Yesterday, I came to the most painful part of writing Chapter 4: tying up the loose ends and deciding if some data should go unreported. As I wrote about the more minor points, I continued to ask myself, "Is this important enough to include in the Chapter?" There are times when I say maybe and others when I say no. I keep the "maybe" points in to get my co-chairs point of view. However, I predict that they will tell me to throw them out, but I'm not certain enough.
Once I got beyond that part, I was able to finish the last section and write the conclusion to the Chapter. I missed my arbitrary page-limit goal by only 3 pages, but I know that I can easily add 3 pages as I noted to myself to flesh out the introduction. Because I'm exhausted, I left the introduction bare bones and it does not reflect what I discovered through the writing process. With my conclusion better written than the introduction, I know that I need to revise the introduction when the time comes to Chapter 4, draft 2.
I have decided to leave Chapter 4 alone for the next week or two. I've been more intimate with Chapter 4 than anything else while I was "vacationing" in Florida, so it's time that I take a break. Incidentally, I finished the first draft with exactly 24 hours remaining in Florida, but I'm a bit too exhausted to celebrate...for now.
Besides exhaustion, how do I feel? I feel that I wrote small islands of good research writing surrounded by an ocean of poor writing with incomplete arguments, positions, or evidence. One reason for these incomplete arguments is that I don't have my full arsenal of literature with me because I'm "on vacation." Another reason is that I gave enough for the beginning of an argument or position, so I could flesh it out later. Like I wrote in the earlier post, there may be some arguments or positions that I may need to remove completely, so I did not want to spend too much time composing well-formed arguments and positions for every point.
I'm also keenly aware of the sentences and paragraphs that are wholly poor reasoning. I changed the font color in the areas to red so I could later decide if that reasoning needs to be either reworked or removed. In those areas, I feel like I was rushing to conclusions and/or making points that may be better made in Chapter 5.
There is one very weak area in my data analysis and Chapter 4 because there is one participant in my study who may be going through a major stage of his adjustment process during the time I was composing Chapter 4. I hope to hear from him after I send him my summary of his experiences and observations as part of the member check.
The member check is the next step in my dissertation process. The responses from my participants will help better inform me when revising Chapter 4, especially the one participant I mentioned in the previous paragraph. Another major step is learning how my colleague coded my data using my themes. She may reveal parts that I either overlooked or misinterpreted. If this is so, my corrections in the revision will definitely improve the quality of the chapter.
I do not expect to hear from my participants about their member checks or from my colleague about her coding procedures for at least a month. In the meantime, I will be making smaller revisions to Chapter 4.
Once I return to Iowa tomorrow, I plan to send my co-chairs my Chapter 4 outline, my conclusion, and a graphic representation of part of my grounded theory. Their input will also greatly affect how I approach the first revision of Chapter 4. I foresee the months of January and February on Chapter 4 revision and Chapter 5 pre-wrtiting.
Showing posts with label coding. Show all posts
Showing posts with label coding. Show all posts
Sunday, January 5, 2014
Sunday, December 8, 2013
Grounded in Grounded Theory
Today I have begun my cognitive transition from data analysis to reflecting upon my research methods, particularly while reading through Constructing Grounded Theory by Kathy Charmaz. The chapters that require my attention the most are Chapters 3-5. Chapter 3 is about coding in grounded theory practice, something which I believe I have completed. Chapter 4 is about memo-writing, which I believe I have been doing and continue to do. Chapter 5 is about theoretical sampling, saturation, and sorting, which I believe is my next step.
The purpose of this posting is to share my thought processes, in which I am making sure I did the best that I could in terms of coding, that I am writing memos as appropriate to myself and my project, and that I am ready for theoretical sampling, saturation, and sorting and maybe even bits of Chapter 6, reconstructing theory into grounded theory studies.
Coding: What have I done?
In September, I began initial coding through my 3000+ pages of data. The most obvious part of this coding was to identify the chunks of data that directly addressed my research questions, that indirectly addressed my research questions, and that did not address my research questions. I have previous blogged about this step before. An important point in Charmaz' book about this stage was to "move quickly through the data" (p.49). I did not want to get bogged down in this tome of data.
