Considering and planning for qualitative focus groups

focus groups qualitative

 

This is the first in a two-part series on focus groups. This week, we are looking at some of the  why you might consider using them in a research project, and questions to make sure they are well integrated into your research strategy. Next week we will look at some practical tips for effectively running and facilitating a successful session.


Focus groups have been used as a research method since the 1950s, but were not as common in academia until much later (Colucci 2007). Essentially they are time limited sessions, usually in a shared physical space, where a group of individuals are invited to discuss with each other and a facilitator a topic of interest to the researcher.


These should not been seen as ‘natural’ group settings. They are not really an ethnographic method, because even if comprised of an existing group (for example of people who work together or belong to the same social group) the session exists purely to create a dialogue for research purposes.


Together with ‘focused’ or semi-structured interviews, they are one of the most commonly used methods in qualitative research, both in market research and the social sciences. So what situations and research questions are they appropriate for?


If you are considering choosing focus groups as an easy way to quickly collect data from a large number of respondents, think again! Although I have seen a lot of market research firms do a single focus group as the extent of their research, one group generates limited data on its own. It’s also false to consider data from a focus group being the same as interview data from the same number of people: there is a group dynamic which is usually the main benefit to adopting this approach. Focus groups are best at recording the interactions and debate between a group of people, not many separate opinions.


They are also very difficult to schedule and manage from a practical standpoint. The researcher must find a suitably large and quiet space that everyone can attend, and is at a mutually convenient time. Compared with scheduling one-on-one interviews, the practicalities are much more difficult: meeting in a café or small office is rarely a good venue. It may be necessary to hire a dedicated venue or meeting room, as well as proper microphones to make sure everyone’s voice can be heard in a recording. The numbers that actually show up on the day will always fluctuate, so its unusual for all focus groups to have the same number of participants.


Although a lot of research projects seem to just do 3 or 4 focus groups, it’s usually best to try for a larger number, because the dynamics and data are likely to be very different in each one. In general you are less likely to see saturation on complex issues, as things go ‘off the rails’ and participants take things in new directions. If managed right, this should be enlightening rather than scary, but you need to anticipate this possibility, and make sure you are planning to collect enough data to cover all the bases.


So, before you commit to focus groups in your qualitative methods, go through the questions below and make sure you have reasons to justify their inclusion. There isn’t a right answer to any of them, because they will vary so much between different research projects. But once you can answer these issues, you will have a great idea of how focus groups fit into your study, and be able to write them up for your methodology section.

 

Planning Groups

How accessible will focus groups be to your planned participants?  Are participants going to have language or confidence issues? Are you likely to get a good range of participation? If the people you want to talk to are shy or not used to speaking (in the language the researcher wants to conduct the session in) focus groups may not get everyone talking as much as you like.


Are there anonymity issues? Are people with a stigmatising condition going to be willing to disclose their status or experience to others in the group? Will most people already know each other (and their secrets) and some not? When working with sensitive issues, you need to consider these potential problems, and your ethics review board will want to know you’ve considered this too.


What size of group will work best, and is it appropriate to plan focus groups around pre-existing groups? Do you want to choose people in a group that have very different experiences to provoke debate or conflict? Alternatively you can schedule groups of people with similar backgrounds or opinions to better understand a particular subset of your population.

 

Format

What will the format of your focus group be, just an open discussion? Or will you use prompts, games, ranking exercises, card games, pictures, media clippings, flash cards or other tools to get discussion and interactivity (see Colucci (2007)? These can be useful not just as a prompt, but as a point of commonality and comparison between groups. But make sure they are appropriate for the kind of group you want to work with, and they don’t seem forced or patronising. (Kitzinger 1994).


Analysis

Last of all, think about how you are going to analyse the data. Focus groups really require an extra level of analysis: the dynamic and dialectic can be seen as an extra layer on what participants are revealing about themselves. You might also need to be able to identify individual speakers in the transcript and possibly their demographic details if you want to explore these.


What is the aim within your methodology: to generate open discussion, or confirm and detail a specific position? Often focus groups can be very revealing if you have a very loose theoretical grounding, or are trying to initially set a research question.


