Academic writing process and associated tools

The main stages of my academic writing process:

  1. Note-taking (empirical data collection or taking reading notes);
  2. Analysis and evaluation, resulting in
    • an abstracted list of observations;
    • a selection of evidence (quotes and notes);
  3. Organising these findings by: [2 and 3 can be done concurrently]
    • Ordering the list into a hierarchical outline;
    • Attaching selected evidence to list items;
  4. Developing a separate outline for a draft (journal article or thesis or book chapter):
    • creating a hierarchical list of items (titles);
    • attaching a selection of evidence (quotes and notes) to the items as inline notes;
  5. Writing the draft;
  6. Adding a reverse outline (headings and subheadings) to the draft [5 and 6 are a more or less simultaneous, recursive process] .

academic writing process

Tools needed:

  1. Note-taking (empirical data collection or taking reading notes);
    1. TOOLS:
      • CT (ConnectedText) – Clipboard Catcher (or clipboard extender)
      • Nebulous Notes on iPad/iPod Touch > Dropbox > CT
      • Dragon NaturallySpeaking (on PC) > CT
      • GoodReader, PDF Expert on iPad > email > CT
  1. Analysis and evaluation, resulting in
    • an abstracted list of observations;
    • a selection of evidence (quotes and notes);
    1. TOOLS:
  1. Organising these findings by: [2 and 3 can be done concurrently]
    • Ordering the list into a hierarchical outline;
    • Attaching selected evidence to outline items;
    1. TOOLS:
      • CT outline (occasionally exported to and imported back from Natara Bonsai)
      • CT outline + linked CT topics
  1. Developing a separate outline for a draft (journal article or thesis or book chapter):
    • creating a hierarchical list of items (titles);
    • attaching a selection of evidence (quotes and notes) to the items as inline notes;
    1. TOOLS:
      • CT Project Outline with links to topics
  1. Writing the draft;
    1. TOOLS:
      • Outline 4D
      • Freeplane
  1. Adding a reverse outline (headings and subheadings) to the draft.
    1. TOOLS:

Update (24/01/2013)

The above chart could have been more representative by having an arrow between ConnectedText in Step 4 and Freeplane in Step 5, as the CT project outline would be exported as a .mm file and imported into Freeplane. This is an important relationship as the links to the selected CT topics will be preserved and remain clickable, thus turning CT+Freeplane into a virtual dual-pane outliner/notes organiser.

Also, Steps 5 and 6 should have been alongside each other or at least there should have been arrows pointing back from 6 to 5, as the reverse outlining process is part of the drafting process. After writing 3 or 4 paragraphs I immediately add headings and sub-headings and slot them into Outline 4D’s single-pane hierarchical outline.

Update 2 (24/01/2013)

Here is an amended chart, taking into consideration the above corrections:

academic writing process 2Update 3 (24/01/2013)

Upon further reflection this is still not an entirely accurate picture of the entire process and set-up. An additional element emerges in Stages 5 and 6: the management of writing fragments that have to be removed from the draft in Outline 4D during interim editing and need to be kept apart. I may reuse some of these later, if and when the need arises.

Initially I just used a text document in NoteTab for this. But as the number of fragments grew, I needed a more sophisticated solution with the ability to split text into paragraphs and rearrange them in a hierarchical outline. For now I’ve been using Scrivener for Windows for this, which works well as a two-pane organiser of notes and also has the handy “split selection with title” command for breaking up a text into segments. However, I find Scrivener a bit of an overkill (and too slow on my Windows 7 system) for this. I’d prefer to use a lightweight, small footprint two-pane outliner that allows for the easy rearrangement of fragments.


6 thoughts on “Academic writing process and associated tools

  1. This is a really helpful blog that I have bookmarked for future reference. I do question however some subtle implications of putting empirical data collection as primary in your work-flow as it does not accurately reflect what you do. Your expectations, or we might say the initial problems, are always prior to observation. They guide your collation of data. Mindsystems Amode software at some point blogged about “method neutral” which again unwittingly propagates this bias. “Key Issues in The New Knowledge Management”, by Joe Firestone and Mark W. McElroy, addresses data issues from a critical rationalist, i.e. Karl Popper, perspective.

