Modelling process workflow for thesis writing

Recently I’ve been finding that whenever I’m stuck in my odyssey towards writing up my dissertation, modelling my process flow in a concept-mapping software (such as VUE) usually helps. In this (hopefully) final stage of my PhD project there are so many resources scattered around in various software and folders on my computer that I need a formal “concept map” (if that’s the right term) to pull them all together and work out the relationships and interactions between them.

Here is for example my last concept map that I’ve knocked up when I was unsure how to proceed with writing up the first four chapters of my dissertation. There is nothing particularly scientific about this map and it probably doesn’t follow any of the conventions of process workflow modelling. But who cares: it did the trick and allowed me to plan out the next stages of what I need to do.

Actually at least 2 or 3 days of deliberation are captured in this chart. First, I needed to decide whether I was going to use ConnectedText or something else for doing the actual writing. Through trial and error I established that it’s better to use another software because however much I love working in CT, it does have some limitations. One of them is that you can only have one instance of CT running and only one edit/view window open. Since I’ve decided to use CT as my database for my reading notes, I need to use another software, so I can be writing in one software in one monitor, while referring to the CT notes in the other. Also, there isn’t an easy way to track the word count of your document while writing in CT.

I had considered WhizFolders briefly as an alternative, but I find its interface too busy to be able to concentrate on the actual writing. So I settled on Scrivener for Windows, which works well both as a two-pane outliner and as a writing tool with decent word-count tracking.

As the sequence of the process flow is not apparent from the chart, let me describe it briefly. I start with importing my master outline with inline notes from Outline 4D (via Word). The reason I created my outline in Outline 4D is because it is a single-pane outliner that allows you to have inline notes, which you can also view in an index card view on a corkboard. Then I use Scrivener to break up the imported document into a 2-pane outline using Scrivener’s handy “Split with Selection as Title” command. As I start writing the actual text (I’m working on the first 4 chapters of my thesis, which need to be contextualised within their respective literatures, namely the Introduction, the Literature Review, the Conceptual Framework, and the Methodology), I begin to review my existing reading notes.

Over the years I have read all kinds of things that are no longer relevant. Therefore I need to deploy some kind of a filtering process to select the most important notes, as well as any new reading that still needs to be done. To consolidate my final reading list (a list of bibliographic references), I use a Natara Bonsai outline. First I import into Bonsai an existing outline document that contains some of my selected references that I have kept on my iPod/iPad in CarbonFin Outliner. Then I go through my old conference papers and other writings to extract references that are still relevant and which are kept in Word files and an old Scrivener project.

Simultaneously to this process I have also designed a ConnectedText project for keeping my final reading notes and quotes, using a similar model to the one I have developed for my empirical analysis. As my old reading notes and quotes are kept in a WhizFolders database, I will need to review those and transfer them one-by-one to the CT database (I deliberately don’t want to import them en mass, as I need to separate the wheat from the chaff). I will also use the CT project for recording any new reading I still need to do. I am designing this CT database not simply just for this writing project. Very likely it will become my main database for all my future readings for years to come. This is just an opportune moment to get started with it, as I no longer want to use WhizFolders for this.

Getting back to the chart, there are basically two important elements to it: 1) the big blue Scrivener rectangle which represents my writing, and 2) the big green rectangle below it which represents the CT reading notes database. If we look at the arrows pointing to the latter, we see mostly the data that needs to be transferred (by carefully sifting through) from my old files, as well as new reading notes that will be created in iPad.

As for the arrows coming in or out of the Scrivener project, those have to do mostly with referring to external sources. In the end I won’t need Excel for planning the word count because Scrivener has good enough tools for that. I will also use Dragon NaturallySpeaking for dictating, whenever I feel the need. Sometimes it’s easier to write without it, other times it speeds things up. As for EndNote, it is simply the central database for my references, which are linked to the PDFs that may need to be read for the first time or reviewed.

But my main point here is that it was the creating of this concept map that was crucial for getting me started with the whole writing stage. Without it I would have probably sat in front of a blank page with a writer’s block for days. Now I feel fairly confident that I know what I need to do next.

Designing your QDA project for ConnectedText

If you have completed the steps suggested in the previous posts (here and here) for this tutorial on how to use ConnectedText for qualitative data analysis (QDA) (or if you are already a CT pro), then you are ready to move on to the next stage of the CT QDA process, which has to do with designing your QDA project for CT. I am suggesting that before you import and dump all your qualitative data and other notes in CT, it might be a good idea to come up with an overall shape for your project and work flow. It is certainly possible to ignore this advice and dump all your data in CT first and worry about organising them later. However, I found that being methodical and designing the project first and then importing data (strategically and incrementally) has helped me keep my head above the water – the data ocean, so to speak.

Let me first present you with my concept map (created in VUE) for the design of my PhD research project in CT, and I will explain how it works below.
Essentially what you are seeing here is a mixture between a top-down hierarchical model for organising topics in ConnectedText and a flow chart indicating a process flow (roughly from left to right) for the qualitative analysis of data and the production of the eventual report, in this case a PhD dissertation. At the very top sits the “project dashboard,” which is the tip of the data iceberg, if represented in this hierarchical model, or the central node of a flat network, if you try to visualise it as the home page of your personal Intranet system.

At the second (horizontal) level of the hierarchy (which are the topics that are linked to from the project dashboard/home page) you will find the main elements of the project. Let’s tackle these one by one.

