An early spring update on Quirkos for 2016

spring snowdrops

 

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 tradition?!

 

It’s really amazing how much Quirkos has grown over the last 18 months since our first release. We now have hundreds of users in more than 50 universities across the world. The best part of this is that we now get much more feedback and suggestions from qualitative researchers who are using Quirkos for different projects. Although we have always had a ‘road-map’ for developing new features for Quirkos, it’s been an aim to keep that flexible so we adapt to people’s needs.

 

We are planning a new update for Quirkos (free of course) for the end of March 2016. This version (1.4) will be a fairly major upgrade, but as ever will be released at the same time for Windows, Mac and Linux, with identical features and compatibility across all three.

 

The most significant improvement will be speed. Although v1.3 did improve this a little, it was not enough. The underlying ‘engine’ for coding and highlights was laggy and slow with large projects, and required complete rewriting from scratch. It has justifiably been the biggest source of criticism so far about Quirkos, but we hope this will now remove the last thing holding many users back. This has taken months, which is why this release is a little later than our typical quarterly updates. However, the difference so far is amazing: a near 10 fold increase in speed when loading, coding and editing sources. Although the interface will still look the same, everyone will notice the under-the-hood difference in small and large projects alike.

 

There will also be a few minor bug fixes in this release. We had reports that when moving encrypted projects between Windows and Mac, passwords were not accepted. We’ve fixed this issue, and a few others that people have reported. There are also several small improvements suggested by users that should make exploring the data easier. So please always e-mail us with bugs or suggestions, everything reported gets investigated, and we try and fix issues as soon as we can!

 

We will be sending the new version out to an international group of beta-testers at the end of February, so we are confident that everything works as intended before we make it publicly available. The best way to keep abreast of updates is to follow our Twitter feed: twitter.com/quirkossoftware which is usually updated every day.

 

Looking forward, the next release (v1.5) is due for the summer, and will add some exciting new features, probably including the second most frequently requested addition: memos! Proper note taking functionality is top of many people’s request lists, and will make it much easier to record researcher’s ponderings during the analysis process. For the meantime, check out our blog post article on how to record and code your notes in Quirkos. We also hope to add a lot more tools to help look at word-frequency in their qualitative data sets, including the ever popular word clouds!

 

In addition to all this, we will have a major new collaboration to announce in the next few months. This is going to represent a major leap forward in functionality for Quirkos, bringing some top minds into the fray to work on the next generation of qualitative analysis software.

 

So far, we have reinvested all our sales income into development, to make sure that we keep making the software better, and keep current and future users happy. Since all our updates are free, the best way to support further development is to buy a licence, and you will always benefit from work we do in the future to add new capabilities, and be able to suggest the features that will make your qualitative research easier and more fun.

 

 

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 back, and share a few tips from researchers into what makes for good quality audio that will be easy to hear and transcribe.

 

1. Phones aren’t good enough
While many smartphones can now be used in a ‘voice memo’ mode to record audio, you will quickly find the quality is poor. Consider how tiny the microphone is in a phone (the size of a pin head) and that it is designed only to pick up your voice when right next to your face. Using a proper Dictaphone or voice recorder is pretty much essential to pick up the voice of interviewer and respondent(s) clearly.

 

2. Choosing a Dictaphone
Even if you want to buy one, a cheap £20 ($30) voice recorder will be a vast improvement over a phone. Most researchers won’t need one with a lot of memory: just 2GB of storage will usually record for more than 30 hours at the highest setting. There is usually little benefit in spending a lot more money, unless your ethics review board states that you need one that will securely encrypt your data as it records. These might cost closer to £250 ($400). However, you can often borrow one from your library or department.


A recorder should always be digital. There is no real advantage to a tape one – they are expensive, have less capacity, use batteries faster, are larger, and are much more prone to losing your data with erased, overwritten or mangled tape. This is one part where the advanced technology wins hands down! The format they record in doesn’t really matter, as long as your computer and transcriber can play it back. MP3 is the most compatible, note that some of the older Olympus ones use their own DSS format which is a pain to convert or play back on a computers. Digital recorders will have various settings for recording quality, you will usually want to choose the high or highest setting for clear audio. Test before you do a full interview!

 

2. Carry spare batteries!
I’ve definitely got caught out here before, make sure you have a fresh (or recharged) pair of batteries in the Dictaphone, and a spare set in your bag! Every few minutes during the interview, have a quick look to make sure the recording is still running, and before you start, check you have enough time left on the device.

