beginning data analysis – orienting yourself

This post is a response to a question about how to begin data analysis.

When you were little, I bet you played sorting games. You might have organised pencils into colours, or blocks into various shapes. Later on, you may well have sorted those same blocks into sizes as well as shapes, perhaps even added their material into the mix. You probably also transferred pattern-making into paper and pencil activities.  

Maths teachers understand this kind of pattern-making to be early mathematical thinking.

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However, sorting games do more than provide a foundation for number. Sorting and categorising are thought processes that we use to understand the world in general – and in particular, they are the very processes we use when we are analysing  data – sorting and categorising the ‘stuff’ we have generated. 

We look to make patterns and establish commonalities in data by finding connections and associations. And we develop categories for our chosen patterns and groupings   – the equivalent of children’s sorting of red and yellow pencils  – and categories for the overall exercise – the equivalent of the child’s “Look Mum, I’ve done a big pencil sort”. So by the time we are adults we have internalised ways we orient ourselves to pattern-finding/making. 

It’s not unhelpful for we researchers to make these tacit orientations to pattern-making more explicit, so we can see if they are up to the job of data analysis, and to see which strategies are good for what tasks. 

If you want to do just this , you might like to check your orienting pattern-making strategies against these sentence starters – just for starters. (Note – these sentence starters are geared to qual data, but many of them also apply to quant.)

When we are finding commonalities, we generally say things like…

  • This is similar to… because
  • This is different from… because
  • This relates to… because

We often then find ourselves refining our ideas of what our data might allow us to see – and we begin to look for particular things…

  • What if…
  • This raises the question of….
  • What is meant by…
  • How is it possible that…
  • I wonder why…

And we begin to make some initial inferences from what we see in the data…

  • It seems as if…
  • I’m guessing that…
  • I’d expect to see more of …
  • If this… then probably also…

In quants research, we statistically test out these initial ideas and inferences, while in qual data we look for additional confirming or disconfirming material. In both traditions, frequency and/or quantity of the ‘this’ is important for a pattern to hold fast.

And then we begin to generate overarching analytic categories

  • This is primarily about…

And evaluative summaries

  • The most important things here are…

We often look for associations and categories using our pre-existing scholarly knowledge – in research this is usually from the literatures.

  • This is connected with the literature on a … because …
  • This also appears in b and there it is about…
  • This reminds me of c which…

As we get on with association-making, we generally begin to think about explanations for the patterns we are seeing.

  • Why is it like this?

Of course, we may find ourselves going back over the data and our analysis as we think…

  • This doesn’t seem to fit. Why is that?
  • I am confused about…
  • I couldn’t quite grasp …
  • I need to check back about…

But. But there are also things we are less likely to do/ask ourselves when we start grappling with data.

Unless we are doing practitioner research, practice research or a professional doctorate, where the use of prior non-scholarly knowledge is important, we usually don’t allow ourselves to dwell on associations such as…

  • This happened to me when…
  • When I experienced this I…
  • The usual response/reason is…
  • Before I have…

And unless we are doing ethnography or some kind of research in which our own affective responses are important, then we also don’t often think…

  • I can picture…
  • This made me feel…
  • I now imagine…

Some people, and I am one of them, would suggest that both our prior knowledge and experience and our emotional responses are always involved in pattern making. They/we would argue that it’s better to acknowledge these influences, and see what lurking ideas and feelings are producing our researcher-self pattern-making – recognition that you can ‘see’ your own taken-for-granted ideas.

And a benefit. You can also sometimes find surprising clues about new patterns and new ways to write about them when you look at your experiences and emotions. Ask yourself for instance – what data made me feel sad – and see if any new associations and inferences occur.

 

Image: Studio Alijn

 

Posted in data, data analysis, orienting questions, qualitative data, Uncategorized | Tagged , , , , | 1 Comment

play with your data

Data analysis can be pretty scary.  That moment when you realise that making sense of the stuff you’ve so painstakingly generated comes down to you – just you.  Well, relax. It’s not just you that has to leap into the unknown. We all put ourselves on the line when we make decisions about definitions, scales, codes, themes – and when we saywhat the analysis eventually means. But that realisation can be alarming – maybe it’s even paralysing.

