This week we are releasing the first major update for Quirkos, Version 2! A huge thanks to all our beta testers who have been putting the new version to the test for the last few months, and sharing their suggestions for improvements.

 

While there is a lot more to come in the next year, here are 10 new things that make Quirkos more powerful and more intuitive.

 

 
1.       The new User Interface
 
Quirkos already has the simplest layout of any of the qualitative software packages, with a visual and colourful design that puts everything within a single click. However, we’ve had 4 years of listening to comments and suggestions, and also running training sessions, watching new users to see where they get stuck and what isn’t obvious. So we’ve made a lot of changes to the layout which should make life easier for new and existing users alike. Plus, we’ve spent a lot of time polishing the layout and colour scheme, making the design more modern and just gosh-darn handsome.
 

We have moved the Actions bar to the top of the screen from the left, again making the layout similar to other software, but also opening up a lot more space for the canvas (and the new memos). We’ve also tweaked the whole screen to allow for more space for your Quirks, something a lot of people have asked for. The tree view is also more compact, allowing you to see more at once, even on smaller screens. We are also keeping the dark view which has proved so popular with qualitative researchers (and their eyes) on long into-the-night coding sessions. This now has a much more consistent layout as the result of some terrific UI feedback from beta testers.

 

 
2.       The new tab bar

Source tabs have now moved to the top of the screen (in common with your favourite browsers) which makes the interface more familiar. There is also a new tab layout for the text and properties, making it much easier and more obvious when shifting between the different parts of your sources. The flow is also much more logical this way, and you can change and compare properties by tabbing through the sources just like you do with the text.

 

3.       Groups

There are a series of new functionalities to help users who have a large number of Quirks/themes to manage. One of the most powerful is the Groups, which replaces the levels feature. Now you can assign any Quirk to any number of Groups, allowing you flexible and comprehensive ways to group your Quirks without limit. But you can now also choose just to show some or all of the groups on the canvas, with the drop down Groups menu. Now you can work just with one set of Quirks at once (useful if you are working with multiple iterations or stages of coding) and turn the rest off and on at a click. This makes much better use of the screen, and you can also use the Query view to see results just from certain groups.

 

 
4.       Quirk search

Another much requested Quirk management feature is the quick search for quirk title. Just start typing in the box, and only Quirks that match the letters you type will be displayed. This makes finding bubbles a lot easier, and it even works for nested Quirks.

 

 

 

5.       Memos!

They are finally here! Now you can just drag and drop a section of your text onto the memos column and get a little note to attach your comments and thoughts. There’s no limit to their length, and you can code directly from the memo selection, allowing better support for IPA and in-vivo coding. You can see your memos in any of the overviews, reports and CSV export. More to come for this feature!

 

 
6.       Rich/styled text

A lot of work behind the scenes now means that Quirkos can support all kinds of styles in the text, like bold, underline and italic. This will help keep the nuance of your transcripts, and allow you to preserve much more information and richness from your sources, especially historical documents.

 

7.       Improved exports

Not only have we moved all the exports into one convenient location, they’ve all been updated. The Reports now can be saved as Word files, allowing you to edit and customise them to your heart’s content. The spreadsheet/CSV files have been simplified, but also have more detail in them now, allowing a lot more cross analysis and drilling down to the detail of your quotes and data.

 

8.       Source information by Quirk

A long requested feature from market research customers has been a way to drill down into the respondent data by theme. We’ve added this as a properties tab in the Quirk Overview screen – now you can see exactly how many quotes came from any type of respondents you have source properties or demographic data for. Did most people coded to this view come from a particular age range? Now you can see… Lots more to come in this area in the future.

 

9.        Highlights column shows Quirk name

It was already the case that hovering the mouse cursor over a stripe in the highlights column showed the name of the associated code in the bottom of the screen, but this was missed by most people. Now a floating pop-up makes it clear right next to the text.
 
 

10.  10 More little things!

 

    • Quirks with subcategories now show total of quotes from child quirks (in brackets)
     
    • Improved CSV dialogue with scroll bar

    • Fix for text search views that got stuck

    • Ability to remove projects from recently used list

    • Streamlined source import with fewer clicks

    • Each quote in the report gets a unique number – much more reliable referencing system than line numbers

    • Longer source browser lists more results

    • Simplified language across Quirkos, with less technical jargon

    • Updated introduction tutorial

    • Clearer and more consistent icon styles

     

There are dozens more small tweaks and fixes. But as we keep saying, this is the start of the next few years of Quirkos, and we will again be adding a lot more features as free updates over the coming months and years.

So if you want to give it a go, download from our website today, and get three weeks to try it out, even if you have tried or used Quirkos before. This is still the longest free trial offered for qualitative software, and has absolutely no restrictions. You can even get an extra week by filling in a quick feedback survey!

And for users that want to upgrade, you can do this directly on our website and get a code for unlocking Quirkos 2 sent to you instantly. The link is right here.

