Qualitative analysis software

learn qualitative software

Qualitative Analysis Software

Articles on using and learning Qualitative Analysis Software in general, and Quirkos in particular. Also known as CAQDAS software or QDA software tools.



General qualitative software articles

Starting a qualitative research thesis, and choosing a CAQDAS package

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...

Comparing qualitative software with spreadsheet and word processor software
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...

Does software lead to the homogenisation of qualitative research?
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...

Practice Projects and learning Qualitative Data Analysis Software
Coding and analysing qualitative data is not only a time consuming, it’s a difficult interpretive exercise which, like learning a musical instrument, gets much better with practice. However, lots of students starting their first major qualitative or mixed method research project will benefit from completing a smaller project first...

Stepping back from qualitative software and reading coded qualitative data
There is a lot of concern that qualitative analysis software distances people from their data. Some say that it encourages reductive behaviour, prevents deep reading of the data, and leads to a very quantified type of qualitative analysis...

Reflections on qualitative software from KWALON 2016
Last week saw a wonderful conference held by the the Dutch network for qualitative research KWALON, based at the Erasmus University, Rotterdam. The theme was no less than the future of Qualitative...

Include qualitative analysis software in your qualitative courses this year
A new term is just beginning, so many lecturers, professors and TAs are looking at their teaching schedule for the next year. Some will be creating new courses, or revising existing modules,...

Using qualitative analysis software to teach critical thought
It's a key part of the curriculum for British secondary school and American high school education to teach critical thought and analysis. It's a vital life skill: the ability to...

Fracturing and choice in qualitative analysis software
Fundamental to the belief behind starting Quirkos was a feeling that qualitative research has great value to society, but should be made accessible to more people. One of the problems that we...

What can CAQDAS do for you: The 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...

The CAQDAS jigsaw: integrating with workflows
I'm increasingly seeing qualitative research software as being the middle piece of a jigsaw puzzle that has three stages: collection, coding/exploring, and communication. These steps...

Is qualitative data analysis fracturing?
Having been to several international conferences on qualitative research recently, there has been a lot of discussion about the future of qualitative research, and the changes happening in the...

Paper vs. computer assisted qualitative analysis
I recently read a great paper by Rettie et al. (2008) which, although based on a small sample size, found that only 9% of UK market research organisations doing qualitative research were using...

Analysing text using qualitative software
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...


Quirkos tutorials, guides and comparisons

7 unique things that make Quirkos awesome
Quirkos is now 3 years old! To celebrate, we’re taking a break from our regular programming of qualitative method posts to remind everyone why Quirkos is the best qualitative analysis software around...

Quirkos vs Nvivo: Differences and Similarities
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...

How Quirkos can change the way you look at your qualitative data
We always get a lot of inquiries in December from departments and projects who are thinking of spending some left-over money at the end of the financial year on a few Quirkos licences...

Making qualitative analysis software accessible
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)...

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

Sharing qualitative research data from Quirkos
Once you've coded, explored and analysed your qualitative data, it's time to share it with the world. For students, the first step will be supervisors, for researchers it might be peers...

Teaching qualitative analysis software with Quirkos
When people first see Quirkos, we often hear them say My students would love this! The easy learning curve, the visual feedback and the ability to work on Windows or Mac appeal...

Tips and advice from one year of Quirkos
This week marks the one-year anniversary of Quirkos being released to the market! On 6th October 2014, a group of qualitative researchers, academics and business mentors met in a bar in...

Freeing qualitative analysis from spreadsheet interfaces
The old mantra is that a picture tells a thousand words. You've probably seen Hans Rosling's talks on visualising quantitative data, or maybe even read some of Edward Tufte's books...

10 reasons to try qualitative analysis with Quirkos
Quirkos is the newest qualitative research software product on the market, but what makes it different, and worth giving the one-month free trial a go Here's a guide to the top 10 benefits to...

Levels: 3-dimensional node and topic grouping in Quirkos
One of the biggest features enabled in the latest release of Quirkos are 'levels', a new way to group and sort your Quirks thematically. While this was always an option in previous...

How to organise notes and memos in Quirkos
Many people have asked how they can integrate notes or memos into their project in Quirkos. At the moment, there isn't a dedicated memo feature in the current version of Quirkos (v1.0),...

Upgrade from paper with Quirkos
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...

True cross-platform support
Another key aim for Quirkos was to have proper multi-platform support. By that, I mean that it doesn't matter if you are using a desktop or laptop running Windows, a Mac, Linux, or a tablet,...


Quirkos release information

Announcing Quirkos v1.5
We are happy to announce the immediate availability of Quirkos version 1.5! As always, this update is a free upgrade for everyone who has ever brought a licence of Quirkos, so download now and enjoy the new features and improvements...

Quirkos 1.4.1 is now available for Linux
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...
Quirkos 1.4.1 is now available for Linux

Quirkos version 1.4.1 is here
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...

Quirkos version 1.4 is here!
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...

An early spring update on Quirkos for 2016
About this time last year, I posted an update on Quirkos development for the next year. Even though February continues to be cold and largely snow-drop free in Scotland, why not make it a...

What's in your ideal qualitative analysis software
We will soon start work on the next update for Quirkos. We have a number of features people have already requested which we plan to add to the next version, including file merge, memos, and a...

