Jacket Image
Format:
Paperback
ISBN:
9781783301614
Published:
Dimensions:
228mm x 152mm x 10mm
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Library Improvement through Data Analytics

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£59.95



This book shows how to act on and make sense of data in libraries. Using a range of techniques, tools and methodologies it explains how data can be used to help inform decision making at every level.

Sound data analytics is the foundation for making an evidence-based case for libraries, in addition to guiding myriad organizational decisions, from optimizing operations for efficiency to responding to community needs. Designed to be useful for beginners as well as those with a background in data, this book introduces the basics of a six point framework that can be applied to a variety of library settings for effective system based, data-driven management.

Library Improvement Through Data Analytics includes:

  • the basics of statistical concepts
  • recommended data sources for various library functions and processes, and guidance for using census, university, or government data in analysis
  • techniques for cleaning data
  • matching data to appropriate data analysis methods
  • how to make descriptive statistics more powerful by spotlighting relationships
  • 14 practical case studies, covering topics such as access and retrieval, digitization, e-book collection development, staffing, facilities, and instruction.

This book's clear, concise coverage will enable librarians, archivists, curators and technologists of every experience level to gain a better understanding of statistics in order to facilitate library improvement.

PART I: OVERVIEW 1. Introduction 2. Planning with Six Sigma PART II: SIX SIGMA STEPS 3. Defining the Project 4. Measure the Current Situation 5. Analyze Existing Processes 6. Improve or Introduce the Process 7. Control the Process PART III: A STATISTICS PRIMER 8. Cleaning Data 9. Getting Started with Statistics 10. Matching Data Analytic Methods to Data 11. Statistical and Survey Software for Libraries PART IV: CASE STUDIES 12. Access and Retrieval: Case Study 13. Benchmarking Library Standards: Case Study 14. Data Sets: Case Study 15. Digitization: Case Study 16. Ebook Collection Development: Case Study 17. Facilities: Case Study 18. Information Audit: Case Study 19. Instruction: Case Study 20. Knowledge Management: Case Study 21. Lending Devices: Case Study 22. Marketing Virtual Reference Services: Case Study 23. Optimizing Online Use: Case Study 24. Reference Staffing Patterns: Case Study 25. True Costs of Acquisitions: Case Study with Implications for Selection Practice
Dr. Alan M. Safer is a professor at California State University, Long Beach (CSULB) in the Department of Mathematics and Statistics. He received his PhD in Statistics from the University of Wyoming and his MS in Marketing Research from Southern Illinois University Edwardsville. He first came to CSULB as an assistant professor in 2000 and has been a full professor since 2010. Early in his career at the university, he created a MS degree in Applied Statistics and later a professional accelerated MS degree in Applied Statistics for industry students from companies such as Boeing, Raytheon, and Northrop Grumman. He served as the graduate advisor for 7 years, and in 2009 was awarded university advisor of the year at CSULB. Dr. Safer's research has been very interdisciplinary; he has over 25 publications in diverse statistical areas such as finance, library science, marketing, health science, linguistics, and forensics. His primary statistical research focus is data mining and quality control. In 2012, he was appointed coordinator of a national conference on quality control sponsored by the American Statistical Association. In the last few years, Dr. Safer helped create the Orange County/Long Beach chapter of the American Statistical Association and served as its vice president

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