We will also be releasing the last update this week for Quirkos 1, version 1.6. This ensures backward and forward capability for the future, and also adds the rich text support and many small bug fixes. This is free for all existing users, and while this will be the last update for version 1, there is no expiry date on any of our licences, so you can keep using it as long as you want.

We are so excited to share this with you, it has been months and months of hard work from our developer Lorinc and myself, so we would love to hear any feedback and suggestions. And don’t forget to spread the word about how the most visual and intuitive qualitative analysis software just got a whole lot better!

 

 

 

Next week we will release Quirkos 2.0 on the 31st of October! It’s our first major update in 4 years, and will not only provide a number of major new features, but also sets the platform for a lot more new things to be added in the next few years.

 

Actually, next week we will be releasing two versions of Quirkos: 2.0, but also a final release for the 1.x series, version 1.6. This adds rich text support, all the bug fixes and forward compatibility with version 2.0. This is because we don’t want to have a situation where anyone who wants to stay with the older version of Quirkos can’t share files back and forward with others using the new version. We hate the ‘planned obsolescence’ and forced upgrades that certain other CAQDAS software forces on their users. With Quirkos, we’ve promised to always make sure that there are no compatibility issues with new and old versions. Users can upgrade to the new version if they want the new features, but not because they feel they have no choice. For those that want to stay with the current version of Quirkos, 1.6 will be the last update, but will keep forward and backward compatibility.

 

But Quirkos 2 brings a lot of exciting new things. For a start, the rich text support means that you can work with a much wider variety of sources, and keep much more detail and expression from your research transcripts.
Next, the memo feature is a whole new system for annotating and commenting on your text data, which can be used with the coding system. This opens up new ways for you to interpret and analyse your data, improving support in Quirkos for approaches like in-vivo coding and IPA. There's also the new 'Groups' feature, better exports and editable reports, and ways to see your source data by Quirk.

Most obivous is the refreshed interface, developed after feedback from dozens of users. We’ve improved the look and feel of Quirkos, keeping all the major functions in the same place, while regrouping some buttons to make them more logical and easier to find. I’ll go into this a lot more in the release post next week.

 

If you’ve brought Quirkos in the last 3 months (from August 2018), the upgrade will be free. Just drop us an e-mail with your licence code, and we will replace it with a 2.0 licence. For existing users, we want to make upgrading as simple as possible. From next week you’ll be able to buy an upgrade from a simple order page, you just need the e-mail address or licence code associated with your previous purchase.

An upgrade will cost £260/€295/US$330 for commercial or public sector users, £140/€160/$180 for academic and charity, and £40/€46/$55 for students. We think this strikes a fair balance between supporting loyal users to get new features and continued support, with the (frighteningly) high cost of software development. Also, the features in the first release of Quirkos 2 next week are a small part of what is to come in the next few years. Come join us and help support Quirkos into the future!

 

In the run-up to the release of Quirkos 2.0, we are running a series of workshops in Glasgow, Stirling and Edinburgh in October.

 

These are all interactive bring a laptop sessions, where participants can bring their own laptops and data, or use example data sets and follow along. We'll show all the basics of creating projects, bringing in text data, creating and managing codes, and exploring and exporting coded data. They are all free, and can be booked with just an e-mail to support@quirkos.com. However, places are going fast, so do get in touch soon!

 

University of Stirling

Monday 15th October 2018, 10:30 am

 

Edinburgh University (Teviot)

Friday 19nd October 2018, 10:00 am

 

Edinburgh Napier (Sighthill)

Monday 22nd October 2018, 10:00 am

 

Glasgow (Central) Workshop

Tuesday 23rd October 2018, 14:00 pm

 

 

Full details are on our workshops page, but if you can't make any of these sessions in person, we are also running a free online workshop, showing the new features in the upcoming Quirkos 2:

 

Introducing Quirkos 2.0!

Friday 26th October, 3:00pm London/BST (10am NY/Eastern Time)

https://www.youtube.com/watch?v=3lP1kdyvmpo

 

We'll have a lot more information about the next version of Quirkos on the blog soon, and we will also be appearing at a series of conferences over the next few months that will showcase Quirkos further, and give you a change to learn this simple qualitative analysis tool. We'll be at the SRA conference in London in December, the ECQI in February, and the Qualitative Health Research Network conference in London in March.

 

We hope to see you at one of these sessions!

 

 

It’s a new academic year, and many professors, lecturers and TAs will be reviewing their course materials and slides for this semester and beyond. Those teaching qualitative methods will also be looking at how to teach qualitative analysis, and wondering about including software as part of that process.

While qualitative analysis software is only one small part of the qualitative methods puzzle and journey, it can take a disproportionate time to teach. Sometimes there is a lack of local support for software training, which even if provided by library or IT services, may have with specific classes scheduled at times that aren’t right for certain modules and coursework deadlines. If internal training isn’t available, tutors may teach the software training session themselves, assuming they have the time and knowledge to do this. This is one reason why there are so many (often excellent) dedicated trainers for qualitative software – although these courses come with an extra price tag to consider on top of the software itself.

