Tag Archives: digital

Setting the tone of academic writing


There is a lot of nonsense talked about “academic writing” in some circles. A central myth is that it needs to be “complex”. In fact, exactly the reverse is the case! In writing an academic text, the author needs to be aware of some of the same key issues as any author, whether the writing is fact or fiction, science or humanities. Firstly, the text needs to convey information to the readership. Even complex ideas and intricate research can be conveyed as a story which captivates the reader’s attention and (hopefully) helps their understanding. So good academic writing is not simply about the message, it is also, to some extent, about the style. A well-written chapter or article will be a pleasure to read and will stimulate the interest of the reader, even if they may not follow (or even agree with) everything that you claim. For this reason, it is just as important to pay close attention to spelling, grammar, and the structure of an academic article as it is for a good piece of journalism.

An academic article requires another couple of essentials, however, and these are ‘evidence’ and ‘analysis’. The main reason for writing an academic article (or PhD chapter) is to make known to the readership some new ideas – perhaps the results of a new experiment (or the confirmation by repetition of an earlier experiment) or perhaps simply bringing together scattered information to present a new way of thinking about the topic. Either way, the ‘story’ that is written will probably build upon earlier work, perhaps quoting some examples, or statistics, attempting to construct a picture of how the new information was obtained. In this synthesis, it is imperative that the writer identifies the sources of evidence which are being referred to – even in passing – in the construction of the storyline. This sometimes gives academic writing a bit of a staccato appearance, with frequent interruptions e.g. (Rennie and Smyth, 2017) to the flow of sentences that would be the norm for a non-academic article. Nevertheless, these citations to the sources of evidence are absolutely essential in order to place the new piece of writing within the context of what is already known about the topic. Remember, the purpose of research, and the PhD in particular, is to make an original contribution to knowledge, by extending what is known into an area which is less well known, and by definition extending the sum total of our knowledge of the discipline. There are different conventions on how to draw attention to the sources of evidence which are used  to give support, reliability, confidence, to the new ideas being expressed, and these citation styles – such as Harvard, Vancouver, APA – will vary with different academic disciplines. Students should check with their supervisors on what is most appropriate (sometimes the required styles will vary between different journals).

With respect to the ‘analysis’ component of the writing, this will vary between different academic levels, and occasionally even within the same piece of academic work. For instance, early-stage undergraduates may be allowed to be more descriptive in their writing, but late-stage undergraduates are expected to be more highly analytical, rather than purely descriptive. By the stage of embarking on a research degree, the student is expected to understand the importance of critical analysis, (and practice it) so that although a literature review chapter may in broad terms describe the state of current knowledge about the research topic, the reviews of the individual sources of evidence should not be solely descriptive, and should critically evaluate the strengths and possible weaknesses of the source publications.

For this reason, I try to give a particularly thorough feedback on the early work of any research student that I am supervising. I use “track changes” to comment on every missing comma, typographic error, lack of citation, or inappropriate style format. If the supervisor can quickly and clearly set the tone required for the relevant level of the student’s work, a benchmark can be established, and thereafter the student should be clear about the quality, style, conventions, and expectations required for the final product. At least, that is the theory…


Storing and archiving data


When I was doing my own PhD, I had a filing cabinet with three or four drawers, and even then I had hundreds of photocopies of academic papers stacked in small piles according to theme and relevance to the section that I was writing about next. My raw research data, however, was compactly contained in electronic format in the form of tables and graphs; row after row of numbers on spreadsheets which could be tabulated and correlated in any format that I desired. When I left the department, the files were archived for a few years, and then I suspect they were all dumped when the department moved to another building on another campus.

Now, when I generate research data, it is almost entirely in electronic format, and it is automatically stored in several places. I have my personal space in the memory banks of the university computing system, and this space is automatically backed-up overnight. I also usually back-up to my own cloud-space, so that I can access the data wherever and whenever I want. Usually, I also store data for individual projects on a separate memory stick or portable hard-drive. The digital age means that after two or three clicks, I can be assured that copies of my data are safely held in four or five independent locations. Research students can simultaneously share data with a colleague or supervisor in a different part of the world without even leaving their own desk.

