What do you think you ‘know’ about your research problem?

*You can listen to this post on the podcast player.

I am currently supervising several students at PhD and MA level, and a few of them are starting out with reading, writing, constructing their gap and setting out the shape and size of their research problems. This is an exciting and also at times frustrating process for students and supervisors. It is often characterised by steps forward and then backward or perhaps sideways, and sometimes what feels a lot like going around in circles. I’m not focusing on this whole process here, but rather one dimension of this part of doing research: the things we think we “know” before we start our research, the assumptions we bring into our reading and writing that we may not always see clearly, and why we need to think reflexively about these issues to do more ethical and credible research.

We often approach research, perhaps more at doctoral than any other level, from the starting point of our own interests, passions, questions: what do we want to know more about? What do we want to use this research project to find out or change or do? This means that we may be coming into this research with existing knowledge about the topic we are researching. And, crucially, our approach to the new research may be shaped or influenced by certain assumptions about what we think the project will be about, or how it might unfold, or what we might discover or find out. This is all really normal: when I started my PhD I had been working as a lecturer and tutor for almost 10 years and I had a lot of ideas about what we should be doing better or what we might be doing poorly and what could be improved and how. Many of these were founded in what might be termed a form of experiential knowledge or practice wisdom*, and some of them were probably quite common-sense understandings of teaching, learning and higher education.

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This is knowledge we all have, whatever field we are working in, I think. This may be more pronounced in some fields than in others, especially when you come from a professional space you have been working in to the doctoral research space. Some of this knowledge may come from overt forms of learning, such as reading research or attending seminars and making deliberate notes and working to make sense or meaning of all of this input in relation to our work and research. But, much of it can also be gained more tacitly, through time, experience, being in the environment and working things out as you go. This can feel like “common-sense” knowledge: this is the way we do things, it works, it’s fine. But we cannot rely on common-sense knowledge as a basis for research because it is too partial, too biased, and perhaps not necessarily supported by peer-reviewed, theorised research in our field of study.

I’ll offer an example from my own research journey. I work in higher education, as many of you may know by now. Specifically, I am a writing and academic development specialist and practitioner. I started my PhD a year after I started managing a university writing centre, and about 6 years after I has been consistently involved in teaching academic writing to undergraduate students at three different universities in different faculties. So, I had a lot of ideas about the “problems” with student writing itself, with students’ “motivation” for writing, and with the teaching that was happening. These ideas were shaped by colleagues I had worked with, the departmental ethos and course structures and materials I worked within and with, and my own experiences with students. I had not read very much of the scholarly research because I was not asked to or required to, and thus it didn’t seem relevant. It was pretty much all I could do most weeks to keep up with the teaching and marking. When I started my PhD and wrote the first few drafts for my supervisor, I was really challenged on a lot of my assumptions – assumptions I did not realise I had – that revealed positions and ideas that were not supported by the more critical parts of the conversation the field was having about literacy development, student writing, and effective teaching and learning. I wanted – needed – to join this critical conversation and this meant that I had some difficult thinking, reading, and reflecting work ahead. Specifically, I had to unlearn some of what I had learned about curriculum design and teaching, and notions of student “deficits” and discourses that put individual development and change over and above deeper and more sustainable systemic development and change.

This was hard work: not just intellectually, but also emotionally because it challenged who I thought I was as a teacher at that time. I always thought I was helpful, kind, made tough things like writing essays easier for my students. I think I was that person for some of my students, but the reading I did and the related thinking and writing work at PhD level made me realise that I was probably not that person for all my students because I had blind spots around questions of knowledge, access, success, systemic dis/advantage and how university opens spaces for some students to succeed and closes places for others, for a range of reasons that need to be made visible, named and addressed.