How will the group data triangulate as part of a mixed methodology? Will the same people be interviewed or surveyed? What explicitly will you get out of the focus groups that will uniquely contribute to the data?

 


So this all sounds very cautionary and negative, but focus groups can be a wonderful, rich and dynamic data tool, that really challenges the researcher and their assumptions. Finally, focus groups are INTENSE experiences for a researcher. There are so many things to juggle, including the data collection, facilitating and managing group dynamics, while also taking notes and running out to let in latecomers. It’s difficult to do with just one person, so make sure you get a friend or colleague to help out!

 

Quirkos can help you to manage and analyse your focus group transcriptions. If you have used other qualitative analysis software before, you might be surprised at how easy and visual Quirkos makes the analysis of qualitative text – you might even get to enjoy it! You can download a trial for free and see how it works, but there are also a bunch of video tutorials and walk-throughs so you quickly get the most out of your qualitative data.

 


Further Reading and References

 

Colucci, E., 2007, Focus groups can be fun: the use of activity-oriented questions in focus group discussions, Qual Health Res, 17(10), http://qhr.sagepub.com/content/17/10/1422.abstract


Grudens-Schuck, N., Allen, B., Larson., 2004, Methodology Brief: Focus group fundamentals, Extension Community and Economic Development Publications. Book 12.
http://lib.dr.iastate.edu/extension_communities_pubs/12


Kitzinger, J., 1994, The methodology of Focus Groups: the importance of interaction between research participants, Sociology of Health and Illness, 16(1), http://onlinelibrary.wiley.com/doi/10.1111/1467-9566.ep11347023/pdf

 

Robinson, N., 1999, The use of focus group methodology with
selected examples from sexual health
research, Journal of Advanced Nursing, 29(4), 905-913

 

 

Circles and feedback loops in qualitative research

qualitative research feedback loops

The best qualitative research forms an iterative loop, examining, and then re-examining. There are multiple reads of data, multiple layers of coding, and hopefully, constantly improving theory and insight into the underlying lived world. During the research process it is best to try to be in a constant state of feedback with your data, and theory.


During your literature review, you may have several cycles through the published literature, with each pass revealing a deeper network of links. You will typically see this when you start going back to ‘seminal’ texts on core concepts from older publications, showing cycles of different interpretations and trends in methodology that are connected. You can see this with paradigm trends like social captial, neo-liberalism and power. It’s possible to see major theorists like Foucault, Chomsky and Butler each create new cycles of debate in the field, building up from the previous literature.


A research project will often have a similar feedback loop between the literature and the data, where the theory influences the research questions and methodology, but engagement with the real ‘folk world’ provides challenge to interpretations of data and the practicalities of data collection. Thus the literature is challenged by the research process and findings, and so a new reading of the literature is demanded to correlate or challenge new interpretations.

 

Thus it’s a mistake to think that a literature review only happens at the beginning of the research process, it is important to engage with theory again, not just at the end of a project when drawing conclusions and writing up, but during the analysis process itself. Especially with qualitative research, the data will rarely neatly fit with one theory or another, but demand a synthesis or new angle on existing research.

 

The coding process is also like this, in that it usually requires many cycles through the data. After reading one source, it can feel like the major themes and codes for the project are clear, and will set the groundwork for the analytic framework. But what if you had started with another source? Would the codes you would have created have been the same? It’s easy to either get complacent with the first codes you start with, worrying that the coding structure gets too complicated if there you keep creating new nodes.

 

However, there will always be sources which contain unique data or express different opinions and experiences that don’t chime with existing codes. And what if this new code actually fits some of the previous data better? You would need to go back to previously analysed data sources and explore them again. This is why most experts will recommend multiple tranches through the data, not just to be consistent and complete, but because there is a feedback loop in the codes and themes themselves. Once you have a first coding structure, the framework itself can be examined and reinterpreted, looking for groupings and higher level interpretations. I’ve talked about this more in this blog article about qualitative coding.


Quirkos is designed to keep researchers deeply embedded in this feedback process, with each coding event subtly changing the dynamics of the coding structure. Connections and coding is shown in real time, so you can always see what is happening, what is being coded most, and thus constantly challenge your interpretation and analysis process.

