research groups’ social capital and phd students’ performance: the case of slovenia petra...

Research GroupsResearch Groups’’ Social Social CapitalCapital and PhD and PhD

Students’ Performance: Students’ Performance: The Case of SloveniaThe Case of Slovenia

Petra Ziherl,Petra Ziherl, CATI d.o.o. CATI d.o.o.

Hajdeja Iglič, Anuška FerligojHajdeja Iglič, Anuška Ferligoj, ,

University of LjubljanaUniversity of Ljubljana

OUTLINEOUTLINE• Theoretical background• Data• Methods cluster analysis, structural equation model - SEM

• Results• Discussion

THE AIM OF THE PROJECTTHE AIM OF THE PROJECTAn 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.

THE MAIN HYPOTHESIS AND THE MAIN HYPOTHESIS AND THE AIM OF A STUDYTHE 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?

THEORETICAL BACKGROUND:THEORETICAL BACKGROUND:DEFINITION 1DEFINITION 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).

DEFINITION 2DEFINITION 2One 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.

STRENGTH OF TIESSTRENGTH 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.

COHESIONCOHESION• 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.

GROUP HETEROGENEITY 1GROUP HETEROGENEITY 1BURT’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.

GROUP HETEROGENEITY 2GROUP 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)

THEORETICAL MODELTHEORETICAL MODEL

O th e r s

S iz e

C oh e s ion

Tie s tr e n g th

P h .D . s tu de n t' spe r for m an c e

In s ti tu tion s S tr u c tu r al h ole s

H .1H .2

H .3 a

H .3 b

H .3 c H .3 d

DATA COLLECTIONDATA 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).

Data Collection – Phase 1Data Collection – Phase 1

236 doctoral students and their mentors

204 contacted mentors

190 defined research groups

contacting mentors and arranging a

personal interview

incomplete data, refusal (e.g., concerns about

security of personal data, lack of time)

Data Collection – Phase 2Data Collection – Phase 2

1355 researchers

194 doctoral students190 mentors971 others

711 participated

117 (60,3%) doctoral students99 (52,1%) mentors495 (51,0%) others

Wave Response rate Response (N)

E-mail 36,5% 494

Reminder 1 48,7% 660

Reminder 2 52,5% 711

+ 12,2%

+ 3,8%

Types of supportTypes of supportTypes 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).

Ego-centered networks vs. Ego-centered networks vs. Complete networksComplete 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.

DATA 1DATA 1Social 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

DATA 2DATA 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

Representativity of Representativity of thethe sample 1 sample 1

Variables Chi-square test Complete networks Ego-centered networks Non-respondents

Gender Number Percentages Number Percentages Number Percentages

Men 13 56.5 71 60.7 38 50.7

Women 10 43.5 46 30.3 37 49.3

Total

0.84 (p-value=0.66)

23 100 117 100 75 100

Science area Natural sciences 18 78.3 94 80.3 61 81.3 Social sciences 5 21.7 23 19.7 14 18.7 Total

0.99 (p-value=0.61)

23 100 117 100 75 100

PhD students’ year of employment in current department

Before 2000 2 8.7 14 12.7

Year 2000

0.99 19 82.6 89 80.9

After 2000 2 8.7 7 6,4

Total (p-value=0.61)

23 100 110 110

Representativity of the sample Representativity of the sample 22

Size of a network Number of institution

Mean 6,09 2,39

N 23 23

Std. Deviation 2,27 1,08

Mean 7,65 3,20

N 94 94

Std. Deviation 3,28 1,90

Mean 6,68 2,77

N 73 73

Std. Deviation 3,10 1,77

Mean 7,09 2,94

N 190 190

Std. Deviation 3,15 1,78

F-statistics 3,33 2,49

Sig. 0,04 0,86

Total

Complete networks

Ego-centered networks

Non-respondents

VARIABLES USED IN ANALYSES VARIABLES USED IN ANALYSES

Tie strength average frequency of cooperation between PhD student and

other members in research groupCohesion average frequency of cooperation between all members of

research groupGroup Diversity:Size of a research group (original one)Number of different institutions that people from research

group are employed inOthers = number of people with who PhD student cooperates

outside the research group defined by mentorBurt's measure of constraints for PhD students

Burt’s measure of constraintsBurt’s measure of constraints

j

qjq

iqiji )pp(pC

j

jiij

jiijij )zz(

zzp

where zij is the frequency of interaction between person i and person j

CLUSTER ANALYSISCLUSTER ANALYSIS

The goal is to obtain clusters of researchgroups according to the networkcharacteristics