 

3. Choose a quiet location if possible
While cafés can be convenient, relaxed and neutral places to meet respondents for a one-on-one, they tend to be noisy. You will pick up background music, other conversations, clattering plates and especially noisy coffee machines that make the audio difficult to transcribe. A quiet office location works much better, but if you do need to meet at a café, try and do a bit of reconnaissance first: choose one that is quiet, don’t go at lunchtime or other busy times, choose a part of the café away from the kitchen and coffee grinders, and ask them to turn off any music.

 

4. Position the Dictaphone
Usually you will want the Dictaphone to point towards the respondent, since they will be doing most of the talking. But don’t put the Dictaphone directly on a table, especially if you are having tea/coffee. You will pick up loud THUD noises every time someone puts down their mug, or taps the table with their hand. Just putting the recorder on a napkin or coaster will help isolate the sound.

 

5. Prevent stage fright!
Some people will get nervous as soon as the recording starts, and the conversation will dry up. To prevent this, you can cover the scary red recording light with a bit of tape or Blu-Tack. However, it can also help to start the recorder half-way through the casual introductions, so there isn’t a sudden ‘We’re Live!’ moment. You don’t need to transcribe all the initial banter, but it helps the conversation seamlessly shift into the research questions. Also, try and ignore the Dictaphone as much as possible, so that you both forget about it and have a natural discussion.

 

6. Watch your confirmation noises!
Speaking of natural conversation, it is rare while listening for the interviewer not to make ‘confirmation sounds’ like ‘Yes’, ‘Uh-ha’, ‘Mmm’ etc. Yet these are a pain for qualitative transcription (as most people will want to keep the researchers comments, especially for discourse analysis) and it also breaks up the flow of the transcript. Obviously, just staring silently at your participant while they talk can be disconcerting to say the least! It takes a little practice, but you can communicate and encourage the flow of the conversation just with periodic eye contact, nodding and positive body language. If someone makes a request for confirmation such as: ‘So of course that’s what I did, right?’ Rather than actually verbally responding, you can nod, turn your palms up and shrug, and roll your eyes. This way, it shows you are listening and engaging with the conversation, without constantly interrupting the flow of the narrative.

 

7. Use a boundary mic for group discussions
For focus groups or table discussions, use a cheap ‘boundary’ microphone so that it will pick up all the voices: ideally stereo ones that give some sense of direction to help identify who-said-what during transcription. Again, these don’t need to be expensive: I’ve used a cheap £20 ($30) button-battery powered one with great results. It’s something you can spend a lot of money on for high-end equipment, so again look for opportunities to borrow. 

 

8. Get a group to introduce themselves
For qualitative group sessions, you will almost always want to be able to assign contributions to individual participants. If you are doing the transcription and know the people very well, this can be easy. However, it is surprisingly difficult to differentiate a group of voices that you don’t know just with a recording. For voices to be identified, make sure you start the recording by getting everyone to go round the table and introduce themselves with a few sentences for context (not just their name).

 

9. Backup immediately!
Got your recording? Great! Now back it up! All the time it exists only on your Dictaphone, it can be lost, stolen or dropped in a puddle, losing your data for ever. As soon as you can, get it back to a computer or laptop and copy it to another location. Make sure that your data storage procedure matches your data protection and ethics requirements, and try not to carry around your interview recordings longer than you need to.

 

10. Finally, listen and engage!
Try not to worry about the technical aspects during the interview, shift into researcher and facilitator mode. Take notes if you feel comfortable doing so: even though you are getting a recording, some brief notes can make a good summary and helps concentration. Tick off your qualitative research/interview questions as you go, and write a few notes about how the interview went and the key points immediately afterwards.

 

If you need more advice, you can also read our top 10 tips for qualitative interviews, to make sure things go smoothly on the day. Hopefully, following these steps will help you get great audio recordings for your research project, that will make transcription and listening to your data easier.

 

 

Once you’ve got it transcribed, you'll find that Quirkos is the most intuitative and visual software for qualitative analysis of text data. You can download a free trial for a month, and see an overview of the features here…

 

 

Transcription for qualitative interviews and focus-groups

transcription and a dictaphone

 

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 interpretations of what your participants say. Yet most researchers and students will want to have typed transcripts of their qualitative interviews.

 

Text gives many advantages during the qualitative analysis process. You can read or skim read text much faster than you can listen to audio, and your eyes are good at quickly picking out keywords. Transcribed text can also be searched for keywords or synonyms, taking you directly to that point in the text.

 

It’s also easier to code text than multimedia data, and since most research outputs (especially journal articles, a thesis or book) tend to remain stubbornly text-based, including quotes is a standard way to embed qualitative data. Text can also be analysed in other ways, including statistical analysis and automated sentiment analysis.