This is one of the main reasons we designed Quirkos – we wanted to increase the number of people able to learn qualitative analysis software. While the other tools on the market are much more powerful, this also makes them more difficult to learn, and for basic projects many of these features are not needed. For those starting out in qualitative analysis, Quirkos is perfect – a good step up from highlighters, Word or Excel, but probably simpler and more flexible than those shoehorned alternatives.

This means it’s not just easy for students to learn, it’s also easy to teach. You can download pre-prepared teaching slides for modification to your course requirements (and branding) and all our resources including video tutorials, manuals and getting started guides are all available on our website without registration. There’s a 20 minute video guide which covers all the basics, so you can show this in your class and spend the rest of the session focusing on actual analysis, rather than just learning the tools.

And since Quirkos is identical on Windows, Mac and Linux, it’s easy to teach – everything is in the same place whatever platform your students are using. This really helps the technical holdups that slow down classroom learning.

We also offer the best student licences around: around half the price of other qualitative software, but without a time limit, so young researchers can keep using Quirkos through their full post-grad studies and careers. If your university or college doesn’t have a site licence for Quirkos yet, we can also help set this up, and make sure that everyone has access to it.

If you want more information on how Quirkos works for qualitative pedagogy, read how it fits into existing qualitative curriculms here, or and article providing links to a lot of the teaching resources we have already created for educators, including example data sets that can be used in teaching.

If you are wanting to teach Quirkos this year, please get in touch with us. We can help provide free or extended trial licences for educators, and point you in the direction of resources to help prepare teaching materials. We also have real qualitative research experience in our support and help staff, so you can get good tips on qualitative teaching as well as technical support.

If you haven’t tried Quirkos before, you can also download the full version of the software, without any registration for a full month to see for yourself how simple it makes qualitative analysis. Give Quirkos a try, and find out why researchers at more than 200 universities across the world are using for their qualitative research.

 

Today we are announcing that a major new version of Quirkos is coming in September! Version 2  will offer big new features that users have requested, including memos, rich text support, new editable reports, an improved interface, and much more.

 

Memos are a feature that people have been requesting for a while, and we are excited to have this coming in the next version. This allows users to write notes which are attached to specific segments of your text sources. You can write long or short comments, and these can be used in approaches like IPA and in-vivo coding which were difficult to achieve in Quirkos before.

 

These memos are visible anywhere your text is, so you will see them connected to your text in the quotes overview, in search and query results as well. We’ve made adding and working with memos as simple and intuitive as the rest of Quirkos – jut drag and drop a section of text into the memo column to add a new memo, and type straight away. You can also toggle the memo column open and closed if you want to focus on just your text and coding at any time.

 

It has been nearly 4 years since we released the very first beta of Quirkos to users, and since then all our updates have been free, and kept backward and forward project compatibility. We don’t charge ongoing fees, or have licences that expire, and these options will continue into the future. For these reasons we think that Quirkos offers the fairest and best value licences for qualitative software.

 

While there will be a small upgrade fee for users on version 1.x wanting the extra features of 2.x, we will create a final free release for version 1 (1.6) to make sure that it will be backward and forward compatible. We never want to see a situation where people can’t share project files because they are using different versions of Quirkos, and lock their projects into an outdated version. Unlike some other qualitative software packages, we will never do this. So don’t worry: even if you don’t need the new capabilities of 2.0, you won’t be forced to upgrade because of lack of support or backward compatibility. We are qualitative researchers too, and want to minimise these headaches!

 

We’re also announcing that anyone that buys Quirkos from now until the release date will get a free upgrade to version 2 when it is available, an offer that also applies to anyone that brought Quirkos in July and August.

 

If you do want to upgrade, there will be a simple process to change your licence code and download the new version, and you will still be able to keep working with your projects without doing anything. We’ll let you know when it’s available and how much the upgrade will be for different users.

 

We also want to assure people about what isn’t changing in Quirkos 2. We will keep the same interface (with a few tweaks), identical compatibility with Windows, Mac and Linux versions, and all the features that were there before. We also don’t plan to add complexity to Quirkos with the new capabilities, and most people will be able to move to version 2 without getting stuck or needing training. We have thousands of dedicated users in hundreds of universities and other organisations, and keeping them happy is what keeps us going!

 

But version 2 allows us to work towards a new platform that enables a lot of new and exciting capabilities in the future. There are exciting technical innovations and collaborative capabilities coming in Quirkos 2 in the not too distant future, and we are really looking forward to detailing more information in the coming months. We will have more blog posts soon outlining the new things coming to Quirkos this year, and note that 2.0 is only the tip of the iceberg!

 

At the National Council for Research Methods ‘Research Methods Festival’ last month, Steve Wright (from the University of Lancaster) mentioned in his talk the frustrations he has with students that do the bog-standard ’12 semi-structured interviews’ methodology for their qualitative research projects. This prompted a lot of discussion and empathy over lunch, with many tutors lamenting how students weren’t choosing some of the more creative methods for qualitative research.