On the next page, Charmaz describes word-by-word and line-by-line coding, both of which I could do for my interview data but would last months if not years if I did so for the blog data. After I selected blog posts that directly and indirectly addressed my research questions, I was then able to do line-by-line and sometimes paragraph-by-paragraph coding. This coding process was different than my coding practice for two qualitative methods courses that I took because the data in those courses were limited to either a certain number of pages or a certain amount of time (for interviews). In those courses, 20 pages of data seemed daunting to most of us.
After my initial coding process, I shared the themes that emerged from the data with my dissertation committee co-chairs. They helped me to discern a common heuristic that unified most of the themes I identified. This heuristic, in term, helped initiate the second stage, which Charmaz called focused coding. I spent the last few weeks engaged in this focused coding, which was faster than I anticipated for a number of reasons:
The second type is theoretical coding, which I thought of because the guiding heuristic is very close to a theoretical model. I decided against invoking the theoretical model for theoretical coding as I wanted the data to emerge without a theory at first as I believe the codes already have a coherent relationships.
Memo-writing: What am I doing?
I have been writing memos ever since my pilot study data collection last year, before my research proposal was approved. The most significant writing times were at various stages of data collection and all throughout the data analysis phase. I discovered that my memo writing at the beginning of data collection revealed that I was too eager to find patterns. I believe I see evidence of my maturity to let the patterns emerge from the data instead of forcing it. I also found that I began to embrace the tenet of KISS (keep it simple, stupid) as I began to gather over a thousand pages of data.
Charmaz writes that there is no formal way or writing memos, and that it depends on the researcher's writing style, personality, and data. While reading through chapter 4, I found many of the techniques to be similar to those from my creative writing courses in how to best organize your thoughts. Although I prefer to be modest, I have discovered that this is one of my strengths. I know how to take notes and communicate effectively to myself.
My favorite bit of advice is to write memos as soon as possible. In one class, we practiced memo writing immediately after collecting data. I found that memo writing after analyzing a selected chunk of data to be more helpful. For data collection, my best memos were when I saw a pattern emerging after the midpoint of the data collection process. Like I wrote earlier, the first part of my data collection memos is most garbage for the study, although it reveals a lot about me.
Theoretical Sampling, Saturation, and Sorting: Am I doing this?
The first question in Chapter 5 that stands out is, "Can you clearly define your themes?" I can easily answer yes. The next concern is that I may not have enough data for these themes, which I do not. When I begin a cross-case analysis, each theme should have at least a full page of evidence; some may even have more than five pages.
Because I believe I have enough data for each category, I do not feel that I need to interview my participants more. However, I believe that a member check with each of my participants may prompt one or more of them to clarify my interpretation of the data.
Reading further in Chapter 6, I realize that, for some of my participants, I have clearly saturated the data because they have conclusions to the experiences I am investigating. Those participants who are still going through these experiences may have a change of perspective by the time of the member check, but I had to decide upon an arbitrary cut point in time or I would have a difficult time ending the project. Besides, I already have 3000+ pages of data, which seems like a good indicator of saturation.
It is clear to me that most theoretical sampling is done and that I have most likely saturated the data for each major category, but I have not physically sorted the categories yet. I have done this to some extent in my head, but that's not reliable enough. I need to write down and visualize theoretical sorting. Once this is done, then I seem to be ready for the next chapter in the textbook, "Reconstructing Theory in Grounded Theory Studies."
The first step is here is to make sure I have a consistent worldview when I use the word "theory" and apply it in my report. Reading through this section of Chapter 7, I am surprised to find myself more on the positivist end than the interpretive end of defining theory. Perhaps this is so because, in the larger scheme of things, I would prefer a mixed-methods approach to the study. Although I'm not measuring anything in this project, I hope to open the door for measuring variables if this study is compelling enough for further investigation.
In the next section of Chapter 7, Charmaz compares constructivist grounded theory to objectivist grounded theory, and I believe I share more the of constructivist's perspective, especially in that one participant's narrative differs slightly between interview data and blog data, mostly in tone. I am a bit surprised to discover that some withhold information from the interview that was revealed (earlier) in the blog.
Since I haven't developed my grounded theory yet, I will stop with a reflection on "Theorizing in Grounded Theory" on page 133. Charmaz shares how researchers view or define the theory of grounded theory:
The purpose of this posting is to share my thought processes, in which I am making sure I did the best that I could in terms of coding, that I am writing memos as appropriate to myself and my project, and that I am ready for theoretical sampling, saturation, and sorting and maybe even bits of Chapter 6, reconstructing theory into grounded theory studies.
