editor allows you to create categories and values to define your sources. There are also built in values for True/False, Yes/No, options from 1 -10 or Likert scales from Agree to Disagree. These let you quickly enter common types of data, and select them for each source. It’s also possible to export this data later as a CSV file to bring back into spreadsheet software.

 

mixed method data entry in quirkos

 

Once your data has been coded in Quirkos, you can use tools like the query view and the comparison views to quickly see differences between groups of respondents. You can also create simple graphs and outputs of your quantitative and discrete data. Having not just demographic information, but also your notes and thoughts together is vital context to properly interpreting your qualitative and quantitative data.

 

 

A final good reason to keep a good database of your research data is to make sure that it is properly documented for secondary analysis and future use. Should you want to ever work with the data again, share it with another research team, or the wider community, an anonymised data table like this is important to make sure the research has the right metadata to be used for different lines of enquiry.

 

 

Get an overview of Quirkos and then try for yourself with our free trial, and see how it can help manage pure qualitative or mixed method research projects.

 

 

 

7 things we learned from ICQI 2016

ICQI conference - image from Ariescwliang

 

I was lucky enough to attend the ICQI 2016 conference last week in Champaign at the University of Illinois. We managed to speak to a lot of people about using Quirkos, but there were hundreds of other talks, and here are some pointers from just a few of them!

 

 

1. Qualitative research is like being at high school
Johnny Saldaña’s keynote described (with cutting accuracy) the research cliques that people tend to stick to. It's important for us to try and think outside these methodological or topic boxes, and learn from other people doing things in different ways. With so many varied sessions and hundreds of talks, conferences like ICQI 2016 are great places to do this.

 

We were also treated to clips from high school movies, and our own Qualitative High School song! The Digital Tools thread got their own theme song: a list of all the different qualitative analysis software packages sung to the tune of ‘ABC’ - the nursery rhyme, not the Jackson 5 hit!

 

 

2. There is a definite theoretical trend
The conference featured lots of talks on Butler, Foucault, but not one explicitly on Derrida! A philosophical bias perhaps? I’m always interested in the different philosophy that is drawn from between North American, British and Continental debates…

 

 

3. Qualitative research balances a divide between chaos and order
Maggie MacLure gave an intriguing keynote about how qualitative research needs to balance the intoxicating chaos and experimentation of Dionysus with the order and clarity of Apollo (channelling Deleuze). She argued that we must resist the tendency of method and neo-liberal positioned research to push for conformity, and go further in advocating for real ontological change. She also said that researchers should do more to challenge the primacy of language: surely why we need a little Derrida here and there?!

 

 

4. We should embrace doubt and uncertainty
This was definitely something that Maggie MacLure's keynote touched on, but a session chaired by Charles Vander talked about uncertainty in the coding process, and how this can be difficult (but ultimately beneficial). Referencing Locke, Golden-Biddle and Feldman (2008), Charles talked about the need to Embrace not knowing, nurture hurdles and disrupt order (while also engaging with the world and connecting with struggle). It's important for students that allowing doubt and uncertainty doesn't lead to fear – a difficult thing when there are set deadlines and things aren’t going the right way, and even true for most academics! We need to teach that qualitative analysis is not a fixed linear process, experimentation and failure is key part of it. Kathy Charmaz echoed this while talking about grounded theory, and noted that ‘coding should be magical, not just mechanical’.

 


5. We should challenge ourselves to think about codes and coding in completely different ways

Johnny Saldaña's coding workshop (which follows on from his excellent textbook) gave examples of the incredible variety of different coding categories one can create. Rather than just creating merely descriptive index coding, try and get to the actions and motivations in the text. Create code lists which are based around actions, emotions, conflicts or even dramaturgical concepts: in which you are exploring the motivations and tactics of those in your research data. More to follow on this...