Quirkos for Linux!
We are excited to announce official Quirkos support for Linux! This is something we have been working on for some time, and have been really encouraged by user demand to support this Free and...

Quirkos 1.3 is released!
We are proud to announce a significant update for Quirkos, that adds significant new features, improves performance, and provides a fresh new look. Major changes include: PDF import Greater...

Quirkos v1.1 is here!
We are excited to announce that the first update for Quirkos can now be downloaded from here! Version 1.1 adds two main new features: batch import, and mutli-language reports. If you...

Spring software update for Quirkos
Even in Edinburgh it's finally beginning to get warmer, and we are planning the first update for Quirkos. This will be a minor release, but will add several features that users have been...

Quirkos is launched!
It's finally here! From today, anyone can download the full 1.0 release version of Quirkos for Windows or Mac OS X! Versions for Linux and Android will be appearing later in the month, but since...

Announcing Pricing for Quirkos
At the moment, (touch wood!) everything is in place for a launch next week, which is a really exciting place to be after many years of effort. From that day, anyone can download Quirkos, try it free...

Quirkos is just weeks away!
It's been a long time since I've had time to write a blog article, as there are so many things to put in place before Quirkos launches in the next few weeks. But one-by-one everything is...

Touching Text
Presenting Quirkos at the CAQDAS 2014 conference this month was the first major public demonstration of Quirkos, and what we are trying to do. It's fair to say it made quite a splash! But...



Qualitative coding and analysis

analysis and coding of qualitative data

Articles on the analysis and coding of qualitative data


Qualitative analysis

What is qualitative analysis?

How do you actually analyse qualitative data? How do you turn the results from your research into findings that can answer your research questions? Analysing qualitative data requires drawing meaning from it...

Making the leap from qualitative coding to analysis
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...

Circles and feedback loops in qualitative research
The best qualitative research forms an iterative loop, examining, and then re-examining. There are multiple reads of data, multiple layers of coding, and hopefully, constantly improving theory and insight into the underlying lived... world.

Stepping back from qualitative software and reading coded qualitative data
There is a lot of concern that qualitative analysis software distances people from their data. Some say that it encourages reductive behaviour, prevents deep reading of the data, and leads to a very quantified type of qualitative analysis...

What actually is Grounded Theory? A brief introduction
“It's where you make up as you go along!” For a lot of students, Grounded Theory is used to describe a qualitative analytical method, where you create a coding...

An introduction to Interpretative Phenomenological Analysis
Interpretative Phenomenological Analysis (IPA) is an increasingly popular approach to qualitative inquiry and essentially an attempt to understand how participants experience and make meaning of their world...

Triangulation in qualitative research
Most qualitative research will be designed to integrate insights from a variety of data sources, methods and interpretations to build a deep picture. Triangulation is the term used to describe this comparison and meshing of different data...

Analytical memos and notes in qualitative data analysis and coding
There is a lot more to qualitative coding than just deciding which sections of text belong in which theme. It is a continuing, iterative and often subjective process, which can take weeks or even months. During this time...

Coding Qualitative Data

Developing and populating a qualitative coding framework in Quirkos
In previous blog articles I've looked at some of the methodological considerations in developing a coding framework. This article looks at top-down or bottom-up approaches, whether you...

Qualitative coding with the head and the heart
In the analysis of qualitative data, it can be easy to fall in the habit of creating either very descriptive, or very general theoretical codes. It's often a good idea to take a step...

Play and Experimentation in Qualitative Analysis
In the last blog post article, I talked about the benefits of visualising qualitative data, not just in the communication and dissemination stage, but also during data analysis. For newcomers to the...

Top-down or bottom-up qualitative coding
In framework analysis, sometimes described as a top-down or 'a-priori' approach, the researcher decides on the topics of interest they will look for before they start the analysis, usually...

Turning qualitative coding on its head
For the first time in ages I attended a workshop on qualitative methods, run by the wonderful Johnny Saldana. Developing software has become a full time (and then some) occupation for me...

In vivo coding and revealing life from the text
Following on from the last blog post on creating weird and wonderful categories to code your qualitative data, I want to talk about an often overlooked way of creating coding topics using...

Against entomologies of coding
I was recently privileged to chair a session at ICQI 2017 entitled “The Archaeology of Coding”. It had a fantastic panel of speakers, including...

Integrating policy analysis into your qualitative research
It’s easy to get seduced by the excitement of primary data collection, and plan your qualitative research around methods that give you rich data from face-to-face contact with participants. But some research questions may be better illustrated or even mostly answered by analysis of existing documents...

Merging and splitting themes in qualitative analysis
To merge or to split qualitative codes, that is the question... One of the most asked questions when designing a qualitative coding structure is “How many codes should I...

Balance and rigour in qualitative coding frameworks
Training researchers to use qualitative software and helping people who get stuck with Quirkos, I get to see a lot of people’s coding frameworks. Most of the time they are great, often they are fine but have too many codes, but sometimes they just seem to lack a little balance...

Word clouds and word frequency analysis in qualitative data
What’s this blog post about? Well, it’s visualised in the graphic above! In the latest update for Quirkos, we have added a new and much requested feature, word clouds! I'm sure you've used these pretty tools before, they show a random display of all the words in a source of text...

