In this post I will describe the process flow of how ConnectedText can be used as a CAQDAS for coding qualitative data (see my caveats and qualifications in the previous post). Unfortunately I can’t use my own CT project as an example because the information in it is confidential and it would just take too long for me to anonymise it. Instead, I will use the silly little example I came up with in a previous post.
Imagine you are conducting a research study into how people use technology. As part of your research, you have decided to interview me about my unorthodox use of CT as a CAQDAS tool. You have recorded and transcribed the interview. You have followed the procedure recommended in an earlier post to import the document into CT and use the suggested naming convention to entitle the topic containing the interview as “DRA1 Why CT.”
The “Why CT” bit is a reference to the main interview question: “Why do you use ConnectedText for qualitative data analysis?” This was the first interview in a series of interviews with Dr Andus, and it was “filed under” the Dr Andus case study, which in your CT is represented by a topic called “DRA case study.” We thus have a case study topic (the parent topic) and several interview topics linked from that topic as child-topics.
Using this example, I will now describe the overall coding work flow and the structure of the CT project before and after the coding, so you can have an overview before we delve into the details. The whole thing might not immediately make sense because you may also need to see how this was actually implemented in CT (which I will show you partly below, and partly in my next post). I strongly recommend that you try to replicate this in your CT test project, so you can experience how these various commands and markups work.
Here is a visual representation (created in VUE) of the coding process flow and the resulting structure of topic relationships. Please note that this follows the same overall CT project design I had suggested and described in this post. This chart just adds our concrete example of the DRA case study to the previous general model (this time only focusing on the “empirical data” and “findings” topic branches). It is a hierarchical structure with horizontal levels, where the Home page is level 1, “Empirical data” etc. is level 2, “DRA case study” etc. is level 3, “DRA1 Why CT” is level 4, and DRA1.1 etc. is level 5 (and it could go on as far down as you need it to):
The other thing to note about this chart is that it shows the end result of the coding process. Initially when you first import your document to code, there would only be level 1, 2, 3, and 4 topics. Level 5 topics (DRA1.1 etc.) grew out of the coding process. While you could set up the “DRA case study findings” topic in advance (I usually do), initially it would be empty (except for some “include” markup which sits there patiently until the topics they point to get gradually filled up with content) [more about the “include” markup below].
Let me now explain what we are seeing in this chart in full detail. As I said, it is a mixture of a process flow chart (follow the arrows) and an organisational structure chart (top-down hierarchy), which shows the elements of the CT project structure. All the boxes represent individual CT topics. Black arrows represent the parent-child relationships between the topics (links emanate from the parent topic to the child topic).
Red arrows represent the direction and operation of the “include part of other topics” markup, which is a critical command for this whole system to work. The “include” operation generally flows upwards from child topic to parent topic (remember my analogy of meaning emerging like “bubbles or cream rising to the top“?), except in the case of the relationship between “DRA case study” and “DRA case study findings”, in which case they are “siblings”, sitting at the same level of the hierarchy.
Let me explain how the “include” markup works because it is such an important part of this system. When you add the following markup to the body of a topic in CT, it includes (pulls) the contents of the target topic in the current topic:
((target_topic_name))
More importantly (and this is the killer feature for this project design and process flow), you can also specify to only include text that has been entered under a specific heading, by adding the title of that heading to the “include” markup like this:
((target_topic_name==heading_title))
To use a concrete example from our chart above, we will need to add the following markup to the bottom of the text in the topic “DRA1 Why CT”, if we want it to collect the conclusions that have been derived from and gathered at the bottom of topics “DRA1.1”, etc.:
((DRA1.1==Conclusions))
((DRA1.2==Conclusions))
((DRA1.3==Conclusions))
As if by magic, the text contained under the heading “Conclusions” in these sub-topics will instantly appear in “DRA1 Why CT”. Moreover, should you make any changes to that content in DRA1.1 for instance, the changes will be immediately updated in the “collector” topic as well. If you now take another look at the chart above, you will see that the red arrows effectively describe a daisy chain structure by which elements of some topics are included in the next level of topics until we reach the top level. Here is the whole hierarchy of “include” commands for the structure in the chart, cross-referenced with the topics they would need to be added to:
((DRA case study findings==Final findings)) - to be included in "Findings"
((DRA case study==Findings)) - to be included in "DRA case study findings"
((DRA1 Why CT==Final conclusions)) - to be included in "DRA case study"
((DRA1.1==Conclusions)) - to be included in "DRA1 Why CT"
You should also consider that this is not simply a mechanical operation but also a conceptual one. Each new inclusion at the level above includes content that has been extracted and abstracted from the topics at the level below. Meaning is becoming purified as it rises to the top, while retaining physical linkages (references) that allow you to trace your findings and conclusions all the way back to the ground level of the empirical material. You have to agree that that’s a brilliant functionality!
