Tag Archives: quantitative analysis

Finding My Process

It’s a dissertator’s right of passage (I think): figuring out one’s process.

It’s messy.

Sometimes it’s downright disgusting, as the coffee cups pile up and the books spread out across your desk, mingled with piles of artifacts collected from teachers, your sweaty water bottle from this morning’s run, and half of an apricot that you put down and then completely forgot about when an idea struck you.


This process began (for me) with a couple of weeks of ToTaL confusion. The school year at my research site ended, and I wandered about unsure of what to do next. I:

  • went to a conference (Computers and Writing 2013, which was a blast), and talked about some of the preliminary stuff I was noticing during data collection.
  • exercised.
  • napped on and off in the afternoon, with the dog on me and a book open, pretending to read.
  • transcribed a couple interviews.
  • met with committee members, hoping that would inspire me to get moving, and asked one of them what in the world I should be doing with these mountains of data staring me in the face.
  • organized a happy hour outing.
  • had lunch with some friends.


Then I came to a realization. I imagine most dissertators have this happen to them at some point. The Realization can take many forms. Here are a few that I have experienced or that I have caught wind of from others:

  • Oh crap. I need to apply to that conference. Must find panel find chair write proposal send quickly!
  • Oh crap. I haven’t read enough about ________ (or _________) (or _________). I am stupid and know nothing how did anyone ever agree to let me do this.
  • Oh crap. I just got an email from a committee member asking me where that draft is. I have not written that draft. I better start writing that draft.
  • Oh crap. I’m gonna run out of funding. (Or: oh crap, I wanted to apply for that funding.)
  • Oh crap. My partner just got a job and we need to move.

My realization went a little like this: oh crap, I took an incomplete because I couldn’t find time to write a seminar paper in the midst of data collection, and now I need to write up findings from my quantitative data before the end of the summer.

Then a slightly less awful realization: hey, I could turn that into a dissertation chapter.

Then I decided to get. to. work.

Followed By

As soon as I decided it was time to get to work, I realized I have no idea how to work. I knew a few things needed to happen, like organization of some of my variables. And I knew I needed — NEEDED — to figure out how to work with the SPSS syntax for my statistical model. What followed was some serious procrastination while I organized, reorganized, coded, and recoded some of my quantitative data.

The problem at this stage: my brain could barely focus on one task at a time, because each task made me think of something else I needed to do. For example, every time I want to run a model to examine whether a variable is important to it, I need to clean up (or even create) that variable to include it in the model. Today this has happened to me twice. Every time it happens I have a tendency to leave the task I’m on to go do that, which means in any given span of two to three hours, I’ll leave my central task and not get back to it again until half a day has gone by. Very, very frustrating. And I know this isn’t the best way to work, it’s just that I haven’t figured out what the best way looks like yet.

And Eventually…

VICTORY! Not only did I get my model program to work this week, but I have some pretty interesting findings that I think I can say a lot about. For example, I discovered that teachers’ consultation with colleagues does seem to have an impact on (or at least explain a significant amount of the variance in) teachers’ digital practices in the classroom, but that this is only true for some of the most central teachers in the network — the ones with lots of nominations from their colleagues. I also discovered that this seems to hold even more true in teachers’ “close colleagues” network, meaning friendship matters a lot to teachers when it comes to influencing their practice. Wahoo! I have stuff to write about! (And just in time, too. That paper deadline looms.)


After a month of screwing around and trying to figure out what this dissertating nonsense looks like, I

processstarted writing some stuff down. At which point I discovered that not even this part of the process gets to remain the same. See, I’m what you might consider a “neat” writer. Even my messiest drafts look pretty clean if you look at them when they’re in progress. By this I mean, I don’t tend to write a lot of notes to myself when I write, nor do I tend to leave citations out during first passes. I tend to plug through a paragraph until I’m really happy with it, then move on, even if this means I’m going back and forth between reading and writing. This might extend to a three-page or even ten-page section, depending on the length of the piece, but I don’t leave chunks of self-commentary in drafts for later pickup.

I’ve been self-conscious about this for a very long time, because many of my colleagues do this frequently. Their drafts are marked up, cut up, divided into chunks with lots of meta-commentary. I’ve always wondered if I was a worse writer because I didn’t do as much reflection in writing, in the draft, as they did.