You might think that qualitative researchers suffer most from nervousness about interpretation –  facing up to those mountains of transcripts and/or field-notes and/or photographs can be really daunting.  But of course surveys also often contain open questions too so  mixed methods and quants researchers also often face lots of words, not to mention the task of actually converting numbers into something convincing and believable.

Doctoral researchers often get the worries at the beginning stages of data analysis. What if I am just being stupid? What if I am barking up the wrong tree altogether? What kind of criticisms might be levelled at the choices I have made? 

So it’s not at all surprising that in the face of masses of data that doctoral researchers opt for a technique-led approach to analysis. After all, regardless of whether we are doctoral or highly experienced researchers, we all have to demonstrate that we have approached the processes of data analysis systematically, and consistently.

But sometimes the need to be technically proficient is more like clutching at a life raft. If I just do the analysis ‘the right way’ then that’s OK. Showing I can follow a set of ‘rules’ is all that matters. I’m not vulnerable because my exemplary technical prowess is all that counts.

We researchers quite rightly try so hard to get rid of anything that might seem dodgy. For example, one of the strategies that people employ to guard against idiosyncratic individual judgements is ‘inter-rater reliability’. This is where two people code the same transcript. An interpretation is reliable if both people agree – if they ‘see’ the same thing.

But isn’t that also a worry? If we are looking at two people with the same sets of understandings because they are in the same disciplines and have had the same kind of research training, then something else might be going on. In finding a common account of and for their data, they might be just acting really predictably and conventionally. They might be simply finding what they expect to find. Processes of choice and interpretation of data are precisely where taken-for-granted ways of thinking can really get in the way. You see this pattern, you opt for this direction because it just ‘seems right’ or ‘obvious’ or ‘expected’.

But what if it’s not so straightforward?

I like to occasionally stand the safety of data analysis technique and the cosy togetherness of being ‘right’ on their orthodox little heads. What if the discomfort with choice and uncertainty is actually helpful?

The grey area of researcher decision-making is where things get most interesting. It’s where you get to be most creative. And it’s where you can build a set of practices that help you step momentarily away from the usual and purely technical ways of doing data analysis.

I reckon it’s good to make time, when getting to grips with your data, to mix it up. Perhaps after you have done an initial analysis. Not right at the start. Get into the material as you usually would. Find the narratives in the interview, the codes in the transcript. Whatever. Then PLAY. Yes play.

But not any old play. Data play. Play that is designed to help you see new ways of connecting, new patterns, new groups, new associations, new commonalities, new aspects of context. In your data. Get out of the rut of the anticipated. Get rid of the grip of the immediately obvious.

And to that end here’s a few suggestions for data–play which does just that.

  • Random associations

Write each theme you have identified on a post-it note. Put all the post-its face side down, as you would with scrabble tiles. Now pick up three. Stick them to a large sheet of paper so you make a large triangle. Draw a line which connects them all. Now write what that line means – what connects them. The ask yourself – What do these three things have in common? Write this in the middle of the triangle.

You can keep doing this exercise as long as its generative. And you can vary the number of postits too  – four, six, ten, whatever you can manage. You do have to keep replacing the post-its that you’ve stuck to the paper of course, but remember that the object is for the set that you pick up to be random. Not planned.

You can also substitute what goes on the post-its too. You might put down ideas that you are currently working with.

A further variation is to work with data and possible theoretical explanations you might employ.  Make two piles of different coloured post-its with one for ideas or themes, and  the other for theoretical concepts. Now you simply pick up two or three themes or ideas and one theory post-it, stick them on a paper and see what lines and connections you can make.

  • Scatter gun

This is a variation on random associations. Cut all of your themes up into individual strips. Throw them down onto a large sheet of paper. You now have the categories for a random mind map.

Now draw the associations between the post-its  and connections – what links to what and why? What larger concepts are you working with as you make these connections? Are there associations that you haven’t seen before?

  • Redactions

Take a page from an interview transcript and a marker. Cross out all of the words that don’t immediately strike you as interesting. When you have finished, you will have a combination of phrases and individual words.

If this is an A4 sheet of paper, it may help at this point to enlarge it to A3.

Now pin the sheet on the wall and read aloud the words that are visible. What strikes you as you listen to yourself reading? Next, look for repetitions and patterns among the words and phrases that remain on the page.