 

Queries, questions and sub-set analysis should also be easy to run and dynamic, because good qualitative researchers shouldn’t only do interrogation and interpretation of the data at the end of the analysis process, it should be happening throughout it. That way surprises and uncertainties can be identified early, and new readings of the data illuminate these discoveries.

 

In a way, qualitative analysis is never done: and it is not usually a linear process. Even when project practicalities dictate an end point, a coded research project in software like Quirkos sits on your hard drive, awaiting time for secondary analysis, or for the data to be challenged from a different perspective and research question. And to help you when you get there, your data and coding bubbles will immediately show you where you left off – what the biggest themes where, how they connected, and allow you to go to any point in the text to see what was said.

 

And you shouldn’t need to go back and do retraining to use the software again. I hear so many stories of people who have done training courses for major qualitative data analysis software, and when it comes to revisiting their data, the operations are all forgotten. Now, Quirkos may not have as many features as other software, but the focus on keeping things visual and in plain sight means that these should comfortably fit under your thumb again, even after not using it for a long stretch.

 

So download the free trial of Quirkos today, and see how it’s different way of presenting the data helps you continuously engage with your data in fresh ways. Once you start thinking in circles, it’s tough to go back!

 

Triangulation in qualitative research

triangulation facets face qualitative

 

Triangles are my favourite shape,
  Three points where two lines meet

                                                                           alt-J

 

Qualitative methods are sometimes criticised as being subjective, based on single, unreliable sources of data. But with the exception of some case study research, most qualitative research will be designed to integrate insights from a variety of data sources, methods and interpretations to build a deep picture. Triangulation is the term used to describe this comparison and meshing of different data, be it combining quantitative with qualitative, or ‘qual on qual’.


I don’t think of a data in qualitative research as being a static and definite thing. It’s not like a point of data on a plot of graph: qualitative data has more depth and context than that. In triangulation, we think of two points of data that move towards an intersection. In fact, if you are trying to visualise triangulation, consider instead two vectors – directions suggested by two sources of data, that may converge at some point, creating a triangle. This point of intersection is where the researcher has seen a connection between the inference of the world implied by two different sources of data. However, there may be angles that run parallel, or divergent directions that will never cross: not all data will agree and connect, and it’s important to note this too.


You can triangulate almost all the constituent parts of the research process: method, theory, data and investigator.


Data triangulation, (also called participant or source triangulation) is probably the most common, where you try to examine data from different respondents but collected using the same method. If we consider that each participant has a unique and valid world view, the researcher’s job is often to try and look for a pattern or contradictions beyond the individual experience. You might also consider the need to triangulate between data collected at different times, to show changes in lived experience.

 

Since every method has weaknesses or bias, it is common for qualitative research projects to collect data in a variety of different ways to build up a better picture. Thus a project can collect data from the same or different participants using different methods, and use method or between-method triangulation to integrate them. Some qualitative techniques can be very complementary, for example semi-structured interviews can be combined with participant diaries or focus groups, to provide different levels of detail and voice. For example, what people share in a group discussion maybe less private than what they would reveal in a one-to-one interview, but in a group dynamic people can be reminded of issues they might forget to talk about otherwise.


Researchers can also design a mixed-method qualitative and quantitative study where very different methods are triangulated. This may take the form of a quantitative survey, where people rank an experience or service, combined with a qualitative focus group, interview or even open-ended comments. It’s also common to see a validated measure from psychology used to give a metric to something like pain, anxiety or depression, and then combine this with detailed data from a qualitative interview with that person.


In ‘theoretical triangulation’, a variety of different theories are used to interpret the data, such as discourse, narrative and context analysis, and these different ways of dissecting and illuminating the data are compared.


Finally there is ‘investigator triangulation’, where different researchers each conduct separate analysis of the data, and their different interpretations are reconciled or compared. In participatory analysis it’s also possible to have a kind of respondent triangulation, where a researcher is trying to compare their own interpretations of data with that of their respondents.

