Ranking Data Outliers for Collection Budget Analysis: Allocating for the Future

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

2. About Binghamton and Libraries 13,700 undergraduate, 3,600 graduate students754 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

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)

4. Methodology – Data Collection • Four fiscal years spanning 2013-2017 were used. • 43 subjects/programs • Monographs, serials, database funds • 54 points of data per program. • Programs were consolidated for program level, funding.

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)

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.

7. Methodology - Ratios • Created ratios for all 54 data categories. • Narrowed down factors to identify meaningful ratios. (n=43)

8. Methodology - Ratios • Calculated amean 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.

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)

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.

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…

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)

13. Thank You! • Elizabeth Brown (Beth) • Director of Assessment and Scholarly Communicationsebrown@binghamton.edu607-237-1937 • James Galbraith (Jim) • Head of Collection Developmentjgalbrai@binghamton.edu607-777-4912 Katherine Scott/Pipe Dream Photographer:

14. References • KittiCanepie, “Fund Allocation Formula Analysis: Determining Elements for Best Practices in Libraries,” Library, Collections, Acquisitions, & Technical Services 31, no. 1 (March 2007): 21, https://doi-org.proxy.binghamton.edu/10.1016/j.lcats.2007.03.002. • 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): 299-300, https://doi-org.proxy.binghamton.edu/10.1016/j.acalib.2012.07.006.   • 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): 209-211, https://www-tandfonline-com.proxy.binghamton.edu/doi/pdf/10.1080/01462679.2013.792306.       • William H. Walters, “A Fund Allocation Formula Based on Demand, Cost, and Supply,” Library Quarterly 78, no. 3 (July 2008): 305, https://www-journals-uchicago-edu.proxy.binghamton.edu/doi/10.1086/588640. • Debbi Dinkins,  “Allocating Academic Library Budgets: Adapting Historical Data Models at One University Library,” Collection Management 36, no. 2 (March 2011): 128-129, https://doi-org.proxy.binghamton.edu/10.1080/01462679.2011.554163.