Exploring Peer Prestige in Academic Hiring Networks

Slideshow Transcript

1. Slide 1: 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
2. Slide 2: 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.
3. Slide 3: Practical Problems of Identity Academic legitimacy  Organizational survival Student recruitment Student placement Development of scholarly community  Publication  Funding  Interdisciplinary research
4. Slide 4: 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.
5. Slide 5: 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
6. Slide 6: 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
7. Slide 7: 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
8. Slide 8: 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
9. Slide 9: 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
10. Slide 10: 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
11. Slide 11: 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
12. Slide 12: 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
13. Slide 13: 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
14. Slide 14: 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
15. Slide 15: 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)
16. Slide 16: 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?
17. Slide 17: 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.
18. Slide 18: 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.
19. Slide 19: 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
20. Slide 20: 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
21. Slide 21: 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.
22. Slide 22: 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
23. Slide 23: 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
24. Slide 24: 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
25. Slide 25: Full iSchool Hiring Network
26. Slide 26: Full CS Hiring Network
27. Slide 27: iSchool Hiring Network Egos
28. Slide 28: CS Hiring Network Egos
29. Slide 29: 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
30. Slide 30: Betweenness Distributions
31. Slide 31: iSchools - Self-Hiring
32. Slide 32: CS - Self-Hiring
33. Slide 33: 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)
34. Slide 34: iSchool Areas of Study
35. Slide 35: 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
36. Slide 36: 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
37. Slide 37: 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
38. Slide 38: 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
39. Slide 39: 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
40. Slide 40: 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
41. Slide 41: 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
42. Slide 42: Thanks for listening! Presentation slides available at: www.slideshare.net/AniKarenina