Open Access Tenure: Slog Slog Slog
Being a tenure track faculty is supposed to be all prestigious and shiny 24/7 isn’t it? Why else would we sign up?
Anyway, I did start a new project. Yes, another new project. Add it to the pile.
One of the things my department is interested in knowing is where and how our time is being used. Of course we can generally tell you what’s happening: we’re seeing less traffic at the reference desk, what we are getting is directional questions and a lot of those are from people who aren’t affiliated with the university (we’re open to the public and we’re in the middle of four hospitals in a major metropolitan area) and we’re doing more consultations and embedded teaching. But anecdata looks so much shinier when one has stats to back it up.
And, since yours truly is trying to wade through learning data, what better than to tackle a data set that was in house? It was Madame Department Head’s last charge to me before her retirement–to actually get the data out and do something with it.
So I went looking for the data. I have the annual paper summaries on my desk. I’m not worried about those. I can pull out monthly summaries and go to work. What I wanted was the day by day statistics. I checked with our staff person, who has been handling them for at least the last few years. We have everything back to August 2006, though we’re missing a pretty big chunk of 2007. I need to follow up with her about where those are. These are charts that were kept daily at the desk until September 2011.
Fortunately, right now, we have temporary paraprofessional help and a graduate student who are covering some of the desk hours with one of us backing them up. It lets me go slightly more face down in my inbox or whatever it is I’m trying to prep for when I’m not out and about. This week, I started them on transcribing the data.
It’s thankless work. I know, I’m doing some of it too. And because of the way the data was captured, I don’t always have a clear 1:1 relationship between who asked what kind of question. On the paper questions you would tick on one side for who asked the question and on the other side for what kind of question it was. Many times, it’s a one to one because we have questions only from one user group type. Where it’s not, I’ve asked them to make some assumptions as follows (look, I’m writing it down here so in six months I’ll be able to remember):
Assume Other questions are Directional/Technical first.
Assume Student/UIC Faculty Staff questions are the In-Depth first.
Assume Student questions are In-Depth before Faculty/Staff.
So if I have five questions marked for the hour, three are other, two are students, two are directional, two are ready reference, and one is in-depth it will look as follows
Student- Ready Reference
I’m assigning higher level questions to UIC patrons and lower level questions to external patrons. Because the data isn’t clean, I won’t be able to do a whole lot with that insofar as assuming x number of directional questions from UIC patrons etc. I do have clean 1:1 data for the past year. What I will have overall is the number of questions from each type of patron and each type of question. I can look at those questions over hour ranges, over days, weeks, day of the week, etc. Just seeing the number of in-depth reference questions will be interesting.
Because that only captures the desk, I’m also extracting the off desk statistics that each of the information services staff captures. That is going to have to come from the monthly summaries in the annual statistics, as until January we didn’t keep those electronically (a) and (b) we don’t worry about those as much on a day to day level. I’ll go through my email at the end of the week and count the number of email reference questions I’ve done, etc. I’d like to assign a time value (in consultation with coworkers) to each item and see where we’re spending time. It will be average: sometimes a mediated search takes an hour, sometimes it takes three days.
Lest you think this data won’t be used or this is a pointless exercise in me making charts, we’re already using it. Armed with charts and numbers from this past year, my Interim Dept Head and I had a meeting with our Assistant University Librarian to talk about how we’re using our time and to talk about what this will mean in short and long term. There are plans afoot. On a grander scale I’d like to show librarians that data analysis is a project they can tackle–along with showing you all my pitfalls so you don’t repeat them.
And then next week I start a Quantitative Analysis class. But I’ll tell you more about that once I get the syllabus.