All 5 entries tagged Methods
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October 14, 2018
I am in no doubt that after the previous reading session it is difficult to pigeon hole my approach. Even the development of the coding scheme, I am finding that my approach consists of ideas from, though not limited to, inductive reasoning, thematic analysis and grounded theory. There is another idea that I have been working on for quite a while but now am in a position where I can make significant progress in this idea. Still plenty to do, but I am thinking that this research will become a multimodal project. It’s really a case of thinking about the way that is best to present this idea in the thesis, and to think more about the way in which the coding scheme relates to this idea. Now that the coding scheme is improving and I feel is beginning to take the form I want it to take, it is expected that there shall be some significant movement in developing this other idea in the coming weeks.
Along with that, another key current task is to continue to rework the coding scheme: reread the data segments, recheck the coding, and drop codes or amend them as necessary as well as combining data segments to present a complete meaning if I feel that I have divided them a bit too much. The idea also is to continue to go through the rest of the data and recode the data as necessary to reflect new meanings and new insights I am making whilst editing on paper, which is also a continuous process.
The process of rechecking everything, as just mentioned, is being carried out on paper. I have used computer software various times to amend the coding and to think about the data in various ways but sometimes for some objectives, it is best to simply use pen and paper. Print out all of your coding, relevant theoretical memos and other relevant documents and go through everything by hand. This approach I feel is especially relevant to mine because I am comparing a lot of data, within and across data sets, in order to develop the categories and themes, and also to develop an understanding of the behaviour of the phenomenon. This is not to suggest that there is no value in computer based analysis and I plan on using that further in the future.
I am pleased that I made the choice to do this, because I have found new insights and ideas that were not at all obvious when staring at the computer screen. Being at the computer screen and being concerned with navigating the software in order to find relevant pieces of data sometimes distracts you from your objectives and can cause you miss out on important insights. Simply doing things by hand sometimes can really help you find new insights that perhaps were not so obvious before. That being said however when I was editing everything on the computer following the edits by hand, there were some insights I made on paper that actually did not make sense when I really thought about what the particular pieces of data actually meant. It’s like a constant battle between colliding thoughts inside your mind with regards to the meaning of the data and what any particular data represents, but my experiences tell me that sometimes using pen and paper is best.
When you have these colliding thoughts and when you are able to perceive and interpret any date segment or segments in various different ways, remember to think about the context within which the segment is situated and remember that whatever you observe and interpret must be relevant to your research questions.
Regardless of what methods are used to explore the data, as always the key idea is to keep asking questions about the data. Remember that the data is a representation of the phenomenon of interest, and what you observe and place meaning upon might not be the same between multiple researchers. Here, you have to try to make sure that what you observe and what you interpret is as close to the data and as close to some sense of objectivity as is possible, if that is a desire of your research. Therefore, I am continuously asking questions about the data and also about my own observations and interpretations, and the quality of those observations and interpretations.
The only way you can progress is to ask questions. Making an observation or an interpretation of something within the data is fine, but you have to be sure of what it is you are really observing or interpreting. This is a process that I am only just scratching the surface of describing the process here (seriously, it’s taking up pages of my research design chapter!), but it is a process that is worth investment and engagement with. You need to make time and effort in ensuring that your observations and interpretations are as sound as possible, regardless of their ultimate subjective nature.
Just keep asking questions about the data and your own interpretations. You learn and develop only through asking, rechecking, reconfirming, and asking again! And when you are sure you are done with everything and have all the answers (quite frankly, I doubt claims of this sort), then ask more questions again!
Keep asking questions and keep going!
‘till next time!
July 02, 2018
Like a toddler running back and forth into the arms of those that love that child, ideas and visions that were previously considered irrelevant or perhaps not suitable for this project but might be for another project have been running back to me like that happy little toddler. Everyone say aww……..
(Oh by the way, I’m not at all suggesting that toddlers are irrelevant! Even if they turn into screaming delightful door slamming teenagers…………..)