You can do this exercise with entire transcripts. You can also compare random redacted sheets from similar interviewees to see what putting their visible words next to each other shows you.

  • Side by side

If you are using images in your study then switching from thinking about them as illustrations to actual data can be a bit tricky. One way to help make this adjustment is to select three or four images that particularly strike you – they seem to ‘say something’ about your research participants or the place you are researching. Pin the images up on a wall next to each other – or peg them on a clothes line – or lean them up against a wall – until you get what intuitively appears to be a pleasing sequence.

Now think about what holds these images together and why they are better displayed in this particular order.

You can also think about what joins individual images together and what sits in between them. Imagine that these images are the only things that you had to understand your research participants or place – what do you look for in the image that might give you information or a feeling about them?

I’m sure you can think of other ways to do serious data play.

As I’ve already suggested, it is worth putting aside a little time to get playful with data, as it can help you to break out of predictable sets of associating and patterning. And data play isn’t a substituted for more conventional and systematic data analysis – but it is a complementary practice, and a sometimes very necessary disruption.

And it’s great to do something playful like this in teams as these approaches generate great conversations.

 

Image – Ambiguous Cylinder Illusion

 

 

 

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a thesis writing-feedback calendar

How does a thesis get written? What do I as a supervisor do to help? How does feedback work best? A set of inter-related questions that keep many of us mildly, or a lot, worried. 

Well, I have an ‘ideal model’ for feedback on a thesis. I don’t always follow it. Quite often my model doesn’t work in the way I imagine it could and should, because either I or the doctoral researcher, or both of us, haven’t set aside enough time.

But it might be helpful for me – and maybe for PhDers out there – to do a bit of show and tell about my ‘magical feedback calendar thinking’. It’s really just as an example, not a best way. But because I don’t see feedback and calendars talked about very much I thought it might be interesting to put it out there.

A caveat – I’m talking here from a Social Science-Humanities perspective, and about a Big Book thesis. What happens in a lab-based project, or a PhD by publication, will differ quite significantly from what I am about to say. And lots of supervisors in my disciplinary fields won’t share my ideas either. That’s OK. The general point I’m making – that it’s helpful for supervisor and doctoral researcher to develop a calendar for writing and feedback – is applicable to everyone.

So to my little imaginary… What I most like to see by way of thesis writing and feedback are progress through these five stages:

  1. Written chunks of data analysis.

Writing chunks of analysis allows the doc researcher and I to discuss the ideas they are working with, potential arguments, theoretical framings and possible structures for the text. This kind of chunky writing can begin way, way before field work is finished.

2. Thesis abstract and brief abstracts for each chapter.

Writing the overall abstract  and the thesis structure affords discussion of the research contribution and the way it will be set up in the thesis. How will the introduction create the warrant for the research? What will the thesis argue? How will this argument be expressed as  major ‘moves’? This discussion leads naturally into considering how the argument will be choreographed as text – How will the data analysis and discussion be organised into chapters? How will the literatures be presented – separately or together? How and where will any theoretical framing be presented? What will the conclusion emphasise?

There is usually at this stage some writing of individual chapters. Draft chapter writing focuses on the mini-argument made in each separate chapter. Is the chapter argument persuasive? What is included in the chapter that ought to be somewhere else in the thesis? What is excluded from the chapter that needs to be in? What literatures are being cited, and are they enough and are they up to the job? Some preliminary feedback on writing might well happen here too – so common problems such as literature-as- laundry-list can be addressed earlier rather than later.

3. First whole draft.

Feedback on a first draft focuses on the quality of the ideas and the flow of the argument. Does the argument hold up? Are obvious objections and issues deal with? Are there any over-generalisations, false claims, under-assertions? Is the engagement with literatures and the analysis suitably critical and evaluative? I also have other things in my head at this point too. Should things that are currently in one chapter be in another? Are any obvious things left out? What might go in an Appendix? How do headings and sub-headings carry the argument? Are there any obvious writing tics and problems that can be addressed now rather than later? Does the writer have a ‘voice? Are there any stylistic tools that might help the text have more ‘life’ and ‘character’?

