exploring peer prestige in academic hiring networks
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Exploring Peer Prestige in Academic Hiring Networks
Andrea Wiggins
April 24, 2007
Submitted in partial fulfillment of the requirements for the Master of Science in Information degree
at the University of Michigan School of Information
Problem Statement
iSchools don't really know who they are as a community and are forming an intellectual identity as a new breed of
Members of the community must
establish an individual identity in alignment with the iSchool community identity.
interdisciplinary researchers.
Practical Problems of Identity
Academic legitimacy Organizational survival
Student recruitment
Student placement
Development of scholarly community Publication Funding Interdisciplinary research
What is an iSchool?
Relatively young and highly interdisciplinary, with diverse institutional characteristics
Rising from common roots in computer science, information technology, library science, etc.
19 schools of information have self-identified as iSchools, forming the I-Schools Caucus www.ischools.org/oc/ Members are expected to have substantial sponsored
research activity, engagement in the training of future researchers, and a commitment to progress in the information field.
Literature - Interdisciplinary Overview
Reviewed literature from sociology, management, physics, statistical mechanics
Topics such as: Emergence of academic disciplines Adaptation and survival in academia Prestige in academic hiring networks Productivity and prestige Social networks Graph-based ranking algorithms Community structure in networks
Emergence of Academic Disciplines
Hildreth & Koenig (2002) The prevalent survival strategies for LIS schools in
the 1980’s: merger with a larger partner or expansion into IT-related fields
Over half of the iSchools are represented as mergers or realignments
• Merger: Rutgers, UCLA• Realignment: Syracuse, Pittsburgh, Drexel, Florida State,
Michigan, Washington, Illinois, Indiana
Adaptation & Survival in Academia
Small (1999) Academic survival strategy to achieve
organizational legitimacy and stability underlies the way an emergent intellectual enterprise develops its identity
Gioia & Thomas (1996) Academic institutions undergoing strategic change
often use prestige ratings as an image goal to indirectly influence identity
Prestige in Academic Hiring Networks
Burris (2004) In sociology, history and political science,
departmental prestige was an effect of the department’s position in PhD hiring networks
Bair (2003) In finance graduate programs, the majority of new
hires in the top ten programs were graduates of those same top ten programs, suggesting academic inbreeding
Prestige in Academic Hiring Networks
Cawley (2003) Common understanding that most initial jobs for
economics PhDs are in lower-ranked departments than the one from which they received their PhD
Bedeian & Feild (1980) Found extensive cross-hiring among top
management programs, preference among hiring departments to choose grads from self-similarly ranked departments
Prestige in Academic Hiring Networks
Baldi (2005) In Sociology, prestige of the PhD-granting
department was strongest determinant of prestige of initial job placements
Long et al. (1979) In Biochemistry, pre-employment productivity
conferred no significant advantage in job placement
Productivity is not a good predictor of the prestige of job placement, but the prestige of the person’s last affiliation is
Productivity and Prestige
Long (1978) Employing department has a strong effect on
productivity, but productivity has only a weak effect on job allocations
Long & McGinnis (1981) Individuals perform to the expectations of their
current cultural context, irrespective of prior or later productivity
Productivity and Prestige
Adkins & Budd (2006) Evaluated productivity of LIS research faculty
through publication and citation rates, repeating prior studies
Meho & Spurgin (2005) Warn that increasing departmental
interdisiciplinarity and publication database incompleteness pose significant threats to validity of LIS faculty productivity studies
Studies and rankings only evaluate a portion of programs at iSchools with ALA accreditation
Social Networks
Travers & Milgram (1969) Tested theory of small worlds in social networks,
verifying that a chain of acquaintances between 2 people can be very short
Granovetter (1973) Theorized that the degree of overlap between
friendship networks of 2 people is determined by the strength of their tie
You’re more likely to be friends with your friends’ friends
Graph-Based Ranking Algorithms
Page et al. (1999) Defined PageRank, an algorithm to efficiently
compute objective rankings for large numbers of web pages based on network topology
An adaptation of the concept of peer review of the structure of web links
Community Structure in Networks
Burt (1976) & Burt (1977) Theoretical framework of stratification and prestige
in a social network Identifies community structure by topology Structural equivalence or near equivalence
identifies nodes playing similar roles in the network
Numerous physical sciences articles on community-finding algorithms Newman (2006), Guimera et al. (2004), Guimera &
Amaral (2005)
Research Question
Can network measures of centrality predict the peer prestige ratings that are a part of the community context of identity in an academic discipline?
Null Hypothesis 1
In the iSchool hiring network, there is no correlation between a node's LIS USNWR rating and its network measures; specifically, the number of graduates in the network from each institution, indegree, outdegree, total degree, weighted PageRank, and betweenness.
Null Hypothesis 2
In the CS hiring network, there is no correlation between a node's CS USNWR rating and its network measures; specifically, the number of
graduates in the network from each institution, indegree, outdegree, total degree, weighted PageRank, and betweenness.
