Tag Archives: sharing

Editing – deciding what to keep

edits

Deciding what actually needs to be in the final version of the text in a dissertation or a journal paper can be a tough job. Some people do revision after revision, chopping and changing, cutting and adding, re-working the text until they are satisfied. Others (and I am one of them) usually think the subject through, then write the complete text straight off, only making minor changes later before submitting the final version. Whatever way works best for the author is the correct approach. The most important thing to remember is that whatever topic, the dissertation should tell a logical story to the reader. The role of the supervisor is often crucial at this stage, because the writer can frequently get so immersed in the subject matter that it can be difficult to see the wood for the trees. At worst, the writer wants to include everything that they know about the topic – just to be on the safe side. At the other extreme, the writer assumes that the readers understand how they are thinking, and tends to skip on the details, leading to ambiguity or misunderstanding by the readership. Having a “fresh pair of eyes” read over the text can be of immense value – whether it is a friend, a partner, or a supervisor, just having a colleague giving an unbiased view can help to iron out any possible areas for future confusion. Listen to them, and try not to be too defensive: if they have the courage to question you, listen to their opinions. Try not to be pedantic – verbosity and clarity rarely go well together – so consider carefully if your sentence actually contributes towards understanding the text, or is it just padding?

Usually, when writing something as chunky as a 100,000 word PhD dissertation, I would advise that each chapter or section should be drafted, then parked, until the general structure of the full text becomes more clear. Before starting to write the final chapter – the conclusions and any recommendations of the research – the author should pause, go back to the very start of the text, and re-read everything that they have written – making final amendments. Constructing complex narratives, such as dissertations or academic articles, need not be written in a completely linear fashion (i.e. from page one all the way through to the end) so re-visiting the advance draft gives an opportunity to shift paragraphs around, or add/delete information, and generally tidy up the text. This is also a good time to check that all the relevant citations to referenced evidence are included, as well as inserting accurate place-holders for tables, diagrams, and images. The advantage of pausing before starting the last chapter and finalising the earlier text, is that the “story” of the narrative is now fresh in the memory (it may have been a very long time since the author wrote the first few paragraphs of the dissertation). Basically, it is in the best interests of the writer that any readers can follow clearly and understandably the points that are being made. Spelling errors, overly-long sentences, clumsy wording, ambiguous statements, and a lack of referenced evidence all serve to make it more difficult for the reader to understand, and ultimately this reflects badly on the appreciation of the text. A happy reader means a happy examiner, and a better chance that the work will be more widely read and esteemed.

Data analysis

dataset

The data analysis stage is one of the main research areas where the supervisory team can really make a significant contribution to assisting the research student. Obviously, the student still needs to do the work for themselves, but this is a stage in the PhD process where the depth and breadth of experience of the supervisors should shine through and help the student to make sense of a complex set of tasks, and make them a bit simpler to complete. Having collected a mass of data, perhaps even coded and categorised this data according to exacting and laborious protocols and methods of analysis, the student needs to understand what this data is actually saying. This might be a simpler task for some projects than for others, according to the amount of data collected, the form in which it was collected, how detailed or exact the observations or calculations are, or what methods for codifying or interpreting the raw data have been employed in the research methodology.

To the beginner, this might seem straightforward, but there is no “one-way” to analyse data, because there are many, many different forms of data. This data might be collected at different levels of granularity, different levels of accuracy, and embody different assumptions and methodological approaches. At some early stage of the analysis it is usually a good idea for the research student to sit down with the supervisory team, spread the collected data out on a table, and look at it together. The student needs to identify a number of key attributes of this data, such as what does this data actually indicate? How robust is the data? What is its accuracy and what are its limitations? Are there any correlations (positive or negative) and so on, leading to the penultimate question, which is what does this data actually tell us? Hopefully there will be a new insight, or a discovery which, at least in part, will address the initial research question(s). The final question is then, how should I present this information so that it understandable to others who have not been involved in this research, and how transferrable is this knowledge to other (present and future) researchers in this general subject area? The ability to repeat, re-combine, and re-use data (and the results of its analysis) is a particularly useful feature to enable contrast and comparison with other projects in similar or related subject areas.