Let’s now turn to the other two features mentioned in the chart: the “headings” markup and the “Cut to new topic” command. The “headings” markup is also very important for this system because it is actually the notation by which the qualitative coding can be carried out the most easily.
[There are some other, more sophisticated ways of annotating a few words and aggregating them in another topic (such as “properties” and “attributes” – you can read up on those in the Help file or see them in action in this post by Steve Zeoli) but not of larger chunks of text (although this is likely to change, as CT’s developer is actively considering introducing a special command for marking up and collecting larger passages which would work very much like a standard CAQDAS feature).]
The “headings” markup is our best option now for coding, partly because it’s very quick and the results can be seen in real time in the Table of Contents pane, but also because the text under a given heading can be included in other topics, as I have explained above. Here is what you need to type to define a headings hierarchy (there are max. 5 levels) (or select them from the context menu by right-clicking on the selected text in edit mode):
=Heading 1=
==Heading 2==
===Heading 3===
====Heading 4====
=====Heading 5=====
When I talk of “coding”, what I mean is adding your “codes” (annotations) as headings above the sections of the text that you are analysing. E.g. =Heading 1= could be your interview question. Then underneath you could add sub-headings, in effect annotating the interviewee’s responses. The 5-deep hierarchy allows you to structure the interview text in a meaningful way, by bringing out its implicit logical outline, which gets gathered and displayed in the Table of Contents pane.
=Why do you use ConnectedText for qualitative data analysis?=
==Because he didn't like the hierarchical nature of NVivo==
===NVivo separated the codes out of its original context===
Now, if you are doing this kind of coding on a 20,000-word document, sooner or later the Table of Contents might fill up the entire page in its pane. While it can be collapsed, the better option in fact might be to remove the coded content from the workspace altogether, partly to make it less cluttered but also in order to organise the content into thematic groups.
This is where the very handy “Cut to new topic (CTRL+ALT+N)” command comes in. Just highlight the section you have already coded that constitutes a coherent thematic group (e.g. the answers given to question 1, 2, 3 etc., if you have numbered your interview questions), right-click, choose “Cut to new topic” and voilà! – CT packs away the selected topic into a new topic, leaving a link to it in the original topic. This is where the Navigator window comes in handy, as you can monitor it visually how the sub-topics that you have coded and cut away gradually grow and appear at a sub-topic level.
When you are finished coding (marking up your topic with headings and sub-headings) and you have cut all of them away, all you should end up with in the body of your original document (“DRA1 Why CT” in our case) is a bunch of links to all the sub-topics (the double-square brackets signify that these are internal wiki links) (and you can of course give the topics more descriptive names after the initial code, such as “DRA1.2 about NVivo”):
[[DRA1.1]]
[[DRA1.2]]
[[DRA.1.3]]
etc.
Let’s say you started with a 20,000-word document (a 2-hour interview transcript). After coding, you might have ended up with 15 sub-topics on average with 1300-words each. Now you can go into each of those sub-topics (DRA1.1 etc.) and complete your evaluation of the codes and the content there.
The simplest way of doing this is to copy the contents of the “Table of Contents” (not in the TOC pane but in the view mode of the topic itself) in this sub-topic and then either paste it into the Notes pane for some further evaluation, organisation or pruning, or you can directly paste it at the bottom of the topic after a heading called =Conclusions=. Here you can carry out additional operations, such as turn this into a numbered or bulleted list or use highlighting to mark out especially important information. (You can also add a “Category” to the sub-topic itself, to have yet another way of classifying the content. See the Help file.)
Remember that only content that is under the =Conclusions= heading in the DRA1.1 sub-topic will be pulled into the parent topic, “DRA1 Why CT.” For this to work, in this parent topic you will need to include the markup ((DRA1.1==Conclusions)). It is best to collect these =Conclusions= from the sub-topics under a heading such as =Summary of conclusions=. Once all your coding is finished, all the conclusions from the sub-topics will be listed in the body of the parent-topic, “DRA1 Why CT.”
This is where the “reverse cascading” of the abstraction process begins, as now you will be ready to evaluate these collected conclusions from level 5, record your conclusions from these conclusions, e.g. by initally writing them in the Notes pane, and then pasting them under a new heading at the bottom of the “DRA1 Why CT” topic, called =Final conclusions=. These “final conclusions” then get pulled into the “DRA case study” topic, where you can repeat the whole process, draw conclusions from the conclusions of the conclusions and call them “findings” this time, under a heading called =Findings=. This in turn will be included in the “DRA case study findings” topic, the =Final findings= of which will eventually be included in the “Findings” topic itself. And that’s it. The meaning (bubble or cream) has reached the surface. Hurray!
I realise it might be a bit heavy to follow all this without seeing what the actual topics look like in CT. However, this post is getting quite long already, so I will summarise all the steps involved in this process in a final post, illustrated with screenshots.
Check out my tutorial here, if you are interested in creating your own research tool.
Dr Andus's toolbox 