Using properties to describe your qualitative data sources

Properties and values editor in Quirkos

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


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

 

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


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

 

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


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


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


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


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


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


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

 


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

 

 

Starting out in Qualitative Analysis

Qualitative analysis 101

 

When people are doing their first qualitative analysis project using software, it’s difficult to know where to begin. I get a lot of e-mails from people who want some advice in planning out what they will actually DO in the software, and how that will help them. I am happy to help out individually, because everyone’s project is different. However, here are a few pointers which cover the basics and can help demystify the process. These should actually apply to any software, not just Quirkos!

 

First off: what are you going to be able to do? In a nutshell, you will read through the sources, and for each section that is interesting to you and about a certain topic, you will ‘code’ or ‘tag’ that section of text to that topic. By doing this, the software lets you quickly see all the sections of text, the ‘quotes’ about that topic, across all of your sources. So you can see everything everyone said about ‘Politics’ or ‘Negative’ – or both.

 

You can then look for trends or outliers in the project, by looking at just responses with a particular characteristic like gender. You’ll also be able to search for a keyword, and generate a report with all your coded sections brought together. When you come to write up your qualitative project, the software can help you find quotes on a particular topic, visualise the data or show sub-section analysis.  

 

So here are the basic steps:

 

1.       Bring in your sources.
I’m assuming at this stage that you have the qualitative data you want to work with already. This could be any source of text on your computer. If you can copy and paste it, you can bring it into Quirkos. For this example let’s assume that you have transcripts from interviews: this means that you have already done a series of interviews, transcribed them, and have them in a file (say a Word document or raw text file). I’d suggest that before you bring them in, just have a quick look through and correct them in a Word Processor for typos and misheard words. While you can edit the text in Quirkos later, while using a Word or equivalent you have the advantage of spell checkers and grammar checkers.

 

Now, create a new, unstructured project in Quirkos, and save it somewhere locally on your computer. We don’t recommend you save directly to a network location, or USB stick, as if either of these go down, you will have a problem! Next, bring in the sources using the (+) Add Source button on the bottom right. You can bring in each file one at a time, or a whole folder of files in one go, in which case the file name will become the default source name. Don’t forget, you can always add more sources later, there is no need to bring in everything before you start coding. Now your project file (a little .qrk file you named) will contain all the text sources in one place. With Quirkos files, just backing up and copying this file saves the whole project.

 


2.       Describe your sources
It’s usually a good idea to describe some characteristics of your qualitative sources that you might use later to look for differences or similarities in the data. Often these are basic demographic characteristics like age or gender, but can also be things about the interview, such as the location, or your own notes.

 

To do this in Quirkos, click on the little grid button on the top right of the screen, and use the source properties. The first thing you can do here is change the name of the sources from the default (either a sequential number like ‘Source 7’ or the file name. You can create a property with the square [+] ‘Quickly add a new property’ button. The property (eg Gender) and a single value (eg Male) can be added here. The drop down arrow next to that property can be used later to add extra values.

 

The reason for doing this is that you can later run ‘queries’ which show results from just certain sources that have properties you defined. So you can do a side-by-side comparison of coded responses from men next to women. Don’t forget, you can add properties at any time, so you can even create a variable for ‘these people don’t fit the theory’ after you’ve coded, and try and see what they are saying that makes them different.

 

 

3.       Create your themes
Whatever you call them: themes, nodes, bubbles, topics or Quirks, these are the categories of interest you want to collect quotes about from the text. There are two approaches here, you can try and create all the categories you will use before you start reading and coding the text (this is sometimes called a framework approach), or you can add themes as you go (grounded theory). (For much much more on these approaches, look here and here.)

 

In Quirkos, you create themes as coloured bubbles, which grow in size the more text is added. Just click on the grey (+) button on the top right of the canvas view to add a new theme. You can also change the name, colour, level in this dialogue, or right click on the bubble and select ‘Quirk Properties’ at any time. To group, just drag and drop bubbles on top of each other.

 

 

4.       Do your coding
Essentially, the coding process involves finding every time someone said something about ‘Dieting’ and adding that sentence or paragraph to the ‘Dieting’ bubble or node. This is what is going to take the most time in your analysis (days or weeks) and is still a manual process. It’s best to read through each source in turn, and code it as you go.