Methods
Collected hiring data for iSchools based on where faculty earned their PhDs
Obtained similar hiring data for computer science departments
Collected statistics for faculties of the hiring affiliation networks
Regression on network centrality & prestige statistics to explain peer prestige ratings
Additional analysis related to self-hiring in iSchools and the areas of study of the faculty
Population
Faculty at 19 iSchools Merged Indiana’s 2 schools to maintain institution
as unit of analysis, leaving 18 iSchool institutions
Full-time faculty with the titles Dean, Associate Dean, Professor, Associate
Professor, or Assistant Professor
Egos & alters An ego is a school for which faculty hiring data
was gathered; an alter is a school whose graduate was hired by an ego
Sampling Frame & Sample
Sampling frame from faculty listings on iSchool web sites as of January 2007
693 faculty met sampling criteria
Manual data collection, 100% response rate Total of 674 PhD degrees in the sample
100% complete data for all PhDs year not available for other terminal degrees, such
as MLS, JD, MD, etc.
Network Data Sources
iSchool hiring network raw data iSchool web sites Faculty web sites and CVs UMI Dissertation Abstracts database
CS hiring network raw data Similarly collected, by Drago Radev and
associates
Ranking Data Sources
US News & World Report graduate school ratings Peer prestige survey data collected in 2005
National Research Council graduate school ratings for CS Similar to USNWR, collected in 1993
iSchool Data
Name, current faculty, title, PhD school, PhD year, PhD Dept/Program
Raw data from 2-mode to 1-mode Was: School A -> Person -> School B Now: School A -> School B, with edge weights
Combined multiple ego networks, one for each iSchool, into one ego network In ego networks, egos and alters are not equal;
some network statistics like PageRank and betweenness are not meaningful for alters
Full iSchool Hiring Network
Full CS Hiring Network
iSchool Hiring Network Egos
CS Hiring Network Egos
Analysis - Comparison
CS is a larger network by many measures, but both are small worlds with high clustering coefficients and small diameters
CS is more tightly connected among egos
Although there are more egos & faculty in CS network, the iSchool network has more nodes and greater hiring diversity
The only large nodes in CS are egos, but some alters are also large in the iSchool network
Betweenness Distributions
iSchools - Self-Hiring
CS - Self-Hiring
Analysis - Self-Hiring
26 of 29 CS egos engage in self-hiring
17 of 18 iSchools engage in self-hiring
On average, 13% of faculty in iSchools are self-hires
64% of self-hires graduated from the program that now employs them, 36% from other departments or schools
For most self-hires from an iSchool, the faculty had degrees related to library science (but not at UCLA)
iSchool Areas of Study
Analysis - Areas of Study
Faculty size matters < 25 usually represent 5 or fewer disciplines 25+ represent 8 - 12 disciplines Maryland is an exception
Distribution of faculty among disciplines varies widely - some iSchools very focused, others very diverse Focused: North Carolina has 1 person in
Bio/Health, 1 in Education, 7 in CIS, 15 in LS Diverse: Michigan has faculty in 11 of 13 areas,
more evenly distributed than in many schools
Hypotheses Revisited
There is no correlation between a node's USNWR rating and its network measures; specifically…
Indegree, outdegree, number of grads & total degree Straightforward prestige measures, based on each node’s
direct connections
Weighted PageRank & betweenness: network centrality measures based on network structure More complex measures, based on each node’s placement
within the larger graph topology
Analysis - iSchool Regression
Small subgroup has USNWR ratings, 11 of 18 schools
Stepwise regression overfits; regression model on weighted PageRank, betweenness & number of grads
These three variables explain 62% of the variance in USNWR ratings (F = 6.5, p = 0.02)
Reject Null Hypothesis 1
Analysis - CS Regression
Stepwise regression validates the regression model on weighted PageRank, betweenness & indegree
These three variables explain 79% of the variance in USNWR ratings (F = 31.7, p << 0.0001), all 3 variables reach at least p ≤ 0.01
Reject Null Hypothesis 2
Negative coefficient for indegree lowers ratings for schools with diverse hiring sources
Conclusions
Self-hiring in iSchools either encourages interdisciplinary diversity or fulfills specific needs for expertise Maintaining ALA accreditation requires hiring
faculty with degrees from a relatively narrow selection of schools
Faculty areas of study in iSchools are diverse, and hiring to support a unique academic focus is a strategy by which iSchools differentiate themselves with respect to the community
Conclusions
Hiring network statistics reflect some aspects of peer prestige captured in USNWR ratings, more strongly in CS than iSchools More data, more established field
Regressions on both networks required both centrality measures, which capture different aspects of social prestige, a very complex concept
Acknowledgements
My committee, Drs. Mick McQuaid and Lada Adamic, provided invaluable mentoring and advice
Dr. Drago Radev and his associates, Sam Pollack and Cristian Estan, shared their CS hiring data set
Many thanks to my husband Everett for his unwavering support of everything that I do
Thanks for listening!
Presentation slides available at:
www.slideshare.net/AniKarenina
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