 

 

6. We still have a lot to learn about how researchers use qualitative software
Two great talks from Ely Lieber and NYU/CUNY took the wonderful meta-step of doing qualitative (and mixed method) analysis on qualitative researchers, to see how they used qualitative software and what they wanted to do with it.

Katherine Gregory and Sarah DeMott looked at responses from hundreds of users of QDA software, and found a strong preference for getting to outputs as soon as possible, and saw people using qualitative data in very quantitative ways. Eli Lieber from Dedoose looked at what he called ‘Research and Evaluation Data Analysis Software’ and saw from 355 QDA users that there was a risk of playing with data rather than really learning from it, and that many were using coding in software as a replacement for deep reading of the data.


There was also a lot of talk about the digital humanities movement, and there was some great insight from Harriett Green on how this shift looks for librarians and curators of data, and how researchers are wanting to connect and explore diverse digital archives.

 


7. Qualitative research still feels like a fringe activity
The ‘march’ of neo-liberalism was a pervasive conference theme, but there were a lot of discussions around the marginalised place of qualitative research in academia. We heard stories of qualitative modules being removed or made online only, problems with getting papers submitted in mainstream journals, and the lack of engagement from evidence users and policy makers. Conferences like this are essential to reinforce connections between researchers working all over the world, but there is clearly still need for a lot of outreach to advance the position of qualitative research in the field.

 

 

There are dozens more fascination talks I could draw from, but these are just a few highlights from my own badly scribbled notes. It was wonderful to meet so many dedicated researchers, working on so many conceptual and social issues, and it always challenges me to think how Quirkos can better meet the needs of such a disparate group of users. So don’t forget to download the trial, and give us more feedback!

 

You should also connect with the Digital Tools for Qualitative Research group, who organised one of the conference Special Interest Groups, but has many more activities and learning events across the year. Hope to see many more of you next year!

 

Quirkos version 1.4 is here!

quirkos version 1.4

It’s been a long time coming, but the latest version of Quirkos is now available, and as always it’s a free update for everyone, released simultaneously on Mac, Windows and Linux with all the new goodies!


The focus of this update has been speed. You won’t see a lot of visible differences in the software, but behind the scenes we have rewritten a lot of Quirkos to make sure it copes better with large qualitative sources and projects, and is much more responsive to use. This has been a much requested improvement, and thanks to all our intrepid beta testers for ensuring it all works smoothly.


In the new version, long coded sources now load in around 1/10th of the time! Search results and hierarchy views load much quicker! Large canvas views display quicker! All this adds up to give a much more snappy and responsive experience, especially when working with large projects. This takes Quirkos to a new professional level, while retaining the engaging and addictive data coding interface.


In addition we have made a few small improvements suggested by users, including:


• Search criteria can be refined or expanded with AND/OR operands
• Reports now include a summary section of your Quirks/codes
• Ability to search source names to quickly find sources
• Searches now display the total number of results
• Direct link to the full manual

 

There are also many bug fixes! Including:
• Password protected files can now be opened across Windows, Mac and Linux
• Fix for importing PDFs which created broken Word exports
• Better and faster CSV import
• Faster Quirk merge operations
• Faster keyword search in password protected files

 

However, we have had to change the .qrk file format so that password protected files can open on any operating system. This means that projects opened or created in version 1.4 cannot be opened in older versions of Quirkos (v1.3.2 and earlier).


I know how annoying this is, but there should be no reason for people to keep using older versions: we make the updates free so that everyone is using the same version. Just make sure everyone in your team updates!

 

When you first open a project file from an older version of Quirkos in 1.4, it will automatically convert it to the new file format, and save a backup copy of the old file. Most users will not notice any difference, and you can obviously keep working with your existing project files. But if you want to share your files with other Quirkos users, make sure they also have upgraded to the latest version, or they will get an error message trying to open a file from version 1.4.

 

All you need to do to get the new version is download and install from our website (www.quirkos.com/get.html) and install to the same location as the old Quirkos. Get going, and let us know if you have any suggestions or feedback! You could see your requests appear in version 1.5!