Coding: What have I done?
In September, I began initial coding through my 3000+ pages of data. The most obvious part of this coding was to identify the chunks of data that directly addressed my research questions, that indirectly addressed my research questions, and that did not address my research questions. I have previous blogged about this step before. An important point in Charmaz' book about this stage was to "move quickly through the data" (p.49). I did not want to get bogged down in this tome of data.
On the next page, Charmaz describes word-by-word and line-by-line coding, both of which I could do for my interview data but would last months if not years if I did so for the blog data. After I selected blog posts that directly and indirectly addressed my research questions, I was then able to do line-by-line and sometimes paragraph-by-paragraph coding. This coding process was different than my coding practice for two qualitative methods courses that I took because the data in those courses were limited to either a certain number of pages or a certain amount of time (for interviews). In those courses, 20 pages of data seemed daunting to most of us.
After my initial coding process, I shared the themes that emerged from the data with my dissertation committee co-chairs. They helped me to discern a common heuristic that unified most of the themes I identified. This heuristic, in term, helped initiate the second stage, which Charmaz called focused coding. I spent the last few weeks engaged in this focused coding, which was faster than I anticipated for a number of reasons:
- I had already gone through the data twice, so I was much more familiar with the participants and their data. I was getting a better feel for the flow of the blog and interview data.
- The initial codes were still in place, so I could easily look for them and determine how to focus on them.
- I did most of the focused coding over Thanksgiving break, in which I had the most free time since I started my full-time job. I was able to completely immerse myself in the data.
The second type is theoretical coding, which I thought of because the guiding heuristic is very close to a theoretical model. I decided against invoking the theoretical model for theoretical coding as I wanted the data to emerge without a theory at first as I believe the codes already have a coherent relationships.
Memo-writing: What am I doing?
I have been writing memos ever since my pilot study data collection last year, before my research proposal was approved. The most significant writing times were at various stages of data collection and all throughout the data analysis phase. I discovered that my memo writing at the beginning of data collection revealed that I was too eager to find patterns. I believe I see evidence of my maturity to let the patterns emerge from the data instead of forcing it. I also found that I began to embrace the tenet of KISS (keep it simple, stupid) as I began to gather over a thousand pages of data.
Charmaz writes that there is no formal way or writing memos, and that it depends on the researcher's writing style, personality, and data. While reading through chapter 4, I found many of the techniques to be similar to those from my creative writing courses in how to best organize your thoughts. Although I prefer to be modest, I have discovered that this is one of my strengths. I know how to take notes and communicate effectively to myself.
My favorite bit of advice is to write memos as soon as possible. In one class, we practiced memo writing immediately after collecting data. I found that memo writing after analyzing a selected chunk of data to be more helpful. For data collection, my best memos were when I saw a pattern emerging after the midpoint of the data collection process. Like I wrote earlier, the first part of my data collection memos is most garbage for the study, although it reveals a lot about me.
Theoretical Sampling, Saturation, and Sorting: Am I doing this?
The first question in Chapter 5 that stands out is, "Can you clearly define your themes?" I can easily answer yes. The next concern is that I may not have enough data for these themes, which I do not. When I begin a cross-case analysis, each theme should have at least a full page of evidence; some may even have more than five pages.
Because I believe I have enough data for each category, I do not feel that I need to interview my participants more. However, I believe that a member check with each of my participants may prompt one or more of them to clarify my interpretation of the data.
Reading further in Chapter 6, I realize that, for some of my participants, I have clearly saturated the data because they have conclusions to the experiences I am investigating. Those participants who are still going through these experiences may have a change of perspective by the time of the member check, but I had to decide upon an arbitrary cut point in time or I would have a difficult time ending the project. Besides, I already have 3000+ pages of data, which seems like a good indicator of saturation.
It is clear to me that most theoretical sampling is done and that I have most likely saturated the data for each major category, but I have not physically sorted the categories yet. I have done this to some extent in my head, but that's not reliable enough. I need to write down and visualize theoretical sorting. Once this is done, then I seem to be ready for the next chapter in the textbook, "Reconstructing Theory in Grounded Theory Studies."
