Tuesday, March 4, 2014

Do you Have Quotitis? How to Diagnose, Treat, and Prevent!

Interview with Chef David Kinch
Interview with David Kinch (Renée S. Suen)

by Nick Hopwood: http://nickhop.wordpress.com/2014/02/02/do-you-have-quotitis-how-to-diagnose-treat-and-prevent/

What is quotitis?

Quotitis is a common disease among qualitative researchers.

It’s a name I have started using to refer to the tendency for people writing about qualitative data to over-rely on raw quotes from interviews, fieldnotes, documents etc.

Why is this a problem?

I used the term over-rely deliberately, implying not only more than is necessary, but too much to the point of being counter-productive by virtue of its excess.

The basic point is this: whether in a journal article, thesis or other scholarly publication, people are giving their time (and quite often paying money, too) to read what you have to say, not what others have said.

The value add in your work comes from expressing your thoughts, interpretations, arguments, and ideas.

How do I know I have quotitis?

Quotitis can be diagnosed both through its manifestations in writing, but also through reflective questioning of the (often tacitly held) assumptions underpinning your writing.

Symptoms to spot in writing

Look at your findings / discussion section. How much is indented as quotes from raw data? How much is “quoting the delicious phrases of your participants” within a sentence? It would be daft of me to give a fixed proportion to limit this, so I’m not going to.

Do you give multiple exemplars to illustrate the same theme? Look at the text around the quotes. Have you given yourself (word) space to introduce quotes appropriately, and to comment on them in detail?

Underlying causes (assumptions)

A full diagnosis requires you to consider what frames your approach to writing up qualitative research. Any of the following assumptions might well give the writing doctor cause for concern:
  1. No-one will trust or accept your claims unless you ‘prove’ each one with evidence in the form of quotes from raw data
  2. Participants express themselves perfectly, and your own words are never as good, and lack authenticity
  3. Not to quote participants directly is to deny them appropriate ‘voice’
  4. Raw data is so amazingly powerful it can ‘speak for itself’
All of these assumptions are false.

Perhaps at times, in certain kinds of research that place high emphasis on sharing knowledge production with participants, you may take issue with point 3.

But still, I would suggest that an academic text will be more valuable by virtue of you developing ideas around data rather than just reproducing it.

Of course, the really uncomfortable truths around some cases of quotitis are as follows:
  1. You may have a fear of your own voice and words (whether self-doubt, uncertainty, insecurity), and prefer to rest in the safety of the words of others
  2. Simple laziness, for example using quotes to pad out a text and increase the number of words.
  3. Lack of analytic insight. Lots of cases of quotitis seem to be to reflect the fact that the researcher hasn’t gone much further than coding her or his data, coming up with a bunch of themes, and wishing to illustrate them with quotes from data in the text. Coding is sometimes useful as a starting point. It is rarely an outcome of analysis.
Prevention rather than treatment or cure

It is better to address underlying causes than to treat surface symptoms, so I’ll deal with this first, before presenting some tips for treatment/cure for an existing text. Let’s challenge those underlying assumptions.

Raw data are needed to convince readers to believe your claims

This is about the ‘evidential burden’ placed on quotes from raw data. Think about it. Does a sentence or two from an interview really prove (or establish credibility) in anything by itself?

Surely we have to think about where the quote came from, how it was treated as part of a sophisticated analytic process, how it relates to other features of the data, and what features of it readers are supposed to notice and interpret in particular ways.

Moreover placing the burden of proof on quotes may be utterly illogical and force (or be a symptom) of highly reductive analyses.

I doubt very much that many of the most interesting analytical insights into qualitative datasets can be accurately conveyed in someone else’s words (in the case of an interview), or in your own field notes (in the case of observation).

In my experience the real value-add ideas can’t be pinpointed to one bit of data or another. They come by looking across codes, themes, excerpts etc.

To prove my point I wrote a paper based on analysis of interviews with doctoral students. It was about relationships they have with other people and their impact on learning and experience. The paper does not contain one single quote from raw data.

Admittedly one of the reviewers found this odd, but I argued my case to the editor and the paper stands with no raw data quoted whatsoever. Don’t believe me? Check it out here at the publisher’s website, or here (full text free) from ANU.

The justification was this: I did my analysis by identifying all the relationships between each participant and others around them (supervisors, students, family etc). I then went through and looked for all the data relating to that relationship.

After several readings, I was able to write a synoptic text, summarising everything I knew about that relationship, its origins, importance and so on. This drew on all available data, and was shaped by a holistic and synthetic reading of the data.