This is only the tip of the iceberg, however, because the production of digital data raises almost as many questions as it provides innovative opportunities. There needs to be an early discussion in the supervisory team, for instance, about not simply which data will be stored, but where will it be stored, for how long, and who will have access to it? This is not simply an issue of security, although security, confidentiality, and appropriate use of the data will certainly figure in the discussion. There is a growing awareness that when public money is used to fund research, there needs to be a transparent return on public interest. Initially this has meant that research results, reports, and journal articles, should be made freely available to the public. This is being extended in the next Research Excellence Framework in the UK to insist that if the journal article is not already published as an open resource, it needs to be added as an open source on the digital repository of the relevant institution. But there’s more.

The argument has been extended to include the research data generated by the public funding, so the datasets themselves are trending to become open and shared property. Whether the data is numbers, interviews, audio recordings, photographs, or other recordable results, the likelihood is that the data being gathered by a researcher today, is probably going to be a shared resource tomorrow. It will be possible for other researchers, in subsequent years, to access your raw data, perhaps combine it with other raw data, and re-analyse, re-interpret, and publish their conclusions. It now begins to matter a great deal more seriously exactly who can gain access to your research data, and for what purposes. As the law currently stands, a bona fide researcher can have access to open datasets for up to ten years after they have been deposited. But here is the catch – if a researcher accesses this data after nine years, the open-access clock is automatically re-set for a further ten years. This ensures the certainty that data which is being collected and digitally stored just now, might be still openly available long after the initial researcher has moved on from that research topic, perhaps changed institutions, changed careers, maybe even passed away. The raw data of open access digital resources is now guaranteed a lifetime longer than the career-span of many individual researchers. So think carefully about what you gather, how you organise and store it, and what your legacy of research data will be!

Getting research ethics approval


Teaching research ethics is almost impossible. Teaching someone about ethics is a different matter, but unless a person actually understands why ethical standards are essential, then everything else is fruitless. It is relatively straightforward to present examples of good ethical practice (and what happens when this practice is ignored) but this simply underpins the implementation of the ethical standards, not the need for them. Fortunately, there are lots of detailed guidelines and professional codes describing the expectations of ethical behaviour, many of them readily available on the web. I say “lots” because the ethical standards vary widely in content and detail, dependent on the subject discipline, the research methods employed, the level of study, and several other factors. This might sound vague, but think about it. There will be a different level of scrutiny required if a researcher seeks access to the confidential medical files of patient, rather than simply asking patients to respond to a few verbal questions. There will be different standards again if the researcher plans to work with animals, or children, or vulnerable adults with diminished responsibility. There is also an ethical code for internet-mediated research, although this is new, variable, and highly contextual, so it is an evolving set of guidelines. Despite these differences, the purpose of research ethics is the same in each case – namely to prevent causing harm to the participants, to preserve their dignity (for example their right to anonymity) and to enable them to withdraw from the study without any undue pressure or penalty.

For these reasons, there is a crucial stage between deciding on what research methods are to be adopted for a study, and the commencement of data collection. This crucial stage is where the researcher submits the details of the design, methodology, and any issues relating to the collection and storage of data, for approval by the university ethics committee. Only after ethical clearance has been approved can the student begin to collect data. Failure to obtain approval before data is collected may result in the university deciding that this data is not admissible for inclusion in the study. If there have been any severe breaches of ethical responsibility, the study may be terminated or the student de-registered. For this reason, the ethical approval of a student research project is a gate-keeper stage of every study.

Fortunately, most research projects have fairly straightforward ethical requirements which are easily satisfied in full. A lot of the ethical safeguards might be regarded as “simply common sense” (and so they are) but you might be surprised by the number of times people say “Oh, there are no ethical issues with my research!” This is almost certainly wrong. Even the issue of whether the researcher with half-formed ideas should be “wasting” the time of an interviewee who almost certainly has something better, perhaps crucial, to do, is an ethical issue. For these reasons, seeking ethical approval for research should be a serious matter, but not something to fret unduly about, if the researcher has properly thought through the research design. Once the ethical approval has been obtained, the researcher is able to jump out of the starting blocks to engage with data collection, and this is where the real fun part starts.

What methods will help to answer the research question?