“Meta project considerations” is the topic that contains or links to information that pertains to the overall organisation of the CT project, the work flow, the project plan and related tasks. This is the place to collect those thoughts and materials that are looking at the project from the outside or from above and are concerned with the overall design and operation of the system as a whole. (For example me reflecting on the design of my PhD project right now is an instance of such a meta-consideration. I will be including the above concept map under this main topic in my own CT project.)

The second main topic, “empirical data (case studies),” is the heart of the project and will contain the bulk of the material. It contains all the empirical data that I have collected as part of my research. It is organised into individual case studies, which contain such material as interview transcripts, participant observation notes and collected files (such as emails, PDFs, MS Office files or even URLs). The red arrows indicate the flow the qualitative data analysis process that I will be focusing on in future posts, showing how CT can be used as a CAQDAS. The main objective of the analysis is to extract findings from each case study, which will be eventually aggregated and evaluated in the fourth major topic, “findings.”

I have skipped over the third topic, “theory notes.” These include notes of all such reflections or interpretations that I have produced myself but which are not strictly speaking part of the empirical materials. It is debatable whether these observations should be included in the empirical data, if they were triggered by – or during – the data collection process. But I prefer to separate out material that was more part of the interpretation of the data than the data itself. Nevertheless, note the dashed lines which indicate that these “theory notes” are closely related to “empirical data” and feed into the “findings.”

While you are analysing your empirical data and evaluating your findings, simultaneously you will start having some ideas about the significance of these findings and how they should be presented later on. You might even want to select quotes to be included and discussed in the final draft itself. So the next two topic areas, “outlines” and “draft” are very closely related to the analytical process (from empirical data to findings) and start developing simultaneously with it. Hence the dashed lines coming out of “case study 1 findings”, which start to inform the outlining and drafting (writing) processes.

You might be in the middle of analysing an interview and have a sudden insight into how this material might fit into the overall or individual chapter outline, and you might even want to engage in some ad hoc writing and type up some paragraphs in the corresponding draft chapter topic. Nevertheless, outlining and drafting/writing will emerge as important processes in their own right, once the coding and analysis of the empirical material had concluded.

The final topic is an “inbox” for uncategorised and unprocessed material that had been imported into CT but has not been allocated to any of the aforementioned topics. I would generally advise against importing too much of such material, as it will just sit in CT and clutter the workspace. Regarding importing material, I found it helpful to work on one case study at a time and only import materials that relate to that case study. Also, I have treated the importing of material as a filtering process and a quality control process. After all, what’s the point of importing stuff that turns out to be utterly useless? It would just end up sitting in CT as dead weight.

Now, “inbox” needs to be understood metaphorically here, as CT is a wiki and therefore there are no folders or boxes into which you can drop stuff. Nevertheless, you can emulate an inbox in two ways: 1) either by creating a category label called “inbox” or “uncategorised” and append it to all new topics that need to be put into this virtual inbox, or 2) use a topic as an inbox and drop text, links to files and URLs into that topic. The same is true for all the other topic “areas”: they are not so much areas or folders as local networks of interlinked topics, for which the top level topic acts as the central node.

As for the overall project, it is essentially a process of trying to find an answer to a question. You can include the research question in your project dashboard, interrogate your empirical data with your chosen conceptual tools (theories), develop your findings, develop outlines to organise your argument, and write the eventual draft, which should hopefully provide an answer to your original research question. The great thing about CT is that it has tools for conducting this entire process in one place, within one software.

There are a few glaring omissions in my model above: a literature review topic, a conceptual framework (theoretical lens) topic, and a methodology topic. You could certainly include them here. I had worked on those phases of my PhD before discovering CT, so I haven’t had a need to include them in my CT project yet. However, as I will be moving onto writing up my dissertation, it is very likely that I will add in those topics and import the related material into CT as well. For the purposes of this blog and this CT tutorial I have decided to focus the above model on the qualitative data analysis process. However, it is easy for you to include those elements. All you need to do is type [[literature review]], [[conceptual framework]], and [[methodology]] into the body of the dashboard topic, and these topics will be automatically created for you and linked to the dashboard.

I have found writing this blog post a very useful exercise. This reflection allowed me to improve my model, as up until this morning it looked a lot less organised in fact. I didn’t have my meta considerations included in an organised way, neither did I have an inbox for uncategorised data.This type of meta-reflection on the design of your project and your work flow can be an important quality process in the ongoing development and improvement of your overall system.

Finally, let me include a couple of screenshots of the above model as implemented in CT. First, the edit mode (I have used a vertical tree view in the Navigator instead of the horizontal tree view of my concept map, so that it could fit into the left-hand-side pane. However, there is also a horizontal tree option in the Navigator, if you prefer that):

And then in view mode:

Please note that in the “PhD project dashboard” (or Home) topic only the words in blue are active links (e.g. “meta considerations“). The bullet-pointed text in black underneath (“CT project design” etc.) is only there to remind me what is inside that top-level topic (or link). Links to “CT project design” etc. are inside the “meta considerations” topic, which is currently not visible in the view window (as we are editing/viewing the “Home” topic), however the relationship can be seen in the Navigator pane on the left. Had I made those links live in the “Home” (PhD project dashboard) topic as well, it would have resulted in a messier Navigator picture, as they would have also showed up as part of level 2 hierarchy.

The Topic List pane on the right simply displays all topics in alphabetical order.