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Objective Collections Evaluation Using Statistics at the MIT Libraries. Mathew Willmott MIT Libraries ACS National Meeting and Exposition August 22, 2010. Overview. Introduction/Background Data Gathering Data Analysis Decision Process Applications Future Work. Introduction: Statistics.

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objective collections evaluation using statistics at the mit libraries

Objective Collections Evaluation Using Statistics at the MIT Libraries

Mathew Willmott

MIT Libraries

ACS National Meeting and Exposition

August 22, 2010

overview
Overview
  • Introduction/Background
  • Data Gathering
  • Data Analysis
  • Decision Process
  • Applications
  • Future Work
introduction statistics
Introduction: Statistics
  • “There are three kinds of lies…”
  • Shortcomings of anecdotal evidence
  • New technology for dissemination enables new technology for evaluation
introduction financial issues
Introduction: Financial Issues
  • In the world
  • At MIT
  • In the MIT Libraries
introduction library collection
Introduction: Library Collection
  • Size of collection
  • Focus of collection
  • Cancellation project feasibility
data gathering what data
Data Gathering: What data?
  • Cost
  • Usage
  • Impact Factor/Subject ranking
  • Papers published by MIT researchers
  • MIT-affiliated editors
  • Citations from MIT-authored papers
data gathering from where
Data Gathering: From where?
  • Our budget commitments database
  • Publisher-distributed reports
  • Journal Citation Reports
  • Licensed databases
  • Journal web pages
  • Local Journal Utilization Report
data gathering how
Data Gathering: How?
  • Mostly manual
  • Some selective
  • Small team gathering for all librarians
data analysis
Data Analysis

Based analysis on four main data categories:

  • Cost per use
  • Average subject ranking
  • Papers published by MIT researchers
  • Presence of MIT-affiliated editors
data analysis1
Data Analysis
  • Ranked journals in each category of data
  • Assigned a “point” to the lowest performing journals in each category:
    • Lowest 50% by cost per use
    • Lowest 33% by subject ranking
    • Lowest 50% by papers published
    • No MIT-affiliated editors
  • Each journal ended up with a score of 0 (high-performing) to 4 (low-performing)
data analysis2
Data Analysis

Data presented to librarian staff in Excel workbook:

  • All raw data
  • Sheets analyzing each category of data
  • Sheet assigning a score to each journal, with changeable criteria
example of spreadsheet1
Example of spreadsheet

Lowest 50%: Cost per use > $20

example of spreadsheet2
Example of spreadsheet

Lowest 50%: Cost per use > $20

Lowest 20%: Cost per use > $50

example of spreadsheet3
Example of spreadsheet

Change the $20 per use criteria value…

example of spreadsheet4
Example of spreadsheet

…to a $50 per use criteria value.

decision process
Decision Process
  • NOT used to make final cancellation decisions; important to note that there are other factors to be taken into account.
  • Used to identify candidates for cancellation that subject librarians would then examine more carefully.
applications
Applications
  • Faculty and other stakeholders are very data-driven; this process allows for clearer explanations and communications
  • Process encourages a big picture view across all disciplines
  • There are some caveats: can’t cancel much from one publisher, society packages aren’t comparable…
future work other data
Future Work: Other data
  • Trends from year to year
  • Eigenfactor/Article Influence Score
  • More LJUR data
future work
Future Work

Can be of use when not in cancellation mode:

  • Evaluate collections
  • Identify where money could be better spent
  • Identify which parts of the collection need better promotion
thank you
Thank you!

Contact:

[email protected]

(photo credit: Flickr user neilio)

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