Building queries to explore qualitative data
So, you've spent days, weeks, or even months coding your qualitative data. Now what Hopefully, just the process of being forced to read through the data, and thinking about the...

6 meta-categories for qualitative coding and analysis
When doing analysis and coding in a qualitative research project, it is easy to become completely focused on the thematic framework, and deciding what a section of text is about. However...



Qualitative methods blog posts

qualitative methods

Articles on qualitative methods



This series aims to introduce qualitative methods and some of the main approaches in collecting qualitative data.



Why qualitative research?
There are lies, damn lies, and statistics It's easy to knock statistics for being misleading, or even misused to support spurious findings. In fact, there seems to be a growing backlash at the...

What is a Qualitative approach
The benefit of having tastier satsumas is difficult to quantify: to turn into a numerical, comparable value. This is essentially what qualitative work does: measure the unquantifiable quality of...

An overview of qualitative methods
There are a lot of different ways to collect qualitative data, and this article just provides a brief summary of some of the main methods used in qualitative research. Each one is an art in its own...

Thinking About Me: Reflexivity in science and qualitative research
Reflexivity is a process (and it should be a continuing process) of reflecting on how the researcher could be influencing a research project. In a traditional positivist research paradigm...



Qualitative Interviews


10 tips for semi-structured qualitative interviewing
Many qualitative researchers spend a lot of time interviewing participants, so here are some quick tips to make interviews go as smooth as possible: before, during and after! 1. Let your...

Designing a semi-structured interview guide for qualitative interviews
Interviews are a frequently used research method in qualitative studies. You will see dozens of papers that state something like We conducted n in-depth semi-structured interviews with...



Focus Groups

Considering and planning for qualitative focus groups
This is the first in a two-part series on focus groups. This week, we are looking at some of the why you might consider using them in a research project...

Tips for running effective focus groups
In the last blog article I looked at some of the justifications for choosing focus groups as a method in qualitative research. This week, we will focus on some practical tips to make sure that focus groups run smoothly...



Participatory Methods

Participatory Qualitative Analysis
Engaging participants in the research process can be a valuable and insightful endeavour, leading to researchers addressing the right issues, and asking the right questions. Many funding...

Participant diaries for qualitative research
I've written a little about this before, but I really love participant diaries! In qualitative research, you are often trying to understand the lives, experiences and motivations of...



Qualitative and mixed method surveys

Bringing survey data and mixed-method research into Quirkos
Later today we are releasing a small update for Quirkos, which adds an important feature users have been requesting: the ability to quickly bring in quantitative and qualitative data from any...

The importance of keeping open-ended qualitative responses in surveys
I once had a very interesting conversation at a MRS event with a market researcher from a major media company. He told me that they were increasingly ‘costing-out’ the qualitative open-ended questions from customer surveys...

How to set up a free online mixed methods survey
It's quick and easy to set up an on-line survey to collect feedback or research data in a digital format that means you can quickly get straight to analysing the data. Unfortunately, most...


Qualitative evaluations

Qualitative evaluations: methods, data and analysis
Evaluating programmes and projects are an essential part of the feedback loop that should lead to better services. In fact, programmes should be designed with evaluations in mind, to make sure that...

Using Quirkos for Systematic Reviews and Evidence Synthesis
Most of the examples the blog has covered so far have been about using Quirkos for research, especially with interview and participant text sources. However, Quirkos can take any text source you can...

Qualitative evidence for evaluations and impact assessments
For the last few months we have been working with SANDS Lothians, a local charity offering help and support for families who have lost a baby in miscarriage, stillbirth or soon after birth. They...


Sampling and sample sizes

Sampling considerations in qualitative research
Two weeks ago I talked about the importance of developing a recruitment strategy when designing a research project. This week we will do a brief overview of sampling for qualitative research...

Reaching saturation point in qualitative research
A common question from newcomers to qualitative research is, what's the right sample size How many people do I need to have in my project to get a good answer for my research...

Triangulation in qualitative research
Most qualitative research will be designed to integrate insights from a variety of data sources, methods and interpretations to build a deep picture. Triangulation is the term used to describe this comparison and meshing of different data...



Recording and Transcribing

Recording good audio for qualitative interviews and focus groups
Last week's blog post looked at the transcription process, and what's involved in getting qualitative interview or focus-group data transcribed. This week, we are going to step...

Transcribing your own qualitative data
In a previous blog article I talked about some of the practicalities and costs involved in using a professional transcribing service to turn your beautifully recorded qualitative interviews and...

Transcription for qualitative interviews and focus-groups
Audio and video give you a level of depth into your data that can't be conveyed by words alone, letting you hear hesitations, sarcasm, and nuances in delivery that can change your...



What is qualitative analysis?

what is qualitative analysis


How do you actually analyse qualitative data? How do you turn the results from your research into findings that can answer your research questions?

Analysing qualitative data requires drawing meaning from it, and getting to some higher level of interpretation than reading the data at face value. This is the process that can seem difficult for newcomers to qualitative techniques, or those used to quantitative methods where the interpretation seems more obvious. However, in statistical analysis of data, the result is often a single figure which still needs to be interpreted in context, by looking at things like the sample size, population and normal distribution.