 

However, you can also use the keyword search to look for words like ‘Diet’ or ‘eating’ and code from the results. This makes it quicker, but there is the risk of missing segments that use a keyword you didn’t think to search for like ‘cut-down’. The keywords search can help when you (inevitably) decide to add a new topic halfway through, and the first few interviews haven’t been coded for the new themes. You can use the search to look for related terms and find those new segments without having to go over the whole text again.

 

 

5.       Be iterative
Even if you are not using a grounded theory approach, going back over the data a second time, and rethinking codes and how you have categorised things can be really useful. Trust me: even if you know the data pretty well, after reading it all again, you will see some topics in a slightly different light, or will find interesting things you never thought would be there.

 

You may also want to rearrange your codes, especially if you have grouped them. Maybe the name you gave a theme isn’t quite right now: it’s grown or got more specific. Some vague codes like ‘Angry’ might need to be split out into ‘Irate’ and ‘Annoyed’. Depending on your approach, you  will probably constantly tweak and adjust the themes and coding so they best represent the intersection of your research questions and data.

 

 

6.       Explore the data.
Once your qualitative data is all coded, the big advantages of using CAQDAS software come into play. Using the database of your tagged text, you can choose to look at it in anyway: using any of the source properties, who did the coding or when, or whether a result comes from any particular group of codes. This is done using the 'Query' views in Quirkos.

 

In Quirkos there are also a lot of visualisation options that can show you the overall shape and structure of the project, the amount of coding, and connections that are emerging between the sources. You can then use these to help write your outputs, be they journal articles, evaluations or a thesis. Software will generate reports that let you share summaries of the coded data, and include key statistics and overviews of the project.


While it does seem like a lot of work to get to this stage, it can save so much time at the final stages of writing up your project, when you can call up a useful quote quickly. It also can help in the future to have this structured repository of qualitative data, so that secondary analysis or adding to the dataset does not involve re-inventing the wheel!

 

Finally, there is no one-size-fits-all approach, and it's important to find a strategy that fits with your way of working. Before you set out, talk to peers and supervisors, read guides and textbooks, and even go on training courses. While the software can help, it's not a replacement for considered thinking, and you should always have a good idea about what you want to do with the data in the end.

 

 

Qualitative evidence for evaluations and impact assessments

qualitative evidence for charities

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 offer amazing services, including counselling, peer discussion groups and advice to health professionals, which can help ease the pain and isolation of a difficult journey.

 

We helped them put together a compilation of qualitative evidence in Quirkos. This has come from a many sources they already have, but putting it together and pulling out some of the key themes means they have a qualitative database they can use for quickly putting together evaluations, reports and impact assessments. Many organisations will have a lot of qualitative data already, and this can easily become really valuable evidence.

 

First, try doing an ‘audit’ for qualitative data you already have. Look though the potential sources listed below (and any other sources you can think of), and find historical evidence you can bring in. Secondly, keep these sources in mind in day-to-day work, and remember to flag them when you see them. If you get a nice e-mail from someone that they liked an event you ran, or a service they use, save it! It’s all evidence, and can help make a convincing case for funders and other supporters in the future.

 

Here are a few potential sources of qualitative feedback (and even quantitative data) you can bring together as evidence for evaluations and future work:

 

 

1.  Feedback from service users:

Feedback from e-mails is probably the easiest to pull together, as it is already typed up. Whenever someone complements your services, thank them and store the comments as feedback for another day. It is easy to build up a virtual ‘guest-book’ in this way, and soon you will have dozens of supportive comments that you can use to show the difference your organisation makes. Even when you get phone calls, try and make notes of important things that people say. It’s not just positive comments too, note suggestions and if people say there is something missing  – this can be evidence to funders that you need extra resources.

You can also specifically ask for stories from users you know well, these can form case studies to base a report around. If you have a specific project in mind, you can do a quick survey. Ask former users to share their experience on an issue, either by contacting people directly, or asking for comments through social media. By collating these responses, you can get quick support for the direction of a project or new service.

 


2. Social media

Comments and messages of support from your Facebook friends, Twitter followers, and pictures of people running marathons for you on Instagram are all evidence of support for the work you do. Pull out the nice messages, and don’t forget, the number of followers and likes you have are evidence of your impact and reach.