Even a lot of the popular textbooks on qualitative research only mention the basic methods, or some variants on textual data collection (eg Braun and Clarke 2013). Even if it’s not interviews of some kind, transcribed focus groups and other textual methods definitely dominate the literature. Helen Kara has a textbook specifically on Creative Methods, which is well worth a read if you are looking for inspiration. But the value of qualitative research can be magnified by choosing the right imaginative methodology, and thinking outside the box a little to redefine what we can collect and analyse as ‘data’.

This is a huge world, but I wanted to give a taster (with lots of examples) of 10 qualitative methods that can go a lot beyond the default ’12 semi-structured interviews’ and engage with participants in new and exciting ways.

 

Diaries

OK, we’ve talked about diaries before. But there is much more to diaries than just hand written journals. You can also have audio diaries (Williamson et al 2015) and video diaries (Bates 2013). There are even diary apps for phones (Garcia et al. 2015), which can notify partipants at reguar intervals to find out what they are doing or feeling. Laura Radcliffe and Leighann Spencer gave a great talk on the challenges and advantages of diary apps at RMF 2018. Each have their own benefits and give you a different level of insight into participants lives, but for certain research, especially where you want to minimise recall issues, regular recording in one of these ways can be really useful.

 

Participant Photography

Although sometimes connected with diaries, getting participants to record their life through Photo Elicitation can get them to reflect on important issues, and provides a good basis for discussion. Usually you give your participants a camera (although with the ubiquity of smartphones this is rarely necessary these days) and ask them to take pictures of things that have meaning to them about your research question. This is the concept of Photo Voice, where you give your paricipants a way to express their lives and experiences pictorially. There’s a nice overview here by Harper (2002).

 

Art

Many of the ‘creative methods’ focus on different ways to integrate art into research. You can basically use any medium, but the idea is often to get participants to reflect on their life experiences and create something (a drawing, clay sculpture, collage) that expresses something connected to the research. Examples include ‘Target drawings’ Tracy, et al. (2006), clay sculptures, (Or 2015), self-portraits (Esteban-Guitart et al 2016), drama and theatre (Norris 2010) or even quilting (Bacic et al. ND). There are many more listed in this presentation by Mannay (2016). This is a huge field, and always fun to see different ways people have been innovative here. However, a key part of the method is getting participants to either label and explain, or discuss with the researchers and other participants the meaning and different interpretations of their creations.

 

Walking methods

If your research is connected to a place, or how people experience an area, there are many interesting approaches you can do with participants while walking with them through a place and getting them to explain their world. These have various names and variations such as the ‘walking interview’ Jones et al. (2008), transecting or walking fieldwork (Goschel 2015). You can record these visually, aurally or with notes and pictures, or get participants to reflect on them afterwards.

 

Mapping / network diagrams

Another good tool for getting people to explore and explain their geographical area with researchers, but mapping tools can also be used to demonstrate other things, such as connections between organisations people use, social networks, or how they see connections between concepts as in mind mapping (Burgess-Allen and Owen-Smith 2010) . There is pictorial narrative mapping Lapum et al. 2015 (which is more like some of the artistic reflection techniques above), body mapping which can be used to show pain (Mukherjee 2002), or getting local people to create and label a map of their area.

 

Secondary Analysis

To some, this may seem even more boring than just doing qualitative interviews, but secondary analysis of other sources of data can be really interesting and insightful, and avoids a lot of practical and ethical issues. You can do document, media or social media analysis or even re-analyse someone’s existing dataset to see if it can reveal something about a different research question. There’s some more advice on our post here.

 

Games and activities

When you do focus groups, don’t just facilitate dry discussion: use games and fun activities to get your participants engaged and sharing. You can use sorting and ranking exercises with cards you make with each card representing a part of the research. You can get people to discuss photos, newspaper articles, made up stories about a controversial issues or flip-charts where you get people to come up with ideas or answer difficult questions. Get people to move: show how strongly they agree with a statement by standing at different positions along a line. In each of these situations, the data can be either the outcome (where people stand / what people share) or the discussion that ensures. There’s a whole book of tips and tricks for making focus groups more interesting (and successful): Participatory Workshop (Chambers 2002).

 

Participatory research

This isn’t always a method in itself, but in some situations it can be really valuable to include participants in the data collection or analysis. In some paradigms they can be seen as the real experts of their own lived experiences, or an ‘insider’ can be a useful co-researcher. Often they are able to make sure that the most relevant questions are being asked, can act as gatekeepers to other participants that might be difficult to reach, or will have their own interpretations of the data that can challenge researchers. It also can shift the power dynamic away from binary researcher and researched. Much more on our blog post on participatory research.