Qualitative analysis also requires this final stage of understanding the results in context, but before then it is often necessary to digest the large amounts of data in some way. While words and meanings can’t be averaged in the same way as numerical values can across different participants or cases, multiple readings of qualitative data may give you a personal perception of the important themes in the data.

This is probably the most important part of your analysis, reading and re-reading your data to become familiar with it and understand what is interesting and surprising. However, you will need to present this insight to others in some digestible way. That’s why I generally describe qualitative analysis as being a cycle of three stages:


Interrogate, summarise, connect


First, ask questions of the data to interrogate it. What do people say about this? What words to people use to describe that? When you are reading through the transcripts of your data create a series of specific lines of inquiry that map onto your research questions. Through this process you can start creating a mental or thematic summary of the data that answers your research question for each of the sources or each of the important topics in your data.

The next step is to draw connections from your summaries, so that you can find common themes across the different sources of your data. So perhaps a particular opinion is shared by lots of respondents, or only some of them. You will also want to look for connections between your themes, to see which parts of your research questions are interrelated, such as if people are often talking negatively about a particular service. One way to do this is to break down data to smaller themes that are common across all sources, then building up to a deeper level of analysis and understanding by looking at these common themes in the wider context of the data. Codes and coding can help you through this, we’ll look at this later.


Although the above gives a general approach to qualitative analysis, but there are a large number of different approaches to how you read and interpret your data. Some of the most common are below, but each of these is really deserving of their own blog post, or chapter in a textbook, so make sure you read about the different options before you settle on one.


Which ever approach you have to reading and interpreting the data, there are two general approaches to dealing with the themes from the data. The first is generally called ‘grounded theory’, emergent coding or inductive (data driven) analysis. Here, the researcher lets the data suggest the themes and codes that will be used to summarise and explore the data. It is assumed that there is no pre-conception of what will be interesting in the data, and the coding framework will grow organically with multiple readings.

The alternative is framework or structured analysis where the coding and analysis are driven by pre-existing theory. In this approach the topics used to explore the data are defined before reading the data, based on the research questions and existing literature or research. Usually the next stages will involve coding and categorising the sources to make sense of the volume and depth in the data in these stages:


  Develop a ‘framework’ – a list of topics or nodes
(except in grounded theory where this is inductive)
  ‘Code’ sections of data to one or more topics
  ‘Retrieve’ data at topics
  Explore connections between the data


If you were to illustrate these stages with examples, it might look like this:


Research Question:
  “What is seen as a healthy breakfast?”

Break down into themes:
  Healthy, sugar, portions, marketing

Find quotes that fit these themes:
  “Muesli is very sweet, but feels healthy”

Draw conclusions across the data:
  Most people thought Muesli was healthy


While CAQDAS or QDA software like Quirkos can help you with each of these steps of the coding process, this is only the middle stage of the process after your reading. You still need to examine the results of the coding and make the jump to proper analysis and interpretation of the data – and that’s the next post to read!


Once you are ready to experiment with coding and qualitative analysis, give Quirkos a try with the one month free trial. It’s the most straightforward and intuitive qualitative analysis software around, with licences that don’t expire, free updates and true Windows, Mac and Linux compatibility. We do qualitative software in all the right ways, so you can focus on your data, not battling software.


Managing coding frameworks in Quirkos

managing qualitative coding frameworks

If you are doing inductive coding or grounded theory, your coding framework can get complex, quickly. If you have hundreds of codes, they can become difficult to mangage which can slow down your coding - the part of your analysis you want as efficient and effective as possible so you can focus on identifying bigger trends.


Fortunately, this is what qualitative analysis software is best at - and whether you are using Nvivo, Atlas.ti or Quirkos there are ways to organise and sort your coding themes. In Quirkos the whole interface is designed to give you great flexibility to group and work with your codes, and this week we are going to look at some of the different ways to do this.


Occasionally I will see coding frameworks that have not been sorted at all, and they can look something like this:


messy coding


If you make no attempt to sort and group your codes, they can become very difficult to work with. The example above has around 100 codes/quirks, but finding the right one is hard, and there is no structure to help you find them thematically. If you are working with other people, it is also nearly impossible to understand what is being investigated - and the same is true for long lists of codes. However, in Quirkos you can move the bubbles around just by dragging them, so you can quickly create rudimentary clusters:


better mess


Already this is looking a lot better, and you can probably now see what the research topic is about... I've just created simple clusters by theme, with composers in the bottom left, playwrights to the right, and genres in the middle. This is quite usable now, and is well worth the 5 minute investment this took to sort. However, we can make even better. Quirkos supports millions of different colours, so you can also colour-code your quirks by theme:


coloured codes


Much prettier, but importantly it's also quicker to find Quirks. If I know that all my composers are shades of red, my eye is drawn to the bubbles immediately, and the colour of the highlight stripes in the coded text immediately tells me what has been coded. This is preserved in the exports as well, so reviewing and sharing data with others gets an extra dimension of organisation and information.


There are still no groups in the above example, and Quirkos makes these very easy to create - just drag bubbles onto each other to create hierarchies:



Now it's getting beautiful! And so much quicker to find the right codes. The only disadvantage to this view is that the sub-categories aren't directly visible. This isn't usually a problem because just hovering the mouse over the parent will allow the subcategories to pop out. But if you are doing a lot of direct coding to the sub and sub-sub categories, it can help to have them always expanded. You can do this with the 'Quirks as Tree view':



This is most similar to the list views in Nvivo and other qualitative analysis software, so can be a good way to get used with Quirkos, or use if you don't like the bubble paradigm. There are also options in the View button to arrange the bubbles by size, or alphabetically. Levels are another way to group themes in Quirkos, which can create non-hierarchical categories.


