Research Groups ’ Social Capital and PhD Students’ Performance: The Case of Slovenia

1. Research Groups’ Social Capital and PhD Students’ Performance: The Case of Slovenia Petra Ziherl, CATI d.o.o. Hajdeja Iglič, Anuška Ferligoj, University of Ljubljana

2. OUTLINE • Theoretical background • Data • Methods cluster analysis, structural equation model - SEM • Results • Discussion

3. THE AIM OF THE PROJECT An international research project (INSOC) in which researchers from Belgium, Germany, Spain and Slovenia study the effect of social relationships (social capital) of doctoral students with their colleagues in their research groups on their academic performance.

4. THE MAIN HYPOTHESIS AND THE AIM OF A STUDY • The main hypothesis is that PhD students’ success depends on characteristics of their research group, which consists of several experts from different areas. • Which of the social capital theories have more explanatory power in the process of knowledge creation in given circumstances?

5. THEORETICAL BACKGROUND:DEFINITION 1 • “Social” implies that it captures interaction between people. • “Capital” indicates that it should be understood as an asset of an individual or a group that comes from relations with others (Rothstein and Stolle, 2003).

6. DEFINITION 2 One of the most cited definitions belongs to Bourdieu: • Social capital is the sum of resources, actual or virtual, that accrue to an individual or group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition. Nevertheless, social capital acquires more than just membership in a certain group that is to change accidental social ties into ties, in which individuals recognize the liabilities to one another.

7. STRENGTH OF TIES • Granovetter’s strength of weak ties • Complex knowledge transfer and knowledge creation HYPOTHESIS 1: The stronger the ties between the PhD student and the other members of research group, the more successful (s)he is.

8. COHESION • Theory of cognitive balance • Coleman’s theory • Norms and sanctions HYPOTHESIS 2: The more cohesive the research group, the more successful the PhD student is.

9. GROUP HETEROGENEITY 1 BURT’S RANGE (1983, 1992) • Size of a network • Different people in the network • Quality of relationships HYPOTHESIS 3: The more heterogeneous the research group, the more successful the PhD student is.

10. GROUP HETEROGENEITY 2 • H.3a:The larger the research group, the more successful the Phd student is. • H.3b:The greater the number of people with whom the PhD student cooperates outside the “primary” research group, the more successful (s)he is. • H.3c: The greater the number of different institutions in which members of research group are employed, the more successful the PhD student is. • H.3d: The more structural holes exist in the PhD student’s network, the more successful (s)he is (Burt, 1983, 1992)

11. THEORETICAL MODEL

12. DATA COLLECTION Phase 1 (June – Sept. 2003): The doctoral student’s research group was defined by his/her supervisor. Phase 2 (Jan. – April 2004): Social ties were measured: • among all members of the research group (complete networks); • between the doctoral student and his/her research colleagues (ego-centered networks).

13. Data Collection – Phase 1

14. Data Collection – Phase 2

15. Types of support Types of support, from members of the research group: • Advice (work related problems), • Co-operation (e.g., on a project), • Technical (e.g., regarding data, software), • Socializing (outside work context, e.g., doing sports), • Emotional (e.g., lack of motivation).

16. Ego-centered networks vs. Complete networks In this presentation analysis on the level of the whole research group will mostly be presented (complete networks). Social relationships between the doctoral student and his/her colleagues (egocentered networks) or between mentor and PhD student (dyadic relations) have also been considered.

17. DATA 1 Social relation based on complete cooperation networks: • Consider all situations of the past year (that is, since 1 November 2002) in which you co-operated with your colleagues, e.g., working on the same project, solving problems together and so on. Minor advice do not belong to this type of co-operation. How often have you been co-operating with each of your colleagues? Scale: from 1 (not in the past year) to 8 (every day), or 0, if the respondent does not know the person

18. DATA 2 • Excluded all research groups in which mentor or PhD student did not respond • Excluded all research groups which did not attain response rate over 60% • Excluded all missing members with low frequencies of cooperation • Other missing members were included, where the ties from him/her to the others were estimated with the values of answers given by the respondents to him/her • Thus, 23 research groups remain for the analysis

19. Representativity of the sample 1

20. Representativity of the sample 2

21. VARIABLES USED IN ANALYSES Tie strength average frequency of cooperation between PhD student and other members in research group Cohesion average frequency of cooperation between all members of research group Group Diversity: Size of a research group (original one) Number of different institutions that people from research group are employed in Others = number of people with who PhD student cooperates outside the research group defined by mentor Burt's measure of constraints for PhD students