4. The second draft.

The fine-grained examiner-hat-on feedback. This is the most time-consuming stage for me, as it involves detailed attention to paragraphing, syntax, choice of terminology, phrasing and so on. While I continue to look at the argument and analytic sophistication, I also check to make sure the references that I think the examiner will want to see are cited. I also check the style of the referencing at this point, looking for consistency. This reading means I do lots of track changes and I need a lot of intensive time at the computer. At best, I manage about two chapters a day of this kind of work.

Sometimes stage ( 3) has to be repeated in order to get the text working before going on to (4). Because of time, a first draft may combine elements of both (1) and (2).  Even (3) might be folded in under worst circumstances. Combinations of feedback stages can be pretty overwhelming, because both substantive content and form are under the supervisory lens. (See bleeding thesis.)

5. The last run-through.

This is not a proof read (that’s the doctoral researcher’s responsibility)  – although if a good proof read is needed it will be obvious at this point in time. Rather it’s a final check on the argument, warrant and conclusion and, most importantly, catching up with the revisions resulting from the fine-grained read. A tracked changes final draft is therefore time-saving for both doctoral researcher and me.

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Now these five stages may equate to more than a twelve-month work-plan. More days than in a whole year. 365 plus days… Scary when you think of it like that. Not only for the doctoral researcher – but also for the supervisor, particularly if there are more than one or two people on the same completion run-down time-table as I have now.

But seeing thesis development as a set of stages – even if yours are vastly different from mine – is good. It’s not only useful as a way for doc researchers to work out what to do when and what to expect by way of feedback. It’s also a guide to developing writing-feedback calendar too. Counting back from submission to the very beginning of chunks of data and putting weeks and months against them provides a real sense of the way ahead. While it might also be daunting, getting your head around the path to completion is also integral to feeling – and staying – in control. And of course, it’s absolutely critical to be realistic about how many weeks it’ll take to write the text for each sequence – and how many weeks it’ll take me to do the feedback. Needless to say, if circumstances change, or if a repeat stage is required, then the calendar has to be adjusted.

The calendar can also help PhDers to sort out where conferences fit in, what to do in between drafts while waiting for feedback and when might be good times to do some writing for publication.

But the calendar is also important for supervision. It helps me enormously if I can also plan my year around the times when I know I will be doing intensive reading and feedback of thesis work. My own book writing depends on finding times when I don’t have to do a lot of feedback work.

Now as I said already, but let me repeat, I don’t expect that all supervisors will have the same way of doing things that I do. Your supervisor may prefer that you work together on all of the chapters one by one so that they are all written to a pretty good standard – then only one draft will need and get feedback. A lot of supervisors don’t work with abstracts like I do. Your supervisor will probably have some other sequence of writing and feedback that works for them and your topic.

But feedback and calendar are a Good Thing to talk about. Setting up a writing-feedback  calendar provides a VERY helpful focus for a discussion sometime towards the end of field, library or lab work.

And it’s a good thing to pin your calendar on your wall above your screen next to your research question. You get to cross off each stage as you finish them – so you see yourself creeping up on completion. Yay. 

Just in case it’s of use, here’s my ideal writing feedback table, just for interest you know… you do your own version. Then make it into a calendar with actual real-life dates. 

TARGET COMPLETION DATE

FINAL DRAFT Date handed into supervisor Date handed back
FINE-GRAINED READ Date handed into supervisor Date handed back
FIRST DRAFT Date handed into supervisor Date handed back
CHAPTERS

1.

2.

3 etc

Dates handed into supervisor Dates handed back
ABSTRACT AND CHAPTER OUTLINES Dates handed into supervisor Dates handed back
CHUNKS OF ANALYTIC WRITING

1.

2.

3. etc

Dates handed into supervisor Dates handed back

 

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social media? sometimes it’s just nasty as ****

 

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Like many of you I’m sure, I’ve been watching the extremely ugly and misogynist twitter assault on Mary Beard over the last week or so. Like many of you I’m sure, this has made me angry, sad and more than a bit worried. It’s a manifestation of a blood sport approach to debate that Mary, and multitudes of the rest of us, deplore.

The abusive combative exchange not only makes me feel ill, but also a bit despairing at the lack of change in academic cultures …. let me explain.