Finally, don't forget to merge! If you have too many codes, some of them are probably superfluous, and can either be merged into other more general topics or deleted all together. Quirkos gives you a lot of flexibility to work with your codes, and manage them so that you can keep focusing on your data. Download a free trial to see for yourself, and get in touch with us if you have any questions.



Integrating policy analysis into your qualitative research

qualitative policy analysis


It’s easy to get seduced by the excitement of primary data collection, and plan your qualitative research around methods that give you rich data from face-to-face contact with participants. But some research questions may be better illustrated or even mostly answered by analysis of existing documents.


This ‘desk-based’ research often doesn’t seem as fun, but can provide a very important wider context that you can’t capture even with direct contact with many relevant participants. But policy analysis is an often overlooked source of important contextual data, especially for social science and societal issues. Now, this may sound boring – who wants to wade through a whole lot of dry government or institutional policy? But not only is there usually a long historical archive of this data available, it can be invaluable for grounding the experiences of respondents in wider context.


Usually, interesting social research questions are (or should be) concerns that are addressed (perhaps inadequately) in existing policy and debate. Since social research tends to focus on problems in society, or with the behaviour or life experiences of groups or individuals, participants in qualitative research will often feel their issues should be addressed by the policy of local or national government, or a relevant institution. Remember that large companies and agencies may have their own internal policy that can be relevant, if you can get access to it.


Policy discussed at local, state or national level is probably easy to get access to in public record. But it may also be interesting to look at the debate when policy was discussed, to see what issues where controversial and how they were addressed. These should also be available from national archives such as Hansard (in the UK) or the House of the Clark in the USA. You can also do comparisons of policy across countries to get an international perspective, or try to explain differences of policy in certain cultures.


Try to also consider not just officially adopted policy, but proposed policy and reports or proposals from lobbying or special interest groups. It’s often a good way to get valuable data and quotes from different forces acting to change policy in your topic area.


But there is also a lot of power in integrating your policy and document analysis with original research. You can cross reference topics coming out of participant interviews, and see if they are reflected in policy document. Discourse analysis, and using keyword searches to look for common terms across all your sources can be revealing.


Looking at how the media represents these issues and the debates over policy can also be interesting. Make sure that you record which outlet an article comes from, as this can be a useful way to compare different representations of events from media groups with their own particular bias or interpretation.


There are of course many different to policy analysis that you can take, including quantitative and mixed-method epidemiologies. While general interpretive qualitative analysis can be revealing, consider also also discourse and meta-systhesis. There’s a short overview video to policy document analysis the from the Manchester Methods Institute here. The following chapter by Ritchie and Spencer is also a good introduction, and for a full textbook try Narrative Policy Analysis: Theory and Practice by Emery Roe (thanks to Dr Chenail for the suggestion!).


Qualitative software like Quirkos can help bring all this data from different sources into one project, allowing you to create a common (or separate) coding framework for your analysis. In Quirkos you can use the source properties to define where a piece of data comes from, and then run queries across all types of source, or just a particular type. While any CAQDAS or QDA software will help you manage document analysis, Quirkos is quick to learn and so lets you focus on your data. You can download a free trial here, and student licences are just US$65.



7 unique things that make Quirkos awesome

quirkos is awesome

Quirkos is now 3 years old!

To celebrate, we’re taking a break from our regular programming of qualitative method posts to remind everyone why Quirkos is the best qualitative analysis software around...


1. All the colours!

Obviously I’m going to start with the most important features first. Some qualitative analysis software restricts you to only 8 colours when customising your themes. Quirkos lets you choose from 16 million colours and that may sound daft, but once you have a large coding framework, giving similar shades of colour to similar themes really makes the coding quicker. Many people find they can identify a colour a lot quicker than they can read a label. You can also easily assign meaning to colours: red things being bad, green things for the environment etc.


2. Interactive coding

It’s the moment I’ve come to love most when doing training workshops, the ‘Ahhh!’ of the audience when they see the bubbles grow for the first time when you drop text on them. And so quickly you realise that it is a lot more than a gimmick: having the size of the themes represent the coding lets you see not just that you put the code in the right place, but what topics are emerging most from your coding. It makes me feel a lot closer to my data, and seeing the themes evolve is really engaging.


download quirkos


3. No Save button

Quirkos is constantly saving after each action, so there is no save button in the interface. I think this initially causes some anxiety in users used to setting up an auto-save or worrying they will loose data. But eventually, it becomes so liberating to just focus on your work. If Quirkos or Windows crashes, or even if you pull out the cord on your computer, when you come back to your project it will be just as you left it.


4. Quick and free to learn.

We designed Quirkos to be simple, with the main features you need to do deep analysis and reading of your data, and no distractions from flashy or complex features. A lot of people come to Quirkos after despairing at the amount of time it takes to learn other software packages. Most people who do qualitative analysis aren’t interested in learning technical software. They just want to focus on their research ideas and the data.