This is where it gets hard, not simply because the research student is venturing out into the unknown, but also because selecting the methods through which the research will be conducted will differ hugely between cultures, between disciplines, and between subjects within disciplines. There is no one-size-fits-all template which will allow a pick-and-choose approach to selecting the most appropriate methods. In one sense, this is an easy step, because it will probably be pretty obvious from the outset what methods will be needed in order to answer the research question(s). Almost all academic research methods will involve reading, either to follow-up on what has already been said about the topic or to put it into a wider context. After that, the methods might include interviews, experiments, observations, questionnaires, focus groups, and a host of other activities which will change in emphasis from discipline to discipline. Getting the “correct” mixture of these methods is what will determine the methodology, that is, the system of methods for further research.

Here is where high technology can come in. I say “high” technology because even using a pen-and-paper or driving a car to conduct an interview is using technology, but of course we generally mean computer-based technology. In educational circles you will frequently hear the assertion that “the technology should never lead!”. This is certainly true, to an extent, but not entirely. For instance, if there are two (or more) ways to record research data, and one way entails using a high-technology solution which makes it easier, more flexible and/or more secure, then surely most sensible people would vote for the use of the technology. Examples might include, the use of RefME to compile the dissertation reference list and store it on the cloud; using Mendeley to store the articles online; the use of SurveyMonkey to conduct a questionnaire online rather than face-to-face, giving time-flexibility, wider geographic coverage, and the ability to utilise automatic data analysis and presentation tools; the use of a free voice-recorder smartphone app to record interviews… The list could go on and on.

A crucial factor in all of this is to consider carefully – right at the start – how these methods will allow you to analyse and hopefully make sense of the data which will be gathered. It makes little sense jumping off a high-point without knowing, even approximately, where you might land. Similarly, it makes little sense to gather mountains of data without any ideas how to begin to make sense of it. The supervisor should be able to give some clear directions, but ultimately each situation, each carefully worded question, is slightly different, and will have different constraints on time, resources, and abilities, so the student will need to be fully comfortable with the methodology before even starting the research. Prior studies in a similar area can help to provide some direction, but the precise mixture needs to be decided for each individual research project.

Writing the methodology


When starting a PhD, there is often a great mystique surrounding the selection (and writing-up) of the proposed methodology. It is important to remember that the term “methodology” means more than simply describing the methods that are intended to be used for the collection of research data, it is the constructed system of methods proposed, and how they interact. Importantly, in order to understand the data which might be generated by the research, it is critical to first understand the rules which govern the various research methods selected, their strengths and their limitations. The selection of a variety of methods will enable the researcher to gather different types of data, and to look at the research area from complementary angles. As always, it is the role of the supervisor to help the research student put together the best methodology for the research project, that is to say, the best combination of methods through which the student proposes to gather new data on the topic. In most circumstances the supervisor will already have an established preference for one or more methods. It might be necessary to include a second, or third, supervisor who has expertise in a complementary a different set of methods, particularly for multi-disciplinary research.

There are many ways of gathering research data, but broadly they can be divided into three major methodological approaches; these are quantitative, qualitative, and mixed methods. I do not propose to go into much more detail here – there are whole volumes written on even the specific sub-categories of these approaches – but briefly, quantitative research explores through the measurement of phenomena, while a qualitative researcher looks for the emergence of themes or patterns in the evidence provided. A “mixed-methods” approach is not simply a randomly constructed “a-bit-of-one-and-a-bit-of-the-other” style, but it does use both qualitative and quantitative analysis to provide complementary perspectives on the same research topic.

The reason that so much early attention is given to establishing the methodology of the proposed research project is partly because the confirmation of the methodology will determine how the researcher looks at the world emerging through the data; partly, also it will condition the forms of analysis, the reliability, and the compatibility of the research data produced. Any fool can go out and collect data, but getting hold of the type of data which will allow reasonably reliable conclusions to be established is a different matter. In some cases, the choice will be easy. There may be a very limited number of tried-and-tested ways in which an experiment can be constructed, or there might be a very similar study already published, the replication of which to the new subject area might facilitated a useful extension and comparison of knowledge. The supervisor may even have pioneered a particular combination of methods over a long research career and therefore be in a position to give the research student advice on very practical issues, as well as the theory. The literature review is, of course, one element of the methods of research, and the published academic records will likely reveal a quite precise range of options to follow. In any event, it is worth thinking hard right at this stage, in order to avoid false starts and perhaps false data later on.