22. Burt’s measure of constraints where zij is the frequency of interaction between person i and person j

23. CLUSTER ANALYSIS The goal is to obtain clusters of research groups according to the network characteristics • Standarized variables • Euclidian distance (between two research groups) • Hierarchical clustering • Ward method

24. RESULTS 1

25. RESULTS 2

26. CLUSTER 1 - WEAK SOCIAL CAPITAL • Small research groups • Rare cooperation between members of research group • Rare cooperation between PhD students and other members • PhD students do not search for cooperation with people outside their “primary” research groups • Members of research group are from the same institution

27. CLUSTER 1 - WEAK SOCIAL CAPITAL Typical research group of cluster 1

28. CLUSTER 2 - BONDING SOCIAL CAPITAL • Small research group • Developed cooperation • The highest average strength of ties between PhD students and others • Some cooperation of PhD students with others outside “primary” research group • Members of research group are from the same institution

29. CLUSTER 2 - BONDING SOCIAL CAPITAL Typical research group of cluster 2

30. CLUSTER 3 - BRIDGING SOCIAL CAPITAL • Large networks • Different institutions • PhD students have numerous cooperation ties with people outside the original group • Moderate strength of ties and cohesion • Network structure shows structural holes

31. CLUSTER 3 - BRIDGING SOCIAL CAPITALTypical representative of cluster 3

32. Index of performance (Coenders and Coromina, 2004) : 2*int_art + 2*pub_rev + pub_norm + pap_conf

33. Index of Performance • int_art - article in an international journal (with/without reviewers), book/chapter in a book - with reviewers. • pub_rev - article, paper in proceedings - with reviewers. • pub_norm - article, book/chapter in a book, paper in proceedings, internal research - without reviewers. • pap_conf - international/national conference/workshop – with/without presentation.

34. Clusters Index of performance Mean 6,63 1 – Weak social capital cluster S td.dev. 5,65 Mean 9,29 2 – Bonding social capital cluster Std.dev. 4,07 Mean 22,13 3 – Bridging social capital cluster Std.dev. 8,87 Mean 12,83 Total Std.dev. 9,44 COMPARISON OF INDEX OF PERFORMANCE BETWEEN CLUSTERS

35. F - statistics Significance ANOVA 12.44 0.00 Bonferroni’s post hoc tests Mean difference Significance 2 - 2.66 1.000 Cluster 1 3 - 15.50 .000 1 2.66 1.000 Cluster 2 3 - 12 .84 .004 1 15.50 .000 Cluster 3 2 12.84 .004 ANOVA RESULTS

36. SUMMARY OF CLUSTERING RESULTS • Three clusters according to social capital variables were obtained: bonding social capital, weak social capital and bridging social capital • Average strength of ties and cohesion in three clusters have non-linear influence on PhD students’ performance • Most successful PhD students are included in large, diverse research groups with network structure that is characterized by structural holes

37. STRUCTURAL EQUATION MODEL DEPENDENT VARIABLE: Index of performance

38. Independent variables Tie strength = average frequency of cooperation between PhD student and other members in research group – the linear and quadratic terms were centralized Cohesion = average frequency of cooperation between all members of research group Range = size of a network + number of different institution + Burt's measure of constraints for PhD students (-) Others = number of people with who PhD student cooperates outside the research group defined by mentor

39. Control variables Job centrality: job centrality was measured by four indicators (scale from 1 to 7): • I'll do overtime to finish my job, even if I'm not paid for it. • The major satisfacion in my life comes from my job. • The most important things that happen to me involve my work. • Some activities are more important to me than work. (-) Mentor’s performance

40. RESULTS 1

41. RESULTS 2 STRONG EFFECT OF RANGE ON PERFORMANCE • size of network (H3a), partially the number of others (H3b) • the number of different institution from which people in research group come from (H3c) • PhD student’s brokering position between unconnected parts in his network (H3d)

42. RESULTS 3 SHOWING SOME EFFECT OF • Quadratic term of strength of ties on performance NO EFFECT OF • Linear term of strength of ties on performance

43. FURTHER PLANS • Analysis on ego-centered and dyad level and the comparison between levels • Comparison across countries