Using Data Analytics for School Library Assessment and Improvement Dr. Lesley Farmer California State University Long Beach

Does this sound familiar?  I can’t find the articles I need!  The catalog says the book is there, but I can’t find it.  What does it take to get a new book on the shelf before it becomes old?  No one uses our self-check out system.  Should we subscribe to ebooks?  Why are kids just using Google and Wikipedia? 2

What data do you collect? 3 Circulation figures Patron usage Facilities usage Computer usage Internet usage Library guides/bibliographies use Instructional sessions Website hits (including tutorials) Database usage vs cost Ordering, processing, cataloging, preservation, weeding workflow and time Ebook usage vs cost Library software usage vs cost Staff scheduling Equipment maintenance and repairs And what tools do you use to collect data?

What do you DO with that data?  Descriptive statistics  Analyze workflow for efficiency  Reveal trends  Benchmark efforts  Control quality  Do cost-benefit analysis  Analyze student learning  Optimize scheduling And how do the data connect with library and school goals? 4

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 What significant trends between 2001 and 2011 exist in California school library resources, services, and usage? To what extent do these trends relate to school demographics, staffing, resources, services, and budgets?  What is the profile of a consistently highly effective school library (in terms of resources, services, and usage)? Concurrently, what is the profile of low-performing school libraries? What relationship do these have to school demographics, if any?  What are the predictors for high – and low -- school library impact over time? What relationship do these usage patterns have to school demographics, if any?  More specifically, does a significant difference exist in school library staffing, resources, services, and usage relative to the grade levels, enrollment, type of school (public vs. private), locale (rural/suburban/urban)? Research Questions Based on California School Libraries Data

 Use California State Department of Education annual school library survey reports datasets ( here)  Code survey variables: e.g., meet standard or not  Transform continuous data to normalize  Compare school libraries that meet state model school library standards baseline criteria with those who did not meet standards  Use several statistical techniques: clustering analysis, decision trees, logistic regression Method

64 Independent Variables

 Meet Standard or Not (binary)  API (Academic Performance Index)  Socio-economic API decile Dependent Variables

 Online library catalog  Internet access  Online DBs  Video DBs  Budget  Collection currency  Reference help  Dependent variable: met standards or not CART (Classification & Regression Trees) Important Independent Variables

 DEPENDENT Variable: API  Materials budget (and book budget)  Access in the evening  Number of books  Having DVDs  Having classified employees  Online subscriptions (including streaming services)  Textbook service CART Best Model: Ultimate Important Predictable Variables

Balanced Scorecard 12

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Decision Tree 14

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Failure Analysis 16

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