Exploring Peer Prestige in Academic Hiring Networks

Andrea WigginsOctober 18, 2007

Research conducted for the Masters Thesis Option Program

At the University of Michigan School of Information

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Evolution of the Research

Independent data collection just to have some “interesting” data to try out SNA , 12/2005

Used the data for exploratory analysis course project in Network Theory, 1/2006 - 4/2006

Presented course project as a conference paper at ASNA 2006 in Zurich, Switzerland, 10/2006

Spent 2006 - 2007 school year on lit review, data re-collection, analysis and writing

Defended thesis 4/2007

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Problem Statement

iSchools are defining an intellectual community identity as a new breed of

Members of the community must

establish an individual identity in alignment with the iSchool community identity.

interdisciplinary researchers.

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Practical Problems of Identity

Academic legitimacy Organizational survival

Student recruitment

Student placement

Development of scholarly community Publication Funding Interdisciplinary research

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What is an iSchool?

Interdisciplinary focus on information, technology and people, with diverse institutional characteristics

Rising from common roots in computer science, information technology, library science,

19 schools of information have self-identified as iSchools, forming the I-Schools Caucus 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.

information studies, and more

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Literature - Multidisciplinary Overview

Reviewed literature from sociology, management, physics, statistical mechanics

Topics included: Emergence of academic disciplines Adaptation and survival in academia Prestige in academic hiring networks Productivity and prestige Topics omitted from this presentation:

• Social networks• Graph-based ranking algorithms• Community structure in networks

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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

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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

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Prestige in Academic Hiring Networks

Burris (2004) In sociology, history and political science,

departmental prestige was shown to be 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

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Prestige in Academic Hiring Networks

Bedeian & Feild (1980) Found extensive cross-hiring among top

management programs, preference among hiring departments to choose grads from self-similarly ranked departments

Baldi (2005) In sociology, prestige of the PhD-granting

department was strongest determinant of prestige of initial job placements

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Prestige in Academic Hiring Networks

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

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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

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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 interdisciplinarity

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

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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?

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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, hiring diversity, and betweenness.

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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, hiring diversity, and betweenness.

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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

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Population

Faculty at 19 iSchools Merged Indiana’s 2 schools to maintain institution

as unit of analysis, leaving 18 iSchool institutions This confounds network statistics for Indiana

Full-time faculty with the titles Dean, Associate Dean, Professor, Associate

Professor, or Assistant Professor

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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.

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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

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Ranking Data Sources

US News & World Report graduate school ratings Peer prestige survey data collected in 2005,

reported in 2006

National Research Council graduate school ratings for CS Similar to USNWR, collected in 1993

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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

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iSchool Egos

Combined multiple ego networks, one for each iSchool, into one ego network An ego is a school for which faculty hiring data

was gathered (iSchool); an alter is a school whose graduate was hired by an ego (iSchool or not)

In ego networks, egos and alters are not equal Some network statistics like PageRank and

betweenness are not meaningful for alters because they are based on characteristics of graph topology that do not apply to alters

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Full iSchool Hiring Network

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Full CS Hiring Network

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Comparing Network Statistics

Network Characteristic CS Network iSchools Network

Nodes 123 152

Egos 29 18

Alters 94 134

Ratio of Alters to Egos 3.2 7.4

Edges 572 429

Average Degree 4.7 2.8

Loops 26 17

Total PhD Degrees 1121 674

Average Edge Weight 1.96 1.57

Density 0.038 0.019

Betweenness 0.021 0.019

Average Distance 2.2 2.3

Diameter 5 (random = 7) 4 (random = 11)

Clustering Coefficient 0.23 (random = 0.05) 0.15 (random = 0.08)

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iSchool Hiring Network Egos

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CS Hiring Network Egos

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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

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Betweenness Distributions

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iSchools - Self-Hiring

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CS - Self-Hiring

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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 iSchool 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)

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Discussion - Self-Hiring

Self-hiring can mean different things Hiring grads of other departments - PSU Intermediary employment - Paul Conway

Some reasons for self-hiring in iSchools: Limited availability of PhDs with specific expertise;

ALA accreditation must be maintained University as the unit of analysis: self-hiring can

represent greater interdisciplinarity due to hires from other departments

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iSchool Areas of Study

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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

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Analysis - Faculty Interdisciplinarity

Disciplinary diversity is operationalized using an information entropy measure on the distribution of faculty areas of study for each iSchool

Most diverse: Michigan, Syracuse

Most focused: Toronto, North Carolina, Georgia Tech, UC Irvine

Entropy measure may differentiate hiring strategies that favor diversity or subject focus

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Analysis - Graduates

Looked at the disciplines of the graduates of iSchool institutions who are now employed at iSchools to look for institutional “halo effect” Are the faculty from institution X from the iSchool? Does network prestige reflect directly on the

iSchool or on the larger institution?