 


3. Local (and international) news

A lot of charities are good at running activities that end up in the local news, so keep clippings as evidence of the impact of your events, and the exposure you get. Funders like to work with organisations that are visible, so collect and collate these. There may also be news stories talking about problems in the community that are related to issues you work on, these can show the importance of the work you do.

 


4. Reports from local authority and national organisations

Keep an eye out for reports from local council meetings and public sector organisations that might be relevant to your charity. If there are discussions on an area you work on, it is another source of evidence about the need for your interventions.


There may also be national organisations or local partners that work in similar areas – again they are likely to write reports highlighting the significance of your area, often with great statistics and links to other evidence. Share and collaborate evidence, and together the impact will be stronger!

 

5. Academic evidence

One of the most powerful ways you can add legitimacy to your impact assessment or funding applications is by linking to research on the importance of the problems you are tackling, or the potential benefits of your style of intervention. A quick search in Google Scholar (scholar.google.com) for keywords like ‘obesity’ ‘intervention’ can find dozens of articles that might be relevant. The journal articles themselves will often be behind ‘paywalls’ that mean you can’t read or download the whole paper. However, the summary is free to read, and probably gives you enough information to support your argument one way or another. Just link to the paper, and refer to it as (‘Author’s surname’, ‘Year of Publication’) for example (Turner 2013).

 

It might also be worth seeking out a relationship with a friendly academic at a local university. Look through Google (or ask through your networks) for someone that works in your area, and contact them to ask for help. Researchers have their own impact obligations, so are sometimes interested in partnering with local charities to ensure their research is used more widely. It can be a mutually beneficial relationship…

 

 

 

Hopefully these examples will help you think through all the different things you already have around you that can be turned into qualitative evidence, and some things you can seek out. We will have more blog posts on our work with local charities soon, and how you can use Quirkos to collate and analyse this qualitative evidence.

 

 

What's in your ideal qualitative analysis software?

Qualitative feature request

 

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 lot of small tweaks and changes to the interface to show more data and make some operations easier.


However, there is still time to let us know what you would like to see in future versions of Quirkos. How about Word import, where highlights can be turned into already coded data? Do you want to see wordclouds and keyword counting?


As for the memos: how would you like these to look? Do you just need memos for parts of text, or for each time you code something as well? How should these be displayed when working, and integrated with the reports? Are these best as a separate section, or integrated as side notes on the rest of the data? Should it be possible to code memos? What about the terminology we use - is it confusing? 


As we grow, it's a challenge to think of all the different ways people want to use Quirkos, including people working on very long qualitative text sources, as well as small snippets from open-ended questions in surveys. We would love to hear your feedback, either by dropping us an email (info@quirkos.com) or by completing this short survey with some questions on what is and isn’t working well for you in Quirkos, and also what features are most important for you in the future.


We are also starting to assemble a team of intrepid beta testers, who have volunteered to try out early releases of Quirkos and test how they work. Since we support so many different platforms (and soon Android as well) it becomes very difficult for us to make sure Quirkos behaves properly on so many different operating systems and computers. So if you were interested in getting involved, again drop us an e-mail, and you’ll get a great chance to shape Quirkos and contribute to making it work just the way you want!


Finally, it’s worth reiterating that these comments really do make a direct difference on what we choose to do. We are a small company, with a smallish number of users at the moment so we can be very responsive. And most of the additions from previous updates were things requested by users. So come and join us, and let’s try and make Quirkos the dream qualitative software for everyone!

 

Teaching qualitative analysis software with Quirkos

students learning quirkos on a laptop

 

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 to students starting out in qualitative analysis. We have an increasing number of universities across the world using Quirkos to teach CAQDAS at both undergraduate and post graduate levels. I just wanted to give a quick overview of why this can be such a good solution for students and educators:

 

1. Fits into tight curriculums
Because Quirkos can be taught from start to finish in an interactive 2 hour lab session, it fits neatly into a full module on Qualitative Methods. In one session students can have the skills to do qualitative analysis using a basic CAQDAS package, where other software would require multiple sessions, or a dedicated workshop as a full day event. Thus other sessions can focus on methods, methodology and coding approaches, with students able to quickly apply software skills to their theoretical knowledge.