All our training materials are freely available online, even our monthly webinars which (unlike others) we don’t charge for or require registration. Some CAQDAS packages can require a lot of extra training, a cost in terms of time and money that institutions sometimes forget to factor in.


5. True cross-platform freedom

Quirkos not only has the same features and interface on Windows and Mac, but is fully supported on Linux as well. And project files are completely compatible, so you can pick up and work on any computer using any operating system. If you have Windows at work and a Macbook at home, no problem. We are the only CAQDAS software to support all these platforms, and unlike Nvivo, we let you go from Mac to Windows (and back) without changing your files.


6. Free updates

When I was working with other qualitative software for my post-doc research, we had serious problems when new versions of the software came out. It would create new (and terrifying bugs), require us to buy a new licence, and made our data no longer compatible with the old version. Since academic organisations aren’t always the most speedy at installing updates, it meant that we always had issues with a collaborator using an older (or newer) version of the software that wasn’t compatible. This frustrated me so much, I have promised this will not happen in Quirkos.

Over the last 3 years we’ve released 6 updated versions of Quirkos now, and they are all free updates, backward and forward compatible. This means that there is no reason for anyone to be stuck using an old version, and even if they didn’t bother to download the free update, they can still collaborate fully with colleagues using different versions.


7. Student licences that don’t expire

In the UK, a typical PhD lasts 4 years, in the US the average is 8.2 years. If you are doing teaching as part of your scholarship or are doing doctoral studies part time, this can get even longer. That’s why our student licences don’t expire. I don’t know why our competitors sell 1 or 2 year licences for students – it always annoyed me when I was studying. Unless you are doing your masters, you’ll probably have to buy another one half way through your research. Sure, you can buy last minute after you’ve done all your data collection, but that is a bad way to do iterative qualitative analysis.


Our student licences are the same price (or cheaper) than most other one or two year licences, but are yours for life: for your postdoc career and beyond. I don’t want to see people loose access to their data, and it’s no surprise that we sell so many student licences.


So try Quirkos for yourself, and see why researchers from more than 120 universities across the world use it to make their their qualitative analysis go a bit smoother. We’ve got a one month free trial of the full, unrestricted version of Quirkos for you to download right here (that’s also the longest free trial offered for CAQDAS!).


Preparing data sources for qualitative analysis

preparing qualitative text


Qualitative software used to need you to format text files in very specific ways before they could be imported. These days the software is much more capable and means you can import nearly any kind of text data in any kind of formatting, which allows for a lot more flexibility.

However, that easy-going nature can let you get away with some pretty lazy habits. You’ll probably find your analysis (and even data collection and transcription) can go a lot smoother if you’ve set a uniform style or template for your data before hand. This article will cover some of the formatting and meta-data you might want to consider getting in a consistent form before you start it.


Part of this should also be a consistent way to record research procedures and your own reflections on the data collection. Sometimes this can be a little adhoc, especially when relying on a research diary, but designing a standard post-interview debriefing form for the interviewer at the same time as creating a semi-structured interview guide can make it much easier to compare interviewer reflections across sources.

So for example you could have a field to record how comfortable the interview setting was, whether the participant was nervous about sharing, if questions were missed or need follow-up. Having these as separate source property fields allows you to compare sources with similar contexts and see if that had an noticeable effect on the participants data.


For transcribed interviews, have a standard format for questions and answers, and make sure that it’s clear who is who. Formatting for focus groups demands particular attention to formatting, as some software will help you identify responses from each participant in a group session when done in a particular way. Unfortunately Quirkos doesn’t support this at the moment, but with focus group data it is important to make sure that each transcription is formatted in the same way, and that the identifiers for each user are unique. So for example if you are using initials for each respondent such as:

JT: I’m not sure about that statement.
FA: It doesn’t really speak to me

Make sure that there aren’t people with the same initials in other sessions, and consider having unique participant numbers which will also help better anonymise the data.

A formatting standard is especially important if you have a team project where there are multiple interviewers and transcribers. Make sure they are using the same formatting for pauses, emphasis and identifying speakers. The guide to transcription in a previous blog post covers some of the things you will want to standardise. Some people prefer to read through the transcripts checking for typos and inaccuracies, possibly even while listening to the audio recording of the session. It can be tempting to assume you will pick these up when reading through the data for analysis, but you may find that correcting typos breaks your train of thought too often.

Also consider if your sources will need page, paragraph or sentence numbers in the transcript, and how these will be displayed in your software of choice. Not all software supports the display of line/paragraph numbers, and it is getting increasingly rare to use them to reference sources, since text search on a computer is so fast.

You’ll see a few guides that suggest preparing for your analysis by using a database or spreadsheet to keep track of your participant data. This can help manage who has been interviewed, set dates for interviews, note return of consent forms and keep contact and demographic information. However, all CAQDAS software (not just Quirkos) can store this kind of information about data sources in the project file with the data. It can actually be beneficial to set up your project before-hand in QDA software, and use it to document your data and even keep your research journal before you have collected the data.


Doing this in advance also makes sure you plan to collect all the extra data you will need on your sources, and not have to go back and ask someone’s occupation after the interview. There is more detail in this article on data collection and preparation techniques.


download qualitative analysis

As we’ve mentioned before, qualitative analysis software can also be used for literature reviews, or even just keeping relevant journal articles and documents together and taggable. However, you can even go further and keep your participant data in the project file, saving time entering the data again once it is collated.