 

Observation / Ethnography

If you have the time to deeply engage with an organisation or a group of people, researchers can become embedded in their research subject with ethnography or participant observation. Usually a researcher will spend weeks, months or even years watching and learning a research context first hand, and it can give very detailed data and understanding. However, there are shorter variations of observation or ‘rapid ethnographies’ (Vindrola-Padros and Vindrola-Padros 2017) which can be a great complement to other qualitative research methods: verifying and expanding on other sources of data.

 

Surveys

Now, this again might seem a bit boring, but I think surveys are often overlooked as a qualitative research method. There are a good way to reach out to lots of people, online, in person or by post, and you can be a lot more creative with questions. Get people to explain what they see in a picture. Use one word to express how you feel about something.  Use emoji’s or get people to rate or rank statements. Ask questions about identity in different ways: which Disney princess do you most associate with, and why? Leave space for lots of open ended answers, but choose creative and engaging questions to get people to think and reflect.

 

Hopefully this post has inspired you to consider or even try out some different qualitatve methods that differ from the normal boring ones. The key with all these is to consider what exactly will constitute the data you collect, and then how you will analyse it. For data that comes back to text or transcripts, Quirkos can be a fun and engaging way to help you analyse differently as well. Give the free trial a go, and see how it makes qualitative analysis a visual method!

 

 

 

 

We are pleased to announce a bug fix release for Quirkos that takes us to version 1.5.2.

 

This is a fairly minor update, but includes 4 bug fixes people had reported:

    • Quirks that got ‘stuck’ and couldn’t be dragged
    • An issue with deleting sources that sometimes caused properties to have extra ‘not defined’ responses
    • A bug with some CSV import that led to “ characters being read incorrectly
    • A bug with docx import that would sometimes create extra spaces in the source text

 

As always, you can download and install the new version over the old one, and the update will not affect your licence or project files. There is no change to the file format, so backward compatibility is also maintained.

 

For this release, we have also created a new distribution method for Quirkos on Linux – a ‘Snap’ image. This solves several issues that Linux users had seen on some distributions, with unresolvable dependency issues and reports not launching correctly.

 

If you haven’t used Snap before, it aims to be a cross-distro package installation system, that should take care of most of the dependencies for you. It is included by default in Ubuntu 18.04, but the package manager snap (or snapd) must be installed first.

 

We’ve tested it on all releases of Ubuntu from 14.04, and Fedora 27/28. Many more distributions should be fine – please let us know if you have any issues, and we will help get around them. We are aware of two dependencies at the moment, on Fedora 28 if you are using the new Wayland driver (to replace the X window system) you will need to install the package qt5-qtwayland. On ubuntu systems using the proprietary Nvidia graphics drivers, you need to manually copy the libs:

sudo cp -r /usr/lib/nvidia-version/* /var/lib/snapd/lib/gl/

This seems to be a known issue with snapd at the moment.

 

Once you have downloaded it, you can install with a command like:

snap install quirkos_1.5.2_amd64.snap --devmode

and Quirkos can then by started from the command line by typing ‘quirkos’. Note that on some distros you will have to log out and back in again before bash is updated and typing ‘quirkos’ will link to the binary.

 

Please let us know how you get on, and as always we keep our older binary (32bit) and AppImage available for people that have had better luck with that. We hope to make Quirkos available in the Snap store in the future, which will make getting Quirks even easier for those on Ubuntu. We love Linux and supporting it, so please let us know your feedback, good or bad – there are so many different distributions and configurations of them we can’t test them all!

 

This now makes the 10th free update since Quirkos was publicly released more than 3 and a half years ago. However, check the blog next week for some very exciting news for later in the year and the future of Quirkos...

 

 

 

Essentially, observation is a type of, or more likely, a part of ethnography. In ethnography, anthropologists (people who study people) turn their observations of people, cultures and organisations into written field notes (a bit like a research diary). While some of this may be reflexive (the participants own thoughts and feelings) most focuses on the activities and interactions of the people being studied.

 

There are broadly two types of observation. The first is participant observation in which the researcher becomes part of and gets involved in the context, area or group they are studying. The second is direct observation, where the researcher does not take part in the activity or setting, but is more of a fly on the wall – passively watching and recording what is happening.

 

There are advantages to both approaches: for example it’s easier to observe and take notes with direct observation, while during participant observation you may be actively taking part in the meeting / surfboarding session (Kawulich 2005). However, participant observation can allow for a deeper level of understanding, embedding and acceptance from the study group, allowing for more significant insights. Taking part in the culture/activity can also provide a ‘Walk two moons in their moccasins’ revelation, allowing the researcher to fully understand and empathise with the decisions and actions of participants.

 

Typically, a participant observer would offer to get involved by volunteering, doing some useful task like taking minutes or driving people around – essentially doing favours that let them help out while being able to see what is going on. It does not need to involve the actual task or skill being researched – for example in an ethnography of two tattoo parlours the author “helped maintain files of tattoo designs, working behind the front desk” although eventually got tattoos herself (Velliquette 1998).

