1. Ranking Data Outliers for Collection Budget Analysis: Allocating for the Future
Elizabeth Brown James Galbraith
LAC 2018, Houston, TX Session 14 12/6/2018

2. About Binghamton and Libraries
13,700 undergraduate, 3,600 graduate students 754 full-time, 293 part-time faculty
6 Transdisciplinary Areas of Excellence (TAEs), formed in 2013, 2018
Library Staff: 80 faculty/professional/paraprofessionals
2018 Total Collections Budget: $6,870,000
Books/Firm Orders: $797,000
Electronic: $2,900,000
Periodicals: $2,700,000
2018 Collections Statistics
2,409,043 volumes
200,000 print and electronic journals
358 databases
Founded in 1946 as a liberal arts college, Binghamton University has evolved into a highly ranked public doctoral research university. Serving 13,700 undergraduate students and 3,600 graduate students. As of fall 2017, Binghamton had 754 full-time and 293 part-time faculty. It is part of the State University of New York (SUNY) system.
University strategic priorities include growing our graduate, research and professional programs while maintaining our traditional strength in the liberal arts. In 2013, Binghamton developed a new approach to supporting faculty and research by creating five Transdisciplinary Areas of Excellence (TAEs). These include Citizenship, Rights, and Cultural Belonging, Health Sciences, Material and Visual Worlds, Smart Energy, and Sustainable Communities. In fall 2017 a sixth TAE in Data Science was established.
The Binghamton University Libraries (Libraries) have nearly 2.5 million print volumes; more than 200,000 print and electronic journals; and 358 databases. In the early 2000s, the library collection budget was relatively flat. In 2013 and 2014, the Libraries was given new funding to support the new TAEs. Starting in 2016, the collection budget was given inflationary increases (3% books and 6% journals and databases).
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3. How collections have been analyzed
Many studies use common data sets. (Canepi)
Choice of data used is subjective. (Walters)
Some studies advocate historical allocation and usage data. (Dinkins)
Data analysis includes percentage-based, weighted multiple variable, factor or regression analysis. (Catalino and Caninano)
Demand, supply, and cost categories analyzed. (Walters)
Ordinal scales and quartiles used. (Lyons & Blosser)
Historical spending and circulation statistics used. (Dinkins)
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4. Methodology – Data Collection
Four fiscal years spanning were used.
43 subjects/programs
Monographs, serials, database funds
54 points of data per program.
Programs were consolidated for program level, funding.
Data Sources
Software
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5. Methodology – Program Ranking
Subject areas were ranked by each data point and compared. 54 rankings total were created, 43 rankings were used. Flagged top and bottom quartiles. (approx. 10 programs)
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6. Collected Rankings Data (n=43)
High-level snapshot of program collection outliers.
Ranking totals were consistent over time period.
Social Sciences and Humanities are “monograph” funds while the journals and databases are dominated by STEM.
Some programs are clearly overfunded and underfunded.
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7. Methodology - Ratios
Created ratios for all 54 data categories. Narrowed down factors to identify meaningful ratios. (n=43)
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8. Methodology - Ratios Calculated a mean for each category. (n=43)
Calculated the ratios, then ranked the subject/program areas.
Flagged top and bottom quartiles. (9-10 programs each)
Used this as a second technique to check rankings data movement.
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9. Ratio Data Observations
Able to identify funds that are outliers. (Art and Comparative Literature)
Relative change or slope can be observed easily. (Management, Sociology)
“Service” teaching departments can be located. (Mathematics, English)
The relatively higher cost of STEM materials and their impact on the budget. (Chemistry, Physics)
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10. More observations Our budget reflects historical program activity.
More recently created departments and professional programs are less supported.
Applying fixed percentage increases based on format doesn’t allow for change.
Nuances in how programs are supported emerge.
New programs and initiatives (such as TAEs) are disruptive and affect existing program support.
Applying a fix percentage increase based on format is not an optimal way of allocating the budget
It helps keep you within budget, but… does not account for campus and library strategic plans, evolving University priorities, new programs and schools. Rich get richer, etc. – Does not help create an argument for more funds, except in terms of inflation.
One exception to this budgeting evolution was the allocation of a significant amount of collections funding for five (and later six) TAEs. Over a three year period, funds were allocated to each of the five TAEs to address collections needs identified from faculty requests and internal library collections discussions.
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11. What our data means for collections planning
Change the way we allocate our collection budget.
Consider factors such as need and campus growth.
Determine optimal allocations for each program.
Consider more active needs assessment and collaborate with other SUNY libraries.
We have to be willing to change – this study indicates we have been too conservative with our budget practices…
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12. Some Next Steps
Update ratios with most recent budget figures. (gauge impact of recent cancellation project)
Allocate new funds to firm orders. ($20,000)
Develop models for determining optimal allocations for subjects, types of content and formats.
Develop a new strategy for allocating the collection budget. (how to achieve the desired budget)
Update with newest budget figures (cancellation project)
Allocate new funds to firm orders ($20,000)
Develop a new process/strategy for the collection budget process
Determine optimal allocation ranges for major types of collections content and formats
Move beyond a comparative model for analyzing collections, for example, looking at subject funds in relation to each other
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13. Thank You! Elizabeth Brown (Beth) James Galbraith (Jim)
Director of Assessment and Scholarly Communications
James Galbraith (Jim)
Head of Collection Development
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Katherine Scott/Pipe Dream Photographer:

14. References
Kitti Canepie, “Fund Allocation Formula Analysis: Determining Elements for Best Practices in Libraries,” Library, Collections, Acquisitions, & Technical Services 31, no. 1 (March 2007): 21,
Lucy Eleonore Lyons and John Blosser, “An Analysis and Allocation System for Library Collections Budgets: The Comprehensive Allocation Process (CAP),” The Journal of Academic Librarianship 38, no. 5 (August 2012): ,  
Amy J. Catalano and William T. Caniano, “Book Allocations in a University Library: An Evaluation of Multiple Formulas,” Collection Management 38, no. 3 (June 2013): ,      
William H. Walters, “A Fund Allocation Formula Based on Demand, Cost, and Supply,” Library Quarterly 78, no. 3 (July 2008): 305,
Debbi Dinkins,  “Allocating Academic Library Budgets: Adapting Historical Data Models at One University Library,” Collection Management 36, no. 2 (March 2011): ,
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