The first step is here is to make sure I have a consistent worldview when I use the word "theory" and apply it in my report. Reading through this section of Chapter 7, I am surprised to find myself more on the positivist end than the interpretive end of defining theory. Perhaps this is so because, in the larger scheme of things, I would prefer a mixed-methods approach to the study. Although I'm not measuring anything in this project, I hope to open the door for measuring variables if this study is compelling enough for further investigation.
In the next section of Chapter 7, Charmaz compares constructivist grounded theory to objectivist grounded theory, and I believe I share more the of constructivist's perspective, especially in that one participant's narrative differs slightly between interview data and blog data, mostly in tone. I am a bit surprised to discover that some withhold information from the interview that was revealed (earlier) in the blog.
Since I haven't developed my grounded theory yet, I will stop with a reflection on "Theorizing in Grounded Theory" on page 133. Charmaz shares how researchers view or define the theory of grounded theory:
- An empirical generalization - I think I may go down this path. To what extent, I don't know.
- A category - I believe I created a category (a group of English language teachers or sojourners) before grounded theory, but I'll need to investigate this category with the my theory.
- A predisposition - This is similar to "an empirical generalization," and I'm unsure at this point which of the two is stronger.
- An explication of a process - This was my intention for the project, but I believe my data may only reveal a partial explication
- A relationship between variables - There is a good possibility for my grounded theory to do this.
- An explanation - Of course my grounded theory will explain something. It will most likely be a partial explanation. To declare a whole explanation is impossible for an exploratory study like mine.
- An abstract understanding - I hope not. With so much data, I believe I can analyze certain categories more deeply for a grounded theory that is an abstract understanding.
- A description - I hope this is the by-product of my grounded theory. I want to shed more light on the experiences of my target population.
Saturday, November 23, 2013
The Long Road to Analysis
In late September, I completed my data collection, which actually ended in early August when including the removal of the 6th participant. In early October, I met with one of my dissertation co-chairs to discuss the data and what to do with it. It was agreed that I should code for themes with a date set in late October, which I didn't believe I could meet because my full-time job was becoming more hectic and the added stress would take away from productive time for my dissertation. While that was true for a week or so, I was able to meet the deadline because there was a long enough lull in October for me to complete coding for themes.
Although only 10 themes were requested, I submitted 11 themes that stood out among all the participants. I also included smaller themes that only emerged across one or some of the participants. Once my co-chairs received my themes, I knew that I wouldn't hear from them soon because I sent them a lot more than probably both parties anticipated. Additionally, I submitted the themes right before a busy conference season and shortly after the midterm week of the fall semester.
To bide my time, I took the suggestion from the same co-chair to revise chapter 3, in which I proposed my research methods. Now that the data collection was finished and the data analysis had begun, I could change the whole chapter from future tense to past tense. I also deleted the report on my pilot study from the chapter because the pilot study was not approved by IRB, thus prohibiting me from using and publishing the data.
I got a little anxious a few weeks later when I felt like I wasn't being productive with my dissertation. I couldn't move forward with the data analysis because I hadn't received feedback from my themes yet. To feel productive, I started looking for scholarly journals to inspire me to write about my seemingly unique data collection process as it regards to social media. I have received feedback from a few peers that believe it is worth writing about for publication. In addition, I wanted to start engaging social media more directly too.
Just as I was gaining steam with these projects, I got an automated message from IRB asking for an annual update on the project. I hadn't realized that it was almost a year since I started filling out the IRB application. Because data collection was over, I didn't expect IRB to find any issues with my progress. I was wrong. On my application, I didn't describe my member checking process that I planned to initiate after I completed data analysis. Although I implied it in the email scripts and interview questions, it was not made clear in the application. Understandably, IRB loves clarity. Fortunately for me, it only took about a week to resolve this issue. A few days later, I received feedback about my themes, and it was a joyous occasion.
Getting the green light to continue my analysis was one reason for my elation, but another was that the feedback was more helpful than I expected as a guide. I could instantly plan a strategy for analysis that would take me to forming a grounded theory in line with some established models and theories from the literature. It took me a good week to calm down from this euphoria before I could resume analysis. Another reason for the week delay was yet another hectic week at my job.