 

One specific field of observational research is ‘Organisational Ethnography’, where researchers look at organisations, management or work places. (Ybema et al. 2009). Here ethnographers may look at a wide range of organisations from parliament (Crewe 2018) to a steel mill in Sheffield (Ahrens and Mollona 2007).

 

However there are also methodological limitations to observation. Even with direct observation, there can be an effect from having the researcher in the room – people’s behaviour may not be normal, and maybe modified if participants have a sense of being watched or judged (see the Hawthorne Effect). With time and acceptance of the research, the effect may become less, but it is still difficult to claim pure objectivity in observational research, especially when the researcher is talking part directly in the culture of the researched.

 

This is why reflexivity is so important in ethnography and participant observation, because the prejudices and interpretations of the researcher need to be untangled (or at least made explicit) from the data.

 

Observational data

Any method of observation has a myriad of practical and theoretical challenges. The first is to consider what kinds of data will be produced during the observation. Usually these will be field notes, but may also include documents (minutes from meetings, policy), audio, video, music and direct comments from people in the field of study. Many ethnographers use a dictaphone to record either the whole session live, or more likely their own thoughts and reflections afterwards. This creates audio data which probably will need to be at least partially transcribed.

Researchers need to have a loose plan before they start their fieldwork of what kinds of data will be collected and how, so that they can make sure the data can be effectively analysed. However, there will often be unexpected sources and type of data in a long and embedded fieldwork project like this, so prepare for some flexibility. Also, consider the volume of data that participant observation will generate (like most qualitative methods). For one study 40 hours of observation generated 28,000 words when transcribed (Conway 2017).

It’s also worth thinking about triangulation, and paring with other qualitative methods. For example, semi-structured interviews can be a good compliment to observation, as interviews allow you to ask questions one-on-one with people who have been part of the ethnography. These can be used to check assumptions, and ask for questions and clarifications on aspects of culture that are not obvious (e.g. Why do you all wear these hats?). Just remember that these direct questions are generating a different type of data to the observation: the participant during an interview is conscious of being questioned about their culture, and is giving an expressed opinion (These hats are stylish) which may not match the researchers interpretations based solely on observation (People wear hats to emulate the cool kids).

In fact, there is usually a little informal observation going on in most qualitative research projects. It’s hard just to meet a series of people for interviews without watching the culture around them and how they act with others (as Katz (2002) says – social researchers are always in ‘the field’). And often finding the right people to interview (if this is your designated research method) involves some participant observation to identify the most interesting respondents.

 

 

Gaining access, consent and trust

Yet for any type of observation, there will be significant issues around access and consent. The first hurdle is to persuade a group of people that it is a good idea to have a nosey researcher hanging out with them for months at a time. It can sometimes be tempting to claim that the research will be useful to them; in getting their situation better understood, or identifying issues and problems in their culture. However, this is a difficult thing to promise. While all good researchers should provide feedback and share findings with participants, the things that an academic researcher is investigating may not match with the immediate problems of participants. Qualitative observation of this type is usually based around fairly speculative exploration, with a sort of grounded theory approach, so there is little guarantee from the start exactly what the area of focus will be.

Usually, gaining access will be done through ‘gatekeepers’ (see more on gatekeepers in this article on recruitment  – https://www.quirkos.com/blog/post/designing-a-qualitative-recruitment-strategy). This may be a senior leader (mayor, tribal leader) in a cultural setting, or manager in an organisation. However, it is worth considering wider issues of consent with the many people a researcher will encounter. Although a senior manager may have given permission for the research, this does not automatically mean that their subordinates also give consent. There may be situations where this is explicit ‘Everyone must take part’ but individuals may not be freely giving consent if they are scared of going against the wishes of their boss.

Getting access for this type of in-depth observation can be a lengthy and fractional process, where researchers are only given access to certain areas at first, and as trust grows they are invited to more closed-off activities (such as weddings or management meetings). Building trust and rapport is an important skill that ethnographers must develop, and to which there are rarely shortcuts – long periods of time are usually required to negotiate access. Indeed, some researchers have come to see the difficulty of negotiating access as an important part of the ethnography itself (Frandsen 2015).

 

 

We are going to look more at ethnography in a future blog post, but what ever type of observation you are making, you might consider qualitative analysis software like Quirkos to help analyse and find themes in your qualitative text data. Download a free trial today, and see why people describe Quirkos as ‘intuitive’, ‘colourful’, and even ‘fun’!

 

 

 

This post is based on a talk I gave at ICQI 2018, which itself leads on from a talk from last year on the Entomologies of qualitative coding.

Good qualitative data is rich, and detailed - a fertile medium for understanding and interpreting the world. But the detail of the data comes at a price, usually qualitative data sources are lengthy, and are about a lot of different things. You don't just ask a single question that can be answered with a one word answer, you inquire and explore a range of issues around the topic to draw out detail and explore the 'why' behind the answers.

