Who are you?
No, really, take a moment right now, before you continue on with reading this post, to think about this question. Who are you?
I’ve been thinking a lot about autoethnography, which feels like a logical progression of my identity as a researcher. I’ve always been reflective, and the main theme of this blog has been reflecting and learning from my own experiences. My goal in these posts has always been equal parts learning for myself and sharing what I’ve experienced so that anyone who happens to read this blog might learn from my experiences too. Marshall et al. (2022) define autoethnography as “a reflexive approach to understanding the human condition through critical and engaged analysis of one’s own experiences” (p. 30). I believe that, in an informal way, this is what I’m trying to do with this blog.
In a recent discussion board in my qualitative inquiry class, the idea of autoethnography being vain or self-indulgent was raised. Perhaps it is self-indulgent to study oneself, but if we don’t, who will? I’ve learned so many lessons through studying myself in the writing of each post on this website. The best example to come to mind was in my recent post about having COVID over Christmas. In that post, I wrote:
Wow. I won’t lie, I started this with the intention of pointing out how unproductive I was over break, and how coming down with COVID derailed my whole two weeks of getting ahead, but honestly… seeing it written out like that, maybe it was good that my plans got derailed.“An Unexpected COVID Christmas“
Through the action of self-reflection and of writing about myself, I learned a lesson that I needed in that moment. Maybe it’s vain of me to think that my experiences or my blog can impact others, but I hope that maybe someone else who read that post was able to also learn something about themselves and about the benefits of taking breaks, whether they’re planned or unexpected.
But anyway… I say all of this as an introduction to the second exploration I’m undertaking from How to be an Explorer of the World by Keri Smith. I’ve flipped through the pages of this book several times now, looking for the explorations that speak to me and that I want to undertake. The number of blue post-its sticking from the pages of my book is growing daily.
Exploration #45: Self-Ethnography
Of all the pages I’ve flagged, my recent reflections on autoethnography lead me to Exploration #45: Self-Ethnography. And to be honest, the image across these pages also called to me. I like the visualization of the word “self-ethnography” written across one’s arm. It feels more symbolic than some of the other illustrations in this book, and I wonder if there’s even more to read into the lack of color on this page, when so many others feature that pop of orange.
This exploration asks the reader to “document in detail all of your movements, activities, behaviors, and conversations through the course of a week.” To be honest here, I almost changed my mind about choosing this exploration when I thought about the immensity of data that would entail. Thinking about all of the conversations I have when I’m working, all of the things that I do throughout the day before, during, and after work. Between three jobs, full time school, taking care of six animals and two fish tanks, spending time with husband, working on my hobbies… I honestly think that seeing everything I do over the course of a week laid out in front of me as data might cause a mental break, not to mention the potential privacy concerns of documenting any details of my conversations with faculty or students.
Lesson #1: There is such a thing as too much data.
Fortunately, before I turned the page, I saw the alternate option: “Choose one specific aspect of your existence to document.” Now, that I can do.
First, I have to say that I absolutely love the way that’s worded. The diction there is stunning.
Ah, but now, I faced a new dilemma. What aspect of my existence is worthy of documentation? I considered taking the suggestion in the book and documenting my steps; I do have a Fitbit, so it would be easy. But do I want easy? And what would I learn from that? I’m already pretty well aware of my appallingly low step count (c’mon, I work from home – the most walking I do is from the office to the backyard). Since I was already thinking about my Fitbit, I thought maybe my sleep could be an interesting aspect to document, especially because I have a habit of going to bed and watching television or reading my book for much too long before actually falling asleep. Documenting the time I crawl into bed and then letting my Fitbit tell me when I fall asleep and wake up could yield interesting results.
Ultimately, I decided to forego the Fitbit entirely and return to my roots as an English teacher. My first exploration focused on reading, so I figured why not focus on writing this time? I know that my keyboard gets quite a bit of use, why not find out just how much use?
I opened up another Google Sheets and wrote a couple of column headers: Date, Activity, and Word Count. I started Saturday, January 22nd and went a full week until Friday, January 28th.
I had to make some tough choices about what data to collect and what to exclude. Ultimately, I had to decide to collect only what I could feasibly collect without interrupting my work flow. I had to exclude my text messages as it was just not feasible to manually count words on my phone. I considered counting the number text messages, but when I logged into Sprint and saw how many texts I send on average, I tossed that whole medium right out the window. No cell phone writing. I similarly cut out my Teams chat messages to my colleagues. These go back to Lesson #1: There is such a thing as too much data, but also…
Lesson #2: Some data is just not feasibly collectable.
If I had more time and more manpower, I could have copied and pasted all of my messages from Teams into a Word document to count, but I couldn’t do that without taking too much time from my work during the week.