 

2. Suitable for both post-grads and undergraduates
Quirkos offers enough features and flexibility to be included in research-based masters or PhD training. RTP modules can easily link to a session delivered by university based instructors, without needing external experts to come in and deliver specialist software training. However, Quirkos is simple enough to teach that undergraduate courses in social science can include it in a module on qualitative approaches, and include lab sessions on the basics of software. This is a great basis for later doing research based projects, as well as a useful transferable skill for many industries, including public sector and market research. Since the basic operation of the software is the same, departments have the option to integrate undergraduate and post-graduate training, and use the same materials and course guides.

 

3. Approach agnostic
Quirkos does not encourage a specific analytical approach, and is just as suitable for emergent analysis as grounded theory. Students can be tasked with example projects to analyse with either approach, and choose a middle ground that works best for their own research project. The software gets out of the way, and lets teachers focus on the theory without worrying about how it fits with available tools.

 

4. A visual approach that underscores learning
Visual-based learning can help both understanding and retention and the way that Quirkos makes the coding process live and interactive helps students see their coding, and how it affects the analysis of a project. A very visual approach not only lets students see their findings emerge, but also understand visually what happens during qualitative analysis. By moving their themes and grouping them by drag-and-drop, students can also group topics in their framework, and use colours to represent different groupings. This provides a way of working that is inherently creative, experimental, and satisfying. Quirkos is the only software package based around a graphical user interface, and offers a unique way for students to understand the functionality behind CAQDAS.

 

5. Self-support and learning options
Students increasingly prefer online course materials they can consume in their own time. Quirkos helps educators by providing all our online support guides for free, giving students great flexibility in how they can learn. They can choose either written materials, or video guides of varying length and specificity, and access them without registration or any intervention from the department. Signposting to the materials is easy, and requires no special software or platform to access. We are always around to directly answer technical issues or queries from students.

 

6. Example projects
We provide several example datasets for students to use either in independent learning or guided workshops, at basic and advanced levels. These materials are free for course leaders to include in their materials, or students can download them as they wish. These can be very useful when undergraduates are practicing different qualitative approaches, or if postgraduate researchers wish to experiment with example data before working on their own projects. Since many RTP programmes are requirements in the first few years of a PhD or research masters (before data collection) this high-quality and challenging real data is a great practice resource to put training in practice.


7. A gateway to more advanced techniques
Quirkos aims to provide all the basic features of CAQDAS software, but without any of the bloat that confuses first time users who should be more focused on the data and methodological considerations. However, should students need to later move on to more advanced packages such as Atlas TI, MAXQDA or Nvivo, learning Quirkos is an easy access point, and encourages familiarity with the basics of coding. We also offer export options that help people get their data from Quirkos into other packages for further statistical exploration. Since the basics between all these packages are the same, Quirkos is the perfect first step in the door, and students with advanced needs can quickly learn other packages.

 

8. Flexible licensing for departments and individuals
While everyone can download and use Quirkos with the free trial, we also make sure that we can provide institutions with affordable and accessible permanent access to Quirkos and updates. We offer a site-wide ‘floating’ licence, ideal for teams or lab work that allows a set number of users at any one time, with the ability to add more users at any time. Smaller evaluations and research groups can also buy individual based licenses immediately with a credit or debit card. We are always here to help with purchase orders, IT and other logistical requirements. With significant group discounts, we are confident that we will always be the cheapest option for qualitative analysis software, and the best place for students to start out into the word of qualitative research.

 

 

Quirkos is in Toronto!

Canadian flag in Toronto

This week’s Quirkos blog comes live from the IIQM Qualitative Health Research 2015 conference, in lovely Toronto. It’s been fun talking to people who are coming to the city for the first time, going up the CN Tower, watching the Blue Jays, and seeing the election results come in!


The conference has had a couple of themes, one of which was realist synthesis and evaluations, kick started by a keynote talk from Geoff Wang. He outlined his interpretation of the realist approach, and examining the world in terms of context, mechanisms, and outcomes. In this way we can plan and evaluate intervention strategies, and break down complex systems and interactions.