 

 

While there is a lot written about the theory of triangulation, there is not as much about actually doing it (Jick 1979). In practice, researchers often find it very difficult to DO the triangulation: different data sources tend to be difficult to mesh together, and will have very different discourses and interpretations. If you are seeing ‘anger’ and ‘dissatisfaction’ in interviews with a mental health service, it will be difficult to triangulate such emotions with the formal language of a policy document on service delivery.


In general the qualitative literature cautions against seeing triangulation as a way to improve the validity and reliability of research, since this tends to imply a rather positivist agenda in which there is an absolute truth which triangulation gets us closer to. However, there are plenty that suggest that the quality of qualitative research can be improved in this way, such as Golafshani (2003). So you need to be clear of your own theoretical underpinning: can you get to an ‘absolute’ or ‘relative’ truth through your own interpretations of two types of data? Perhaps rather than positivist this is a pluralist approach, creating multiplicities of understandings while still allowing for comparison.


It’s worth bearing in mind that triangulation and multiple methods isn’t an easy way to make better research. You still need to do all different sources justice: make sure data from each method is being fully analysed, and iteratively coded (if appropriate). You should also keep going back and forth, analysing data from alternate methods in a loop to make sure they are well integrated and considered.

 


Qualitative data analysis software can help with all this, since you will have a lot of data to process in different and complementary ways. In software like Quirkos you can create levels, groups and clusters to keep different analysis stages together, and have quick ways to do sub-set analysis on data from just one method. Check out the features overview or mixed-method analysis with Quirkos for more information about how qualitative research software can help manage triangulation.

 


References and further reading

Carter et al. 2014, The use of triangulation in qualitative research, Oncology Nursing Forum, 41(5), https://www.ncbi.nlm.nih.gov/pubmed/25158659

 

Denzin, 1978 The Research Act: A Theoretical Introduction to Sociological Methods, McGraw-Hill, New York.

 

Golafshani, N., 2003, Understanding reliability and validity in qualitative research, 8(4), http://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1870&context=tqr


Bekhet A, Zauszniewski J, 2012, Methodological triangulation: an approach to
understanding data, Nurse Researcher, 20 (2), http://journals.rcni.com/doi/pdfplus/10.7748/nr2012.11.20.2.40.c9442

 

Jick, 1979, Mixing Qualitative and Quantitative Methods: Triangulation in Action,  Administrative Science Quarterly, 24(4),  https://www.jstor.org/stable/2392366

 

 

100 blog articles on qualitative research!

images by Paul Downey and AngMoKio

 

Since our regular series of articles started nearly three years ago, we have clocked up 100 blog posts on a wide variety of topics in qualitative research and analysis! These are mainly short overviews, aimed at students, newcomers and those looking to refresh their practice. However, they are all referenced with links to full-text academic articles should you need more depth. Some articles also cover practical tips that don't get into the literature, like transcribing without getting back-ache, and hot to write handy semi-strucutred interview guides. These have become the most popular part of our website, and there's now more than 80,000 words in my blog posts, easily the length of a good sized PhD thesis!

 

That's quite a lot to digest, so in addition to the full archive of qualitative research articles, I've put together a 'best-of', with top 5 articles on some of the main topics. These include Epistemology, Qualitative methods, Practicalities of qualitative research, Coding qualitative data, Tips and tricks for using Quirkos, and Qualitative evaluations and market research. Bookmark and share this page, and use it as a reference whenever you get stuck with any aspect of your qualitative research.

 

While some of them are specific to Quirkos (the easiest tool for qualitative research) most of the principles are universal and will work whatever software you are using. But don't forget you can download a free trial of Quirkos at any time, and see for yourself!

 


Epistemology

What is a Qualitative approach?
A basic overview of what constitutes a qualitative research methodology, and the differences between quantitative methods and epistimologies

 

What actually is Grounded Theory? A brief introduction
An overview of applying a grounded theory approach to qualitative research

 

Thinking About Me: Reflexivity in science and qualitative research
How to integrate a continuing reflexive process in a qualitative research project

 

Participatory Qualitative Analysis
Quirkos is designed to facilitate participatory research, and this post explores some of the benefits of including respondents in the interpretation of qualitative data

 

Top-down or bottom-up qualitative coding
Deciding whether to analyse data with high-level theory-driven codes, or smaller descriptive topics (hint – it's probably both!)

