收集预算分析的数据异常值排序:为未来分配

E. Brown, James Galbraith, J. Dixon, M. Tuttle
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引用次数: 1

摘要

寻找一种客观可靠的方法来分配年度馆藏发展预算是研究型图书馆长期面临的挑战。许多图书馆往往依赖于对所有或某些类型的基金应用标准通货膨胀增长等方法。这些方法倾向于维持和延续每年的资金优先次序。不断变化的校园需求,包括新的项目和课程,研究方法和教学的创新,以及新的校园范围内的战略优先事项,不断挑战我们克服收集分配惯性,如果不使用经验数据来测试预算假设,然后分配资源以满足不断变化的优先事项,则可能会出现这种惯性。面对新的校园范围内的优先事项,包括启动新的学术项目和校园范围内的多学科倡议,以及财政压力,如图书馆材料的预算削减和年度价格通胀,宾厄姆顿大学图书馆开发了一个系统来分析我们的收藏预算,并使用17个数据点评估我们的预算分配方法。我们的方法包括比较各数据点的项目资金排名,并利用排名差异来确定潜在的资金过剩和不足的项目。我们首先收集了2013/14-2016/17四个财政年度的所有图书馆支持项目的数据。数据来自图书馆内部和校园数据,外部为专著和期刊创建成本信息。图书馆数据包括专著、期刊和数据库的费用以及流通和馆际互借数据。校园数据包括教师的全职工作时间,授予的学位,按级别(本科生和研究生)划分的学生人数,以及按系或专业划分的课程时数。外部数据包括各学科领域的平均专著成本和连载成本。数据集创建后,每个数据类别按价值从高到低进行排序。然后,将专著、期刊和数据库的图书馆预算排名与所有数据点的排名进行比较。编制了一份汇总表,以确定资金指标过剩和不足的项目。摘要表还显示了所审查预算年度的趋势。通过统计一个项目在排名比较中被标记为“资金过剩”或“资金不足”的次数,我们确定了应该被检查为潜在资金过剩和资金不足的项目。通过分析和考虑定性措施,如项目对通识教育课程的支持,跨学科性质,以及对专著或期刊的依赖,我们将能够确定重新分配收藏资金的潜在领域,并更好地解决预期的校园课程和研究需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ranking Data Outliers for Collection Budget Analysis: Allocating for the Future
Finding an objective and reliable means of allocating annual collection development budgets is a perennial challenge in research libraries. Many libraries tend to rely on methodologies such as applying standard inflationary increases across all or some types of funds. These methods tend to maintain and perpetuate funding priorities from year to year. Changing campus needs, including new programs and curriculum, innovations in research methodologies and teaching, and new campus-wide strategic priorities constantly challenge us to overcome the collection allocation inertia that may set in if empirical data is not used to test budgetary assumptions and then allocate resources to meet changing priorities. Faced with new campus-wide priorities, including the launch of new academic programs and campus-wide multidisciplinary initiatives, as well as fiscal pressures, such as budget cuts for library materials and annual price inflation, the Binghamton University Libraries developed a system to analyze our collection budget and evaluate our budget allocation methodology using 17 data points. Our methodology involved comparing the rankings of program funds across data points and using disparity in rankings to identify programs that are potentially overand underfunded. We began by gathering four fiscal years of data, 2013/14–2016/17, on all library supported programs. The data was drawn from internal library and campus data, and externally created cost information for monographs and journals. Library data included costs for monographs, journals, and databases as well as circulation and interlibrary loan data. Campus data included faculty FTE, degrees granted, number of students by level (undergraduate and graduate), and course hours by department or program. External data included average monograph cost and serials cost by subject area. Once the dataset was created, each data category was ranked from highest to lowest in value. Then, library budget rankings for monographs, journals, and databases were compared to the rankings of all data points. A summary sheet was compiled to determine programs with overand underfunded indicators. The summary sheet also indicates trends over the budget years examined. By tallying the number of times a program was labelled as either “overfunded” or “underfunded” across the ranking comparisons, we identified programs that should be examined as potentially overand underfunded. Through this analysis and consideration of qualitative measures, such as a program’s support of general education courses, interdisciplinary nature, and dependence on monographs or journals, we will be able to identify potential areas for reallocation of collections funds and better address anticipated campus curriculum and research needs.
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