The day has been a productive coding session. As I have been coding the data and observing patterns and meanings within the data, I have come to realise that certain patterns and meanings that were once considered irrelevant are now becoming more relevant and, also, I have observed new patterns and meanings that I had not previously observed when previous sets of data were coded. Or at least, new patterns and meanings that have not made themselves obvious till now, even though I might have observed them before but had not consciously acknowledged them, for whatever reason. I think this is a psychological thing: the more you become sensitised to a particular pattern or meaning you start to think later in the coding process that you have observed similar before in different contexts and then you start to identify the bigger picture or wider pattern of behaviour. It’s a very interesting and a very involving process. What I have found during the day is making me rethink what I have coded previously, and the way in which I have interpreted and perceived what is occurring in the data, which might lead to recoding the data again as I go through a more deeper coding phase as I go further into building an understanding of the phenomenon of interest. I’ll be talking more about this in another post later this week.
In the meantime however it is clearer to me now more than ever, and what might be good practice for other Ph.D. candidates to adopt, not to throw away any old ideas and visions that were previously considered irrelevant. This is an approach that I have adopted from the beginning of the Ph.D., as I have folders upon folders of books and research papers and thesis related documents and notes, and a fair percentage has been sent back and forth between the archive folders and the working folders as they were continuously examined for relevance at particular times of the project so far.
Now some of the oldest ideas and visions I had right at the earlier stages of the Ph.D. are becoming more relevant for answering my research questions and addressing the research problems. But more than that: what I was writing about earlier in a theoretical memo that documented my thinking of what I was observing was an attempt at building upon those earlier visions. It’s really interesting when you have built your earliest visions upon a section of existing literature and then to observe what you thought was irrelevant within the data brings back home the thinking that nothing is really impossible. There is a slight problem, however.
It is a fair way into the reanalysis and coding phase that these older ideas and visions have occurred, so this leaves me with a couple of questions. Do I carry on with the coding and analysis and simply suggest at what point I observed a new aspect of a phenomenon to be relevant? Or, do I reanalyse the data again and code for these additional observations that I made later in the coding?
Methodological literature that I have come across so far has not been clear on this subject although it is a subject I shall read more about. I have come across a paper that did suggest that you don’t have to reanalyse the data to code any new observations but this from what I remember was associated with grounded theory based Open Coding, where you are basically coding to build a theory and not coding to identify and relate themes. I am leaning towards yes, I would have to recode the data to code for more instances and examples of what I have observed in order to validate and authenticate the existence of what it is I have been observing.
Of course this then leads onto other philosophical questions such as does repeatability really represent truth? If you observe something often enough does it really exist in an external reality or does it exist within our own interpretations? What about if others are not able to perceive or observe what a researcher finds observable? In what way can I tell that something might exist in an external reality? In what way can I possibly know what I know to be true? These, and more, are challenging questions, but the key I think is to keep everything grounded in the data and make sure that arguments and observations are built from the data. You cannot build from existing theory; you can, however, build from a relationship between data observations and existing theory, but I shall cover that point at a later time.
With all that in mind, what I am thinking about is to analyse the data but keep the original copy of the data and embed evidence of a change in perspective or the observation of a potentially key new theme. This would be in the form of a theoretical note embedded within the data that would mark precisely the point that I began to observe the importance and relevance of an event or meaning that could form a part of a theme. This would show and evidence the progression of thinking and the way in which my thinking and thought pattern progressed to the point that I began to observe the importance and relevance of what it was I was observing. I am not really sure what the literature says on this subject, but I am becoming convinced that this might be the best approach.
The key lesson here really is, don’t throw out your old ideas. Whether that idea is represented as a few lines of writing on a scrappy piece of paper or rushed serious of paragraphs on the word processor, keep it! Archive it or put it in some relevant folder or whatever storage system you have so that you can refer back to those ideas in the future if they prove to be relevant. Another lesson is don’t focus your mind exclusively on what you found previously.
In other words, don’t code one set of data and then focus the next set of data on what you have discovered before (I know this is rather a contentious point in academic discussion from what I can understand about coding approaches and debates) (another contentious point is whether or not anything is actually discovered at all, but is actually interpreted), but keep an open mind. Of course what you find whilst you are coding and thinking about the data is exciting, overwhelmingly exciting, but keep a level head, keep an open mind, and don’t be distracted by what you have observed previously. If you become too focussed on what you have observed previously you’ll begin to lose the meaning of innovation and originality, and become potentially enslaved by previous observations. Keep an open mind and keep coding for original insights and meanings, and think and plan carefully to determine if there is a real need to reanalyse the data when you find something new a fair way into your data analysis process. This really depends on your research questions, research problem, and the way in which what you have observed relates to explaining the phenomenon of interest.