Finally, being well prepared will help at the end of your research as well. Having a consistent style defined before you start data entry and transcription can also make sure that any quotes you use in write-ups and outputs look the same, saving you time tidying up before publication.

If you have any extra tips or tricks on preparing data for analysis, please share them on our Twitter feed @quirkossoftware and we will add them to the debate. And don’t forget to download a free trial of Quirkos, or watch a quick overview video to see how it helps you turn well prepared data into well prepared qualitative analysis.



Balance and rigour in qualitative analysis frameworks

image by https://www.flickr.com/photos/harmishhk/8642273025


Training researchers to use qualitative software and helping people who get stuck with Quirkos, I get to see a lot of people’s coding frameworks. Most of the time they are great, often they are fine but have too many codes, but sometimes they just seem to lack a little balance.

In good quality quantitative research, you should see the researchers have adopted a ‘null hypothesis’ before they start the analysis. In other words, an assumption that there is nothing significant in the data. So statisticians play a little game where they make a declaration that there should be no correlation between variables, and try and prove there is nothing there. Only if they try their hardest and can’t possibly convince themselves there is no relationship are they allowed to go on and conclude that there may be something in the data. This is called rejecting the null hypothesis and may temper the excitement of researchers with big data sets, over enthusiastic for career making discoveries.

Unfortunately, it’s rare to see this approach described in published quantitative analysis. But there’s no reason that a similar approach can’t be used in qualitative research to provide some balance from the researcher’s interpretation and prejudices. Most of the time the researcher will have their own preconception of what they are going to find (or would like to find) in the data, and may even have a particular agenda they are trying to prove. Whether a quantitative or qualitative methodology, this is not a good basis for conducting good impartial research. (Read more about the differences between qualitative and quantitative approaches.)


Steps like reflexivity statements, and considering unconscious biases can help improve the neutrality of the research, but it’s something to consider closely during the analysis process itself. Even the coding framework you use to tag and analyse your qualitative data can lead to certain quotes being drawn from the data more than others. 

It’s like trying to balance standing in the middle of a seesaw. If you stand over to one end, it’s easy to keep your balance, as you will just be rooted to the ground on one side. However, standing in the middle is the only way you are challenged, and it’s possible to be influenced by sways and wind from one side to another. Before starting your analysis, researchers should ideally be in this zen like state, where they are ready to let the data tell them the story, rather than trying to tell their own data from selective interpretations.

When reading qualitative data, try to have in your head the opposite view to your research hypothesis. Maybe people love being unemployed, and got rich because of it! The data should really shout out a finding regardless of bias or cherry picking.

When you have created a coding framework, have a look through at the tone and coverage. Are there areas which might show any bias to one side of the argument, or a particular interpretation? If you have a code for ‘hates homework’ do you have a code for ‘loves homework’? Are you actively looking for contrary evidence? Usually I try and find a counter example to every quote I might use in a project report. So if I want to show a quote where someone says ‘Walking in the park makes me feel healthy and alive’ I’ll see if there is someone else saying ‘The park makes me nervous and scared’. If you can’t, or at least if the people with the dissenting view is in a minority, you might just be able to accept a dominant hypothesis.


Your codes should try and reflect this, and in the same way that you shouldn’t have leading questions “Does your doctor make you feel terrible?” be careful about leading coding topics with language like “Terrible doctors”. There can be a confirmation bias, and you may start looking too hard for text to match the theme. In some types of analysis like discourse or in-vivo coding, reflecting the emotive language your participants use is important. But make sure it is their language and not yours that is reflected in strongly worded theme titles.


All qualitative software (Quirkos included) allows you to have a longer description of a theme as well as the short title. So make sure you use it to detail what should belong in a theme, as if you were describing it to someone else to do the coding. When you are going through and coding your data, think to yourself: “Would someone else code in the same way?”


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Even when topics are neutral (or balanced with alternatives) you should also make sure that the text you categorise into these fields is fair. If you are glossing over opinions from people who don’t have a problem with their doctor to focus on the shocking allegations, you are giving primacy to the bad experiences, perhaps without recognising that the majority were good.


However, qualitative analysis is not a counting game. One person in your sample with a differing opinion is a significant event to be discussed and explained, not an outlier to be ignored. When presenting the results of qualitative data, the reader has to put a great deal of trust in how the researcher has interpreted the data, and if they are only showing one view point or interpretation they can come across as having a personal bias.


So before you write up your research, step back and look again at your coding framework. Does it look like a fair reflection of the data? Is the data you’ve coded into those categories reflective? Would someone else have interpreted and described it in the same way? These questions can really help improve the impartiality, rigour and balance of your qualitative research.


A qualitative software tool like Quirkos can help make a balanced framework, because it makes it much easier than pen and Post-It notes to go back and change themes and recode data. Download a free trial and see how it works, and how software kept simple can help you focus on your qualitative data.



Word clouds and word frequency analysis in qualitative data

word clouds quirkos


What’s this blog post about? Well, it’s visualised in the graphic above!


In the latest update for Quirkos, we have added a new and much requested feature, word clouds! I'm sure you've used these pretty tools before, they show a random display of all the words in a source of text, where the size of each word is proportional to the number of times it has been counted in the text. There are several free online tools that will generate word clouds for you, Wordle.net being one of the first and most popular.


