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Exploring Peer Prestige  in Academic Hiring Networks Andrea Wiggins October 18, 2007 Research conducted for the Masters Th...
Evolution of the Research <ul><li>Independent data collection just to have some “interesting” data to try out SNA , 12/200...
Problem Statement <ul><li>iSchools are defining an intellectual community identity as a new breed of </li></ul><ul><li>Mem...
Practical Problems of Identity <ul><li>Academic legitimacy </li></ul><ul><ul><li>Organizational survival </li></ul></ul><u...
What is an iSchool? <ul><li>Interdisciplinary focus on information, technology and people, with diverse institutional char...
Literature - Multidisciplinary Overview <ul><li>Reviewed literature from sociology, management, physics, statistical mecha...
Emergence of Academic Disciplines <ul><li>Hildreth & Koenig (2002) </li></ul><ul><ul><li>The prevalent survival strategies...
Adaptation & Survival in Academia <ul><li>Small (1999) </li></ul><ul><ul><li>Academic survival strategy to achieve organiz...
Prestige in Academic Hiring Networks <ul><li>Burris (2004) </li></ul><ul><ul><li>In sociology, history and political scien...
Prestige in Academic Hiring Networks <ul><li>Bedeian & Feild (1980) </li></ul><ul><ul><li>Found extensive cross-hiring amo...
Prestige in Academic Hiring Networks <ul><li>Long et al. (1979) </li></ul><ul><ul><li>In biochemistry, pre-employment prod...
Productivity and Prestige <ul><li>Long (1978) </li></ul><ul><ul><li>Employing department has a strong effect on productivi...
Productivity and Prestige <ul><li>Adkins & Budd (2006) </li></ul><ul><ul><li>Evaluated productivity of LIS research facult...
Research Question <ul><li>Can network measures of centrality predict the peer prestige ratings that are a part of the comm...
Null Hypothesis 1 <ul><li>In the iSchool hiring network, there is no correlation between a node's LIS USNWR rating and its...
Null Hypothesis 2 <ul><li>In the CS hiring network, there is no correlation between a node's CS USNWR rating and its netwo...
Methods <ul><li>Collected hiring data for iSchools based on where faculty earned their PhDs </li></ul><ul><li>Obtained sim...
Population <ul><li>Faculty at 19 iSchools </li></ul><ul><ul><li>Merged Indiana’s 2 schools to maintain institution as unit...
Sampling Frame & Sample <ul><li>Sampling frame from faculty listings on iSchool web sites as of January 2007 </li></ul><ul...
Network Data Sources <ul><li>iSchool hiring network raw data </li></ul><ul><ul><li>iSchool web sites </li></ul></ul><ul><u...
Ranking Data Sources <ul><li>US News & World Report graduate school ratings </li></ul><ul><ul><li>Peer prestige survey dat...
iSchool Data <ul><li>Name, current faculty, title, PhD school, PhD year, PhD Dept/Program </li></ul><ul><li>Raw data from ...
iSchool Egos <ul><li>Combined multiple ego networks, one for each iSchool, into one ego network </li></ul><ul><ul><li>An  ...
Full iSchool Hiring Network
Full CS Hiring Network
Comparing Network Statistics 0.15 ( random = 0.08) 0.23 ( random = 0.05) Clustering Coefficient 4 ( random = 11) 5 ( rando...
iSchool Hiring Network Egos
CS Hiring Network Egos
Analysis - Comparison <ul><li>CS is a larger network by many measures, but both are small worlds with high clustering coef...
Betweenness Distributions
iSchools - Self-Hiring
CS - Self-Hiring
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-h...
Discussion - Self-Hiring <ul><li>Self-hiring can mean different things </li></ul><ul><ul><li>Hiring grads of other departm...
iSchool Areas of Study
Analysis - Areas of Study <ul><li>Faculty size matters </li></ul><ul><ul><li>< 25 usually represent 5 or fewer disciplines...
Analysis - Faculty Interdisciplinarity <ul><li>Disciplinary diversity is operationalized using an information entropy meas...
Analysis - Graduates <ul><li>Looked at the disciplines of the graduates of iSchool institutions who are now employed at iS...
Hypotheses Revisited <ul><li>There is no correlation between a node's USNWR rating and its network measures; specifically…...
Hypotheses Revisited <ul><li>There is no correlation between a node's USNWR rating and its network measures; specifically…...
Analysis - iSchool Regression <ul><li>Small subgroup has USNWR LIS ratings, 11 of 18 schools </li></ul><ul><li>Stepwise re...
Analysis - CS Regression <ul><li>Stepwise regression validates the regression model on weighted PageRank, betweenness & in...