 

This means that the analysis of qualitative data starts with reading the data, to get a sense of the landscape of it, but an intermediary stage is coding - and this is the part of qualitative analysis that qualitative software like Quirkos can help with. We create codes which are like themes, and read through the text and put sections of text which are relevant to them into each code. Tagging the data in this way lets us bring quotes together that fit a theme, to eventually support (or disprove) a hypothesis. But what should these codes be? What features do we highlight that help us see the similarities and differences in the data?

 

Broadly speaking these themes can of two types: very low order, basic descriptive codes, or 'higher level' conceptual codes. It's difficult to describe the process and difference between these types of coding, but you can conceptualise it as moving from the lowest common denominator across the data to the highest (although it's also possible to do it the other way around).

 

This concept is found often in high-school level math when trying to add fractions. If they have different denominators (the bottom bit which shows how many sections they are) you have to multiply them out to get a common denominator - in other words a number that can be used to divide both fractions. It’s also the same game that entomologists are playing when trying to create a taxonomy of insects or other animals. Think about how you would describe what features are common to the butterflies in the top image? It can be a specific spot of a certain colour (a basic low level feature) or a feature that may look very different, but has a similar purpose - like an antenna (high level).

 

This is a bit like what qualitative coding often tries to do - find common themes that occur across all the sources. At a very basic level these will probably be very simple. Everyone in our sources is talking about 'Politics' in a general sense, 'The Media' and 'Opinions'. Creating these descriptive codes, and putting text into them is a useful way to start the coding process. It creates a 'map' or list of everything everyone is saying about 'The Media' and we can then read all these quotes together and look for patterns.

 

But there is a risk of creating codes that are so 'common' and so basic that they are pretty meaningless on their own. The more vague the theme, the more data will fit into it, but the less useful filtering of the data you are getting. Remember, the end goal is to find data that will answer your research question, and it is unlikely that these are as vague as 'What do people say about Politics?'. Usually you are looking for a much more specific insight, such as 'How do libertarian leaning people distance themselves from the policies of the Republican party?'. To do this, and to make a meaningful conclusion, we need to move to something more akin to the highest common denominator. In other words, what are the highest level, most significant and specific insights that are common themes in the data?

 

These are the 'highest common divisors' - in math the largest number you can give that allows you to divide and compare numbers or fractions. Every number is dividable by itself, and one. In the same way, every thing an individual says is true about themselves, and each word or statement they make is true in itself. However, neither of these is particularly interesting or insightful in itself, without some point of comparison. It's not important that an opinion or experience must be common to everyone to be relevant in qualitative research, in fact that's the strength of this methodology. However, you could argue that dissenting or different views are only interesting in comparison.

 

However, the highest common denominator codes should be at a high enough conceptual level to cover a variety of opinions, but bring them together under a common theory. It's not just saying people think this, or some people think the other way about their political leanings, but how they create a political identity. High level codes should be a close match to a theoretical interpretation of the world, such as “Gender is a performative act” (Butler 1988). These may be an existing theory, or a new theory you are discovering by applying a grounded theory approach.

 

But it's usually pretty hard to jump straight to this level of understanding of your data. Maybe you can read though all the sources once and just see a new conceptual understanding of the world emerge. However, this is rare, and you would probably still want to have quotes to illustrate and support your understanding. That's why creating your coding structure of the lowest common denominator first can help you to get to the next levels. And there may be multiple levels of coding, involving moving up, grouping and refining codes to support a deeper hypothesis. It's one reason why qualitative analysis is often described as a cyclical, iterative process.

 

Quirkos is designed to help you create and manage these different stages of coding with the ‘levels’ feature. This lets you create groups of codes from different coding iterations, and even have some that belong to all or just some of the levels. There’s a lot more information about how they work (and can be used to do other things) in this blog post on levels and groups. However, you can also always download the full version of Quirkos for free and try it for a month. It’s the easiest qualitative analysis software package to learn, as well as being one of the cheapest and most visual.

 

But remember, that even once you have created and populated a high level coding framework, this is not the same as analysis. You still need to make the leap from coding to qualitative analysis and actually read through the coded data, keep re-conceptualising it, and eventually match it to your research questions so that they can be answered. However, if you can keep coming back to the butterfly categorisation or fraction addition metaphors above, it might help you keep your eye looking out for both the low and high level themes in your research, and developing a rich coding framework that will help your insights and conclusions bubble up from your data.

 

 

So this much is obvious: quantitative research uses numbers and statistics to draw conclusions about large populations. You count something that is countable, and process results across the sample.

 

Qualitative methods are more elusive: however in general they revolve around collecting data from people about an experience. This could be how they used a service, how they felt about something, and could be verbal or written. But it is generally speech or talk, albeit with a variety of levels inferred above and below this (if they are truthful, or if what they say has deeper or hidden meaning). Rather than applying a statistical test to the data, a qualitative researcher must read/listen to the data and make an interpretation of what is being discussed, often hoping to discover patterns or contradictions.

 

Interpretation is done in both approaches: quantitative results are still examined in context (often compared with other numbers and data), and given a metric of significance such as a p-value or r-squared to assess if the results, or a part of them, are meaningful.