My early research questions and writing reflected my early blind spots and biases – in the language I was using (“In what ways can we better allow students to succeed”; “How do we deliver the curriculum more effectively”). My verbs, perhaps more than anything else, gave me away: they revealed my assumptions and bias. These needed to be challenged and made visible for two key reasons, I think. The first is that if we cannot or do not see that we have all sorts of assumptions and ideas coming into a project, we may well end up with a significant mismatch between the work we want to do and the work we will probably end up doing. We may say, for example, we want a participatory research design but end up doing something far more directive and hierarchical if we have an assumption that we know what the answers are and all we need to do is work to find them, or that the researcher needs to be an authority in their project, for example. This may extend to disconnects between theoretical and conceptual frameworks and the language you use in writing the thesis, or between the theoretical and methodological frameworks.

The second reason is that these assumptions and biases, if they remain unseen or unchallenged, may affect the way we obtain and make sense of our data. If you think you “know” that student motivation is the issue in whether or not students are successful, even if your reading of the literatures challenges this, you may construct data gathering instruments, such as survey items or interview questions, that are leading – that try to get participants to confirm these assumptions or ideas. You may also only seek out literatures that confirm these assumptions or ideas. In all research, we need to be quite aware of and cautious around confirming our biases, rather than constructing a research design and data gathering process that reflects the literatures we have included and aligned our study with, our theoretical frameworks, and that is open to discovery and analysis, rather than seen as “The answer”. “The answer” may be too coloured by our common-sense knowledge and what we think we “know” to really push us forward in our thinking, challenge some of the more problematic aspects of our work, create new knowledge in our fields of study and practice. In my own study I used Nvivo to code my data and in one of the training videos there was a phrase that stayed with me: “We need to be open to being surprised by our data”. Scientists may tell you: “Believe what you see; Don’t see what you believe”.

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There are many different research paradigms, or worldviews, that shape what we think the world is like, what we think counts as valid or credible knowledge or “truth”, and how we can reliably come to know that knowledge or verify those “truths”. Our worldviews are not always visible to us because we may come to them quite tacitly, over long periods of time through immersion in particular fields or disciplines of research and practice, through prolonged engagement in particular social and political environments, and so on. Surfacing our assumptions about what we “know” and how we know it and why we think it is valid knowledge is a powerful step in making our research paradigm clear enough to ourselves to explain it to readers and examiners. And this is important because it lends further credibility to our choices of research design, analytical framework and methods, and our eventual findings. On what basis do you make claims to “truth” or knowledge? How must others evaluate and make sense of your claims? How do you show the different ways in which you are making your contribution to knowledge and thereby persuade the reader that they matter and are valid?

This is not always easy work and it happens over the course of the project rather than all up front in the proposal. You need to begin there, at the beginning, reading and thinking with the scholarly and additional relevant literatures in your field, keeping a reading journal to write to yourself about how the reading and your own thinking and purposes/plans are connecting and speaking to one another. Speak to others about your research, including but not limited to your supervisors. Share your thoughts as openly as you can with people you know will listen and think with you, and not shut you down. Consider having a critical friend to regularly debrief with about different stages of your research process, so that you have help in spotting and revealing blind spots and biases that may derail your study or undermine the credibility of your arguments. The key thing, I found, was to find as many different useful spaces as I could to be brave enough to talk about my work and my thinking, and be open to listening to the critique and doing the work it demanded. It was not easy, but I do think that my research itself and myself as a researcher are better for it.

*This a term I reference to Veronica Bamber and her keynote at HECU7 in 2016.

Referencing, plagiarism and building credibility

Plagiarism is a big deal in academia. As students we are told often what a terrible thing it is to plagiarise the work or ideas of others, and that if we do plagiarise we could face serious penalties. Academics have lost jobs, status and reputations over plagiarism – in South Africa there was a famous case in 2007 of ‘Chippy’ Shaik having his PhD degree rescinded as a result of allegedly plagiarising 2/3 of it, and in 2013, Dr Jane Goodall was accused of lifting several passages verbatim or near verbatim from other sources in a book that is scheduled to appear this year. Even outside of academia, cases of plagiarism are taken very seriously – US Senator Rand Paul and Transformers actor Shia Lebouf have both been accused of using the words and ideas of others and passing them off as their own without crediting the original sources.