 

Even when working primarily with the video or audio of qualitative interviews, most academic researchers and students will still generate a transcript for these reasons. But at the moment, there is no software that can reliably understand untrained interview audio, find words or create automatic transcriptions. Either the researcher themselves, or often an experienced transcriber will have to listen to the audio and type it up word for word.

 

Thus BEFORE starting interviews, it is worth considering a few ways to make sure that transcription goes smoothly, and cheaply. Finding a transcriber or transcription service is a key part of most qualitative research. But how much will it cost?

 

Well, this depends on the level of detail required. Verbatim transcriptions, especially when there is a need to capture the nuance of the conversation, are very time consuming to produce. These will capture not just the conversation word for word, but also every um, er, pause and hesitation, and sometimes even infliction. When there are gaps or pauses these will be detailed (such as [pause 5 sec]). This level of detail is illuminating, especially for discourse analysis, but expensive. Often researchers would like regular timestamps included (say at the top of each page) so that it is easy to find the position of the text in the audio. This also increases cost.

 

Often you will hear the phrase ‘Intelligent Verbatim’ used by transcription services, which denotes a middle ground where the transcriber chooses which pauses and detail are relevant, but is careful to make sure that the exact wording of the dialogue is recorded. This is what most qualitative research projects use, unless there is a methodological need for more detail.

 

This is still more detailed than what you would get from a standard typing service used in business, where phrases and words may be approximated. These services, sometimes called a ‘clean transcript’ are cheaper and easier to read (since they don’t have breaks or interjections, they are much more like reading dialogue from a novel), but generally lack the rigour and specificity for qualitative analysis. If someone said ‘afraid’ or ‘anxious’ it might represent a difference in your interpretation, so the exact words uttered must usually be noted. For more discussion, there is an interesting paper by Halcomb and Davidon (2006).

 

If you are conducting focus groups, this can also increase cost and difficulty because of the need to identify the different voices in the room. Typically this can add 20% to the transcription costs, some services will charge for each additional participant. Many transcribers will justifiably add an additional 20% or more for bad audio. We are going to look in the next blog post article about how to make sure this doesn’t happen, but noisy environments and bad recordings make the process much more time consuming, as it is necessary to keep going back and forward to correctly hear muffled words.

 

In general, you should expect to pay between £1 (often $1 in the States) at the absolute minimum and £3 ($2.70) per audio minute for transcription. This means a one-hour interview will cost around £80 ($60) to be transcribed, depending on the quality of the service and number of people speaking. For fast turnaround (ie within 24 or 48hrs) expect to pay a premium. After salaries, it is often the most expensive part of a qualitative research project. So if you have 20 interviews, you will need to budget £1600 ($1200). This is why many students end up doing transcription themselves, and while this is good for keeping close to your data, it is not easy, and can be a false economy.

 

As someone who has also done transcription before, it is vital to stress what a difficult and specialist job this is. Almost no-one can type at the same speed that people speak, and so the work takes much longer than the length of the interview. You are not paying someone £60+ an hour, they will work two or three times that long to get everything typed and corrected. It is also exhausting, and mentally draining. I’ve tried automated software for transcription, like Dragon Dictate or Microsoft’s Project Oxford, but these are not yet geared up for this type of work. They struggle with words that run together, require perfect audio recordings with no extra noises, and can’t identify different voices. I know some transcribers that use trained dictation software in their work: the computer recognises their own voice, so they have to listen with headphones to the audio and clearly repeat every word, one by one.

 

There are many online services offering transcription services, easily found with a quick Google search. I don’t have any specific ones to recommend at the moment, but if you want to use a company I would suggest you choose one that specifically works with research interviews, and offers the options above. It is also a good idea to choose one that works in your native dialect! If you are not used to hearing British, Scottish or Indian accents as an American transcriber (or vice versa), there can be odd misunderstandings and discrepancies that arise.

 

A transcriber that is used to working in your field of study is also useful: they will spot commonly used terminology and abbreviations. My favourite transcriber had worked in a medical field before, so was used to most of the NHS acronyms, and if there were terms or phrases he hadn’t come across before, he would Google them to make sure they were right. Good people like this are hard to come by!

 

Personally, I have always used a few freelance transcribers who work exclusively with universities. Ask your department or colleagues for local recommendations, and if part of a research project, one who is already on the university payroll system can save major headaches and delays. Don’t be afraid to give a new transcriber just one transcript to see how they do, before you commit yourself to giving them all the work. It’s also not a bad idea to have a back-up, especially if a transcriber gets sick, or you need a large batch of transcripts in a hurry.