When I was a young person, thinking about whether to pursue an academic career or not, there was a notable propensity for some academics to verbally pulverise anyone who disagreed with them. I have a very vivid memory of a Masters’ tutorial where the topic was whether Stanley Livingstone was an imperialist or not. The tutor, a senior academic, took great umbrage at anyone who wanted to answer in the affirmative and shouted, berated and belittled, effectively silencing everyone. He relied on seniority, bluster and fear to make the case that if someone didn’t think they were an imperialist then they weren’t. (Yes, it was a pretty untenable argument and maybe that’s why he felt he had to defend it so vigorously. And yes, the gender politics were also what you might have predicted. )

This wasn’t the first time he’d yelled and carried on like this – but it was a last for me. I hated it. Everyone in the Department knew about it and no-one did anything. So why sign up for a career where hierarchical intimidation was accepted? I decided then and there that if that’s how the academy conducted itself I wanted no part of it. Instead I went to work with young people who’d been kicked out of school – many of them were young offenders.  They were often very friendly and nice to be around, and if they weren’t, at least there was a good reason for their mean-ness and outbursts.

Coming back into the academy much much older, and at a different time, I was at first relieved to see that academics had largely left insufferable ad hominen brutality behind. Conference behaviour seemed to generally be kinder. Teaching in particular was much more focused on support and reasoned discussion. Yes, people were still often less-than-nice to each other, but hunger games pedagogies and gladiatorial argument seemed to have largely disappeared.

Yet, and yet, here it all is. Still. But now on social media. Not in the relative privacy of a tutorial or seminar room, but full frontal in public. Just not face to face. A senior academic bloke flinging insults into the ether without actually witnessing what happens as a result to the person under attack, to others who are watching or trying to support, or to social media practices/cultures. And then the hangers on join in. Triggered by their ‘guru’ they wade in. More mass nastiness – distanced, much of it anonymous, some of it just smart-assery, a lot of it rage and bile.

This is not the first such incident*. This kind of joustery-to-the-death makes social media a very unhospitable place. Somewhere where narcissism prevails, where tribalisms are constructed and reinforced and a pernicious anti-ethics prevails.

And this kind of social-mediatised behaviour is not something that can be fixed with calls for academic niceness and kindness. We need to name this badness for what it is. It’s not clever. It’s not a sign of intellect or learning. It’s just harassment. Plain old bullying. And sometimes just criminal hate.

Civility in academic discussion – what Martha Nussbaum might call a practice of moral emotions which recognises and values other persons and perspectives – seems sadly at risk on social media. Bravo Mary Beard for continuing to demonstrate what it means.

As many have already noted in print and on social media, red in twitter tooth and claw is academic conduct unbecoming and unwarranted. Encouraging others in attack behaviour in the name of some kind of robustness or anti-‘political correctness’ is equally, perhaps even more noxious.

So what can be done, other than to back off, or to persist graciously and courteously, as Mary Beard has done. Well, at a time when we academics are required to engage more with publics and public debate, we need our institutions to openly condemn violent and combatorial social media practice.Alison Phipps made this point quite some time ago, calling for universities to consider the impact agenda and what it asked of us. She noted that the ways in which those who spoke about questions of equity, gender and race in particular were liable to be vilified and threatened. Phipps called for some kind of institutional action.

Nothing much seems to have happened in the three years since her call for intervention. Institutional ‘duty of care’ seems still to be a notable absence rather than an active presence in social media turmoils . Yet the impact and public engagement conversation continues unabated  – and as if none of this nastiness happens.

Those of us who have no stomach for social media verbal violence need to know that we have institutional backing against macho belligerence – torrents of crude abuse cannot masquerade as intelligent debate. Less Coliseum, more Socratic dialogue please. Universities – we need your explicit and open support if we are to speak about our work with publics who aren’t always receptive, prepared to listen or to behave decently.

 

 

*A new edited book contains systematic analysis of these kinds of incidents. Watch out for BAROUTSIS, A, RIDDLE, S and THOMSON, P Eds Education Research and the Media: Challenges and Possibilities Routledge – coming in late 2018.

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#holidayreading – the forgotten tribe, scientists as writers

Over my “holiday”, actually not holiday I’m working, I’m catching up on research about writing. In fact, I’ve just been reading about the writing practices of scientists.