 

What can CAQDAS do for you? The Five-Level QDA

five level qda

 

I briefly mentioned in my last blog post an interesting new article by Silver and Woolf (2015) on teaching QDA (Qualitative Data Analysis) and CAQDAS (Computer Assisted Qualitative Data AnalysiS). It’s a great article, not only because it draws from more than 20 years combined pedagogical experience, but suggests a new way to guide students through using software for qualitative analysis.

 

The basis of the strategy is the ‘Five-Level QDA’ approach, which essentially aims to get students to stop and think about how they want to do their qualitative analysis before they dive head-first into learning a particular CAQDAS package. Users are guided through a five-step tool that I would paraphrase as:

 

  1. Stating the analysis/research objectives
  2. Devising an analytic plan
  3. Identifying matches between the plan and available tools
  4. Selecting which operations to do in which tools
  5. Creating a workflow of tools and software to meet all the aims above


For more detail, it’s worth checking out the full article which includes example worksheets, and there is also a textbook due out covering the approach in more depth. It’s also interesting to see how they describe the development of their pedagogical approach in the last decade or so.

 

The Five-Level method is designed to be delivered remotely, as well as in workshops, but to start out with being a software agnostic approach, drawing from the experience of the trainers to choose the best approach for each researcher and research project. Based around Analytic Planning Worksheets, it feels like the main aim is to get people to step back and think about their needs before learning the software.

 

This is often badly needed, mostly due to the practical limitations. Firstly, many people (especially new researchers) don’t do a detailed analytical plan when designing their project or research questions. In qualitative research, this is not always a disaster, often one source of investigation ends up being much richer than anticipated, and the variety of methods used mean that data doesn’t always look as we thought it would when we started (for better or worse).

 

However, there are also some very practical limitations which lead to people learning a CAQDAS package before they start their research journey. Often training courses are only offered once a semester (or year), so you need to take advantage of that when you can. While ideally there would be an interactive process between learning the capabilities and refining the analytical strategy, in the timescale of one project this is not always feasible. Often people have learnt so much after their first qualitative project, that the next time their analytical approach is extremely different.

 

The other issue is what CAQDAS is actually available: often a department or school will have a licence for just one (or maybe two) packages, and understandably, the training offered will often focus on those. There are also practical limitations when working as part of a team (especially people in a different institution) who might not have access to the same software. This affects the approach a lot, because it’s important to choose a workflow that everyone can participate in. I’ve been involved in projects where we end up using Excel for analysis, because everyone had access and familiarity with spreadsheet software.

 

I think that this is the kind of consideration that the Silver and Woolf worksheets are trying to tease out, and their examples illustrate how by point 5 people have chosen a number of tools that they can use together to answer their research questions.

 

As a final point, I think that RTP (Research Training Programmes) courses offered for post-grad students sometimes leave a lot to be desired on this front. Even those that are specifically on qualitative methodologies (where available) tend in my experience to have little more than a slide on the whole analysis process, and sometimes just a bullet point on software! I spend a lot of time talking to people about CAQDAS, and I am always surprised at how few people have heard even of NVivo, let alone a dozen alternative packages that I could name off the top of my head. Yet each has its own strengths and weaknesses: Transana is great for video, the Provalis products for stats geeks, MaxQDA a friendly all-rounder, Dedoose for working remotely, and Quirkos obviously for beginners.

 

But it’s a chicken and egg problem – to know what which software is best, you need to know what you can do with it. Which is why it can help so much to draw on the experience of CAQDAS trainers, but not just to go on a course and learn one package, but to go with an open mind and a research question, and let them suggest the best combination for each approach. In short, ask not what your CAQDAS can do for you, ask what you want to do with your CAQDAS!

 

Update (14/8/15):

 

Christain Schmieder has written a response to this blog post and the 5-level QDA, and how it links into his curriculum for qualitative question generation using CAQDAS.