‘till next time!
June 22, 2018
Since my previous update, I have been reading more about thematic analysis and discourse analysis, as well as beginning to recode and reanalyse the previously coded data, a process at the time influenced by Grounded Theory.
The reading has illuminated text analysis to be a complex area and therefore, there is no clear or shared consensus of the way in which a specific type of text can be or should be analysed. Different methods and methodological ideas lean towards different type of texts to achieve different purposes and different outcomes; at least, that’s what is perceived from the research methodology textbooks. I think it’s more complex than even that because since I have ideas about methodological fluidity (check earlier blog posts) I think potentially any analytical method can be used for any type of text. The key to all this is to understand your data within the context of the research problem, research questions, research discipline, and your own philosophical beliefs and the extent to which you are consciously aware of the values and importance that your beliefs bring to your research. Within the context of my current thinking about my philosophical beliefs, the research problem and questions, etc. there actually isn’t a single individual approach that convinces me to be the absolute way to analyse data that achieves what I want to know.
This is a challenge because how can I possibly analyse data if I do not know which analytical method is best?
The answer comes from releasing your mind; from allowing your mind to be chained to this idea that a specific analytical method is required to becoming open and sensitive to the data; to allow yourself to become sensitised and to allow the data to speak to you. Obviously I am being guided by the research questions and I have a very general approach to what I am looking for based on the previous readings and analysis of the data via grounded theory, and identifying aspects of the data that grounded theory in my opinion is not able to capture (check previous blog posts). Beyond that I am allowing the text data to “speak” instead of me trying to apply any frameworks to it.
This is challenging, but my thinking is that I shall eventually arrive at either a specific analytical approach beyond the initial stage of thematic analysis, or I shall be able to pragmatically combine different aspects and ideas of different analytical methods in order to enable me to explore the data fully and therefore, enable me to achieve what I want to achieve with the research.
I have read through a variety of different analytical approaches, and what I am finding is there are aspects of these approaches that I think are relevant and aspects that are not. It is from these readings that I am leaning towards the possibility of adopting some sort of pragmatic, functional approach to analysing the data. This would involve the combination of different elements and aspects of different approaches, as long as what I do is relevant to the research purpose and questions, and aligns with my philosophical beliefs. What I will have to do in the thesis is to very carefully, reflectively, critically and analytically describe, critique, evaluate and explain what I am doing, how I am doing things, why I am doing things the way I am doing them, and also evaluate, critique, contrast and compare my approach with other approaches relevant to the analysis of the phenomenon of interest.
I could probably write eighty thousand words for the methodology chapter, nevermind the entire thesis………
This is effectively where I am with the data analysis! I have recoded the data that I have previously coded now under the thinking of thematic analysis instead of grounded theory, and I view no problem so far with the transition of thinking. The current task is simply to recode the data, meaning that I have dropped some of the previous codes and created new codes in order to better represent what is going on in the data. This has come from an increased understanding and awareness of the subject content and the way in which the content can be expressed. And also, I’m going beyond the data: I am beginning to visualise, theorise and conceptualise relationships and patterns within the data, which shall contribute towards theme development as the next part of the thematic analysis as well as the phase beyond thematic analysis. But before I get to that point I shall have to analyse more data than previously as I have changed the scope of data collection and data sampling procedures but I can discuss that another time and more specifically in the thesis.
As I code through the data, develop the themes and then begin to go deeper into the data and explore the contexts and expressions of these themes I shall be able to understand which analytical method is best used for the particular type of text (again, in the context of the research problem, research questions, and my own philosophical beliefs), or which aspects of relevant analytical approaches are best combined in a more pragmatic sense.
This is challenging but fascinating area of research and exploration!
‘till next time!