Lisa Emerson interviewed 106 scientists –  distributed across four countries and multiple science disciplines (but excluding pure mathematics). She wanted to address/redress the common myth that scientists write badly and don’t care that they do. They allegedly churn out tedious experimental reports that nobody can or wants to read. 

I’m with Emerson on debunking this, if it is something that people believe – some great academic writing comes from scientists. Witness this year’s Royal Society short list for science writing for instance. The list includes Ed Yong’s I contain multitudes and Cordelia Fine’s Testosterone Rex, both of which are highly pleasurable reads, as well as being worthily informative.

The starting point for Emerson’s research was that postgraduate scientists generally learn academic writing differently from those of us in the arts and social sciences. Working in lab teams, postgraduate scientists tend to co-write with a supervisor or with peers. This apprenticeship model differs from the ways in which say, a literature scholar works alone in a library on text, shows their writing to a supervisor and gets feedback. There is no co-writing. Because of this difference, the writing habits of scientists needed a specific research project, Emerson asserts.

coverBefore reading the book, I’d have assumed that some scientists do see writing as very important and also see themselves as writers, whereas others don’t –  much like the rest of the academy then. And indeed, that is actually what Emerson found. Emerson divides her scientist interviewees into two broad groups – those who are routine writers, they don’t like writing much, but they see it as part of the work and they do it – and adaptive writers – they like writing, even if it is hard work sometimes. Adaptive writers spend time working on their writing craft, and experiment with a range of genres and styles of writing. Even as senior scholars, they still write a lot and don’t delegate writing to junior members of their team.

Emerson’s interest is in how her group got to be either routine or adaptive. She is not judgmental about a career choice to engage widely with the public or not  – both are equally valid and both are needed she says. Rather, her concern is with the patchiness of learning about science writing and how that might be understood and changed.

So, here’s a bit of a potted summary of some of her key points.

  • Adaptive writers tended to have positive experiences of writing during childhood and school – and actually most of her adaptive and routine writers had taken language rich subjects at school so this wasn’t about subject choice. But, Emerson notes, schools could do a much better job of teaching scientific writing.
  • Scientific writing was largely learnt through co-authorship, supervision, reading and imitation with most scientists,  routine and adaptive, having negative experiences of working with an advisor. Positive experiences were where supervisors talked through changes – not just rewriting or asking for changes without explanation which was the more common experience. (Emerson’s interviewees did produce some teethgritting examples of worst practice; these are always instructive.)
  • Very few of the entire group had any assistance with guided reading designed to help them learn the rhetorical conventions of their discipline – they learnt this through a kind of immersion in the discipline rather than anything explicit.
  • Support for writing post PhD, including participation in writing groups, was important for all of the scientist interviewees. Mentorship was key, with adaptive writers reporting higher levels of support than routine writers. Two of the group had experienced specific writing programmes during their undergraduate years and reported the significance of this experience. 

Emerson argues that the lack of undergraduate attention to scientific writing, and patchy experiences of the postgraduate apprenticeship mode, clearly point to the kinds of  interventions that might improve scientific community engagement with writing. As she see it, “the challenge for the scientific community is to begin to model and articulate the attitudes and beliefs towards writing that they wish to see in their graduate students”. Emerson sees an answer not in courses or modelling per se, but rather in ongoing and deliberate conversations about writing, conversations that go on throughout an academic science career.

I reckon that this is the kind of book that ought to be part of a resource kit for supervisor development. I can imagine that a discussion of extracts from this book could kick off a pretty interesting intervention to bring writing into greater focus in teaching/learning/supervision. The book isobviously of interest to those in science but it does also have some pointers for those in other disciplinary areas too, particularly as lab-based models of research spread more widely across the academy.

And – yay – Emerson’s book is open access online. 

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that bleeding thesis…

No. I’m not cussing. Let me explain why. My colleague Brigitte Nerlich sent me an email the other day. She said:

I was talking to a PhD student (not one of mine) and this student repeatedly used a metaphor which I found quite interesting – that of the ‘bleeding document’. This was not a swear word! This metaphor described the fact that when PhD students get chapters back from their various supervisors (two or three), the document looks like its bleeding because of all the comments and track changes that have been made. I had the impression that this process provoked a quite visceral feeling which then induced discouragement and, not quite despair, but despondency. It drained the life-blood out of the PhD. I began to wonder whether PhD supervisors should reflect a bit more on how they comment on chapters so as not to let a PhD bleed to death …

Yes, I thought on reading this. Supervisor feedback on writing can be a pretty variable affair.