These visualisations are fun, and can be a quick way to give an overview of what your respondents are talking about. They also can reveal some surprises in the data that prompt further investigation. However, there are also some limitations to tools based on word frequency analysis, and these tend to be the reason that you rarely see word clouds used in academic papers. They are a nice start, but no replacement for good, deep qualitative analysis!


We've put together some tips for making sure your word clouds present meaningful information, and also some cautions about how they work and their limitations.


1. Tweak your stop list!

As these tools count every word in the data, results would normally be dominated by basic words that occur most often, 'the', 'of, 'and' and similar small and usually meaningless words. To make sure that this doesn't swamp the data, most tools will have a list of 'stop' words which should be ignored when displaying the word cloud. That way, more interesting words should be the largest. However, there is always a great deal of variation in what these common words are. They differ greatly between verbal and written language for example (just think how often people might say 'like' or 'um' in speech but not in a typed answer). Each language will also need a corresponding stop list!


So Quirkos (and many other tools) offer ways to add or remove words from the stop list when you generate a word cloud. By default, Quirkos takes the most 50 frequent words from the verbal and written British National Corpus of words, but 50 is actually a very small stop list. You will still get very common words like 'think' and 'she' which might be useful to certain projects looking at expressions of opinions or depictions of gender. So it's a good idea to look at the word cloud, and remove words that aren't important to you by adding them to the stop list. Just make sure you record what has been removed for writing up, and what your justification was for excluding it!


2. There is no weighting or significance

Since word frequency tools just count the occurrence of each word (one point for each utterance) they really only show one thing: how often a word was said. This sounds obvious, but it doesn't give any indication of how important the use of a word was for each event. So if one person says 'it was a little scary', another says 'it was horrifyingly scary' and another 'it was not scary' the corresponding word count doesn't have any context or weight. So this can look deceptive in something like a word cloud, where the above examples count the negative (not scary) and the minor (little scary) the same way, and 'scary' could look like a significant trend. So remember to always go back and read the data carefully to understand why specific words are being used.


3. Derivations don't get counted together

Remember that most word cloud tools are not even really counting words, only combinations of letters. So 'fish', 'fishy' and 'fishes' will all get counted as separate words (as will any typos or mis-spellings). This might not sound important, but if you are trying to draw conclusions just from a word cloud, you could miss the importance of fish to your participants, because the different derivations weren't put together. Yet, sometimes these distinctions in vocabulary are important – obviously 'fishy' can have a negative connotation in terms of something feeling off, or smelling bad – and you don't want to put this in the same category as things that swim. So a researcher is still needed to craft these visualisations, and make decisions about what should be shown and grouped. Speaking of which...


4. They won't amalgamate different terms used by participants

It's fascinating how different people have their own terms and language to describe the same thing, and illuminating this can bring colour to qualitative data or show important subtle differences that are important for IPA[[]] or discourse analysis. But when doing any kind of word count analysis, this richness is a problem – as the words are counted separately. Thus neither term 'shiny', 'bright' or 'blinding' may show up often, but if grouped together they could show a significant theme. Whether you want to treat certain synonyms in the same way is up to the researcher, but in a word cloud these distinctions can be masked.


Also, don’t forget that unless told otherwise (or sometimes hyphenated), word clouds won’t pick up multiple word phrases like ‘word cloud’ and ‘hot topic’.



5. Don’t focus on just the large trends

Word clouds tend to make the big language trends very obvious, but this is usually only part of the story. Just as important are words that aren’t there – things you thought would come up, topics people might be hesitant to speak about. A series of word clouds can be a good way to show changes in popular themes over time, like what terms are being used in political speeches or in newspaper headlines. In these cases words dropping out of use are probably just as interesting as the new trends.


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6. This isn't qualitative analysis

At best, this is quantification of qualitative data, presenting only counting. Since word frequency tools are just count sequences of letters, not even words and their meanings, they are a basic supplemental numerical tool to deep qualitative interpretation (McNaught and Lam 2010). And as with all statistical tools, they are easy to misapply and poorly interpret. You need to know what is being counted, what is being missed (see above), and before drawing any conclusions, make sure you understand the underlying data and how it was collected. However…



7. Word clouds work best as summaries or discussion pieces

If you need to get across what’s coming out of your research quickly, showing the lexicon of your data in word clouds can be a fun starting point. When they show a clear and surprising trend, the ubiquity and familiarity most audiences have with word clouds make these visualisations engaging and insightful. They should also start triggering questions – why does this phrase appear more? These can be good points to start guiding your audience through the story of your data, and creating interesting discussions.


As a final point, word clouds often have a level of authority that you need to be careful about. As the counting of words is seen as non-interpretive and non-subjective, some people may feel they ‘trust’ what is shown by them more than the verbose interpretation of the full rich data. Hopefully with the guidance above, you can persuade your audience that while colourful, word clouds are only a one-dimensional dive into the data. Knowing your data and reading the nuance will be what separates your analysis from a one click feature into a well communicated ‘aha’ moment for your field.



If you'd like to play with word clouds, why not download a free trial of Quirkos? It also has raw word frequency data, and an easy to use interface to manage, code and explore your qualitative data.