June 10, 2018
I have now switched for the time being from the literature review to the methodology chapter(s). Unsurprisingly, there shall be a substantial amount of editing and rewriting of existing chapter sections as they were written at a time I was using a pure grounded theory approach. I think it would be a mistake however to focus any allocated time frame on just a single thesis chapter because, in my opinion, the construction of a thesis is not a linear process particularly in qualitative research. There is fluidity in the intellectual movement across thesis chapters as they are being constructed and / or edited. As you are reading and writing for a particular chapter, ideas and thoughts relevant for earlier or later chapters might be revealed. Do not fight these happenings and occurrences: record them in whatever way is convenient at a particular time, even if it’s just a few words written down quickly on a piece of paper, so that you can follow up on your ideas at a later time. We all develop a strategy for doing this: for example, I write more extensive ideas down on paper before transferring them to the computer and extending and amending accordingly; any terms I want to explore further I simply type some key words into a search engine and save the results for future exploration. Whatever you do, do not dismiss or undermine any ideas that come to you, because during the Ph.D. so far I have found a lot of value in keeping ideas, documents, papers, word processed pages of previous ideas etc. as it was proven recently that lots of previous work has suddenly become quite relevant. Don’t dismiss or discount anything that comes to you!
The current methodological writing process at the moment is on paper instead of on the computer. I find this beneficial because with writing on paper sometimes I feel that I can explore my own ideas and play with my ideas better than I can on the computer. You could call this experimental writing of ideas, where try to carefully elaborate on my ideas and test according to what is suggested in the literature, and to think carefully about the way that literature supports my ideas. I obviously cannot write a thesis chapter on paper, but what I can do more effectively is to experiment with my writing and with my thoughts. I can also do this on the computer, but I feel that it’s best to start with on paper, but that’s just my preference! Opposition is welcome too, because if you engage with opposing views you can carefully construct a reasonable response that continues to support your views. As long as what you construct is logical and counters the opposing claims in a reasonable way with well grounded elaborations and explanations, supported where necessary and appropriate by relevant literature.
The topic of my current methodological writings is philosophy; more specifically, my ontological beliefs and the way that my ontological beliefs are shaping and guiding the utalisation and perspective of the newly assigned methods, as well as the way they are shaping my views of the type and source of data. Briefly, I consider myself an ontological realist (more moderate than staunched), which impacts, as mentioned, the way that I perceive the value of different types and sources of data, and explains the way in which research methods shall be utalised. Being a realist impacts what I perceive to be real, what I consider to be a more truthful or accurate representation of reality, and therefore the way in which different types and sources of data are to be engaged with in order to best understand this reality. These are the topics I have been writing about and obviously there is much more to think about and, therefore, this is an ongoing process. Obviously as time goes on these notes shall be extended and amended in various ways.
What I intend to do is write the methodological chapter as I go through the analysis process. At least, the sections that more closely relate to the utalisation of these research methods, as the methodology chapter(s) contain sections where you have to explain and critique your own understanding and utalisation of whatever research methodology and methods you use for your research. In the meantime however, I shall be working on elaborating on my philosophical beliefs and their relationship with the research method, and the source and type of data before progressing onto engaging with the first stage of analysis, which shall be reanalysing the data.
More on this in the next blog post!
April 04, 2018
This past weekend has encouraged me to re-evaluate and re-explore the value of using both quantitative and qualitative data within my research project. This is an ongoing task that demands careful and reflective thought, and currently constructing diagrams that illustrate aspects of the design and the way in which these different aspects relate to each other, and the way in which the research shall now progress. Once I have completed these diagrams I shall be sending them to my supervisor for further feedback and confirmation of the design’s suitability. There are, not surprisingly, many thoughts, questions and ideas that I have about the emerging research design. As mentioned, going through all these thoughts, questions and ideas is an ongoing process but there are some key questions and ideas that I am focussing on at the moment with regards to the characteristics and aspects of the research design.
Firstly, and probably most importantly, should I reemploy a mixed methods approach? Is a mixed methods approach actually possible given the data collection context? Instead of collecting qualitative and quantitative data separately as is typically found in most mixed methods research, I have collected qualitative data and from this data set, both qualitative and quantitative data analysis shall be applied. I have a vision about what qualitative and quantitative data I want, but I am working through how this is going to be precisely and exactly realised particularly the quantitative aspect. I realise therefore I am not using mixed methods at the data collection level, but there appears to be a mixed approach at the data analysis methods level. This has implications at the methodology level: should mixed methods be confirmed as the appropriate approach to the research, then grounded theory becomes the qualitative method and not a methodology, whilst network analysis or some form of it becomes the quantitative method.