Integrating a qualitative approach into this method was alluded to in the talk, but with a focus in his view on realist synthesis using mixed methods. The debate and discussion in the afternoon continued this theme: for me the challenge is keeping a person and actor centred view.


I’ve also had the pleasure of meeting again with colleagues from the UK working on many qualitative different health issues, and also a chance to meet new faces from around the world. QHR is a very accessible and engaging conference, and people from all stages of their research career are both presenting and attending in force. It’s great to hear from research in so many different areas, and there is a really strong focus on experience in clinical settings.


And of course Quirkos is here as a sponsor! We have an exhibition stand in next to all the food (dangerous) and have been giving demonstrations to people from all over the world. And today we also have a demonstration session over lunch, which gives people a chance to see Quirkos in action.


So a big thank you to the whole conference team from the University of Alberta Institute of Qualitative Methods, especially Yvette, Bailey and Alex for running such a smooth ship, and making us all feel so welcome.

 

 

Tips and advice from one year of Quirkos

birthday cake CC by theresathompson

 

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 Edinburgh, and at 8pm, version 1.0 of Quirkos was launched to the world. We then drank the bar dry of Prosecco (Champagne being much too expensive). Now Quirkos is being used in more than 30 universities across the world, and it's so exciting to see how people have used it for their PhDs, or in major research projects.

 

Obviously, the story didn't begin on that October night. It was the cumulation of nearly 2 years of planning, testing and development, not just of software, but of the skills and networks of many people behind the scenes. This blog post is mostly intended to share some of the things that went wrong, what went right, and to provide encouragement to those starting down the road to their own business for the first time.

 

There are frightening statistics about how many start-ups fail in the first few years. Some say 20% in the first year, others as high as 50% in the first two years. Whomever you believe, the rates are high, and this has to be expected. It's a competitive world out there and the cost of starting a business is high, nearly always higher than people anticipate (Quirkos included in this).

 

Now, 3 years after I quit my job to work full time on Quirkos, it feels like we have beaten the odds (so far). I also know many start-ups that didn't make it, many colleagues in Edinburgh who were embarking on their own adventures, who saw their business fade during that time. But it's interesting that all those people have still done well individually. Whatever gave them that entrepreneurial desire has led them all to new and different things, just maybe not what they originally planned!

 

At the risk of adding to the 3752 (est) other lists of start-up advice on the internet, here are some numbered pointers:


1. Don't develop qualitative research software
This is annoyingly specific advice, but has become a bit of a running joke for me. If you want to get poor quickly, qualitative analysis software is ideal for you, there is not a lot of money in it. It's a niche, and a very un-sexy one at the moment too. People always suggest that Quirkos should branch out into 'big-data' or add more quantitative features, but this is not really what I want to do.

 

Qualitative research is what I am passionate about, and what I know best. I didn't start this project to make a fortune, but because I felt software was holding people back from better understanding the world, and that was a gap in the market (my pain point).

 


2. Advice is free, but guidance is invaluable
Fortunately, it's really easy to get advice and support. Government initiatives (at least in Scotland and the UK) provide lots of basic training workshops and materials for free. We've benefited from advice from Business Gateway and Scottish Enterprise and their partners on strategy, funding, IP, you name it. However, these people won't tell you what to do, and obviously don't have very specific knowledge for your industry.

 

That is where our great mentors have come in, with knowledge from our specific area (software) and in working with our main markets (public sector and academic). This allowed us to plan a lot better, and make much more realistic projections about things like conversion rates, lead times, and even cultural differences selling abroad.

 

 

3. Awareness is everything
Insulting though it may be, people don't go out looking for your product. They are looking for a solution to that problem, and at first they don't know your name is Quirkos. They search for 'qualitative analysis software' in Google, go to qualitative research conferences, and read journals on all manner of related disciplines. It is never enough to 'build it and they will come' – you have to go to where your potential users will be.

 

Awareness is just the first step, then you need people to believe that you can help them. That can only be done by yourself to a certain extent, word of mouth and recommendations are much more important than corporate-sponsored hearsay. That's why I think that quality and customer happiness are so important, because people don't really believe what they read in adverts (I know I don't).