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We know from the research that’s been done on PhD feedback – and there’s not that much of it – that supervisors vary hugely in their approach to responding to writing. While pretty well all supervisors feel responsible for the final dissertation text, this sense of obligation translates into very different feedback practices. Some think a chat about ideas is sufficient. Some focus primarily on the technicalities of writing – sentence structure and grammar. Others deal with the flow of the argument. Some believe that it is not their job to deal with technical language at all, and it’s the doctoral researcher’s responsibility to get the writing right.

We also know from experience and anecdote that getting feedback from supervisors can be tricky. Research tells us that the feedback on doctoral writing can be vague and ambiguous, and thus not understood by doctoral researchers. Feedback also always raises questions of ownership – whose thesis is this anyway? And PhD feedback needs to change over time – what is helpful at the start of the the doctorate may not be so helpful at the thesis stage.

Receiving supervisor feedback is often highly emotional. Some PhDers focus strongly on the negative and find it almost impossible to move away from feeling brutalised. Others, as Brigitte’s email suggests, find that feedback can be pretty demotivating.  But some PhDers do find detailed feedback very helpful and they have strategies to deal with the disappointment of being asked to write again and again – for example, they write lists of supervisor comments and systematically go through them.

And advice given to doctoral researchers and supervisors alike is to discuss the process of feedback – but discussion doesn’t always mean that there will be agreement.

So feedback is obviously a fraught area. It’s often pretty vexatious for all concerned. I claim no particular expertise in it, and I suspect that none of us do. In fact, for many of us, our own experience of supervisory feedback was quite different from what is expected now.

In her email to me, Brigitte reflected on her own and her husband’s experiences of supervisory feedback.

 When I wrote my PhD, I had virtually no supervision and computers were not invented yet, really, so one just had a good chat once in a while. I talked about this with my husband who did his PhD in the 1960s/70s and actually got some supervision! He gave his first chapter to his supervisor who scribbled some stuff in the margins. He retyped it (on his typewriter), gave it back to his supervisor, who scribbled some more stuff in the margins. By the third time, he became aware that this was unsustainable. So he gave his supervisor the second chapter; the supervisor scribbled some stuff on it… But then he didn’t give back the revised version but just the next chapter and so on, which worked much better and involved less typing and retyping.

My PhD experience was twenty years later, but not too different from Brigitte’s.

Universities now have courses and resources for supervisors and many of them do take supervisor development very seriously. However, what’s on offer to supervisors is usually and necessarily general – it doesn’t really address the feedback question with the kind of cultural specificity and discipline-specific and cross-disciplinary detail needed to avoid the bleeding document syndrome.

It’s probably the case that really significant improvements in supervisor feedback practices will take a combination of research-informed supervision resources and the space in busy academic workloads to discuss and process them. That’s largely still to come IMHO.

Meanwhile some PhDers have blood on the page…

Image credit: Paula Grubb

Posted in Brigitte Nerlich, feedback, supervision, supervisor, thesis | Tagged , , , , | 9 Comments

#holidayreading – air & light & time & space

I read a lot of books about writing and research. That’s not surprising, as I write them too and I always want to see what others are writing. And today…  Helen Sword has followed up Stylish academic writing with a study of successful academic writers – it’s called Air & Light & Time & Space.

41dj4QjzaUL.jpgSword identifies four elements that combine to make a successful academic writing practice: behavioural habits such as persistence; artisanal habits such as crafting and artistry; social habits such as collaboration; and emotional habits such as risk-taking. There is an accompanying website that you can use to look at your own writing habits. Once you have your ‘diagnosis’ you can then consider strategies that you might use to change any one of the four ‘cornerstones’.

I was very interested in the empirical basis of the book, a survey followed by 100 interviews.  The book offers many ‘boxed’ condensed portraits of the academic writers who were interviewed, all named. There is also a generous use of quotations which illustrate the diversity of writing practices across countries and disciplines.