 

Finally, in general it is seen that a quantitative approach is a positivist paradigm, while qualitative methods fit better with constructionist or pragmatic paradigms (Savin-Baden and Major 2013). However, both are essentially attempting to model and sample something about the world in which we live so that we can simplify and understand it. And it’s not a case of one is better than the other: just like a hammer can’t be used to turn screws, or a screwdriver to hammer in nails, the different methods have different uses. Researchers should always make sure that the question comes first, and that is used to choose the methodology.

 

But you should also ask, is there a quantitative way to measure what your question is asking? If it’s something as simple as numbers of people, or a quantitative aspect like salary. While there are also quantitative measures of things like anxiety or pain that can be used as a proxy to make inferences across a large population. However, for detailed understanding of these issues and how they affect people, these metrics can be crude and don’t get to the detail of the lived experience.

 

However, choosing the right approach also depends on the how much is known about the research question and topic area. If you don’t know what the problems are in a field, you don’t know what questions to ask, or how to record the the answers.

 

I would argue that even in the physical sciences, qualitative research comes first, and sets questions to answer with quantitative methods. Quantitative research projects usually grow from qualitative observations of the physical world, such as 'I can see that ice seems to melt when it gets warm. At what temperature does ice melt?' or qualitative exploration of the existing literature to find things from other research that is surprising or unexplained.

 

In the classic high-school science experiment above, you would quantitatively measure the melting point of water by taking a sample. You don't try and melt all the ice in the world: you take one piece, and assume that other ice behaves in the same way. In both quantitative and qualitative research, sampling correctly is important. Just as only taking one small piece of impure ice will give you skewed results, so will only sampling one small part of a human population.

 

In quantitative research, because you are usually only sampling for one question at a time (i.e. temperature) it's best to have a large sample size. Especially when dealing with naturally variable, unrestricted variables (for example like a person's height) the data will tend to form a bell curve with a large majority of the answers in the middle, and a small number of outliers at either end. If we were sampling ice to melt, we might find that most ice melts around the same temperature, but very pure or dirty ice will have a slight difference. We would take the answer to be the statistical average, for the mean by adding up all the results and dividing by the sample size.

 

You could argue that the same is true for qualitative research. If you are asking people about their favourite ice cream, you'll get a better answer by asking a large number of people, right? Well this might not always be true. Firstly, just as with the ice melting experiment, sampling every piece of ice in the world will not add much more accuracy to your work but will be a lot more work. And with qualitative research, you are generally asking a much more complicated question for each person sampled, so the work increases exponentially as your sample size grows.

 

As your qualitative data grows, Quirkos can help you manage and make sense of it...

 

Remember, it's rare that qualitative research aims to give one definitive answer: it's more exploratory, and interested in the outlier cases just as much as the common ones. So in our qualitative research question 'What is your favourite ice cream' people may talk about gelato, sorbet or iced coffee. Are these really ice cream? One could argue that technically they are not, but if people consider them to be ice cream, and we want to know what to sell for desert at our restaurant, this becomes relevant. As a result of qualitative research, we usually learn to ask better questions 'What is your favourite frozen dessert?' might be a better question.

 

Now our qualitative research has helped us create a good piece of quantitative research. We can do a survey with a large sample size, and ask the question 'What is your favourite frozen dessert?' and give a list of options which are the most common answers from our qualitative research.

 

However, there can still be flaws with this approach. When answering a survey people don't always say what they mean, and you lose the context of their answers. In surveys there is primacy effect which means that people are lazy, and much more likely to tick the first answer in a list. In this case, the richness of our qualitative answers are lost. We don't know what context people are talking about (while walking along a beach, or in a restaurant or at home?) and we also loose the direct contact with the respondent so we can tell if they are lying or being sarcastic, and we can't ask follow on questions.

 

That's why qualitative research can still be useful as part of, or following quantitative research, for discovering ‘Why’ – understanding the results in the richness of lived experience. Often research projects will have a qualitative component – taking a subset of the the larger quantitative study and getting an in-depth qualitative insight with them.

 

There’s no shame in using a mixed methods approach if it is the most appropriate for the subject area. While there is often criticism over studies that ‘tack-on’ a small qualitative component, and don’t properly integrate or triangulate the types of results, this is a implementation rather than paradigm problem. But remember, it’s not a case of one approach vs another, there are no inheriently good or bad approaches. Methods should be appropriate to each task/question and should be servants to the researcher, not ruling them (Silverman 2013).

 

Quirkos is about as close as a pure qualitative software package as you can find. It's quick to learn, visual and keeps you close to the data. Our focus is on just doing qualitative coding and analysis well, and not to attempt  statistical analysis of qualitative data. We believe that for most qualitative researchers that's the right methodological approach. However, there is capacity for allow some mixed method analysis, so that you can filter results by demographic or other data.

 

The best way to see if Quirkos works for you is to give it a go! Download our one month free trial of the full version with no restrictions, and see if Quirkos works for your research paradigm.