This turned out to be a fun opportunity to play with pivot tables in Google Sheets, which made it so easy to pull some takeaways out of the data without having to pull out the SPSS statistics software. This blurring between qualitative and quantitative inquiry took me by surprise, but considering the data I collected was numerical, it makes sense to use some quantitative methods to dig into it.
Lesson #3: The lines between quantitative and qualitative inquiry can get blurry.
Writing by Activity
Over the course of the week, I wrote a total of 12,839 words across 17 different activities. Three of those activities related directly to the Graduate Student Assembly, of which I am currently Social Media Officer (and running for President-Elect – elections are open now!). Two activities were blogs, one of which was the introduction to this blog and the other was a draft for a blog for Infobase. Three activities could be categorized as homework (Discussion Post Reply, ISDT 7325 Presentation and Presentation Script). Three activities were notes of some kind.
|Activity||SUM of Word Count|
|Discussion Post Reply||598|
|GSA President-Elect Candidate Statement||472|
|GSA Scheduled Social Media||180|
|GSA Social Media||27|
|ISDT 7325 Presentation||41|
|ISDT 7325 Presentation Script||934|
|Personal Social Media||310|
|Research Planning Notes||97|
|Webinar Planning Notes||342|
I also looked at how many times I engaged in each activity over the week. A dozen of the 17 activities had a count of only one. Email was unsurprisingly my most repeated activity with a total of 27, followed by my personal social media with a total of 9.
|Activity||COUNTA of Activity|
|Discussion Post Reply||4|
|GSA President-Elect Candidate Statement||1|
|GSA Scheduled Social Media||1|
|GSA Social Media||1|
|ISDT 7325 Presentation||1|
|ISDT 7325 Presentation Script||1|
|Personal Social Media||9|
|Research Planning Notes||1|
|Webinar Planning Notes||1|
Writing by Day
My daily word counts varied from my highest day on Saturday with 3,205 words to my lowest day on Thursday with only 234 words. This made sense, as Saturday I had more time for novel-writing, while Thursday was a workday full of meetings. If I’d been counting words spoken, those numbers would likely be reversed.
Lesson #4: Outside factors can have seen or unseen impacts on the data.
|Date||SUM of Word Count|
|Saturday, Jan 22||3205|
|Sunday, Jan 23||2747|
|Monday, Jan 24||2566|
|Tuesday, Jan 25||579|
|Wednesday, Jan 26||956|
|Thursday, Jan 27||234|
|Friday, Jan 28||2552|
Throughout the week, I thought about a couple of potential issues with the way that I collected the data.
I counted totals after I completed each activity, so it didn’t count any words that I wrote and then deleted in the process of writing.
I have four email accounts that I use regularly for different purposes, but I did not differentiate between accounts or purpose, though I did differentiate between my personal and GSA social media and between my personal and professional blog posts.
I would not be surprised to realize later on that there was some activity or writing that I missed in my counts. As I was counting each activity as they happened, it would have been easy to overlook something. I also didn’t have anyone else working on this exercise with me, so there wasn’t anyone to check my work or remind me to count.
Lesson #5: Having a co-author can catch things you’ve missed.
I’ve also considered how different the numbers could be depending on the week that I measure. This week, I didn’t have as much writing to do for course assignments, and my current research projects are in the data collection and analysis phase, not the writing phase. I also have only recently (finally) jumped back into writing my book, which accounted for 5,683 words or 44% of my total word count. If I’d counted words the week before, my totals likely would have been a lot lower as I wasn’t working on the book at that time. Next week will likely be different again depending on my homework. Would calculating a full month give a better picture? A year? Where does one draw the line?
Finally, in the spirit of full transparency, I have to say that counting my words likely had an effect on how many words I wrote. Watching my own word count grow throughout the week might have provided a bit of motivation to keep writing, especially when it came to my novel. Word counting is a common activity among writers for this reason.
Lesson #6: The act of collecting the data can affect the data.
As I think about this exercise over the week, I think I learned as much from what I didn’t record as from what I did. Several times throughout the day, I would think “Oh I just did a lot of typing, did I count all of that” only to realize that it was typing in the Teams chat, which I’d decided not to count. I honestly think that counting my Teams and text messages would easily double my weekly word count.
I knew that I do a lot of writing in my day-to-day activities, but it was still a fascinating activity to record it in a spreadsheet, especially when I looked at how my writing was spread across my different roles.
However, I think that the biggest effect of completing this exploration was in how attentive I was of my own activities through the week. Even though I was only counting words, the exercise made me pay close attention to everything that I did, especially on my computer, over the course of the week, and it has lead me to think more critically about how I spend my hours.
I think that now that I’ve completed this exploration, I will likely continue to keep a close eye on how many words I write, but maybe now I’ll also start to think a little more deeply about what those words are.