    Observations may be surprising and thence modify the initial problems, but they are not the beginning of the scientific or cognitive process.

    The agent’s pre-existing information provides the world with structure, data is a type of information. Without structure it is not data. What is normally treated as information is in Firestone and McElroys’ view, “just information”, that is to say information with conceptual commitments plus interpretations. Knowledge is a subset of information (not a superset) that has been evaluated without being proven.

    Thus rather than a model based on a misconceived pyramid, it is epistemologically more appropriate to picture a Knowledge Life Cycle in which data, “just information” and knowledge are types of information. New data and knowledge are made through the Knowledge Life Cycle from pre-existing information. That is from “just information”, data, knowledge, and problems.

    Karl Popper, to whom the authors are indebted, said that all life is problem solving. One might say that all knowledge management is problem solving. I think it is important in knowledge management to avoid and reject the bias that the function of knowledge systems is to be a bucket for pure data. The knowledge cycle exists to solve problems and the problems in turn structure the questions to be asked and the information model that is tentatively appropriate.

  2. Bruce, thank you for your kind words. You raise some very relevant points about the relationships between one’s research philosophy, research design, and conceptual and actual workflow, as reflected by the arrangement and use of software tools. In fact this particular workflow above has been constructed purposefully this way to be consistent with my inductivist (empiricist, pragmatist) approach (think of ethnographic description). So we could say that I’m implementing quite the opposite of the Popperian views you’ve described above. If I were to implement a critical rationalist methodology, then the above workflow would need to look different, so you’re absolutely right about that.

  3. Thanks, Dr Andus, for the gracious response. I do wonder though about the purity of your inductivism, after all your Amode workflow or mind map does start from a central topic (problem). Julius Thomas Fraser made the point that there is no such thing as immaculate perception: perception of objects depends on the active participation of the perceiver.

  4. Bruce, the above chart only focuses on the writing process, so it’s not necessarily the best representation of my entire research methodology. Amode is not part of the above workflow. I only use Amode as a project management tool to manage my overall research project. There is a mind map in there (Freeplane), but it is part of Step 5, as a final outline for the first draft, so it’s very much an outcome of the analysis and an organisation of the findings. I do use concept maps during Stage 3 (Organising findings), but I use VUE (not Amode).

    My research did not start with a clearly defined central research question (even though there are institutional pressures on PhD students to define one upfront, due to the prevalance of deductivism in academia). It was an explorative process and the research question emerged over time from engaging with the empirical material and the literature. But I have not said much about my research philosophy or the contents of my research on this blog so far, partly because I’ve been focusing on the software and hardware tools, partly because I wish to remain anonymous.

    I do not actually claim to be the purest of inductivists. I only use the “inductivist” label when I need to define myself against “deductivism,” but otherwise I don’t think it is possible to do only one or the other in practice. Whether a piece of work is classified as inductivist or deductivist depends on its final presentation, which does not say much about the mental or physical processes involved in constructing that final presentation.

    Nevertheless, you are absolutely right that there is a strong relationship between one’s research philosophy and one’s use of software tools and the underlying research design. You might be interested in this respect in the “Tools & Techniques” section of the CASTAC blog, where I have discussed why I prefer to use a desktop wiki for organising my research data, rather than the mainstream qualitative analysis software packages.

  5. P.S. But just to be clear, my research is a qualitative, not quantitative (and it’s social science, not natural science). So when I’m talking about empirical data collection, I don’t mean “collecting data in order to prove a hypothesis using statistical methods” or “conducting a laboratory experiment in order to test a hypothesis.” Instead, data collection for me consisted of participant observation, living and working in the midst of the “tribe” that I was studying. Only once I had a basic understanding of the culture, practices and concerns of the “tribe,” was I in the position to start formulating some research questions that may be interesting to existing knowledge about that “tribe.” This is not to say that I didn’t do any reading prior to empirical data collection or during that time. But much of that early reading proved irrelevant in light of what I found in the field and the research question had crystallised fairly late during the data collection process.

  6. Pingback: Workflows and toolchains | Dr Andus's toolbox

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