The Network Perspective of Social Capital and its

Transkript

PARADOKS Ekonomi, Sosyoloji ve Politika Dergisi
PARADOKS Economics, Sociology and Policy Journal
The Network Perspective of Social Capital and its
Relationship with Students’ Performance: An Empirical
Research at the Faculty of Education
Sosyal Sermayenin Ağ Perspektifi ve Öğrencilerin
Performansı ile İlişkisi: Eğitim Fakültesinde Ampirik Bir
Araştırma
Yrd.Doç.Dr.Selim TÜZÜNTÜRK
Uludağ Üniversity
Faculty of Economics and Administrative Sciences,
Department of Econometrics
[email protected]
Temmuz/July 2015, Cilt/Vol: 11, Sayı/Num: 2, Page: 5-33
ISSN: 1305-7979
© 2005 - 2015
Temmuz/July 2015, Cilt/Vol: 11, Sayı/Num: 2
ISSN: 1305-7979
Editör/Editor-in-Chief
Yayın ve Danışma Kurulu / Publishing and Advisory Committee
Doç.Dr.Sema AY
Prof.Dr.Veysel BOZKURT (İstanbul Üniversitesi)
Prof.Dr.Marijan CINGULA (University of Zagreb)
Prof.Dr.Recai ÇINAR (Gazi Üniversitesi)
Prof.Dr.R.Cengiz DERDİMAN (Uludağ Üniversitesi)
Prof.Dr.Aşkın KESER (Uludağ Üniversitesi)
Doç.Dr.Sema AY (Uludağ Üniversitesi)
Assoc.Prof.Dr.Mariah EHMKE (University of Wyoming)
Assoc.Prof.Dr.Ausra REPECKIENE (Kaunas University)
Assoc.Prof.Dr. Cecilia RABONTU (University “ Constantin Brancusi” of TgJiu)
Doç.Dr.Elif KARAKURT TOSUN (Uludağ Üniversitesi)
Doç.Dr.Emine KOBAN (Gaziantep Üniversitesi)
Doç.Dr.Ferhat ÖZBEK (Gümüşhane Üniversitesi)
Doç.Dr.Senay YÜRÜR (Yalova Üniversitesi)
Dr.Zerrin FIRAT (Uludağ Üniversitesi)
Dr.Murat GENÇ (Otago University)
Dr.Hilal YILDIRIR KESER (Uludağ Üniversitesi)
Editör Yardımcıları/Co-Editors
Doç.Dr.Elif KARAKURT TOSUN
Dr.Hilal YILDIRIR KESER
Uygulama/Design
Dr.Yusuf Budak
Tarandığımız İndexler / Indexes
Dergide yayınlanan yazılar- daki
görüşler ve bu konudaki sorumluluk
yazarlarına aittir. Yayınlanan
eserlerde yer alan tüm içerik kaynak
gösterilme- den kullanılamaz.
All the opinions wriVen in artic- les
are under responsibilities of the
authors.
None of the contents published
cannot be used without being
cited.