 

 

4. You ultimately invest in yourself
The last few years have been a roller-coaster, but I have learned so much. I learnt about running a business, accounting, tax, sales, marketing, search engine optimisation, PHP, Javascript, SSL certification, social media, software testing, promotional printing, exhibitions, conferences, planning, strategy, and on and on.

 

I've always thought at the back of my mind, what if this fails? Is all that time and money lost? Well, yes, but through the experience I have learnt so much, and developed real skills in real situations. I can't say I was worried about finding employment afterwards, since I was always adding so much to my CV.

 

 

5. Getting funding costs money and time
However you want money: grants, competitions, loans, equity investment, all these things are very expensive in terms of time and paperwork. We spent a long time going to the final (term-sheet) stages of angel investment, before deciding that the timing wasn't right, and the costs of the transaction were going to be too high.

 

So you need to pick your battles carefully, but plan for redundancy. Assume that only 1 out of 3 sources of funding will come through, so always have a back-up ready to pursue. For Quirkos, a friends and family round worked really well for our first funding cycle, and allows us flexibility in the future.

 


6. Critical Path Analysis
I'm a little obsessed by this, but I have to admit, I actually learnt it from a children's book decades ago. It was 'Truckers' by Terry Pratchett, and it describes it thus: “It’s something called critical path analysis. It means there’s always something you should have done first. For example, if you want to build a house you need to know how to make bricks, and before you can make bricks you need to know what kind of clay to use. And so on”.

 

I actually do this in my head all the time now, whether I am doing a marketing strategy, releasing software or even making dinner. It just means working backwards from what you want to achieve, and working out the things that will hold you back if you don't get them done first. For example, if I am having flyers handed out at a conference, they need them 3 days before the conference. To get there, they have to be in the post 5 days before that. They take 3 days to come back from the printer, take me 1 day to design, and my colleague who has agreed to proof read them is on holiday next week. So quickly I can see that the last day I can do the first draft of the flyer is 17 days before the conference!

 

In a small business you end up doing everything, so being able to plan your time like this is essential. With experience, you also learn that the uncertain part in the chain is always when you have to rely on other people (who can be late, sick, forgetful) so you always factor in more time for the post, printers, and proof readers. Not that you won't be late, sick or forgetful yourself sometimes, but generally you know when this is happening. It's no coincidence that most of the 'Truckers' book is actually about managing people (seriously, it's the best management book I've ever read).

 


7. Network, network, network
Actually, I hate netwo rking, or at least that kind of endless socialising in large groups without direct purpose . But very targeted networking is essential to getting the word out, and cultivating positive relationships with key people and organisations is essential. A case-study or positive review is always more valuable than just a quick sale, and there are always influential people in any industry who have a large audience. Engaging with these networks is essential.

 

 

8. Love, love, love
I couldn't have done this on my own. Over the years so many people have given time, support, money and advice, and I can't thank them all here. If I was thinking in terms of social capital (Putnam style) I would have used up a lifetime of favours and goodwill. To be honest, I don't think I could have got this far without them, and the  love and belief from friends, family, colleagues and spouse. So I'm going to finish on a song, and say “Thank You!”

 

  “When you were giving me advice, that I seldom ever took
  But your head never shook - That's love

 

  Both knowing you were right, never shook it left and right
  Just gave me that look - That's love

 

  When I had to learn the hard way, and you would let me fall
  But never did it out of spite - That's love

 

  You told me never burn a bridge
  If you build it, then you need it
  Whether a river or a brook”

 

That's Love – Oddisee (from the album The Good Fight)
Performed live here from the awesome NPR Tiny Desk series!

 


 

Play and Experimentation in Qualitative Analysis

by Artaxerxes @ commons.wikimedia.org/wiki/File:Our_Community_Place_Sandbox.jpg

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 world of qualitative research, the analysis process often seems intimidating and mysterious. How is the coding framework created? How are decisions made about categorising quotes? How do I know I have taken the right approach? When do I know when I am finished?


But as qualitative researchers will often tell you, the analysis process is so much more than a step-by-step regulated process. It is often like an exploratory journey, during which connections will be discovered, hypotheses challenged, and new unexpected paths will be taken. Personally I feel that qualitative analysis is a very creative process, something that Patton (2014) describes as drawing from “both critical and creative thinking – both the science and the art of analysis”.