Plagiarism is not just copying verbatim the words or ideas of others and passing them off as your own. Even when you summarise, paraphrase or incorporate the ideas of others in your own work without citing the original source, you are regarded as a plagiarist. Plagiarism is generally defined as the intentional or reckless use of the words and ideas of others, rather than unintentional or accidental actions, but the burden of proof about intentions is often on the student – how do you show that, if you did plagiarise, you did so accidentally rather than on purpose? One way you could do this is by showing your ‘paper trail’ – all your notes, and reference lists and planning that went into your writing. But this is a hassle, and it’s by far easier to do what you can to avoid plagiarism altogether by keeping track of all your source material, and by working hard on developing your own voice in your writing.

But, for a PhD student, this is not as simple as it may sound for a couple of reasons. The first, big reason, is that as PhD students we read a lot. We read and read and read, we make a lot of notes, and after a while these ideas become part of our own ideas, and the theory we are influenced by and that ‘gels’ with the way we see our research or the world around us really influences how we think. It starts to become difficult to tell our own ideas pre-reading apart from our ideas post-reading. We can, after a while, write 3 or 4 or 5 pages of a ‘literature review’ without even needing to consult a reference. This is how well you end up knowing your stuff. This is a good thing – as a PhD student you need to know your stuff that well. But, the downside is that you have to be really careful not to plagiarise. Which ideas are yours and which are the ideas of other authors and researchers whose work has influenced your own? Which ideas do you have to reference and which ones can you claim as your own?

The second, connected reason, is that avoiding plagiarism and being an honest and ethical writer and researcher is about more than just including references in the text and in a bibliography. If you copy your whole literature review piece by piece and reference it perfectly, you really are still committing a form of plagiarism, and you’re not doing your own research either. Being an honest and ethical writer and researcher requires an understanding of knowledge, and how it is built, debated, challenged and changed in academic communities of practice and research. We build when we research and write – we build on the ideas of others, on their research, on their data, on their methodologies and on their words. We join conversations, and we debate, challenge, dispute, agree and slowly establish our own voice and our own ideas, claims and positions which we hope (and fear) that others will challenge us on. So, even if the idea is also yours (and the writers’ you are citing), referencing well does more than just help you to avoid plagiarism; it helps you to establish the credibility of your voice, your ideas and your developing argument. This is essential for writing a credible and acceptable dissertation, and for ensuring that you do actually get full credit for your own ideas and arguments. If you reference poorly, lose sources and get sloppy, you risk being accused of plagiarism, you risk being discredited and you risk shortchanging yourself on your own intellectual development and growth.

I used two simple tools to help me: One was the P(oint), E(vidence), E(xplanation) paragraph writing structure, which helped me to ensure that I started and ended each paragraph with my own ‘voice’ and included accurately cited and referenced ‘Evidence’ to support my ‘Points’ and ‘Explanation’. As a tool it can be adapted and played with and it really works. The other was a bit techno-backwards, but I kept a manual list in separate file of all the sources I was using as I wrote. I cross-checked this regularly. I had it open whenever I was working on part of a chapter and I copied across the in-text references either at the end of a section or as I wrote, depending on how well or smoothly the writing was going. I regularly went into this list and filled in all the information I needed, and reorganised, cut and added as I went. I know I could have used Refworks, Endnote, Mendeley or other similar tools, but I actually found the effort of learning to use these tools effectively too much for my already-taxed brain. I trusted my system, and that gave me peace of mind. (I don’t think I left out any references, but frankly I am not going back to find out!)

Any tool you use, whether un-technological one like mine or more automated like Refworks etc, is only as good as the person using it. These tools can’t do your writing for you, or make sure you don’t plagiarise or leave references out of your reference list (or leave too many in after chapters get cut). They can’t do the work of developing your voice. But, learning to use particular tools, whichever ones work for you, can save you a lot of time and reduce your anxiety about keeping track of your sources as your PhD progresses. And using these tools to help you reference accurately can also help you show your reader your emerging voice that much more clearly as you write.