© 2005 - 2015
Temmuz/July 2015, Cilt/Vol: 11, Sayı/Num: 2
ISSN: 1305-7979
Hakem Kurulu / Referee Committee
Prof.Dr.Veysel BOZKURT (İstanbul Üniversitesi)
Prof.Dr.Marijan Cingula (University of Zagreb)
Prof.Dr.Recai ÇINAR (Gazi Üniversitesi)
Prof.Dr.Mehmet Sami DENKER (Dumlupınar Üniversitesi)
Prof.Dr.R.Cengiz DERDİMAN (Uludağ Üniversitesi)
Prof.Dr.Zeynel DİNLER (Uludağ Üniversitesi)
Prof.Dr.Hasan ERTÜRK (Uludağ Üniversitesi)
Prof.Dr.Bülent GÜNSOY (Anadolu Üniversitesi)
Prof.Dr.Erkan IŞIĞIÇOK (Uludağ Üniversitesi)
Prof.Dr.Sait KAYGUSUZ (Uludağ Üniversitesi)
Prof.Dr.Aşkın KESER (Uludağ Üniversitesi)
Prof.Dr.Bekir PARLAK (Uludağ Üniversitesi)
Prof.Dr.Ali Yaşar SARIBAY (Uludağ Üniversitesi)
Prof.Dr.Şaban SİTEMBÖLÜKBAŞI (Süleyman Demirel Üniversitesi)
Prof.Dr.Abdülkadir ŞENKAL (Kocaeli Üniversitesi)
Prof.Dr.Veli URHAN (Gazi Üniversitesi)
Prof.Dr.Uğur YOZGAT (Marmara Üniversitesi)
Doç.Dr.Hakan ALTINTAŞ (Sütçü İmam Üniversitesi)
Doç.Dr.Hamza ATEŞ (Kocaeli Üniversitesi)
Doç.Dr.Canan CEYLAN (Uludağ Üniversitesi)
Doç.Dr.Kemal DEĞER (Karadeniz Teknik Üniversitesi)
Assoc.Prof.Dr.Mariah Ehmke (University of Wyoming)
Doç.Dr.Kadir Yasin ERYİĞİT (Uludağ Üniversitesi)
Doç.Dr.Ömer İŞCAN (Atatürk Üniversitesi)
Doç.Dr.Burcu GÜLER (Kocaeli Üniversitesi)
Doç.Dr.Vedat KAYA (Atatürk Üniversitesi)
Doç.Dr.Ferhat ÖZBEK (Gümüşhane Üniversitesi)
Doç.Dr.Veli Özer ÖZBEK (Dokuz Eylül Üniversitesi)
Doç.Dr.Serap PALAZ (Balıkesir Üniversitesi)
Assoc.Prof.Dr. Cecilia RABONTU (University “ Constantin Brancusi” of TgJiu)
Assoc.Prof.Dr.Ausra Repeckiene (Kaunas University)
Doç.Dr.Sevtap ÜNAL (Atatürk Üniversitesi)
Doç.Dr.Sevda YAPRAKLI (Atatürk Üniversitesi)
Doç.Dr.Gözde YILMAZ (Marmara Üniversitesi)
Yrd.Doç..Dr.Aybeniz AKDENİZ AR (Balıkesir Üniversitesi)
Yrd.Doç.Dr.Doğan BIÇKI (Muğla Üniversitesi)
Yrd.Doç.Dr.Cantürk CANER (Dumlupınar Üniversitesi)
Doç.Dr.Emine KOBAN (Gaziantep Üniversitesi)
Yrd.Doç.Dr.Ceyda ÖZSOY (Anadolu Üniversitesi)
Doç.Dr.Senay YÜRÜR (Yalova Üniversitesi)
Dr.Zerrin FIRAT (Uludağ Üniversitesi)
Dr.Murat GENÇ (Otago University)
Dr.Hilal YILDIRIR KESER (Uludağ Üniversitesi)
Temmuz/July 2015 - Cilt/Vol: 11 - Sayı/Num: 02
Sayfa/Page: 05-.33
THE NETWORK PERSPECTIVE OF SOCIAL CAPITAL AND ITS RELATIONSHIP
WITH STUDENTS’ PERFORMANCE: AN EMPIRICAL RESEARCH AT THE
FACULTY OF EDUCATION
SOSYAL SERMAYENİN AĞ PERSPEKTİFİ VE ÖĞRENCİLERİN PERFORMANSI İLE
İLİŞKİSİ: EĞİTİM FAKÜLTESİNDE AMPİRİK BİR ARAŞTIRMA
Yrd.Doç.Dr.Selim TÜZÜNTÜRK
Uludağ Üniversity, Faculty of Economics and Administrative Sciences
Abstract:
This study deals with the social capital concept from the network perspective and analyzes the relationship
between network perspective of social capital and performance. Within this scope, a research study was performed in
the Department of Special Education at the Faculty of Education of Uludağ University. A social network
questionnaire which is different than the conventional data collection tools was conducted to the Mentally
Handicapped and Education course students. With the gathered data social network analyses were performed and
social network variables were computed by using PAJEK programme. Then, several linear regression analyses were
performed to check the proposed relation. The originality of this analysis lies behind the usage of social capital
variables that were derived from the network through human interactions. On the basis of the sample results, it was
found that the primary independent network variable (social capital-constraint) has effect on the dependent variable
individual performance. Moreover, secondary network independent variable “closeness centrality”, and a control
variable “gender” have effect on the individual performance. These results indicate that sample students’
performances are directly related to network perspective of social capital.