 

Finally, there will always be errors and uncertainties. You still need to have at least a cursory read through of the transcript to make sure it makes sense and there aren’t typos. A feedback loop is a valuable thing to set up with a good transcriber, so they can learn about common phrases and terms they are mishearing, and the accuracy will improve. Words misheard will usually be marked with [inaudible] and you will need to go through and fix these. Often, it will be obvious to you as the researcher who was there in the room, but not for someone else, especially when it occurs just as the noisy expresso machine turns on!

 

I hope this is illuminating, it’s one of those things that is difficult to find much written advice on. Very few articles discuss this essential part of the research process – Davidson 2009 is a notable exception. Check out some of our other blog post articles for more on this stage, including how to get good quality recordings, and 10 tips for qualitative interviewing, and let us know if you have any suggestions or tips of your own!

 

 

Once you’ve got a transcript, you will be ready to start qualitative coding your text data, and Quirkos is an ideal software tool to bring your interview and focus-group data to life, with a visual and intuitive interface. Download a free trial, or watch a video overview showing you how to start a new analysis project in just 20 minutes.

 

 

Building queries to explore qualitative data

qualitative analysis with queries in Quirkos

 

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 underlining themes has revealed a few likely points of interest. Now is a good time to step back, put your research questions in front of you, and think about what the data is telling you about the main topics, and how you can work this into a convincing argument.

 

But it’s also a good time to try something different: to challenge your assumptions and come at the data sideways.

 

If you have been using a qualitative software package like Quirkos, you may already be able to see some trends and connections in the data. For example, what are the themes or nodes which you have coded most to? Are these surprising? Step back a little more: what has been coded more or less than you thought? Don’t forget to look beyond the numbers as well, click on major themes and see what is actually being said.

 

Next, it’s a good idea to try and look at connections between your coded topics. Most CAQDAS software will let you do this: in Quirkos the ‘overlap’ view will visualise which topics were coded together for any of the Quirks in your project. You might be intrigued to see that quotations coded as being ‘Negative’ were particularly correlated with statements about ‘Diet’ or ‘Experience’. Again, make sure you look at the actual quotes, so you can understand the substantive reasons for these associations.

 

Contradictions are also a good thing to look out for. When are people making comments that contrast from the general view? Are there particular contentious issues?  Hopefully, by looking at the quotes in context, and thinking about the narrative or demographics of the person, there will be an obvious reason for this. But don’t bet on it! It’s always OK to flag things that aren’t understood, either for further discussion with colleagues, revisiting the literature, or the fabled ‘more research is needed’.

 

 

A really useful way to explore the themes and trends in coded qualitative data is to do subset analysis. Is there something different about responses from people in difference age ranges? What about gender, or from people who have children? If it’s a literature review, what does one particular author have to say in their later works? Are articles in one journal defending a particular view more than others? A great advantage to using any qualitative software package is the ability to bring up results from just certain sources or responses.  Most CAQDAS will let you do this in some way, but I’m going to go through the process in Quirkos, and how it can illuminate the expected or unexpected.

 

Most of this functionality is built around how you have described your data, also known in Nvivo as ‘Source Classifications’ or the ‘Document Variables’ in MAXQDA. In Quirkos, these are ‘Source Properties’ and can be used to describe anything about the source; be it demographics of the respondent, circumstances of the recruitment, or where and when an article was discovered. See this blog article for more detail on how this can be useful.

 

In Quirkos, you can use the ‘Query View’ to explore your data, by seeing only coded results that meet certain criteria. For example, you can see results just from women, or people in a particular age range. However, you can also see work coded by particular people working on the project, or coding done during a specific period of time. If you’ve created a grouping of Quirks (such as different emotions) you can just see results from these topics.

 

Default query view in Quirkos

 

By default, the first button in the query view shows ‘PR’ for Properties (the source properties you have described). These will automatically be shown the next drop down box to the right, allowing you to select one of any of the properties and values defined in the project. By clicking on the PR button, you can choose from any of these filter options:


PR            Properties                     Which source properties match
HA            Highlight Author           Who coded a segment
QA            Quirk Author                  Who created the Quirk
HD            Highlight Date             When the coding was done
QD            Quirk Date                    When this Quirk was created
QL            Quirk Level                    Which level grouping the Quirk belongs to

You will also see the   =   symbol, by clicking on this, you can change the logic matching to  !=  or ‘not equal to’, so you can get results where Gender was not equal to Male.

