Objective collections evaluation using statistics at the mit libraries
This presentation is the property of its rightful owner.
Sponsored Links
1 / 21

Objective Collections Evaluation Using Statistics at the MIT Libraries PowerPoint PPT Presentation


  • 58 Views
  • Uploaded on
  • Presentation posted in: General

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.

Download Presentation

Objective Collections Evaluation Using Statistics at the MIT Libraries

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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

  • “There are three kinds of lies…”

  • Shortcomings of anecdotal evidence

  • New technology for dissemination enables new technology for evaluation


Introduction: Financial Issues

  • In the world

  • At MIT

  • In the MIT Libraries


Introduction: Library Collection

  • Size of collection

  • Focus of collection

  • Cancellation project feasibility


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?

  • Our budget commitments database

  • Publisher-distributed reports

  • Journal Citation Reports

  • Licensed databases

  • Journal web pages

  • Local Journal Utilization Report


Data Gathering: How?

  • Mostly manual

  • Some selective

  • Small team gathering for all librarians


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 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 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 spreadsheet


Example of spreadsheet

Lowest 50%: Cost per use > $20


Example of spreadsheet

Lowest 50%: Cost per use > $20

Lowest 20%: Cost per use > $50


Example of spreadsheet

Change the $20 per use criteria value…


Example of spreadsheet

…to a $50 per use criteria value.


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

  • 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

  • Trends from year to year

  • Eigenfactor/Article Influence Score

  • More LJUR data


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!

Contact:

[email protected]

(photo credit: Flickr user neilio)


  • Login