The document discusses a visualization approach for identifying peer institutions for faculty salary benchmarking using CUPA data. It outlines the goals of finding a larger, meaningful peer set, and describes a method involving IPEDS data analysis and Tableau for creating interactive visualizations. The method includes multiple views and parameters to allow for a comprehensive analysis across various institutional characteristics.

1. A Visualization Approach to Peer Identification
October 5, 2010
Pacific Northwest Association for Institutional Research & Planning
2010 Conference

2. Context and goals
• Context
– Usual set of peers was too small to provide coverage
across disciplines in CUPA faculty salary survey
• Goals
– Find a large set of peers for discipline-level salary
benchmarking via CUPA based on meaningful criteria
– Process should be transparent and accessible
– Ability to make adjustments

3. Initial scope
• Institutions
– Active
– Degree granting
– Private not-for-profit institutions or Public institutions
using FASB
• Faculty Salary
– 9-Month equated contracts
– All faculty ranks total
• Student
– All students total

4. Small multiples
• Tufte
– Envisioning Information (1990) 1
– Illustrations of postage-stamp size are indexed by
category or a label, sequenced over time like the
frames of a movie, or ordered by a quantitative
variable not used in the single image itself.
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1 – Via http://www.infovis-wiki.net/index.php/Small_Multiples

5. Small multiples crosstab
• Few
– An Introduction to Visual
Multivariate Analysis
(2006)
– Adds another dimension to
Tufte’s model of small
multiples
– Enables additional
variables
– Better than 3D
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6. Multiple Concurrent Views with Brushing
• Few (2006)
– Presents multiple graphs and tables of the same dataset
– These views are linked such that selecting or clicking on a
range of values in one view highlights the same records in
other views
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7. Method
• IPEDS data compiled in SAS
• Selected variables
– Type of institution
• Sector
• Carnegie Classes
• Geographic and location
– Financial qualities
• Endowment
• Total expenses
• Tuition
– Size
• Number of students
• Number of faculty
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8. Method (cont)
• Flag for CUPA 2009-10 participation
• Calculated deciles for quantitative variables
– Noting position of my institution
• Exported to Tableau
– Build univariate and bivariate vizes
– Assemble into matrix
– Tableau 6.0
• Build parameters for each variable
– Actually two: one for upper and the other for lower limits
• Create a variable to combine the parameters and indicate if an institution fits
the conditions
• Add parameter controls to the dashboard/supergraphic
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9. The resulting selection dashboard/supergraphic
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10. Supplemental views: Carnegie 2005 Classes
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11. Supplemental views: Geographic
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