• Standarized variables• Euclidian distance (between two research

groups)• Hierarchical clustering• Ward method

RESULTS 1RESULTS 1

RESULTS 2RESULTS 2

CLUSTER 1 - WEAK SOCIAL CLUSTER 1 - WEAK SOCIAL CAPITALCAPITAL

• 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

CLUSTER 1 - WEAK SOCIAL CLUSTER 1 - WEAK SOCIAL CAPITAL CAPITAL

Typical research Typical research group of cluster 1group of cluster 1

62225

R7EYK

9L23T

ZL2Y9

4G6WN

Pajek

Average frequency of cooperation Few times a year

Average frequency of cooperation between PhD students and other members

Not in year before interviewing

Number of »outside« people with who PhD student cooperates

None

Number of different instititutions 1

Size of research group 6

Index of constraints 0,78

CLUSTER 2 - BONDING SOCIAL CLUSTER 2 - BONDING SOCIAL CAPITALCAPITAL

• 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

85QC1

HKG4Z

UBBQB

RDCKA

Pajek

Average frequency of cooperation Once a month

Average frequency of cooperation between PhD students and other members

Few times a year

Number of »outside« people with who PhD student cooperates

3

Number of different instititutions 1

Size of research group 4

Index of constraints 0,85

CLUSTER 2 - BONDING SOCIAL CLUSTER 2 - BONDING SOCIAL CAPITAL CAPITAL

Typical research Typical research group of cluster 2group of cluster 2

CLUSTER 3 - BRIDGING CLUSTER 3 - BRIDGING SOCIAL CAPITALSOCIAL 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

CLUSTER 3 - BRIDGING SOCIAL CLUSTER 3 - BRIDGING SOCIAL CAPITALCAPITAL

Typical representative of cluster 3Typical representative of cluster 3ESXBW

TRMXP

HKZ2Y

XN3IH

4B76Z

T3K6B

Pajek

Average frequency of cooperation Once/few times a year

Average frequency of cooperation between PhD students and other members

Few times a year

Number of »outside« people with who PhD student cooperates

2

Number of different instititutions 4

Size of research group 8

Index of constraints 0,56

Index of performance Index of performance (Coenders and Coromina, 2004)(Coenders and Coromina, 2004) : :

2*int_art + 2*pub_rev + pub_norm + pap_conf

Index of PerformanceIndex of Performance

1. int_art - article in an international journal (with/without reviewers), book/chapter in a book - with reviewers.

2. pub_rev - article, paper in proceedings - with reviewers.

3. pub_norm - article, book/chapter in a book, paper in proceedings, internal research - without reviewers.

4. pap_conf - international/national conference/workshop – with/without presentation.

COMPARISON OF INDEX OF COMPARISON OF INDEX OF PERFORMANCE BETWEEN PERFORMANCE BETWEEN

CLUSTERSCLUSTERS

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

ANOVA RESULTSANOVA RESULTS

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

SUMMARY OF CLUSTERING SUMMARY OF CLUSTERING RESULTSRESULTS

• 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

STRUCTURAL EQUATION STRUCTURAL EQUATION MODELMODEL

DEPENDENT VARIABLE: Index of performance

Independent variables 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

Control variablesControl variablesJob 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

RESULTS 1RESULTS 1

R an g e

M e n tor 'spe r for m an c e

C oh e s ion

Tie s tr e n g th

Tie s tr e n g th 2

P h .D . s tu de n t' spe r for m an c e

W or k c e n tr al i ty O th e r s

0 .3 2

0 .5 7

0 .3 9

0 .3 1

0 .0 6

-0 .0 0

0 .2 6

0 .5 0

0 .3 3

0 .0 2

0 .5 4

1 .0 0

1 .0 0

0 .8 9

0 .8 5

0 .7 5

0 .4 2

0 .9 0

1 .0 0

t-valu e s above 2

t-valu e s above 1 .5

t-valu e s above 1 .0

t-valu e s be low 0 .5

LEGEN D :

R 2 = 0 .5 8 4

RESULTS 2RESULTS 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)

RESULTS 3RESULTS 3

SHOWING SOME EFFECT OF • Quadratic term of strength of ties on

performance

NO EFFECT OF• Linear term of strength of ties on

performance

FURTHER PLANSFURTHER PLANS

• Analysis on ego-centered and dyad level and the comparison between levels

• Comparison across countries