Hoarding is not collection development
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Making a Collection Count

Practical Librarian

This is the place that we put more serious discussions of librarianship.

Using Excel with Collection Development

excellibrarians1Holly and I are in the process of writing a new edition of Making a Collection Count. As we go through the chapters and start really re-thinking the content and updating this book, I really wanted to make the idea of using statistics more palatable to those folks who avoid math at all costs. I used to be the queen of math avoidance. As I got older, went to business school and started working in libraries, I realized that I am actually more mathematically inclined or maybe more comfortable with with data, than my fellow library people.

Aside from budgets, there are lots of ways numerical literacy helps in managing a collection. What I am talking about is really thinking in terms of expressing library functions and trends with hard data. Modern technology makes this much easier than back in the 1970s when I was trying to pass algebra. Calculators and my personal favorite, Excel, can make analysis of library collections an absolute dream.

So how do you get over your aversion to analysis in library science? Use a small example of something you love and work it to death. Start with a small piece of a shelf list of a particular collection. Practice with excel in organizing this spreadsheet in various configurations.

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Collection Analysis: Median vs. Average

There is more to understanding a collection age beyond average and thanks to Emma, who made a comment on my last collection analysis post, I thought it would also help to discuss median age in a collection. My experience has been that often “average” and “median” are used interchangeably (which is so very wrong!).  Median age of a collection really has some serious power in helping librarians talk about collection age.

First, let us get clear on the difference is between median age and average age of a collection. (Again, as I have done in previous posts, the best way to get a handle on the process is to use a small set of numbers until you feel comfortable.) The average is the sum of all the dates in the set divided by the number of items in the set. (If you are using Excel, it will be the @average function)

Here is the example of some publication dates:

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Collection Metrics Part 2: Dewey Call Numbers

spine labelsNow that we’ve talked about using publication dates, let’s talk about metrics that use call numbers. What do we still have in the collection after that big weed? It’s too big a chart to include here, but I can tell you that the top three subject area holdings in my 500s are:

1. 599 Mammals – 161 items
2. 551 “Geology, hydrology, meteorology” – 153 items
3. 523 “Specific celestial bodies and phenomena” – 139 items.

Now we need to find out where the holes in the collection are. Still sorted by call number, we look at the subject areas that have very few holdings and decide if they are popular enough to warrant buying more items in those areas. There isn’t necessarily a lot of quantitative data to help you make this decision. I feel like I’m aware enough of my community’s needs (curriculum, local clubs, common reference questions, collection policy) to make an educated guess about what subject areas we need more materials in and which ones we don’t. We also have collection management guidelines and a selection policy to follow.

The bottom three subject area holdings in the 500s are:
1. 562 Fossil invertebrates – 1 item
2. 565 Other fossil invertebrates – 1 item
3. 596 Vertebrata – 1 item

We have plenty of items on vertebrate fossils, but apparently very little on invertebrate fossils. Ok, we’ll try to buy a few.

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