Chapter 4 proved to be quite a long chapter, so I split it across two posts; this is the second and discusses ethics, data management, data analysis, and integrity.
Ethics
Although the formal stages of making ethics applications first for a pilot and then the main study are where my ethical thinking began, it continued through the data collection phase, on through analysis and presentation, and remains as a duty of care through the publishing of the thesis. By maintaining an ongoing ethical sensibility, I became increasingly attuned to ethical issues as they arose. I’ve discussed the thinking underpinning my ethical sensibility over a series of posts, so here I’ll simply summarise what was carried forward into the thesis.Read More »
In an earlier post I discussed some of the thinking behind the analytical process I was leaning towards; much of that made it into an earlier draft of the thesis. Feedback from my supervisors pointed out that detail on how I actually proceeded through the analysis was rather thin. Any reader (examiner!) would therefore not be clear about the steps I took … and therefore my thesis fails one of the characteristics I set for its integrity – that of transparency. I’ll now attempt to set out the analytical moves I made in a little more detail.
Analysis was a multi-stage process, although not one which proceeded linearly from start to finish. Instead it involved a series of back and forth iterations moving between and across the different data sources. As data offer themselves either as words participants deliver during interviews, tweets that appear through observation, or blog posts at the end of hyperlinks, analytical seeds begin to germinate during this period of familiarisation.Read More »
At the interview for entry onto the PhD programme, one of the panel asked me if I could sum up my research proposal in a tweet. Although it shouldn’t have, that question stumped me at the time, but as a result, it did stick with me. A reasonable question to ask in my viva might be ‘Can you sum up your PhD in a tweet?’ Currently I’m struggling to get it under 90 000 words, so I still have some way to go! Following the first draft of my thesis, one of the feedback points was that I needed to be able to synthesise my findings into a handful of bullet points, even if I didn’t subsequently present them as such. It’s about having a distillation that’s brief enough to fit into the abstract and encapsulate what my study found, whilst leaving room for the other bits that also need to be in the abstract like the methodology, methods, theoretical approach etc. I thought I might try to go a step further and get it down to tweet length; after all, since I started the PhD, Twitter’s generously provided double the characters to play with.Read More »
In earlier posts, including this one, I’ve attempted articulate what flânerie involves. Like the urban wanderer, explorer, observer and chronicler of city life, I’ve approached my research as flâneur. Initially, that was in attempting to find an alternative way of describing my ‘ethnographic’ approach to Twitter. Initially, only somewhat playfully, I called this a ‘flanography.’ More recently, I included it within my thesis; it had become a ‘thing!’ What struck me at the time, and what was recently reinforced during a supervisory meeting, was that I need to articulate clearly what distinguishes flanography from ethnography. In this post I want to thrash around a few thoughts how that might be done.Read More »
In a previous post, I outlined how creating an image, initially for a competition, also illustrated how that visualisation process often became an analytical (flânalytical?) technique. Having been inspired by the @metropologeny city maps, as I began planning the vis, it always struck me that tweets seemed to naturally fit the mainly rectangular shapes of the buildings on the map. In being drawn towards the tweets however, I wondered about the other data sources which were part of my study, but temporarily parked that aspect until I’d resolved the technical aspects of producing the image. Now, with that task completed and the image submitted for the competition, I now turned back to the other data. How might blog posts or interviews also contribute to the vis?
Before delving into how I moved forward, perhaps it might help to rewind somewhat and look at how the map was built in the first place. This animation shows the different stages
Here at SHU there’s a couple of PhD researcher competitions on at the moment as part of the forthcoming Doctoral Showcase series. There’s the ‘Three Minute Thesis’ heats and local final, but the one that attracted my interest was the ‘SHU Doctoral Research Image Competition 2018.’ I’ve been producing visualisations throughout my study and I had in mind one I wanted to produce, but hadn’t because I knew it would suck up time. The competition provided the final impetus and although I suspect from the information and instructions, the organisers are expecting photographic images, I thought I’d have a shot at pushing the boundaries.
We welcome attention-grabbing images to intrigue, inform or excite a lay/non-specialist research audience about your research. Images may be arresting, beautiful, moving or even amusing but they must relate to your doctoral research project.
Entrants are also allowed 150 words of accompanying text; here are mine:
The flâneur of 19th Century Paris was an observer and chronicler of city life. In exploring the bold claims some teachers make that ‘Twitter is the best PD ever!’, I called on the spirit of the flâneur to guide my ethnographic approach.
One of several methods I employed in the study was participant observation; this image is formed from tweets collected during that process. Each of the districts or ‘quartiers’ contains tweets on one of the emerging themes, each typified by a magnified example.
Since flânerie inspired my approach to observation, analysis of the data, and presentation of the findings, I sought an image which spoke to that activity. Although somewhat playful, creating this image, and other visualisations during the study, was more than simple representation. On each occasion I found the attention to compositional detail which was demanded also yielded additional analytical insights.
In preparing for a forthcoming supervisory meeting, I’ve been asked to share what I felt were the standout insights from my empirical observations, but for each one explain how I know, how I convinced myself of that, and how I can convince others. I guess what I’m being asked here is to justify my claims to knowledge; how do I assert that my interpretations are plausible? Lincoln and Guba (1985:290) phrase it as follows:
“How can an inquirer persuade his or her audiences (including self) that the findings of an inquiry are worth paying attention to, worth taking account of?”
For them it is about trustworthiness and the arguments which can be mounted to make the case, however, assessing the quality of research findings is far from straightforward and is contested in a number of ways. Traditionally, research quality has been judged on the criteria of validity, reliability, generalisability, and objectivity. Validity, simply put, is the extent to which an account adequately represents the phenomenon it purports to. Reliability is related to the replicability of the data generation and analysis; if different people conducted the same study, or the same person on different occasions, would the outcomes be the same? Generalisability refers to the extent what has been learned can be extended to wider populations and objectivity, to how the biases and interests of researcher and researched have been reduced or accounted for.Read More »
Recently Stephen Downes released an updated version of a graphic outlining how groups and networks differ in what he proposes as ‘The Semantic Condition,’ a network design principle. He helpfully explains how the diagram was produced in this video:
In addition to the criteria he uses to distinguish between the two ways in which people might organise or aggregate, what attracted my attention was the appearance of Twitter within the arguments. One of the aspects coming through in the data from my research, is how educators on Twitter describe the ways in which they come together. The most common term people seem to use is community, but group, tribe and network also appear. Although these terms are conceptualised differently, I suspect in most cases, a particular term is used simply because it happens to be the favoured choice, rather than having an awareness that there is a distinction between it and the others. If I was to explore this more carefully, I might be able to tease apart the ways in which people see these different terms, but suspect that what for one person is a community, could just as easily be what a tribe is for another. It was from wondering how these different groupings are distinguished from one another in the literature, that I was attracted to Stephen’s graphic.Read More »
I was attempting to write a vignette yesterday about how the tweet which prompted this post actually got in front of the eyes of some people who might be so inclined to respond. If we’re asking a question or seeking advice, rather than sharing a resource or thought, then the audience becomes even more significant than it normally would. Without an audience, like the falling tree in the forest needing someone to hear it, the tweet and the query it carries may as well not be there.Read More »
During the exchange I was discussing in the previous post, Becky (Wood) shared an example of one of the resources she had been using to provide whole class feedback:
Here's a couple of examples I've done. The S's quite like it. Next lesson all do Dirt + check for Spag errors pic.twitter.com/KylGhwthR0
Marginal Notes 