Data: collecting, gathering or generating?

I’m thinking about data again – mostly because I am still in the process of collecting/gathering/generating it for my postdoctoral research. I had a conversation with a colleague at a conference I went to recently who talks about ‘generating’ his data – colleagues of mine in my PhD group use this term too – but the default term I use when I am not thinking about it is still ‘collecting’ data. I’m sure this is true for many PhD scholars and even established researchers. I don’t think this is a simple issue of synonyms. I think the term we use can also indicate a stance towards our research, and how we understand our ethical roles as researchers.

Collect (as other PhD bloggers and methods scholars have said) implies a kind of linear, value-free (or at least value-light) approach to data. The data is out there – you just need to go and find it and collect it up. Then you can analyse it and tell your readers what it all means. Collect doesn’t really capture adequately, for me, the ethical dilemmas that can arise, large and small, when you are working in the ‘field’. And one has to ask: is the data just there to be collected up? Does the data pre-exist the study we have framed, the questions we are asking, and the conceptual and analytical lenses we are peering through? I don’t think it does. Scientists in labs don’t just ‘collect’ pre-existing data – experiments often create data. In the social sciences I think the process looks quite different – we don’t have a lab and test tubes etc – but even if we are observing teaching or reading documents, we are not collecting – we are creating. Gathering seems like a less deterministic type of word than collecting, but it has, for me, the same implications. I used this word in my dissertation, and if I could go back I would change it now, having thought some more about all of this.

Generating seems like a better word to use. It implies ‘making’ and ‘creating’ the data – not out of nothing, though; it can carry within it the notions of agency of the researcher as well as the research participants,  and notions of the kinds of values, gazes, lenses, and interests that the parties to the research bring to bear on the process. When we generate data we do so with a particular sense in mind of what we might want to find or see. We have a question we are asking and need to try and answer as fully as possible, and we have already (most of the time) developed a theoretical or conceptual gaze or framework through we we are looking at the data and the study as a whole. We bring particular interests to bear, too. If, as in my study, you are doing research in your own university, with people who are also your colleagues in other parts of your and their working life, there are very particular interests and concerns involved that impact not just on what data you decide to generate, but also how you look at it and write about it later on. You don’t want to offend these colleagues, or uncover issues that might make them look bad or make them uncomfortable. BUT, you also have a responsibility, ethically, to protect not just yourself but also the research you are doing. Uncomfortable data can also be very important data to talk about – it can push and stretch us in our learning and growth even as it discomforts us. But this is not an easy issue, and it has to be thought about carefully when we decide what to look at, how and why.

These kinds of considerations, as one example, definitely influence a researcher’s approach to generating, reading and analysing their data, and it can help to have a term for this part of the research process that captures at least some of the complexity of doing empirical work. For now, I am going to go with others on this and use ‘generating’. Collecting and gathering are too ‘thin’ and capture very little if any of the values, interests, gazes and so forth that researchers and research participants can bring to bear on a study. Making and creating – well, these are synonyms for generating, but at the moment my thinking is that they make it sound too much like we are pulling the data out of nothing, and this is not the case either. The data is not there to be gathered up, nor is it completely absent prior to us doing the research. In generating data, we look at different sources – people, documents, situations – but we bring to bear our own vested interests, values, aims, questions, frameworks and gazes in order to make of what we see something different and hopefully a bit new. We exercise our agency as researchers, not just alone, but in relation to our data as well. Being aware of this, and making this a conscious rather than mechanical or instrumental ‘collection’ process can have a marked impact, for the better I think, on how ethically and responsibly we generate data, analyse it and write about down the line.

Fieldwork: custom, character and questions of ethics

I have been thinking about fieldwork a lot lately, and how to improve on what I did with it during my PhD because I am doing it all again, post-doctorally. I have started a new, connected research project which I will probably write about later on, and I am wondering if the way I am doing my fieldwork is the best way. I am not really doing anything too different yet, and I’m working in the same two departments although with different lecturers. This will probably be one of three posts thinking through different aspects of doing fieldwork, so I’d like to start with considering the question of ethics, and the ethical behaviour of researchers in ‘the field’.