Now that Thanksgiving week has arrived, I will have plenty of time to get a large chunk of the deeper analysis completed. I am now projecting to complete these stage of analysis by the second week in December, leaving me the time up to Christmas to determine if I have enough evidence and a strong enough argument for a grounded theory. I am certain that I have enough evidence, but I'm not as confident with my argument yet, and that's what this next stage of analysis is all about.
You don't need to guess what I will be thankful for this year's Thanksgiving! All in all, I am still enjoying the majority of this dissertation process. The key to this enjoyment is my passion for the project and already realizing practical implications through social media and my current full-time job helping faculty with professional and curriculum development.
Although only 10 themes were requested, I submitted 11 themes that stood out among all the participants. I also included smaller themes that only emerged across one or some of the participants. Once my co-chairs received my themes, I knew that I wouldn't hear from them soon because I sent them a lot more than probably both parties anticipated. Additionally, I submitted the themes right before a busy conference season and shortly after the midterm week of the fall semester.
To bide my time, I took the suggestion from the same co-chair to revise chapter 3, in which I proposed my research methods. Now that the data collection was finished and the data analysis had begun, I could change the whole chapter from future tense to past tense. I also deleted the report on my pilot study from the chapter because the pilot study was not approved by IRB, thus prohibiting me from using and publishing the data.
I got a little anxious a few weeks later when I felt like I wasn't being productive with my dissertation. I couldn't move forward with the data analysis because I hadn't received feedback from my themes yet. To feel productive, I started looking for scholarly journals to inspire me to write about my seemingly unique data collection process as it regards to social media. I have received feedback from a few peers that believe it is worth writing about for publication. In addition, I wanted to start engaging social media more directly too.
Just as I was gaining steam with these projects, I got an automated message from IRB asking for an annual update on the project. I hadn't realized that it was almost a year since I started filling out the IRB application. Because data collection was over, I didn't expect IRB to find any issues with my progress. I was wrong. On my application, I didn't describe my member checking process that I planned to initiate after I completed data analysis. Although I implied it in the email scripts and interview questions, it was not made clear in the application. Understandably, IRB loves clarity. Fortunately for me, it only took about a week to resolve this issue. A few days later, I received feedback about my themes, and it was a joyous occasion.
Getting the green light to continue my analysis was one reason for my elation, but another was that the feedback was more helpful than I expected as a guide. I could instantly plan a strategy for analysis that would take me to forming a grounded theory in line with some established models and theories from the literature. It took me a good week to calm down from this euphoria before I could resume analysis. Another reason for the week delay was yet another hectic week at my job.
Now that Thanksgiving week has arrived, I will have plenty of time to get a large chunk of the deeper analysis completed. I am now projecting to complete these stage of analysis by the second week in December, leaving me the time up to Christmas to determine if I have enough evidence and a strong enough argument for a grounded theory. I am certain that I have enough evidence, but I'm not as confident with my argument yet, and that's what this next stage of analysis is all about.
You don't need to guess what I will be thankful for this year's Thanksgiving! All in all, I am still enjoying the majority of this dissertation process. The key to this enjoyment is my passion for the project and already realizing practical implications through social media and my current full-time job helping faculty with professional and curriculum development.
Monday, September 9, 2013
Diving Into Analysis
My sixth and apparently final participant has finally found time for the interview part of my data collection. Last week, I finally completed viewing about 40 of his online videos regarding teaching English in Japan and/or adjusting to living and working in Japan. The remaining 1000 videos were more about current events in Japan or general information about Japanese culture.
As the interviews take place over the course of the week, I hope, I will start the more in-depth phase of analysis of all of my participants' data. At the same time, I will begin writing my first draft of Chapter 4. Actually, I already wrote the first paragraph giving a general introduction to the participants. Besides the final interview, I perceive much time of this week spent on organizing my approach to analysis as I write Chapter 4.
Coincidentally, as I am diving into analysis of my data, I am also beginning my new weekly routine of swimming. Since I have started my full-time job, there are two things that I want to continue that abruptly stopped back in April: 1) using the university's facilities that I'm paying for, 2) getting some good exercise. Although this seems like aside to the rest of this blog, I believe that physical exercise may enhance my mental exercise for data analysis.
