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