This paper aims to define a group of institutions based on IPEDS data for use in making comparisons in the CUPA salary benchmarking system. This group should have similar qualities in terms of financial and size characteristics, but should be large enough to provide sufficient coverage of the disciplines in the CUPA benchmarking system. A group of institutions was defined that were fairly similar on several measures of institutional size and financial circumstance. The final group defined through this process was also more reasonable in terms of Carnegie and regional representation than the overall CUPA population.

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.
PNAIRP - 2010 Conference4
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
PNAIRP - 2010 Conference5

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
PNAIRP - 2010 Conference6

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
PNAIRP - 2010 Conference7

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
PNAIRP - 2010 Conference8

9. The resulting selection dashboard/supergraphic
PNAIRP - 2010 Conference9

10. Supplemental views: Carnegie 2005 Classes
PNAIRP - 2010 Conference10

11. Supplemental views: Geographic
PNAIRP - 2010 Conference11