As I read it, the message Sword drew from her data is this – everyone sorts out their own academic writing practice… but the four habits derived from successful academic writers can be used to examine what you do and how you might work on your own academic writing.

I particularly like the way that Sword debunks writing advice full of shoulds, and focuses instead on what writers may do.

May I ignore the advice of all of those productivity-pumping books, articles, and websites that tell me I should write in a certain way, at certain intervals, for a certain length of time? Yes, you may. Alternatively, you may choose a prescriptive formula and follow it to the letter if doing so works best for you. (p. 42)*

Sword lists a number of metaphors that dominate writing advice – blast or sculpt, bungee or map, lines or boxes, roots or rhizomes, snack or binge, sprint or marathon, march or dance. She suggests that while writing advice often lands on one side of the binary – snacking good, binging bad for instance – academic writers often have a personal preference for one or the other, and many people do both at different times. Besides giving permission to become non-binary, Sword also offers some alternative couplings – stew or marinate, juggle or bowl, track or float, cloisters or commons – which address other aspects of the writing process.

But finding your own writing practice is not just about rethinking what you do. It’s also about being informed about producing text. Sword’s chapter on the craft of writing (Chapter 5) contains a very helpful list of the ‘stuff’ that experienced and successful writers attend to – elegance of expression, concision, structure, voice, identity, clarity, accessibility, vocabulary, syntax, agency, audience, telling a story, “the big picture”, the visual (typography, pagination and layout) and the technologies of writing. Any budding academic writer could easily use this list as a self-guided map for working on their own writerly artisanship.

Each of Sword’s chapters concludes with ‘things to try’ – examining your own practice, examining the ways in which you think about academic writing, and further reading. The further reading is VERY comprehensive and ranges through literary, creative writing and academic writing texts. Years of fun could be had checking these out, and for anyone thinking about a career in academic writing, this is a handy ready-made bibliography.

I really enjoyed reading the book and I recommend it to you (but see note below). It’s got loads of helpful pointers, and a reassuring stance. The habits of successful writers can help you to think about what strategies might work for you.

And if you are interested in following up Sword’s debunking of the daily writing practice mantra, she has written a journal article where she addresses Robert Boice’s original argument – she describes the evidence for the advice to write daily as a ‘perilously thin research thread’. She argues on the basis of her empirical research that:

The bottom line is that Boice’s austere methods do not reflect – and in some cases are antithetical to – the real-life practices of productive academics. For the vast majority of the colleagues I interviewed, writing is neither a daily routine nor a rare occurrence, neither an immovable constant nor a random event, neither a public activity nor a rigidly sequestered one; writing is the work that gets done in the interstices between teaching, office hours, faculty meetings, administration, email, family events, and all the other messy, sprawling demands of academic life. The secret to their academic success lies not in any specific element of their daily routine but in a complex cluster of attributes and attitudes that I call their ‘writing BASE’: behavioural habits of discipline and persistence, artisanal habits of craftsmanship and pride, social habits of collegiality and collaboration, and emotional habits of positivity and resilience (Sword, 2017).  (p. 320)

Even though I’m a daily writer like Sword, I completely agree with her argument about the dangers of assuming a One Best Way. (I’ve posted about daily writing as therapeutic desensitising treatment for writer’s block  – my attempt to show its specificity and potential uses.) Free writing  can be very helpful – but it is not mandatory. In Sword’s words you may choose to do it. There really is no one way to write, you have to sort out what works for you, when, and on what. You have agency.

And Sword’s latest book can certainly help widen your thinking about how to build your own repertoire of writing strategies and practice. Also see her piece in the Times Higher about how her research might inform academic writing provision in universities.

Important note

I don’t want people to read my posts about books as me saying that they must rush out and spend some of their limited funds on these books. Books are expensive. You need to pick and choose which ones you will buy.  If you’re short of cash, you want to choose books which you’re going to use over and over again. So I often say at the end of writing about a book – get your library to order it.  So please read this post, and the other one or two that will appear over the next few weeks as having that message. These books are interesting and worth reading – so if Sword’s book isn’t already there, get your library to get it in because then others will benefit from it too.

 

 

 

 

 

 

Posted in academic writing, crafting writing, free-writing, good academic writing, Helen Sword | Tagged , , , | Leave a comment