Conclusions - Comparisons <ul><li>Hiring network statistics reflect some aspects of peer prestige captured in USNWR rating...
Conclusions - Hiring in iSchools <ul><li>Self-hiring in iSchools either encourages interdisciplinary diversity or fulfills...
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Exploring Peer Prestige in Academic Hiring Networks Brown Bag

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A brown bag presentation of the results of my completed masters thesis research, delivered at the Syracuse University School of Information Studies on 10/18/07. Changes from thesis defense include revised results and added analysis of diversity through information entropy measures.

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  • Transcript of "Exploring Peer Prestige in Academic Hiring Networks Brown Bag"

    1. 1. Exploring Peer Prestige in Academic Hiring Networks Andrea Wiggins October 18, 2007 Research conducted for the Masters Thesis Option Program At the University of Michigan School of Information
    2. 2. Evolution of the Research <ul><li>Independent data collection just to have some “interesting” data to try out SNA , 12/2005 </li></ul><ul><li>Used the data for exploratory analysis course project in Network Theory, 1/2006 - 4/2006 </li></ul><ul><li>Presented course project as a conference paper at ASNA 2006 in Zurich, Switzerland, 10/2006 </li></ul><ul><li>Spent 2006 - 2007 school year on lit review, data re-collection, analysis and writing </li></ul><ul><li>Defended thesis 4/2007 </li></ul>
    3. 3. Problem Statement <ul><li>iSchools are defining an intellectual community 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>
    4. 4. 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>
    5. 5. What is an iSchool? <ul><li>Interdisciplinary focus on information, technology and people, with diverse institutional characteristics </li></ul><ul><li>Rising from common roots in computer science, information technology, library science, </li></ul><ul><li>19 schools of information have self-identified as iSchools, forming the I-Schools Caucus </li></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><ul><li>information studies, and more </li></ul>
    6. 6. Literature - Multidisciplinary Overview <ul><li>Reviewed literature from sociology, management, physics, statistical mechanics </li></ul><ul><li>Topics included: </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>Topics omitted from this presentation: </li></ul></ul><ul><ul><ul><li>Social networks </li></ul></ul></ul><ul><ul><ul><li>Graph-based ranking algorithms </li></ul></ul></ul><ul><ul><ul><li>Community structure in networks </li></ul></ul></ul>
    7. 7. 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>
    8. 8. 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>
    9. 9. Prestige in Academic Hiring Networks <ul><li>Burris (2004) </li></ul><ul><ul><li>In sociology, history and political science, departmental prestige was shown to be 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>
    10. 10. Prestige in Academic Hiring Networks <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><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>
    11. 11. Prestige in Academic Hiring Networks <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>
    12. 12. 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>
    13. 13. 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 interdisciplinarity 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>
    14. 14. 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>
    15. 15. 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, hiring diversity, and betweenness. </li></ul>
    16. 16. 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, hiring diversity, and betweenness. </li></ul>
    17. 17. 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>
    18. 18. 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><ul><li>This confounds network statistics for Indiana </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>
    19. 19. 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>
    20. 20. 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>
    21. 21. Ranking Data Sources <ul><li>US News & World Report graduate school ratings </li></ul><ul><ul><li>Peer prestige survey data collected in 2005, reported in 2006 </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>
    22. 22. 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>
    23. 23. iSchool Egos <ul><li>Combined multiple ego networks, one for each iSchool, into one ego network </li></ul><ul><ul><li>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) </li></ul></ul><ul><ul><li>In ego networks, egos and alters are not equal </li></ul></ul><ul><ul><li>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 </li></ul></ul>
    24. 24. Full iSchool Hiring Network
    25. 25. Full CS Hiring Network
    26. 26. Comparing Network Statistics 0.15 ( random = 0.08) 0.23 ( random = 0.05) Clustering Coefficient 4 ( random = 11) 5 ( random = 7) Diameter 2.3 2.2 Average Distance 0.019 0.021 Betweenness 0.019 0.038 Density 1.57 1.96 Average Edge Weight 674 1121 Total PhD Degrees 17 26 Loops 2.8 4.7 Average Degree 429 572 Edges 7.4 3.2 Ratio of Alters to Egos 134 94 Alters 18 29 Egos 152 123 Nodes iSchools Network CS Network Network Characteristic
