Hoarding is not collection development
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Making a Collection Count

Another Baby Step in Collection Analysis

As we discussed in my post Using Excel in Collection Analysis, go slow and work a small data set into the ground until you feel comfortable. While you are in “learning mode”, use enough data to fit comfortably on the screen, so you can “see” everything at once. For this example, I am using 20 random records from my Juvenile Fiction collection and I am going to focus on evaluating age.

Average is a term thrown around a lot in data analysis. Calculating average is adding up all the dates and then dividing by the number of titles. In other words a couple of really old books can skew the average. So, for library and collection purposes, you need to look deeper. As you can see in my sample data set, the average age of this group of items is 1990.3 or for our purposes 1990. That seems really old, given we are starting 2013. I am now going to re-order the spreadsheet to organize the 20 records in Date Published order (oldest to newest).

sample data 1

In this particular group, 3 of the titles are almost 50 years old and another is a whopping 71 years old. I can feel the panic as everyone collectively gasp: “you can’t weed these classics just because they are old!”. Before everyone collectively panics and writes to tell me I am a no-good, censoring book burner, hell bent on destroying classics, relax!* The purpose of this sheet is to flag your attention to the collection. When I say “flag” your attention, that means investigate further. It doesn’t mean weed.

For this set, I am going to pull the items highlighted in RED as my oldest examples and the first thing I am going to do is check on condition. For Juvenile fiction, I expect paperbacks to hold up about 5 years and hardbacks will last maybe 10 years, if people are gentle. Your mileage may vary.

sample data2

Average age discussions are best used in the context of sections of the library. When you go through sections of the library and use pieces of the collection, it is easier to catch those needing attention. I particularly like using the average age when dealing with nonfiction areas of legal, business and medical.

Again, I want to remind everyone, what I consider hanging onto in my tiny library in Michigan, is not going to be the same as what goes on in your library. To give you an idea what I did for the above “red” titles, I weeded the  Ghostly Tales and bought new editions of the others.

Now go forth and play with the average of your collections and see what pops out!

Mary

* People have already written that shortly after Holly and I started Awful Library Books

Originally published at http://www.practicallibrarian.net/another-baby-step-in-collection-analysis on January 3, 2013.
updated 11/6/204