 

There are also standard comparison options:


<                      Less than
<=                    Less than or equal to
>                      More than
>=                    More than or equal to


These are useful for date ranges, or numerical source properties like age. So you could get results from all respondents older than 42.

 

But wait, that’s not all!

 

You can also add up to 9 extra criteria to the search, by clicking on the (+) button at the end of the row. This means you can stack search criteria, for example where Gender = Male AND Age > 42. You can even change the AND operand to OR, and thus make your searches wider, rather than more specific. This would give you results from all sources that were Male, as well as respondents (regardless of gender) that were over 42. The example below shows what the response from a search with two criteria might look like:

 

Quirkos qualitative query results

 

Using tools like this, you can explore your qualitative data through many different lenses, and see what interesting things might emerge. You might have a theory, backed up by your own experience or the established literature that certain respondents will behave in certain ways. Or (if methodologically appropriate) you can experiment and try looking at different groups of participants until an interesting pattern emerges. Maybe you will discover that women like toast more than men (as in our example project) or hopefully, something with deeper significance for your research question!

 

Regardless of your discoveries, CAQDAS software like Quirkos can make it easy to filter your coded data, and get an extra level of insight into the underlining intricacies. For further reading, try this chapter from Lewis (2001), and the section on how queries can be used to "test ideas and theories".

 

 

Delivering qualitative market insights with Quirkos

delivering fashion

 

To build a well-designed, well-thought-out, and ultimately useful product, today’s technology companies must gain a deep understanding of the working mentality of people who will use that product. For Melody Truckload, a Los Angeles tech company focused on app-based freight logistics, this means intense market research and a focus on training sales agents as researchers.

 

Kody Kinzie, director of Melody’s special research and operations team, Cythlin Intelligence, was faced with introducing qualitative social research and analysis to people who had never considered themselves researchers before.

 

“Quirkos was the first truly accessible qualitative program I found,” Kinzie said.

 

Quirkos was designed with the philosophy that anyone can become a qualitative researcher. The goal is to allow companies and agencies to adopt unique ways to understand their staff and the wider marketplace. By making qualitative data visual and easy to code, users can see their results emerge and gain quick overviews of complex issues.

 

Companies like Melody are at the forefront of developing the next generation of qualitative insight, and Quirkos is helping to open the door to innovative new methods of business intelligence.

 

Kinzie started training his team members to use Quirkos but said he soon discovered that the simple coding allowed even a novice to develop complex data structures with notable uniqueness. Often, he found that these code structures were well suited to analyzing particular elements the researchers were interested in, and he began documenting the experiment to evaluate the resulting structures.

 

Here’s how Kinzie and his team use Quirkos:
One team member will send a Quirkos database to another team member — a researcher who examines the code structure and walks the requesting team member through an explanation of the thought process that went into creating the code. The data structure’s strengths and weaknesses are then assessed and distilled into a report. The researcher examines Melody’s code construction to discover what kind of information it is most effective at analyzing or categorizing, as well as whether the code tags and organizes information or clusters information into meaningful relationships.

 

This helps researchers understand what kind of questions these information structures should be applied to, and where a particular researcher’s methods might excel. The ability to use Quirkos to build and analyze unique and flexible databases from these structures has given Melody an edge in developing and sharing insights throughout the team.

 

While Melody Truckload’s app currently wraps up beta testing with commercial partners, the Quirkos approach has been put to the test most recently on the Melody team’s latest project, Melody Fashion.

 

“In the complex world of L.A.’s Fashion District, which is the part of town that houses the city’s fashion industry wholesale market, freight consolidation desperately needs to be modernized,” Kinzie said. “The objective of Melody Fashion is to provide a platform for fulfilment and consolidation that takes into account a detailed understanding of a market with many players.”

 

To that end, sales agents were trained to analyze interactions using grounded theory on Quirkos and to aggregate data garnered in their interactions with customers. It led to valuable insights, including a partnership with local shipping experts to bring Melody Fashion’s technology to the district.

 

Melody operations manager Marcus Galamay, who introduced new agents to Quirkos software and guided them through their first qualitative exercises, said, “Quirkos provides an intuitive introduction to qualitative analysis for our sales agents, augmenting their role in a way that’s expanded our insights into our client base. It’s a niche that many might not think to pursue, but it’s already delivered results in terms of better understanding of the data we generate and refining our market strategy based on that.”

 

Thanks to its ease of use and its powerful ability to assist in important social research, Quirkos was instrumental in providing Melody with the insight necessary to build smart and useful technology for a distinct and totally new customer base.

 

 

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.