    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 iSchool 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. Discussion - Self-Hiring <ul><li>Self-hiring can mean different things </li></ul><ul><ul><li>Hiring grads of other departments - PSU </li></ul></ul><ul><ul><li>Intermediary employment - Paul Conway </li></ul></ul><ul><li>Some reasons for self-hiring in iSchools: </li></ul><ul><ul><li>Limited availability of PhDs with specific expertise; ALA accreditation must be maintained </li></ul></ul><ul><ul><li>University as the unit of analysis: self-hiring can represent greater interdisciplinarity due to hires from other departments </li></ul></ul>
    35. 35. iSchool Areas of Study
    36. 36. 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>
    37. 37. Analysis - Faculty Interdisciplinarity <ul><li>Disciplinary diversity is operationalized using an information entropy measure on the distribution of faculty areas of study for each iSchool </li></ul><ul><li>Most diverse: Michigan, Syracuse </li></ul><ul><li>Most focused: Toronto, North Carolina, Georgia Tech, UC Irvine </li></ul><ul><li>Entropy measure may differentiate hiring strategies that favor diversity or subject focus </li></ul>
    38. 38. Analysis - Graduates <ul><li>Looked at the disciplines of the graduates of iSchool institutions who are now employed at iSchools to look for institutional “halo effect” </li></ul><ul><ul><li>Are the faculty from institution X from the iSchool? </li></ul></ul><ul><ul><li>Does network prestige reflect directly on the iSchool or on the larger institution? </li></ul></ul><ul><li>Challenging to interpret </li></ul><ul><ul><li>Names of degrees have changed over the years with the changes in focus, identity of iSchools </li></ul></ul><ul><ul><li>Notable exception: Syracuse </li></ul></ul>
    39. 39. 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/centrality measures, based on each node’s direct connections </li></ul></ul><ul><li>Weighted PageRank & betweenness </li></ul><ul><ul><li>network centrality measures based on position in the larger network structure </li></ul></ul>
    40. 40. Hypotheses Revisited <ul><li>There is no correlation between a node's USNWR rating and its network measures; specifically… </li></ul><ul><li>Hiring diversity: information entropy measure </li></ul><ul><ul><li>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 </li></ul></ul><ul><ul><li>Strongly affected by size of faculty - Indiana would be differently ranked if the department was the unit of analysis </li></ul></ul>
    41. 41. Analysis - iSchool Regression <ul><li>Small subgroup has USNWR LIS ratings, 11 of 18 schools </li></ul><ul><li>Stepwise regression overfits; regression model on weighted PageRank, betweenness, hiring diversity & number of grads </li></ul><ul><li>These four variables explain 77% of the variance in USNWR ratings ( F = 9.3, p < 0.01 ) </li></ul><ul><li>Reject Null Hypothesis 1 </li></ul>
    42. 42. Analysis - CS Regression <ul><li>Stepwise regression validates the regression model on weighted PageRank, betweenness & indegree (very similar results with hiring diversity in place of 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>
    43. 43. Conclusions - Comparisons <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>
    44. 44. Conclusions - Hiring in iSchools <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>
    45. 45. Looking Forward <ul><li>Hope to re-collect iSchool data </li></ul><ul><ul><li>for longitudinal comparison and analysis as the field develops </li></ul></ul><ul><ul><li>would like to make a comparison data set for all ALA schools, but this is very labor intensive </li></ul></ul><ul><li>Submitting to iConference 2008 </li></ul><ul><li>Could use suggestions for other rankings to compare to USNWR and other stats </li></ul><ul><ul><li>Preferably more inclusive (not just ALA schools!) </li></ul></ul><ul><ul><li>Not based on scholarly productivity </li></ul></ul>
    46. 46. Acknowledgements <ul><li>My committee, Dr. Mick McQuaid and Dr. 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>
    47. 47. Thanks for listening! <ul><li>Presentation slides available at: </li></ul><ul><li>www.slideshare.net/AniKarenina </li></ul>
    48. 48. References <ul><li>Adkins, D. & Budd, J. (2006). Scholarly Productivity of US LIS Faculty . Library and Information Science Research, 28(3), 374-389. </li></ul><ul><li>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. </li></ul><ul><li>Baldi, S. (1995). Prestige Determinants of First Academic Job for New Sociology Ph.D.s 1985-1992. The Sociological Quarterly, 36(4), 777-789. </li></ul><ul><li>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. </li></ul><ul><li>Burris, V. (2004). The Academic Caste System: Prestige Hierarchies in PhD Exchange Networks. American Sociological Review, 69(2), 239. </li></ul><ul><li>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. </li></ul>
    49. 49. References <ul><li>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. </li></ul><ul><li>Long, J. S. (1978). Productivity and Academic Position in the Scientific Career. American Sociological Review, 43(6), 889-908. </li></ul><ul><li>Long, J. S., Allison, P. D., & McGinnis, R. (1979). Entrance into the Academic Career. American Sociological Review, 44(5), 816-830. </li></ul><ul><li>Long, J. S., & McGinnis, R. (1981). Organizational Context and Scientific Productivity. American Sociological Review, 46(4), 422-442. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul>
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