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NASIG 2015 - The Postconference

I chose to attend a half-day postconference on using and understanding COUNTER 4 reports, which, if you know anything about stats collection for online resources, you know sounds like a potential torture session. Luckily Jennifer Leffler made it relatively painless.

I think one of the most useful things about this session was the chance to slow down and just focus on the methods of stats collection. So often I'm trying to grab stats quick to answer a question or make a point and I'm focusing more on the end result instead of the process going into it. This helped me realize that it's important not to skip the process, that it's just that - a process - so it will take time to get it right. But, at the same time, if I take some time to set up the process correctly, then going forward it should be faster and easier and not require all that time for set up. I have done some of this with stats collection, but hearing someone else's process and a more in depth overview of general considerations in stats collection has given me ideas for how I may want to adjust my process.

My main reason for attending this session, though, was that, as a public librarian, I've made some quick assumptions about which reports are useful for me and which aren't, so getting a quick run down of all the reports, what they report, and how they might be used was super helpful. For instance, I've pretty much written off journal reports because, unlike academic libraries, we don't really care about specific titles, just general usage. While I'm still probably not going to use journal reports in my routine reporting, it's good to know that if we're evaluating subscriptions or doing some activity where title level data is important, I know what I'm doing.

Also, it was good to finally have a long discussion about the meanings of regular searches, searches federated & automated, result clicks, and records views, because I was never very confident on which I should be using. I'm still sad they got rid of sessions in R4, but I understand now why it was a problematic number and how the numbers currently provided are, at least in theory, easier to count.

This was a lot of information to take in and I'm sure when I get back to work and start looking at my numbers I'll still have a million unanswered questions, but now at least I've contacted the hive mind and know that many others share my struggle.
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