As I mentioned earlier, my preliminary analysis took part during data collection of the blog data. I created two very broad themes: 1) teaching English and 2) cultural adjustment. The golden ticket is when these themes overlap, which seems to happen often for some participants and not much for others. This is where I will look to develop a grounded theory. Although this seems easy, I'm dealing with the murky nature of my participants' beliefs, attitudes, and perceptions which are made more complex with own beliefs, attitudes, and perceptions. My dissertation chairs and committee will demonstrate how this gets even more complicated when my interpretation is interpreted by them. This is not science.
Another matter I need to consider is condensing my analysis into a limited number of pages for Chapter 4. My first 3 chapters are already longer than most other dissertations I have seen. I've been considering a page limit when describing the context and adjustment process for each participant. I have arbitrarily chosen 4 pages, thus 24 pages for all 6 participants. This does not include the general introduction or the cross-case analysis or the establishment of any grounded theory. I am the type of person who can write a lot and does not mind writing a lot, so I need to exert some type of self-control. I'd like to thank all of my writing teachers who brought me to this point. However, I didn't mention the quality of my writing. Perhaps you can take a guess by the mostly informal prose you see here.
As the interviews take place over the course of the week, I hope, I will start the more in-depth phase of analysis of all of my participants' data. At the same time, I will begin writing my first draft of Chapter 4. Actually, I already wrote the first paragraph giving a general introduction to the participants. Besides the final interview, I perceive much time of this week spent on organizing my approach to analysis as I write Chapter 4.
Coincidentally, as I am diving into analysis of my data, I am also beginning my new weekly routine of swimming. Since I have started my full-time job, there are two things that I want to continue that abruptly stopped back in April: 1) using the university's facilities that I'm paying for, 2) getting some good exercise. Although this seems like aside to the rest of this blog, I believe that physical exercise may enhance my mental exercise for data analysis.
As I mentioned earlier, my preliminary analysis took part during data collection of the blog data. I created two very broad themes: 1) teaching English and 2) cultural adjustment. The golden ticket is when these themes overlap, which seems to happen often for some participants and not much for others. This is where I will look to develop a grounded theory. Although this seems easy, I'm dealing with the murky nature of my participants' beliefs, attitudes, and perceptions which are made more complex with own beliefs, attitudes, and perceptions. My dissertation chairs and committee will demonstrate how this gets even more complicated when my interpretation is interpreted by them. This is not science.
Another matter I need to consider is condensing my analysis into a limited number of pages for Chapter 4. My first 3 chapters are already longer than most other dissertations I have seen. I've been considering a page limit when describing the context and adjustment process for each participant. I have arbitrarily chosen 4 pages, thus 24 pages for all 6 participants. This does not include the general introduction or the cross-case analysis or the establishment of any grounded theory. I am the type of person who can write a lot and does not mind writing a lot, so I need to exert some type of self-control. I'd like to thank all of my writing teachers who brought me to this point. However, I didn't mention the quality of my writing. Perhaps you can take a guess by the mostly informal prose you see here.
Saturday, August 10, 2013
The Last But Not Least Participant
After my web conferencing interview, I have completed interviewing three of the six participants who gave me their informed consent. Two of the remaining three participants have completed two of their three interview sessions, and I believe one will be done by the end of the weekend. I have not yet begun interviewing the sixth and perhaps last participant for my study because he has been on vacation for the past few weeks, but he should be back soon if not already.
The last participant provides an interesting approach to data collection as he is more of a vlogger than a blogger. (A vlogger is a video blogger.) Actually three of my participants have or had vlogs on their own YouTube channels, but this participant is the most dedicated towards his videos. Of the other two participants, one started vlogging but switched to blogging, and that is where most of my document data for him is found. For the most part, the other participant's videos support her blog, and she doesn't produce as many videos as she used to.
So how dedicated is this last participant to his online videos? I say very much so as I spent the last few weeks cataloging his videos, which basically means listing all of his videos in chronological order and initially coding them when their titles are directly relevant to my research questions. Unlike for most other bloggers, I did not date each video because to find the date, I have to play the video, and when I play a video, I lose my place on YouTube where I was cataloging videos. This isn't usually a problem, but, as of yesterday, I have cataloged 1036 videos!
He has more than 1036 videos because halfway through my cataloging I stopped listing repeated videos, which don't happen often, and a series of videos on sumo wrestling, which do not directly answer my research questions. If sumo wrestling videos dominated his channel, then I would have considered that they may, but they don't.