Well I can quit worrying about that, because now I’m doing a lot more of it, apparently. As I started writing what will be my first findings chapter, I realized that my old “plug on through till it’s good” strategy was NOT going to work if I wanted to get anything accomplished today. So I left notes to myself and to my committee members, questions and follow-ups for further analysis, and a few spots where I need to go back and add citations.

Just looking at this page makes me nauseated, because it reminds me of all the things I need to do before this piece can be finished (no wonder I used to avoid all that meta-commentary).

So I’m finding my process.
Which I’m discovering is, in and of itself, a process.
It’s going to take me a while.

Debugging a Program, Debugging my Brain

I have spent a number of days over the past few weeks grappling with my network data, trying to figure out how to run models that will tell me (theoretically) whether teachers’ behaviors are shaped by their network affiliations. More specifically, I’ve been randomizing participant IDs, developing variables, and adding variables to an influence model syntax for SPSS that someone else wrote.

*insert screeching halt sound here*

WHAT!? Let’s back up.

There are a few different ways I am using network data. My friends and colleagues are familiar with my general obsession with network graphics, but that’s only a small part (and, honestly, a somewhat misleading and distracting part) of analyzing networks. Don’t get me wrong. It’s fun. Check out, for example, this network graphic, which depicts the technology advice network at my research site:

a teacher tech consultation network

a teacher tech consultation network

This one, in particular, shows who teachers nominated as individuals they “consult” about technology and teaching. Nodes (dots, which represent teachers) are sized by in-degree (number of nominations received) and are colored based on an attribute variable (number of devices used in the classroom). The darker blue the node, the more devices used in that teacher’s classroom.

Setting aside the academic for a moment, these graphics are cool (I’m obsessed with them). But they can also tell me a lot about the social dynamics at my research site. I can see who is in the “center” (according to their colleagues) of the school’s tech network. I can see who receives the most nominations in the network, which can tell me a lot about where perceived expertise lies in the school. I can use this to inform interviews during data collection, to help me interpret interview and observational data afterwards, and to examine other networks (for example, the “close colleagues” network in the school). And all of that applies in the opposite directions, too.

But this is the icing on a very complicated cake. Beneath these graphics lie layers upon layers of statistical analysis possibilities — these are the layers of the cake that I have not discussed with my colleagues or in my conference presentations because I’m still trying to understand them. I’m good at graphics, and I picked up the graph theory elements of network analysis quickly. But my stats are shaky, and I don’t have much experience grappling with data (not to mention grappling with the sheer volume of data I collected for my diss).

Besides these badass graphics, one can also “create models” with network data, by which I mean, they can develop equations that predict the potential for one’s behaviors to be shaped by their social interactions. OR, equations that will predict how particular behaviors might lead one to choose particular types of friends or collaborators. OR, equations that will predict how individuals within a particular network cluster together into groups. And the list goes on and on.

The value of these models? I admit, I was skeptical when I first learned about this element of network science. I wasn’t in it for the quantitative models — I was in it for the power it would give my descriptions and analysis of teachers on a qualitative level. I wanted to know more about the people, not reduce their behaviors and relationships to mere numbers and equations. But over the past few months to a year of doing this work, I am beginning to see how these quantitative data reveal trends that I might have missed in my observations and conversations with teachers at the school. And these trends may help schools rethink how they lay out their schools, plan for professional development, or purchase equipment.

But in order to do this powerful analysis, I need to gain some basic programming skills. I have been staring at this screen all freakin’ day, folks…

today's work

today’s work

…trying to make that list of errors at the bottom diminish. Trying to, more specifically, incorporate one more variable into a program someone else wrote (which means learning their coding scheme and inserting my own code into it). This is something my counterparts over in Ed Measurement can do in a few swipes on the keyboard, but I’m learning the hard way, with syntax reference sheets and help tabs open in the background.

But I’m learning a lot, and it’s worth it. By debugging this program to meet my needs, I’m forcing myself to relearn the math (and thus the theory) behind the models, I’m gaining valuable statistical analysis skills that I will use in future studies, and I’m thinking about how my quantitative findings (assuming I ever get any actual findings) line up with what I saw and heard at my research site this semester.

And this is all valuable work, even if I feel like I haven’t accomplished much of anything today.

Because I’m debugging my brain. And as it happens, this is an important part of dissertating.