Fieldwork is generally defined as ‘an investigation or search for material, data, etc, made in the field as opposed to the classroom, laboratory, or official headquarters’ (http://www.thefreedictionary.com/field+work).  I gather data in classrooms, and in conversations with lecturers, and from documents. This year I am also adding student voices to the mix. This may not fit this definition but for the sake of using one word instead of two, and because it tends to be a ‘catch-all’ term for this phase of a research project, I am going to use the term fieldwork to talk about the phase of gathering different kinds of data from different sources, even is a classroom or lecture hall.

The lecturers I worked with last year and the ones I am working with this year are colleagues. I know them and they know me, and we respect and like one another professionally and personally as well. So it is very important that we can work together well, and that I am ethical in the way I behave as a researcher because that will affect the way I am received as a colleague. There are several benefits to working like this: access to data is much less complicated – I have been welcomed with open minds and arms, and they are interested in what I am doing; I can ask questions after class and even in class if I want to, so I feel like part of the environment rather than a detached outsider (more about this in another post perhaps); I am really enjoying this work a great deal because these departments are so interested and welcoming.

However, there are also drawbacks. The biggest, for me, stemmed quite specifically from the kind of data gathering I was (and am still) doing – what Paul Trowler calls doing research in your own ‘backyard’ and learning what Kevin Williams has called ‘guilty knowledge’. I work at the same university I gather my data in, and I do other kinds of work from time to time with the lecturers who are talking to me and letting me into their classrooms. So, I am not a detached outsider. I am part of this environment, and it is thus a challenge to try to be more objective about what I am seeing and thinking, and not get too emotionally involved with the courses or the lecturers and students and therefore end up skewing the representations of my data, or omitting important observations because they may not paint the lecturers or students in a good light.  Williams especially talks about this in his paper – he argues that doing research with colleagues in your own university, in a place in which you have invested part of yourself, is difficult because sometimes you learn things you are not sure you should disclose, or dig into deeper. This can leave you and your research in a tricky place, as your professional identity as a staff member can conflict with your identity as a researcher. Choices may have to be made, and this is where custom can clash with character.

Essentially, the literature on research ethics talks about ‘custom’ as being chiefly about the forms you fill in and the ethical protocols you agree to abide by. These are the standard ethical rules to live by in your field. ‘Character’, on the other hand, is to do with how you behave as a researcher when confronted with guilty or difficult knowledge or situations that present you with ethical dilemmas. This is an important distinction. I filled in forms, and got ethical clearance and promised, quite truthfully, to abide by the ethical rules laid down by my university. But when I got into the field, I was confronted by a couple of dilemmas that those forms and rules did not necessarily help me to solve. I had to call more in my character as a researcher, reflect very carefully on the dilemma, and speak to my participants openly about the problem yet without compromising myself or my research in that process. I had to rely on character, rather than on custom, to get me through and to keep the integrity of my research project intact.

This was not easy, but through this part of my fieldwork phase, I realised that while the rules and protocols are there for a reason and need to be observed diligently, there are also things they cannot account for. It is when these unexpected twists and turns arise that you need to call on your own character as a person and as a researcher. You need to cultivate relationships, as far as you can, with your participants that are open, so that when difficulties arise that include or affect them you can share these and reach an understanding, solution or compromise as needed. Share with them, if it’s helpful, pieces of what you are writing and get their feedback. Show them your classroom or interview transcripts, and ask for their input and whether they would like anything omitted. Discuss their requests for omissions or changes with them openly, especially if they may compromise your research. Talk about this in your methodology, so your reader knows what happened too.

It’s important to actually be ethical, and not just to say you will be, and it’s important to realise that things don’t always go according to plan in the field, so having an ethical and upfront character and approach to your research will stand you in good stead in case the unexpected is part of your journey too.