There was no one line or even paragraph from an interview that could demonstrate, illustrate, or even support what I had to say. Because what I had to say was at a different level from what students told me directly.

This is an extreme example, and I’ve written plenty of other papers where I use quotes from raw data. But I use them sparingly and I don’t operate from misplaced assumptions about evidential burden.

The problem is, many referees do apply these unfortunate ideas, so be ready to defend yourself when they do!

Participants express themselves perfectly, your words are worse

Do people really speak in the most considered, informed and evocative ways? Sure, sometimes the odd gem of a quote comes out.

But I’d suggest that the craft we can put into our written text, playing around with word order, phrasing, vocabulary, emphasis and so on, means we can reach much tighter and considered words than the on-the-spot responses in interviews, or madly rushed field notes.

What are raw data ‘authentic’ expressions of that your words in the paper or not? They may authentically capture what someone said or what you wrote in the field. But is that really what your paper is about?

Is it not about reading into what people say, constructing a new argument out of those comments. In which case authenticity lies at a different level: what is authentic to your argument or contribution may not be what is authentic to a participant. Unless your contribution rests solely on reproducing what others say or feel about something, for example.

Not to quote is a denial of participant voice

I never promise participants they will be ventriloquized in my writing about them (though I know in some qualitative approaches this can be important). And anyway, I would never get chance to quote from all participants equally, so there would always be some who are denied more than others.

Why should those who happen to say something in a particular way (the ‘real gem’ quotes) be given voice, while those who are less articulate be silenced? Not a useful or valid basis for my writing.

Neither is giving everyone blanket the same ‘voice’ because that doesn’t seem likely to be a sound foundation for a balanced, well structured text either.

What’s more as I’ve hinted above, there’s another denial going on when you over-quote from raw data: denying readers access to your opinions and insights.

You’re the author of the paper: it’s your interpretations and arguments I’m interested in. Don’t deny me, the reader, chance to benefit from your thoughts by hiding behind the words of others.

Raw data speaks for itself

No it doesn’t. Or at the best this is rarely the case. This is a continuation of the point above.

If raw data really was that powerful and self-evident, we would simply present interview transcripts as papers and let it be. But we don’t.

Why? Because readers need help and guidance in making sense of those data. You need to hold my hand, shine the light on relevant features, make links, show connections, read between the lines, and provide contextual information that is not contained in the quote itself.

So the way you introduce quotes is important - is this ‘typical’, ‘illustrative’, or chosen for some other reason? How does it relate to other quotes you could have chosen?

And you need to provide a commentary on each quote. What work is it doing in the development of your argument? What do you want readers to take from it? Why is it important? Raw data speaks most powerfully when you speak on its behalf.

Treatment and cure of quotitis

Maybe you’re working on a text and you can diagnose a likely case of quotitis: the symptoms are there in the text itself, and your assumptions are in need of some serious questioning. What can you do? Here are some tips:

Ask yourself some really difficult questions, and be ready for answers you don’t want to hear: Are you over-reliant on quotes because your analysis is half-baked? Are you presenting a list of themes or categories but not doing much with them? Are you hiding behind your data because you aren’t clear about what you actually have to say or want to add to them?

Challenge yourself to sort the wheat from the chaff: are any of your quotes absolutely essential? I promise you, not all of them will be. So bin the one’s that aren’t, and start adding better introductions and commentaries on those that are most crucial.

A good way to start the sorting process is by asking: am I giving three (or more) quotes when one would do? You don’t have to prove that three (or more) people said something relating to a theme by presenting three (or more) quotes. You can quote once and say something about the occurrence of these theme across your dataset.

Ask yourself ‘what is going on here’ when you read a bunch of quotes. I mean, in the sense, what do these quotes collectively say about a particular phenomenon or idea. How can you read between the lines, analyse, synthesise, interpret them together? Perhaps you can swap heaps of raw data for paraphrasing and making a higher-level argument.

Address your anxiety about evidential burden by being really clear in your methods section why readers should trust in your evidence (because your methods of data generation were appropriate and high quality) and what you have to say about it (because your methods of analysis are clearly explained so people have a sense of how you arrived at the claims you make without having to have everything ‘proved’ with a quote).

In conclusion

Quotitis can be painful, especially for readers. Left undiagnosed and untreated, it can be deadly (for your publications, scholarly reputation etc). Fortunately it is easy to spot, treatable, and its underlying causes can be addressed with some critical and honest reflection. Over to you …
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