Challenging to interpret Names of degrees have changed over the years

with the changes in focus, identity of iSchools Notable exception: Syracuse

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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/centrality measures,

based on each node’s direct connections

Weighted PageRank & betweenness network centrality measures based on position in

the larger network structure

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Hypotheses Revisited

There is no correlation between a node's USNWR rating and its network measures; specifically…

Hiring diversity: information entropy measure Based on weighted link structure of the network,

takes into account both the number of links to other schools and the weight of those links

Strongly affected by size of faculty - Indiana would be differently ranked if the department was the unit of analysis

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Analysis - iSchool Regression

Small subgroup has USNWR LIS ratings, 11 of 18 schools

Stepwise regression overfits; regression model on weighted PageRank, betweenness, hiring diversity & number of grads

These four variables explain 77% of the variance in USNWR ratings (F = 9.3, p < 0.01)

Reject Null Hypothesis 1

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Analysis - CS Regression

Stepwise regression validates the regression model on weighted PageRank, betweenness & indegree (very similar results with hiring diversity in place of 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

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Conclusions - Comparisons

Hiring network statistics reflect some aspects of peer prestige captured in USNWR ratings, more strongly in CS than iSchools More data, more established field

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Conclusions - Hiring in iSchools

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

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Looking Forward

Hope to re-collect iSchool data for longitudinal comparison and analysis as the

field develops would like to make a comparison data set for all

ALA schools, but this is very labor intensive

Submitting to iConference 2008

Could use suggestions for other rankings to compare to USNWR and other stats Preferably more inclusive (not just ALA schools!) Not based on scholarly productivity

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Acknowledgements

My committee, Dr. Mick McQuaid and Dr. Lada Adamic, provided invaluable mentoring and advice

Dr. Drago Radev and his associates, Sam Pollack and Cristian Estan, shared their CS hiring data set

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Thanks for listening!

Presentation slides available at:

www.slideshare.net/AniKarenina

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References

Adkins, D. & Budd, J. (2006). Scholarly Productivity of US LIS Faculty. Library and Information Science Research, 28(3), 374-389.

Bair, J. H. (2003). Hiring Practices in Finance Education. Linkages Among Top-Ranked Graduate Programs. American Journal of Economics and Sociology, 62(2), 429-433.

Baldi, S. (1995). Prestige Determinants of First Academic Job for New Sociology Ph.D.s 1985-1992. The Sociological Quarterly, 36(4), 777-789.

Bedeian, A. G. & Field , H. S. (1980). Academic Stratification in Graduate Management Programs: Departmental Prestige and Faculty Hiring Patterns. Journal of Management, 6(2), 99-115.

Burris, V. (2004). The Academic Caste System: Prestige Hierarchies in PhD Exchange Networks. American Sociological Review, 69(2), 239.

Gioia, G. A. & Thomas, J. B. (1996). Identity, Image and Issue Interpretation: Sensemaking During Strategic Change in Academia. Administrative Science Quarterly, 41(3), 370 - 403.

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References

Hildreth, C. R. & Koenig, M. E. D. (2002). Organizational Realignment of LIS Programs: From independent standalone units to incorporated programs. Journal of Education for Library and Information Science, 43(2), 126-133.

Long, J. S. (1978). Productivity and Academic Position in the Scientific Career. American Sociological Review, 43(6), 889-908.

Long, J. S., Allison, P. D., & McGinnis, R. (1979). Entrance into the Academic Career. American Sociological Review, 44(5), 816-830.

Long, J. S., & McGinnis, R. (1981). Organizational Context and Scientific Productivity. American Sociological Review, 46(4), 422-442.

Meho, L. I. & Spurgin, K. M. (2005). Ranking the research productivity of library and information science faculty and schools: An evaluation of data sources and research methods. Journal of the American Society for Information Science and Technology, 56, 1314-1331.

Small, M. L. (1999). Departmental Conditions and the Emergence of New Disciplines: Two cases in the legitimation of African-American studies. Theory and Society, 28(5), 559 - 607.