As you may have noticed, on Wednesday, 18 January 2012, Wikipedia didn’t work.Wikipedia and a wide range of other sites including blogs like BoingBoing went black and many other sites including Google and Flavorwire used censored logos and content in protest against legislation proposed in Congress to protect copyright. The two pieces of legislation, SOPA (Stop Online Piracy Act) and PIPA (Protect IP Act), would ostensibly block sites that illegally provide copyrighted content for free, but would also have a detrimental effect on access to websites and information legally and legitimately available on the Internet.
On Wednesday morning, I received one request from a colleague for clarification on why Wikipedia wasn’t available. For another user, I tried unsuccessfully to track down the posts I’d seen the previous day about how to make one’s blog go dark; the posts themselves were difficult to find because the blogs supporting them were on strike. For my own benefit, I found myself visiting sites I knew were down, simply to see what was up in place of the usual content. A couple library-themed online comics and most of the library-related blogs I follow had messages in protest and links to resources for learning more about the laws and contacting government representatives. I eagerly read the information posted on the website of the Electronic Frontier Foundation (EFF) and thoroughly enjoyed the LOLcats protest video with appropriate lyrics set to the tune of Don McLean’s signature song “American Pie.”
Recently, I blogged about analyzing qualitative interview data. Using a constant comparative method, themes emerged upon multiple readings of the data. Done by hand, the process was laborious, time-consuming, and highly instructive. In other words, I learned a lot. However, as in other areas of life (electric screw drivers, anyone?) having the right tool for the job can save an enormous amount of time, not to mention muscle power. Time and energy that might be spent in better ways, say in writing up the analysis or catching up on your professional reading, or even polishing off the last of your online Christmas shopping. So when I received over 50 pages of newly transcribed interview data, I decided it might be time to investigate tech tools to do the job more efficiently, even more precisely.
One of the most powerful graduate school experiences I had drove home the point that we all “see” the world through our own filters. In small groups, my classmates and I were asked to visit a local coffee shop, sit together, observe the environment for 20 minutes and write a description of what we saw. Then we were to share and compare our field notes. Of course, our notes shared similarities; after all we were in a small, communal space. We all noted the menu, the number of tables, and an overstuffed couch in the corner. However, we had each noticed different things. One of my classmates, who’d built furniture as a hobby, included a lengthy description of the chairs. Another, the mother of two children, focused on describing the play area at the back of the shop where the owner’s four year old was playing. Coming from a family of movie buffs, I described in detail the vintage film posters on the walls. In short, what we noticed revealed as much about ourselves as it did about our surroundings. In a way, it reminded me of that cartoon where a group of people were looking at a house. The real estate agent saw her commission; the young couple saw their first home; the roofer saw loose shingles: well, you get the idea.
After a week and a half away from work, the volume of unread messages in my e-mail inbox had shot up, as one might expect. However, careful clearing away of the clutter made opening my inbox far less daunting on my first day back. What was less manageable, though, was the “1000+” on my Google Reader and the loss of the Share functionality. Much as I wished to, I hesitated to delete or mark all new posts as read, analogous to an approach recommended by Danah Boyd for avoiding e-mail overload following a sabbatical. There were gems in that mountain of blogposts, I was certain. A simple slash-and-burn method of attacking the feed reader overload would have meant missing out on this jewel in particular: Katy Meyers’s post at GradHacker entitled, “Taking a Chance: My Blog is a Publication”.
It’s fall. The white, wet blanket of last weekend’s unseasonable snowstorm has melted; the sky is an unblemished, brassy blue; streaky orange, red, and yellow leaves spiral off and skitter underfoot, and the markets overflow with pumpkins of all varieties: long neck, sugar, Autumn Gold’s and miniatures. (How many ways are there to prepare pumpkin?)
Recently, I was asked to analyze and write a narrative for a qualitative interview data set. I hadn’t done the interviews myself, although I was familiar with the purpose, participants, and interview questions. So I started by reading, and re-reading the interview transcripts. While walking to the local grocer to get the ingredients for pumpkin muffins, and also to clear my head, I thought about making meaning from pages of qualitative data. It’s one thing to read about data analysis and quite another thing to actually do it. Which is probably why my advisor thought that it would be such good pre-proposal practice.
You may have heard the world is made up of atoms and molecules, but it’s really made up of stories. When you sit with an individual that’s been here, you can give quantitative data a qualitative overlay. – William Turner, 16th Century Scientist and Naturalist
As I mentioned last week, I have been working through a mountain of data that I believe I underestimated as I went in to this project. Having 10 participants for a pilot, I thought that it was going to be too small a sample size, or that it would not yield as much data as I needed. But humans are complex beings. Everyone has a story that at least spans the years they have lived, if not working in the complex histories of families and societies. So, from this one experience, I have many individuals with histories and perspectives that have yielded a ton of great work. So now, the question stands, is how to make sense of the rich qualitative data I’ve collected along with the quantitative that comes from pre-/post-surveys. I realized quickly that I was going to need to look to bolster my understanding of what unique challenges and opportunities mixed methods research poses.
One of my friends moved recently. She really likes her new digs in an historic house and loves the proximity to town center. My friend is a walker, so it was no surprise that she now makes regular jaunts to “her” coffee shop, a miniscule (by corporate standards), independently owned, convivial space with the requisite collection of slightly quirky customers. She’s come to know the servers and is starting to say hello to regulars, some of whom are her neighbors. In her personal life, my friend is gentle, quick to laugh, and a sympathetic observer of her fellow humans. In her professional life, she is a qualitative researcher, perceptive and well-versed in the art of eliciting other people’s perspectives and lived experiences in naturalistic settings.
Interviewing and observing participants where they work or live are staple tools of qualitative researchers. Often, interviews and observations take place in interior spaces; in offices, classrooms, or homes. Frequently, one or all parties are sitting down, engaged in a somewhat formal and dialectical exchange. In other words, the environment and actors are relatively static. By contrast, the tools of “street ethnography,” such as neighborhood walks and go-longs, are less frequently used. They take place in exterior spaces; sidewalks, neighborhood paths, or public spaces. Frequently, both or all parties are ambulatory, engaged in a much more informal and somewhat more egalitarian exchange.
As I work through a pile of data – interviews, documents, and yes, even some quantitative – it is slowly occurring to me that this is a lot to work through. During the collection phase, you fret and think about what happens if a participant drops out, or if they are not forthcoming with information, or if they do not complete your post-test. Upon completion, you sigh a big sigh of relief, and then start chugging away at what you have amassed. As I sat with my advisor today trying to sift through mountains of qualitative data, I started to feel overwhelmed at even the shortened profiles of participants that I created. There is a lot of information in each, and on top of that, I am looking to make connections to quantitative data in a way that shows a meaningful picture. And that’s where the white board came in…