Measure Up! Data Analysis Tools to Optimize Library Management Dr. Lesley FarmerCalifornia State University Long Beach

 Research data analytics to assess California school libraries, and identify variables to improve their impactData analysis statistics  Choosing data analysis tools Agenda

 What significant trends between 2007 and 2012 exist in California school library programs?  What is the profile of a consistently highly (and low) effective school library progarms?  What are the predictors for high – and low -- school library impact over time? Research Questions Based on 2007 and 2012 California School Libraries Data

 Trend analysis of California school libraries  Predictive models of impactful California school libraries, which might be generalizable  Increased use of data analytics to improve libraries Needs

 Use California State Department of Education annual school library survey reports datasets ( and )  Code survey variables: e.g., meet standard or not  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

Sample California School Library Reports Distribution

64 Independent Variables

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

 Kth nearest-neighbor (knn) is a clustering method that uses distances between variables to group observations together.  Those with smaller distances between them are assumed to be similar, so  looking closer at the individual clusters can potentially determine important characteristics. Clustering

 Measures the distance between two clusters  Observations with least differences are clustered  Joins “close” clusters so that resulting within- cluster variance is minimized Ward Method of Clustering

 Enhanced access: on weekends, summer  Book budget Important Ward-based Variables

 Measure distance between the centroids (means) of each cluster  Join 2 nearest clusters Centroid Method of Clustering

Centroid Cluster-based Variables Positive:  Access during breaks  Internet access  Online productivity tools  Reference help Negative:  No access before OR after school  No Internet access  No online library catalog  No “extra” funding

 Flowchart of decisions and possible consequences  Node=test, branch=outcome, leaf=decision  Path from root to leaf is classification rule  Split data into training set and test set  Select “information gain” attribute to separate data  Do tree pruning for optimal selection (aim for homogeneous class)  Useful for predictions Decision Trees

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

 Budget (and funding sources)  Collection currency  Online lib rary catalog  Reference help  # of books  Dependent variable: met standards or not C4.5 decision tree (more than binary splits) Important Dependent Variables

 Probabilistic statistical classification model  Measure relationship between categorical dependent variable and independent (continuous or categorical) variables  Regression line is nonlinear  Run with combination of main effects  Aim for best fit  Predicts outcome of categorical dependent variable Logistic Regression

 Backward Selection: start with all variables and remove insignificant ones  Forward Selection: start with 1 significant variable until model is complete  Stepwise Selection: add or remove a variable depending on making model better Main Effects: Different ways to determine the best logistic regression model

 Use to compare models  Distinguishes classifiers that are optimal under some class and sub-optimal classifiers  Plotting 2 classes: true-positive versus false-positive rates ROC (Receiver Operating Characteristics)

 DEPENDENT Variable: API  Staffing  Online library catalog  Collection currency  Internet access  Online DBs  Budget (and fund sources)  Reference help CART Best Model: Ultimate Important Predictable Variables

What data do you collect? 22 Circulation figures Patron usage Facilities usage Computer usage Internet usage Reference consultations and fill Library guides/bibliographies use Instructional sessions Website hits (including tutorials) Database usage vs cost ILL processing and turnaround time Ordering, processing, cataloging, preservation, weeding workflow and time Ebook usage vs cost Library software usage vs cost Staff scheduling Equipment maintenance and repairs

What tools do you use to collect data?  Surveys  Web statistics  Circulation statistics  Interviews and interviews  Observation  LibQual / LibPAS  Flowfinity  Document collecting 23

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  Optimize queuing 24

 Data: demographics, staff, resources, services  Use: trends over time, correlations between staff and resources/services,  Demographic correlations with staffing, resources and services  AASL membership correlations with staffing, resources and services AASL Longitudinal Data

Copyright Median by State

$/Student by Region

# of Books/Student by School Level

Techniques  Correlation analysis (for relationship between continuous variables)  Multiple Regression(continuous response variable), Logistic Regression(categorical response variable)  Decision Trees  Principle Components, Factor Analysis  Hypothesis testing (paired tests, two sample tests, ANOVA)  Chi-Square tests of independence (for relationship between categorical variables) 29

Graphs  Box Plots  Stem and Leaf Plots  Histograms/Bar Graphs  Pareto Charts  Pie Charts  Time Series Plot  Outlier assessment 30

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Stem-and-Leaf Plot 33

KM ANALYSIS APPROACHDATA ANALYTIC TOOLS Cause identification Fishbone diagram, correlation analysis, regression analysis, ANOVA, clustering, principal components Cost-benefit analysis / ROIPugh matrix, Pearson correlation Customer satisfactionRegression analysis, Likert techniques, chi square DecisionDecision tree, Pugh matrix Error and tolerance analysisPareto analysis, control chart Failure analysisPareto analysis, control chart, clustering Job analysisDemerit systems, flow chart Process capacity Quality analysisPugh matrix, control chart Quality controlControl chart, run chart Quantity analysisHistogram, run chart QueuingPoisson distribution ScalabilityProcess capability Time analysis Run chart, Poisson distribution, activity network diagram Work flow and process analysis Fishbone diagram, activity network diagram, flow chart, run chart

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