With 1036 cataloged videos, I have come to a dilemma. How can I possibly thoroughly code all these videos? Coding all these videos requires thoroughly transcribing all of them. The average length of these videos seems to be around 10 minutes. There are many that go beyond 15 and 20 minutes, but there are also some that are less than 5. If the average is truly 10 minutes, then that's 10,360 minutes of video. That 172 hours and 40 minutes! Now, when I took courses on qualitative research I had to practice transcribing recorded interviews. When I wasn't tired, I could transcribe about 20 minutes of raw data in 1 hour. So to transcribe 10,360 minutes would at least take me over 31,000 hours which is 518 hours. If I dedicated 8 hours a day to transcribing these videos, it would take me over 2 months straight, and that's if I didn't slow down! After that, then I could start coding. I wonder how many pages of text that would be.
Now, if I were rich or well-funded, I would hire an army of people to transcribe and code his videos. No!
So...I decided to not be as rigorous with analyzing his videos as I am with my other participants, who average about 10 videos each. This is not a case study, it's a multiple case study, and I want to spend roughly the same amount of time analyzing the data for each participant. Otherwise, this last participant would definitely dominate my time and perhaps my study.
Fortunately, fortunately, my initial coding turned up only 42 videos of the 1036 that directly answer my research questions. One of my other participants didn't even blog 42 times. Anyway, once I transcribe one of these videos, I will check how many pages long it is because nearly all of my bloggers didn't blog more than a page per post. I think I can handle a rough estimate of 420 minutes (7 hours) of videos to transcribe and code.
And I didn't even get to write about how I will collect and analyze the last participant's interview data. That's another interesting post for later.
The last participant provides an interesting approach to data collection as he is more of a vlogger than a blogger. (A vlogger is a video blogger.) Actually three of my participants have or had vlogs on their own YouTube channels, but this participant is the most dedicated towards his videos. Of the other two participants, one started vlogging but switched to blogging, and that is where most of my document data for him is found. For the most part, the other participant's videos support her blog, and she doesn't produce as many videos as she used to.
So how dedicated is this last participant to his online videos? I say very much so as I spent the last few weeks cataloging his videos, which basically means listing all of his videos in chronological order and initially coding them when their titles are directly relevant to my research questions. Unlike for most other bloggers, I did not date each video because to find the date, I have to play the video, and when I play a video, I lose my place on YouTube where I was cataloging videos. This isn't usually a problem, but, as of yesterday, I have cataloged 1036 videos!
He has more than 1036 videos because halfway through my cataloging I stopped listing repeated videos, which don't happen often, and a series of videos on sumo wrestling, which do not directly answer my research questions. If sumo wrestling videos dominated his channel, then I would have considered that they may, but they don't.
With 1036 cataloged videos, I have come to a dilemma. How can I possibly thoroughly code all these videos? Coding all these videos requires thoroughly transcribing all of them. The average length of these videos seems to be around 10 minutes. There are many that go beyond 15 and 20 minutes, but there are also some that are less than 5. If the average is truly 10 minutes, then that's 10,360 minutes of video. That 172 hours and 40 minutes! Now, when I took courses on qualitative research I had to practice transcribing recorded interviews. When I wasn't tired, I could transcribe about 20 minutes of raw data in 1 hour. So to transcribe 10,360 minutes would at least take me over 31,000 hours which is 518 hours. If I dedicated 8 hours a day to transcribing these videos, it would take me over 2 months straight, and that's if I didn't slow down! After that, then I could start coding. I wonder how many pages of text that would be.
Now, if I were rich or well-funded, I would hire an army of people to transcribe and code his videos. No!
So...I decided to not be as rigorous with analyzing his videos as I am with my other participants, who average about 10 videos each. This is not a case study, it's a multiple case study, and I want to spend roughly the same amount of time analyzing the data for each participant. Otherwise, this last participant would definitely dominate my time and perhaps my study.
Fortunately, fortunately, my initial coding turned up only 42 videos of the 1036 that directly answer my research questions. One of my other participants didn't even blog 42 times. Anyway, once I transcribe one of these videos, I will check how many pages long it is because nearly all of my bloggers didn't blog more than a page per post. I think I can handle a rough estimate of 420 minutes (7 hours) of videos to transcribe and code.
And I didn't even get to write about how I will collect and analyze the last participant's interview data. That's another interesting post for later.
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