Using Analytic Tools with California School Library Survey Data

Abstract

Abstract

Objective — California school libraries have new state standards, which can serve to guide their programs. Based on pre-standard and post-standard library survey data, this research compares California school library programs to determine the variables that can potentially help a school library reach the state standards, and to develop a predictive model of those variables.

Methods – Variations of decision trees and logistic regression statistical techniques were applied to the library survey data in order to create the best-fit model.

Results – Best models were chosen within each technique, and then compared, concluding that the decision tree using the CART algorithm had the most accurate results. Numerous variables came up as important across different models, including: funding sources, collection size, and access to online subscriptions.

Conclusion – School library metrics can help both librarians and the educational community analyze school library programs closely and determine effective ways to maximize the school library’s impact on student learning. More generally, library resources and services can be measured as data points, and then modeling statistics can be applied in order to optimize library operations.