It will seem a bit ironic, or a perhaps a bit of a paradox, given that we are often seeking to reveal hitherto unknown facts, and “answers” to high-level research questions, but usually it is better to be less ambitious in the interpretation of the conclusions, but to be absolutely sure of the reliability and credibility of our data, rather than to propose over-ambitious conclusions (exciting thought they may seem) which are based on sketchy evidence and correlations which are skating on thin ice. It the research can be shown to produce solid, incontrovertible evidence, regardless of how large or small the breakthrough, then this small advance can be built-upon by subsequent researchers. If the conclusions are like a house built on thin ice, then there is always a doubt about the credibility of the data, or the results, and therefore the value of the research output is devalued. With data analysis, it always pays to check, cross-check, consider the constraints and limitations, then double-check each stage before you venture to draw conclusions to share with a wider audience.

Storing and archiving data

pen-drives

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!

Recording data

notebook

Firstly, I’m aware that I have broken the first ‘rule’ of blogging, which is to keep the posts short, and keep them coming regularly, but I had a bit of a hiatus due to other interests and demands over the summer. Hopefully, now to get back on track

Starting to record the new data which is being gathered as part of a research project, whether a long-term study like a PhD, or a quick toe-in-the-water project, is the most crucial, but perhaps the subtlest stage of the research. If you gather too little data, the project may flounder even before it gets started; too much data, and a metaphoric mountain of results can be generated by cross-correlation and individual analysis, which can paralyse a project almost as quickly as having no data at all. Then there is the question of what is the “right” data? How will I know it when I see it? In reality, it is as likely to be different for every individual project as the diversity of methods of data gathering. The correct procedure, of course, is to recognise that recording the correct data is integrally dependant on selecting the correct research methodology, and in carefully selecting how the data will be collected, coded, and stored in the future.

One of the most impressive records of research data that I can remember, is from a scientist who was studying birds of prey, and his handwriting in an old notebook recorded what seemed to me to be almost every conceivable factor which might influence nesting success, including several factors that I, personally, would never have begun to consider relevant. He was of course correct, for it is often the correlations with hidden, and often apparently spurious, information which leads to the really stunning breakthroughs in research projects. There are many different ways of the recording research data that you might collect, and there is no one-size-fits-all solution. If you are interviewing people, there is a choice between taking notes, audio recording, or video recording; all these methods have their advantages and disbenefits. Taking notes is less obtrusive, but also can be distracting for the researcher. Audio recording can be done easily with a digital recorder, or a suitable app on a smart-phone, but some people may be more guarded in their responses when they are being recorded, and there is also the problematic issue of what to do with all the data you have gathered. Gathering a huge mass of data can be attractive, but it needs to be proportionate to the scale of the project, because there is little point in generating a mountain of data if 80% is left unanalysed and unused. Great care needs to be taken to strike a balance between collecting a good data-set which provides rich possibilities for future analysis, against de-motivating your participants by presenting them with huge questionnaire or over-long interviews. Similar constraints apply when conducting laboratory experiments, fieldwork, or desk-top studies.

Finally, in addition to having to consider your recording requirements in terms of how you propose to codify and analyse the potential results (there is little point in collecting data so randomly that it cannot be interrogated effectively) there are the issues of long-term storage and access to the data. The research supervisor has a crucial role here, not simply in helping to shape what the research students proposes to gather, or how that might be analysed and interpreted, but in providing the continuity which may extend over several decades and overlap with numerous related research student projects. In an increasingly digital and open educational society, not simply the research results, but also the raw research data is also becoming more open and accessible. It is becoming more possible and more likely that scholars coming after you will read not just your conclusions, but also your original data recording notes, so think carefully about what you collect and how you record it!

Revising the research question

As I wrote earlier, once the student becomes better acquainted with the research area, it will probably become necessary to slightly revise the original research question. There could be several reasons for this, but largely this is because the review of the previous academic literature on the subject has helped to clarify what the academic community already knows about the topic and what still remains to be discovered. Hopefully this will result in minor adjustments, rather than huge changes of emphasis, but it is important to recognise that this is an ongoing process which will require a bit of settling down. For some people, in certain subject areas, this settling-down process will take longer than others, and a crucial resource to help the process along is – surprise, surprise – the research supervisor! It might seem obvious, but when the research student becomes enmeshed in the research problem, it seems that sometimes they forget to communicate effectively with their supervisor(s).

While I always emphasise that the research project belongs to the student, the supervisor also has a very direct interest in the success of the study, and regular discussions between the student and the supervisor are essential. The definition of ‘regular’ can be a bit loose. Does this mean weekly, or monthly, or what? In practice, meetings are usually closer together at the start and towards the end of the PhD, and a bit further apart during the middle period when the student is really getting into the data-gathering and analysis. Perhaps meetings might be every 2-3 weeks at the start, to help orientate the student and discuss the broad plans, and about the same in the later stages to discuss feedback on the writing as each chapter gets produced. Normally I like to meet every 6-7 weeks in the middle phases of the study, just to keep on top of what the student is working on at that time.

