Balance and rigour in qualitative analysis frameworks

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


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


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

 

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


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


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

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

 

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

 

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

 

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

 

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

 

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

 

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

 

 

Thinking About Me: Reflexivity in science and qualitative research

self rembrandt reflexivity

Reflexivity is a process (and it should be a continuing process) of reflecting on how the researcher could be influencing a research project.


In a traditional positivist research paradigm, the researcher attempts to be a neutral influence on  research. They make rational and logical interpretations, and assume a ‘null hypothesis’, in which they expect all experiments to have no effect, and have no pre-defined concept of what the research will show.


However, this is a lofty aspiration and difficult to achieve in practice. Humans are fallible and emotional beings, with conflicting pressures on jobs, publication records and their own hunches. There are countless stories of renowned academics having to retract papers, or their whole research careers because of faked results, flawed interpretations or biased coding procedures.


Many consider it to be impossible to fully remove the influence of the researcher from the process, and so all research would be ‘tainted’ in some way by the prejudices of those in the project. This links into the concept of “implicit bias” where even well-meaning individuals are influenced by subconscious prejudices. These have been shown to have a significant discriminatory impact on pay, treatment in hospitals and recruitment along lines of gender and ethnicity.


So does this mean that we should abandon research, and the pursuit of truly understanding the world around us? No! Although we might reject the notion of attaining an absolute truth, that doesn’t mean we can’t learn something. Instead of pretending that the researcher is an invisible and neutral piece of the puzzle, a positionality and reflexivity approach argues that the background of the researcher should be detailed in the same way as the data collection methods and analytical techniques.


But how is this done in practice? Does a researcher have to bare their soul to the world, and submit their complete tax history? Not quite, but many in feminist and post-positivist methodologies will create a ‘positionality statement’ or ‘reflexivity statement’. This is a little like a CV or self-portrait of potential experiences and bias, in which the researcher is honest about personal factors that might influence their decisions and interpretations. These might include the age, gender, ethnicity and class of the researcher, social and research issues they consider important, their country and culture, political leanings, life experiences and education. In many cases a researcher will include such a statement with their research publications and outputs, just Googling ‘positionality statements’ will provide dozens of links to examples.

 

However, I feel that this is a minimum level of engagement with the issue, and it’s actually important to keep a reflexive stance throughout the research process. Just like how a one-off interview is not as accurate a record as a daily diary, keeping reflexivity notes as an ongoing part of a research journal is much more powerful. Here a researcher can log changes in their situation, assumptions and decisions made throughout the research process that might be affected by their personal stance. It’s important that the researcher is constantly aware of when they are making decisions, because each is a potential source of influence. This includes deciding what to study, who to sample, what questions to ask, and which sections of text to code and present in findings.


Why this is especially pertinent to qualitative research? It’s often raised in social science, especially ethnography and close case study work with disadvantaged or hard-to-reach populations where researchers have a much closer engagement with their subjects and data. It could be considered that there are more opportunities for personal stance to have an impact here, and that many qualitative methods, especially the analysis process using grounded theory, are open to multiple interpretations that vary by researcher. Many make the claim that qualitative research and data analysis is more subjective than quantitative methods, but as we’ve argued above, it might be better to say that they are both subjective. Many qualitative epistemological approaches are not afraid of this subjectivity, but will argue it is better made forthright and thus challenged, rather than trying to keep it in the dark.


Now, this may sound a little crazy, especially to those in traditionally positivist fields like STEM subjects (Science, Technology Engineering, Mathematics). Here there is generally a different move: to use process and peer review to remove as many aspects of the research that are open to subjective interpretation as possible. This direction is fine too!


However, I would argue that researchers already have to make a type of reflexivity document: a conflict of interest statement. Here academics are supposed to declare any financial or personal interest in the research area that might influence their neutrality. This is just like a positionality statement! An admission that researchers can be influenced by prejudices and external factors, and that readers should be aware of such conflicts of interest when doing their own interpretation of the results.


If it can be the case that money can influence science (and it totally can) it’s also been shown that gender and other aspects of an academic's background can too. All reflexivity asks us to do is be open and honest with our readers about who we are, so they can better understand and challenge the decisions we make.

 

 

Like all our blog articles, this is intended to be a primer on some very complex issues. You’ll find a list of references and further reading below (in addition to the links included above). Don’t forget to try Quirkos for all your qualitative data analysis needs! It can help you keep, manage and code a reflexive journal throughout your analysis procedure. See this blog article for more!

 

 

References

 

Bourke, B., 2014, Positionality: Reflecting on the Research Process, The Qualitative Report 19, http://www.nova.edu/ssss/QR/QR19/bourke18.pdf


Day, E., 2002, Me, My*self and I: Personal and Professional Re-Constructions in Ethnographic Research, FQS 3(3) http://www.qualitative-research.net/index.php/fqs/article/view/824/1790


Greenwald, A., Krieger, L., 2006, Implicit Bias: Scientific Foundations, California Law Review, 94(4). http://www.jstor.org/stable/20439056


Lynch, M., 2000, Against Reflexivity as an Academic Virtue and Source of Privileged Knowledge, Theory, Culture & Society 17(3), http://tcs.sagepub.com/content/17/3/26.short


Savin-Baden, M., Major C., 2013, Personal stance, positionality and reflexivity, in Qualitative Research: The essential guide to theory and practice. Routledge, London.


Soros, G., 2013, Fallibility, reflexivity and the human uncertainty principle, Journal of Economic Methodology, 20(4) https://www.georgesoros.com/essays/fallibility-reflexivity-and-the-human-uncertainty-principle-2/