The State of U.S. School Libraries By the Numbers Dr. Lesley Farmer California State University Long Beach

 Use data analytics to assess school libraries in the United States  Identify trends of school libraries in the United States  Identify high-performing U.S. school libraries Agenda

 There are over 100,000 K-12 schools (about 23% are non-public)  93% of public schools have a school library  97% of public school libraries have computers (95% have Internet access)  2/3 of public school libraries have both flexible and fixed schedules  ¼ of public school libraries are open to the public  Decisions may be made at the state and local levels Background Data about U.S. School Libraries

 2/3 of public schools have a full-time paid state certified school librarian (52% have a master’s degree)  Ratio of librarian to students: from 1:300 to 1:7000 by state  Certification bases: test, summer training, credential, master  Teaching basis: course to credential, experience  Standards: state, professional associations School Librarians in the U.S.

 What significant trends between 2009 and 2012 exist in United States school library programs?  What relationship exist between aspects of school libraries? Research Questions Based on AASL School Libraries Data

 Use American Association of School Libraries annual school library survey reports datasets ( )  Code survey variables  Compare school libraries by region and level  Apply descriptive statistics, Pearson correlation Method

Overall Data about School Libraries

Average Number of Hours per Week

Individual and Group Visits

Individual and Group Visits

Computers Elementary Middle School High School Northeast Midwest South West

 # of Group Visits per Week (.062 p<.001)  # of Instructional Hours (.128 p<.001)  Spending per Student (.037 p=.02)  # of Library Computers (.154 p<.001)  # of Books/Student (-.075 p<.001)  Currency of Books (.067 p<.001) Hours Meeting Teachers Correlations

 # of Instructional Hours (-.045 p=.004)  # of Hours Meeting Teachers (.154 p<.001)  # of Books/Student (-.176 p<.001)  Currency of Books (.120 p<.001) Computers Correlations

 DEPENDENT Variable: Academic Progress Index for California  Staffing  Online library catalog  Collection currency  Internet access  Online databases  Budget (and fund sources)  Reference help (Farmer & Safer, 2015) Decision Tree Predictable Variables

What do you DO with the data?  Descriptive statistics  Analyze workflow for efficiency  Reveal trends  Benchmark efforts  Control quality  Do cost-benefit analysis  Analyze student learning  Optimize scheduling  Optimize queuing 21