External and Internal Citation Analyses Can Provide Insight into Serial/Monograph Ratios when Refining Collection Development Strategies in Selected STEM Disciplines

Keywords: evidence summary

Abstract

A Review of:
Kelly, M. (2015). Citation patterns of engineering, statistics, and computer science researchers: An internal and external citation analysis across multiple engineering subfields. College and Research Libraries, 76(7), 859-882. http://doi.org/10.5860/crl.76.7.859

Objective – To determine internal and external citation analysis methods and their potential applicability to the refinement of collection development strategies at both the institutional and cross-institutional levels for selected science, technology, engineering, and mathematics (STEM) subfields.

Design – Multidimensional citation analysis; specifically, analysis of citations from 1) key scholarly journals in selected STEM subfields (external analysis) compared to those from 2) local doctoral dissertations in similar subfields (internal analysis).

Setting – Medium-sized, STEM-dominant public research university in the United States of America.

Subjects – Two citation datasets: 1) 14,149 external citations from16 journals (i.e., 2 journals per subfield; citations from 2012 volumes) representing bioengineering, civil engineering, computer science (CS), electrical engineering, environmental engineering, operations research, statistics (STAT), and systems engineering; and 2) 8,494 internal citations from 99 doctoral dissertations (18-22 per subfield) published between 2008-–2012 from CS, electrical and computer engineering (ECE), and applied information technology (AIT) and published between 2005-–2012 for systems engineering and operations research (SEOR) and STAT.
Methods – Citations, including titles and publication dates, were harvested from source materials and stored in Excel and then manually categorized according to format (book, book chapter, journal, conference proceeding, website, and several others). To analyze citations, percentages of occurrence by subfield were calculated for variables including format, age (years since date cited), journal distribution, and the frequency at which a journal was cited. Top journals for selected subfields were identified based on the percentages of authors citing them in each dataset and, for interdisciplinary journals, according to how often citations for them appeared in subfield groups.

Main Results – For each subfield group, distinct patterns emerged for both internal and external analysis in terms of format, currency, and preferred journals. Regarding format of material cited, journals were dominant for external citations and ranged between 40% of citations (CS) to 94% (bioengineering) of formats cited. Formats were more distributed for internal citations, with ECE, SEOR, and STAT exhibiting journal dominance (61%, 30%, and 59% of citations, respectively) and conference proceedings dominant in CS (43%) and AIT (30%). Regarding currency, almost all cited items (>98% for external citations and 96% for internal citations) were published within the last 50 years, with electrical engineering showing the highest percentage of materials cited within the past five years for external citations (47%). For internal citations, applied information technology illustrated the most use of materials in the five-year timeframe (46%). Top journals for each subfield in which only external data were analyzed include Journal of Biomechanics (bioengineering 54%), Engineering Structures (civil engineering 47%), Water Research (environmental engineering 60%). For CS and AIT, the top journal was Communications of the ACM (external CS citations 29%; internal CS 32%; internal AIT 36%). For electrical engineering, the top journals were Electronics Letters (21% external citations) and Proceedings of the IEEE (50% internal citations). SEOR was broken into three categories (systems engineering, SEOR, and operations research), with Systems Engineering being the top journal according to external citations for the subfield of the same name (48%) and Air Traffic Control Quality as the leading SEOR journal (25% internal citations only). Management Science (77% external citations only) was the top journal for operations research. Top STAT journals were Annals of Statistics (96% internal citations) and Journal of the American Statistical Association (60%). Science was the top interdisciplinary journal for external citations (10%) and IEEE: Transactions on Pattern Analysis and Machine Intelligence for internal citations (13%).

Conclusion – An approach to citation analysis integrating both internal and external components is useful for institutions aiming to develop balanced STEM collections as well as for collection assessment and budgeting purposes and enables adjustment of serial/monograph ratios to create custom local serial/monograph ratio “blends.” In this institution’s case, internal data suggested a 59:41 serial/monograph ratios versus an external data ratio of 75:25, which indicated that a blended ratio of 67:33 might be appropriate for this institution based on an average of both ratios. In the future, cross-institutional collaboration for external analyses would make it easier for institutions to focus on internal analyses in order to develop appropriate local serial/monograph ratio blends.

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Author Biography

Stephanie Krueger, Czech National Library of Technology
Head, Office of Specialized Academic Services
Published
2016-12-15
How to Cite
Krueger, S. (2016). External and Internal Citation Analyses Can Provide Insight into Serial/Monograph Ratios when Refining Collection Development Strategies in Selected STEM Disciplines. Evidence Based Library and Information Practice, 11(4). https://doi.org/10.18438/B8PD2V
Section
Evidence Summaries