But here’s something to think about, and forms my second current thought and question: what comes between grounded theory and network analysis? What acts as the bridge that enables qualitative data to cross over into the quantitative realm? I think the answer lies in visualisation. In my understanding, a network is a visual representation or diagram of what is happening. A phenomenon can be understood through its aspects, features, events or activities and these can be represented as a network of nodes and connections. What I am attempting to do here is convert the concepts, categories and their relationships, products of grounded theory analysis, into a network. I am slowly working through how these grounded theory concepts can be converted into aspects of a network and this is going to take some time, but currently I am thinking that concepts and categories can be represented by nodes, and the relationships between categories can be represented by connections between nodes. What I am also interested in is exploring the relationships between these nodes because it is at these points where interesting observations and values can be obtained, but I’ve yet to figure out the way this can be fully considered. I’m thinking at the moment these relationships shall be related to the hypotheses that shall be developed as well as the properties and dimensions of categories and might also might be involved with quantitative analysis. The quantitative analysis shall be used to analyse these relationships to determine the strength between different types of nodes within different contexts, but the exact relationships and hypotheses that are to be explored are undetermined at this time and shall be until the qualitative data analysis section has been completed. This in a sense brings me to a third concern I am working on.
If my research is to adopt a mixed methods methodology again, what type of mixed methods should it be? My previous approach to mixed methods was a sequential exploratory type where qualitative data were to be collected and analysed first followed by the collection and analysis of quantitative data. This was therefore sequential in nature but I am not sure at this time whether my mixed methods approach now would be sequential or transformative: sequential because qualitative analysis will come after qualitative analysis, or transformative because it might be that some aspects of the qualitative data might be transformed into quantitative data. Is this even possible? It is in some context but I’m not sure if my qualitative data will be able to transform into quantitative and I am probably unable to know this till the qualitative analysis phase is complete and I begin to really look at the findings. At a push at the moment I'd say sequential exploratory: might be best to design both types just in case!
There are many other concerns that I now have that I shall be exploring further as my thinking and experimenting of the potential mixed methods approach progresses: in what way should I now present my research questions? The research questions shall have to change to better represent a potential mixed methods approach as the questions cannot be purely qualitative: a question must be qualitative and another must be quantitative but derived from an overarching question that brings both together.
Also, what are the implications on the use of literature and the roles of the literature reviews? At the moment I cannot imagine there being too many changes because of the important role that grounded theory shall continue to play in terms of identifying the nodes and connections of a network, which shall subsequently have some form or forms of quantitative analysis placed onto it (is this really network analysis, or something else?) although I shall have to double check the role of literature within mixed methods research.
What about the product of or the outcomes of the research? What is the nature of theoretical development within mixed methods research? A key role of mixed methods as described in some of the methodological literature is to both build and test a theory and / or a set of hypotheses. The qualitative aspect builds theoretical constructs and hypotheses and the quantitative strand tests these theoretical constructs and hypotheses.
What shall be or should be the extent to which grounded theory is used? Should I use grounded theory to the extent that a general theme of the learning phenomenon can be established and use that as the basis of the network construction and exploration? Or, should I use grounded theory to the extent that categories, relationships and hypotheses can emerge from the data, but use an existing overarching theoretical framework to guide their use in the network construction, and use quantitative analysis to test the identified relationships and hypotheses that come from the qualitative stage? I am not sure at this time.
What about the case study methodology? Should I return to thinking about the value of a case study methodology with mixed methods approach encased within? There is some debate about whether or not a mixed methods approach really is a methodology and not just a strategy of the way in which methods are to be sequenced or arranged. I shall have to revisit this debate area.
I have so many questions at this time, so many more than answers but I have a plan to work through all these different questions and issues that I have discussed here and more besides. I shall probably be writing on here on a regular basis now if only to document this challenging yet exciting journey and therefore to help me reflect upon my ideas and their development.
Thanks for reading! If you’re on your Easter holidays still, continue to have fun!