What information and statistical tests do I use to find out if moving biographies to their own special display actually increased their popularity? I want to take a rigorous approach that includes confidence levels.
The idea was that it would make the books easier to find and hence increase circulation. The analysis software available to us can hint at things, but it unfortunately does not incorporate the concept of statistical significance. Nor is it very clear about what data it is basing its results on.
However, more detailed database reports can be run. These can be tailored in many ways to show almost all specific collection data imaginable. I want to run some of these detailed reports and statistically analyze them in a spreadsheet to find out whether the data truly supports the decision to separate out biographies into their own area.
The class range is autobiographies and biographies, beginning at 920 and ending at the end of 921. Before Jul 2016, this range was in the usual position within the non-fiction collection. In Jul 2016, the range was taken out and placed in its own special display, near but not spatially integrated into the rest of non-fiction. So we have over a year of data for the new location.
I am starting with the idea of looking at biographies as a percent of total checkouts for a year, 07/01/2015-06/30/2016, before the change, versus a year, 08/01/2016-07/31/2017, after the change. I would leave out July 2016 because the books were being moved then which would cause the numbers to be off. I would normalize on total checkouts because the collection was weeded and decreased in size over those dates.
Three measurable factors would be total checkouts, total items, and checkouts per item. Three possible sets are overall non-fiction including biography, non-fiction minus biography, and biography alone.
Confounding factors are circulation varies seasonally, and there have been reductions in the overall number of non-fiction books, and people are checking out fewer books. Also, focus goes to retaining popular books, so remaining items might have higher circs per item.
If there is statistically significantly more activity after the change, I will consider it to be a valid move. If there is no change, then it is a pointless move. If there is a decrease, then it is a harmful move. Making changes is a big commitment and I want to find out a way to mathematically test the results of our choices.
Help me figure out what to normalize on, whether to compare month-by-month samples or just boil it all down to the overall numbers for a year before versus a year after, whether there is a better time frame than the one I proposed, and what is the appropriate statistical test to run once I've gotten the data sets.
Also if any librarians want to share qualitative, not quantitative, observations about what has worked at your library, I would love to hear it.
Brief answers are ok. Pointed in the right direction, I can do the research to fill in the rest.
I appreciate your help and I look forward to doing math! |
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