Exploring Peer Prestige in Academic Hiring Networks


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Masters thesis defense presentation abstract:

Why do we care about prestige rankings? What does this preoccupation say about our implicit understanding of prestige as a function of image and identity? For an academic community in which identity matters, prestige rankings reveal an important dimension of identity in community context. In the case of existing rankings for the emergent iSchools, interdisciplinary growth has rendered the community context incomplete.

Exploring indicators of prestige in hiring networks as they relate to measures of prestige presented in peer rankings such as US News & World Report rankings provides a new perspective on hiring and identity in the iSchools. This research collected data on the educational pedigrees of 693 full-time faculty at iSchools and constructed a hiring network of institutional affiliations, with connections between the schools based on the institutions from which current iSchool faculty received their PhD degrees. The study quantitatively and qualitatively compares the iSchool hiring network structure to a similar hiring network in the more established academic discipline of Computer Science, and uses regression on network prestige and centrality measures to explain the variance in USNWR ratings. The study projects inclusive prestige ratings for the full CS and iSchool communities, which reveal underlying similarities in the structure of the two networks. Analysis of additional hiring network features, such as faculty areas of study and self-hiring in the iSchools, demonstrates the interdisciplinary diversity of the emergent field of information and its constituent institutions.

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  • Exploring Peer Prestige in Academic Hiring Networks

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