Description versus critical review


In constructing a literature review of any proposed research topic, especially for new arrivals to research, there is often a tension between giving a straight description of the relevant academic articles rather than providing a critical analysis. This is understandable. The main purpose of the literature review is to provide subsequent readers with an introduction to the subject area of the research, and this is done by constructing a narrative – a story – of the evolution of the subject area to the stage that we understand at present. This description describes the “landscape” of the research subject area – the significant and salient points and the less well-known or contested points. The literature review, however, needs to be more than just a simple description of each significant article, more than a sort of “He said… then she said…” list of opinions.

The literature review, to be really useful, needs to critically evaluate the importance of each article, as well as providing a description of what was said, what methods were used, what degree of reliability the data has, etc. The reader has not only to understand the history of the development of the research topic, but to appreciate the relative merits of previous work. This is relatively easy at the start of the project, but by the end, juggling several hundred citations, it becomes a challenge.

A number of students and colleagues have drawn to my attention an app called RefME which is a really interesting piece of software which enables the compilation of a reference list very quickly. Once a (free) account has been created on the app, entries of citations for books, journal articles, and lots of other artefacts can be added instantly by scanning the bar-code of the publication using a phone with the app. The reference list can be built-up and accessed from any device with a web connection. Reference lists can be divided into lists for particular projects (articles, conferences?) and each list can be exported to various formats, including a simple word document. Each citation can also be annotated, so using a simple set of phrases and tags, a critical reference list can be compiled in minutes. The app also allows citations to be input manually, which is required for older publications and those without a bar code. There are several “easy” referencing systems available at present, but the simplicity, elegance, and flexibility of this app really impresses me.

Whichever method is used to compile the reference list, there are two golden rules to adhere to. Firstly, start early to compile the reference list and keep on top of it. As an article or book is read, and if you know it is going to be referred to in the text of the dissertation, it should be immediately added to the reference list. Secondly, keep a list which is an annotated bibliography, not simply the list of all the references, but copy the file and add short notes on each reference. Do not trust the memory to remember details such as page numbers (for direct quotations) and DOI numbers (for direct web access), or even for the key points of analysis and critique. As the numbers of citations begin to mount, the details begin to blur and disappear. This will act as a memory jog, and also as a useful item to share with a supervisor to discuss the merits and demerits of individual articles. As time progresses, because they are focussed on one specific research topic, the PhD student will discover relevant articles which the supervisor(s) may not have seen, and anyway, there is life after the PhD so you might want some of this material again, years down the line. Don’t trust the memory!

Keeping track of articles


One of the key skills in any research project is good organisation. This is especially true for a PhD research project, lasting as they do over three years of full-time study, or up to seven years part-time. Students start off with two or three seminal articles relating to their research topic, but the field of reference will grow dramatically within the first six months, and citations will continue to be added to the reference list right up until the dissertation is submitted. Even then, the external examiner(s) might insist at the viva that the student needs to consider further a certain area of the research which will require further reading. Without a careful system, it does not take long for this growing pile of references to become unmanageable!

Some researchers swear by the old “traditional” system of individual index cards, alphabetically filed for each reference. This has the advantage of being able to add notes, summaries, questions etc., and also it is not dependent on technology, so does not require electricity or a battery. On the other hand, a file of cards is not very portable, can be a bit clumsy to sort, and not being digital, is less flexible to re-purpose. There are number of software packages, both free and commercial, that allow you to store and sort references on a computer. A product called Refworks provides an online database to manage bibliographic data, and this has numerous advantages, including being able to manipulate the data to display in different academic styles, create bibliographies for different publications, and also to access the data from different devices and locations. The university may subscribe to this product or some comparable service. Personally, I use a simple word processed file. This does not have the flexibility of customised bibliographic management software, but it has the advantage of being easy to create and use without specialised training. To create a bibliography for a new article I simply cut-and-paste from my master list (not forgetting to keep back-up copies of the master-list in other locations!)

In addition, Mendeley https://www.mendeley.com/ is a free manager for references and pdf documents which can be used to annotate articles and share online with students and other colleagues. It’s easy to use, see https://youtu.be/qRiAIaqdAOg and allows storage and access to a personalised library collection from any internet location. So, for example, a researcher could import an identified article, store it in a personalised online space, add comments and questions to the file, then share with an online social network which could include a research team, supervisors, or a cohort of students. Whatever filing system for research articles is used by a PhD student, it needs to be able to store, display and allow easy retrieval of anything that has been read over the duration of the study, which is not a simple task when this means five or six hundred individual references.