Similarly, the word ‘discussion’ can be a rather misused term. I don’t just mean quick chats in the corridor or tea-room, and I don’t mean that the student is brought in to hear a monologue from the supervisor. Discussion means both parties exchanging their views. There needs to be a level of trust developed – trust that the student’s half-formed thoughts and ideas can be shared and developed; trust, too, that the supervisor has the student’s best interests at heart and will give detailed feedback which is both supportive and useful. The student is learning the business of advanced research, so it is unrealistic to expect perfection from the outset, yet many students are reluctant to share their ideas and their writing with their supervisor, perhaps in apprehension of looking inadequate. This is completely the wrong attitude. As a supervisor, I cannot give advice unless the student tells me what they are thinking; I cannot give written comment or suggestions until a student submits some text for me to read. The more I learn of how the research project is developing, the more I can share my own thoughts and expertise. After each formal meeting I get the student to email me a half-page summary of what we have discussed and agreed. No-one else need see this summary, but it is a useful record to look back upon as the research project evolves.

The effective research supervisor should be both approachable and knowledgeable, and ideally is the best “critical friend” that a research student could have. For a bit of light-hearted reinforcement of this almost symbiotic relationship, check the parable at

http://www.cosy.sbg.ac.at/~held/fun/thesis_advisor.txt

Don’t say you have not been warned!

The Second (or Third) PhD Supervisor

Scott

Although the main research supervisor normally has the most contact with a research student, the role of the Second Supervisor also provides a useful balance. Normally the second (and perhaps third) supervisor has a limited contact with the student, perhaps as little as three or four discussions per year, but the input they provide is also valuable. It might be because the two supervisors cover different aspects of the same research problem and so can give different suggestions to cope with problems, or they may favour slightly different research methods and emphasis in the investigation. Even when the advice is similar from both supervisors, it can provide a reassurance that the student is on the right (or the wrong!) track. Research has indicated that the way that we supervise research students is often heavily influenced by the manner in which we were supervised ourselves. Some supervisors prefer a distant role, only making contact through formal meetings to check that the research is progressing well. They expect their research students to independent-minded and self-motivated and see a supervisor’s role as a combination of safety-net (for consultation in times of trouble) and manager (ensuring that all the key stages of development and reporting are taken care of). At the other end of the spectrum there are supervisors who seek to micro-manage the PhD project. Resist this temptation! Although the supervisor has a strong self-interest in ensuring that the student’s research project is successfully completed, the work needs to be done by the student, including making the mistakes, false starts, and the hours of working out the best way forward. The relatively light touch provided by the second supervisor can easily be provided via Skype or other such distance-shrinking audio-visual technology. If it is done on a regular basis, perhaps with some periodic face-to-face meetings, this can also be an option for the main supervisor. A benefit of this is that the supervisory team can be brought together on the basis of the skills and enthusiasm that they provide, not simply because they happen to be co-located in the same building or campus. Gone are the days when a PhD student needs to be based just along the corridor for their main supervisor – and anyway, many research students who were residentially based near their supervisor’s office will tell you that their supervisor was so busy globe-trotting to conferences and fieldwork that they hardly saw them for months at a time. Although the UK universities insist upon an external PhD examiner from a different university to ensure the equivalence of the level of the degree, it’s a pity that there is so much emphasis attached to individual institutions as this would seem to be a great opportunity to bring together cross-institutional expertise at the supervision stage, as well as at the final viva voce.

Project planning

Yearplanner

Like many regular users of web technology, I get a lot of new software and apps sent to me, or recommended, for me to try out. Mostly these are designed to ‘make life easier’, some are primarily for fun. While I am in favour of both of these aims, many of the new applications do neither. One I discovered yesterday, however, is worth passing on – Tom’s Planner http://www.tomsplanner.com/software/project-planning/tour.aspx is a very simply tool that allows you to create your own Gantt Chart as a project planning aid. Like many people who have played around with other project planning software, I found that there is often a tendency to get TOO fancy, add one too many icon or feature – but Tom’s Planner is really simple, intuitive, and useful. The plan can be created very quickly and can be shared with project colleagues. It is also free for personal use. Worth passing on!