Play is often the best way to work with qualitative data. Somehow it often feels more enlightening to explore themes and codes with Post-it notes, and manipulate them in a physical way – moving them around as part of a discussion and experimentation in grouping. The thematic bubbles in Quirkos can be manipulated in the same way, grouped and ungrouped by pulling them around, endlessly rearranged or grouped by colour.


I use the term play deliberately, as informal experimentation with data can uncover new things, and encourages the user to try different approaches. I really wish to encourage this during qualitative analysis, because it is so often productive. The graphical and interactive interface in Quirkos is key to this, as is the instant undo and redo buttons: it’s important to be always able to go back, because play so often leads nowhere, but occasionally somewhere very valuable.


Even in adults, this play and experimentation is important part of learning and enacting (Malamed 1987*), whether it is in formal education classes, museums, or in coaching and mentoring.


Because of this, I have long believed that visualisation should be an integrated part of the analysis process, because it is inherently iterative and interactive. I know I am not alone on this: Bradley, Curry, Devers (2007) state that “there is general agreement that analysis is an ongoing, iterative process”. This applies to developing a coding structure, as well as the act of categorising text. For that reason, any approach that not only keeps researchers close to their data, but allows them to play with it, I find inherently beneficial.


The limitation with most software approaches is that they treat these exploratory steps as distinct events and phases, not as part of the process. So if you want to see connections in a data – run a report. If you want to see the text under a code - generate a summary. To visualise a topic, generate a graph of a particular node: all through a series of button clicks and separate windows. To me, this interrupts the discovery process in the same way that a scratched CD track stops the flow of music.


We designed Quirkos to help people play with their data. To make visualisations live and interactive, and to make it touch-friendly, colourful and forgiving. We try not to limit how researchers can sort and group their themes, but make flexible tools that people can adapt to their own preferred ways of working. And of course, to let people share their work with others and extend that creative process to teamwork, because the discussion and challenge that comes from engaging with other researchers is often as valuable as hours (or days!) of solitary coding.


Most of all though, we wanted software that freed the imagination to take more creative approaches, rather than tying researchers down to literally thinking inside a box or spreadsheet. Rather than a node-tree, the primary interface in Quirkos is called the canvas, and for a deliberate reason. It’s a sandbox for creative play, and space for that ingenious combination of art and science.


*Melamed, L. (1987). ‘The role of play in adult learning’, in Boud, D. & Griffin, V.(eds.), Appreciating adults learning: from the learner’s perspective, London: Kogan Page, pp13–24

 

Freeing qualitative analysis from spreadsheet interfaces

spreadsheets and quirkos to visualise qualitative data

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 on data visualisation. The thrust of the argument is clear: “Good displays of data help to reveal knowledge relevant to understanding mechanism, process and dynamics, cause and effect.” (Tufte 1997).


This chapter also describes the dangers of obscuring data in large amounts of text. He provides examples of the reports that were used to decide to launch the Space Shuttle Challenger on a morning that dipped below freezing, despite concerns that the rubber O-rings in the rocket booster would not function at this temperature. They failed, leading to the death of the crew.


Tufte produces a graph of temperatures at which the shuttle had been previously launched, visually showing how far below previous experienced temperatures this launch was: a vast improvement over the cold technical language presented to management. This illustrates the challenge in summarising and presenting not just quantitative statistics, but also the qualitative world of meetings, discussion and decision making.


However, why shouldn’t this apply during the analysis of qualitative data? Obviously it does, we can quickly identify visual elements like graphs, and relative size, and so word clouds and graphs are all popular export methods from qualitative software. But that analysis process, not just the end product is a very complicated cognitive process, and where it is important for researchers to see the big picture; to get an overview of how things are emerging. Visual representations here can show the researcher what is emerging from the data, and a birds-eye view of the project. The difficulty is that at the same time, there is a need to drill down, and examine the minutiae of one quote or connection.


So how can we get a graphical approach during the analysis process as well as the reporting and summaries? Generally software packages use a spreadsheet-style interface, where working with qualitative data is restricted to columns and rows, quickly collapsed into quantitative representations and overviews.
A project like ‘nineteen’ aims to go part way to address this: by making quantitative and qualitative data from spreadsheets more visual. It turns data into more graphical and colourful representations, which help users see patterns and connections in the data.