 

 


Qualitative methods

An overview of qualitative methods
A brief summary of some of the commonly used approaches to collect qualitative data

 

Starting out in Qualitative Analysis
First things to consider when choosing an analytical strategy

 

10 tips for semi-structured qualitative interviewing
Semi-structured interviews are one of the most commonly adopted qualitative methods, this article provides some hints to make sure they go smoothly, and provide rich data

 

Finding, using and some cautions on secondary qualitative data
Social media analysis is an increasingly popular research tool, but as with all secondary data analysis, requires acknowledging some caveats

 

Participant diaries for qualitative research
Longitudinal and self-recorded data can be a real gold mine for qualitative analysis, find out how it can help your study

 


Practicalities of qualitative research

Transcription for qualitative interviews and focus-groups
Part of a whole series of blog articles on getting qualitative audio transcribed, or doing it yourself, and how to avoid some of the pitfalls

 

Designing a semi-structured interview guide for qualitative interviews
An interview guide can give the researcher confidence and the right level of consistency, but shouldn't be too long or too descriptive...

 

Recruitment for qualitative research
While finding people to take part in your qualitative study can seem daunting, there are many strategies to choose, and should be closely matched with the research objectives

 

Sampling considerations in qualitative research
How do you know if you have the right people in your study? Going beyond snowball sampling for qualitative research

 

Reaching saturation point in qualitative research
You'll frequently hear people talking about getting to data saturation, and this post explains what that means, and how to plan for it

 

 

Coding qualitative data

Developing and populating a qualitative coding framework in Quirkos
How to start out with an analytical coding framework for exploring, dissecting and building up your qualitative data

 

Play and Experimentation in Qualitative Analysis
I feel that great insight often comes from experimenting with qualitative data and trying new ways to examine it, and your analytical approach should allow for this

 

6 meta-categories for qualitative coding and analysis
Don't just think of descriptive codes, use qualitative software to log and keep track of the best quotes, surprises and other meta-categories

 

Turning qualitative coding on its head
Sometimes the most productive way forward is to try a completely new approach. This post outlines several strange but insightful ways to recategorise and examine your qualitative data

 

Merging and splitting themes in qualitative analysis
It's important to have an iterative coding process, and you will usually want to re-examine themes and decide whether they need to be more specific or vague

 

 


Quirkos tips and tricks

Using Quirkos for Systematic Reviews and Evidence Synthesis
Qualitative software makes a great tool for literature reviews, and this article outlines how to sep up a project to make useful reports and outputs

 

How to organise notes and memos in Quirkos
Keeping memos is an important tool during the analytical process, and Quirkos allows you to organise and code memo sources in the same way you work with other data

 

Bringing survey data and mixed-method research into Quirkos
Data from online survey platforms often contains both qualitative and quantitative components, which can be easily brought into Quirkos with a quick tool

 

Levels: 3-dimensional node and topic grouping in Quirkos
When clustering themes isn't comprehensive enough, levels allows you to create grouped categories of themes that go across multiple clustered bubbles

 

10 reasons to try qualitative analysis with Quirkos
Some short tips to make the most of Quirkos, and get going quickly with your qualitative analysis

 

 

Qualitative market research and evaluations

Delivering qualitative market insights with Quirkos
A case study from an LA based market research firm on how Quirkos allowed whole teams to get involved in data interpretation for their client

 

Paper vs. computer assisted qualitative analysis
Many smaller market research firms still do most of their qualitative analysis on paper, but there are huge advantages to agencies and clients to adopt a computer-assisted approach

 

The importance of keeping open-ended qualitative responses in surveys
While many survey designers attempt to reduce costs by removing qualitative answers, these can be a vital source of context and satisfaction for users

 

Qualitative evaluations: methods, data and analysis
Evaluating programmes can take many approaches, but it's important to make sure qualitative depth is one of the methods adopted

 

Evaluating feedback
Feedback on events, satisfaction and engagement is a vital source of knowledge for improvement, and Quirkos lets you quickly segment this to identify trends and problems