To a certain extent, this approach can be done in Excel, using Pivot tables and conditional formatting. It can really help tabulated data ‘pop’, and even simple things like colour coding for particular respondents or themes can make it much easier to find quotes and similarities in qualitative data. (Some day, I am going to have to write a blog post on using Excel for qualitative research!). However, this is actually one way in which most qualitative software is inferior to Excel, because it is very difficult to apply any sort of dynamic visualisation or colour to the data while you work with it to illuminate it in different ways.


Quirkos makes not just reports and summaries visual and engaging, but also the analysis interface itself. I think this is vital, because users need to get constant feedback during the analysis process. If a researcher is forced to generate a new graph in a new window to get a project overview, they are taken out of the flow of the process. Results must be live and interactive, and Quirkos aims to provide that as much as possible.

 

The same goes for search and retrieve, a separated list of results is not illuminating to the researcher, the user is presented with compartmentalised data which begins to lack context. Nearly all the other qualitative software packages have this model: a query is a process, and the results another window of data. In Quirkos, we designed a separate query view that is dynamic and interactive. Results are shown again with colour coded bubbles representing number of hits, and the quotes are integrated into the screen to connect back to the full data source. That way, you get the overview, and the context of the data together. I know that people really love the side-by-side comparison view in this screen too, because again it is about getting that context, and again in a visual way.


All the qualitative analysis software out there will let you generate reports based around graphs and visual methods to communicate qualitative data. However, only Quirkos integrates this into the analytical process, making sure you get constant feedback. For myself, and many other qualitative researchers, this is the most important (and lengthy) part of a project, and ways to help people to spot important findings in the data should be fully integrated into the workflow, not delivered as a side-step.

 

10 reasons to try qualitative analysis with Quirkos

10 quirkos qualitative bubbles

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 switching to Quirkos:

 

1. Ease of Use
When we ask people what they like most about Quirkos, we hear one word: ‘intuitive’. We find that most people can get going after just watching a 5 minute video, since the interface is so graphical.


2. Everything is Visual
Bubbles grow as data is added. Colours help your group your themes, and clusters move bubbles together as connections emerge. Graphs show you key stats from your sources. This is not like working with a spreadsheet, Quirkos turns exploring text into an immersive process.


3. Speed of coding
Researchers spend most of their time coding sections of text, so we try and make it as quick as possible. Adding text to a theme is a simple drag and drop operation, but there are also keyboard shortcuts that can make adding text to multiple themes a breeze (or a whirlwind!).


4. Visual exploration
Of course Quirkos has great tools to explore your data once it is coded. But they are all based around visual feedback, giving you an instinctive feel for emerging findings. For example, side-by-side comparisons let you see how different groups of participants are responding.


5. Custom reports
Your work is not useful unless you can share the results. Our reports are very customisable, letting you choose which sections and detail to show, and naturally are full of graphical ways to represent your text data.


6. Connect with other applications
Researchers need to be flexible in how they work, which is why Quirkos supports bringing text in from all kinds of software like Word, PDFs and on-line survey platforms. But it also lets you get data out as well, to explore further in Excel, SPSS or even back to Word.


7. Free training and support
All our support options are available on-line with no subscription or fee. This includes detailed manuals, one-minute video guides, and interactive tutorials, as well as example projects to experiment with. Save time and money in training with software you can teach yourself. If you get stuck, e-mail or Skype the developers directly: qualitative researchers who are there to make your research go smoothly.


8. Cross-platform with no bumps
Work on Windows, Mac or Linux with an identical interface and features everywhere. You can save work on one system and keep going on another with no issues.


9. Cheap as chips
We think we are the cheapest qualitative software around, especially for our student licence. Also, our licences don’t expire, so you can keep working on your data for year after year.


10. Try for free
Our free trial is exactly the same as the full version, and gives you a month to try before you buy. If you like working with Quirkos, you can just buy and enter the licence code, and away you go.


We are proud of our software, which is why we always like people to try before they buy, and see the benefits for themselves. Download the free trial from here, and make your qualitative analysis a less stressful process!