Keywords: Social Networks, Social Network Analysis, Social Capital, Students’ Performance, Linear
Regression Analysis.
Özet:
Bu çalışma ağ perspektifinden sosyal sermaye kavramı ve ağ perspektifinden sosyal sermaye ile performans
arasındaki ilişkinin analizi ile ilgilidir. Bu çerçevede, Uludağ Üniversitesinde Özel Eğitim bölümünde bir araştırma
çalışması yürütülmüştür. Geleneksel veri toplama araçlarından farklı olan bir sosyal ağ anketi Zihinsel Engelliler
ve Eğitimi dersi öğrencilerine yapıldı. Elde edilen verilerle sosyal ağ analizleri yapıldı ve PAJEK paket programı
kullanılarak sosyal ağ değişkenleri hesaplandı. Daha sonra, ileri sürülen ilişkinin kontrol edilmesi için birçok
doğrusal regresyon analizi yapıldı. Bu analizin orijinalliğinin arkasında insan etkileşimleri yoluyla ağdan elde
edilen sosyal sermaye değişkenlerinin kullanılması yatmaktadır. Örnek sonuçlarına göre, birincil bağımsız ağ
değişkeninin (sosyal sermaye-kısıt) bireysel performans bağımlı değişkeni üzerinde etkisi vardır. Ayrıca, ikincil
bağımsız ağ değişkeni “yakınlık merkeziliği” ve kontrol değişkeni “cinsiyet” bireysel performans üzerinde etkilidir.
Bu sonuçlar, örnek öğrencilerin performanslarının doğrudan ağ perspektifinden sosyal sermaye ile ilişkili olduğuna
işaret etmektedir.
Anahtar Kelimeler: Sosyal Ağlar, Sosyal Ağ Analizi, Sosyal Sermaye, Öğrencilerin Performansı,
Doğrusal Regresyon Analizi.
THE NETWORK PERSPECTIVE OF SOCIAL CAPITAL AND ITS RELATIONSHIP WITH.......
1. INTRODUCTION
The first researcher who was credited with using the term “social network” was
John A. Barnes in 1954 (Noble, 1973; Mitchell, 1974; Knoke and Young, 2008). Barnes
viewed social interactions as a “set of points of which are joined by lines” to form a
total network of relations. Examples of social interactions include friendships among
people, membership of people in large social groups (e.g., clubs, companies), contacts
between people, cooperation on a common endeavor and the exchange of resources
(Kolaczyk, 2009: 5).
The term “social network” has been defined in many ways by different writers
in the Literature. For instance, Sandars (2005) defines social networks as a composed
of people and the relationships that hold them together. O’Malley and Marsden
(2008) definition is slightly the same but more technical: A social network consists of
one or more sets of units-also known as nodes, vertices or actors-together with the
relationships or social ties among them. Knoke and Yang (2008) defines a social
network as a structure composed of a set of actors, some of whose members are
connected by a set of one or more relations.
Social network analysis is used widely in the social sciences to analyze and
measure how interaction and communication occur between individuals and groups
(Morton and et all, 2004: 218). The main goal of social network analysis is detecting
and interpreting patterns of social ties among actors (De Nooy et al., 2007: 5).
In general, social network units or nodes are individual persons/actors, e.g.,
employees in a business organization. The relationships or social ties of social
networks have communication transactions such as advice, trust and discuss
transactions or have exchange contents such as goods or services exchanges. These
social networks of human interactions have complex relationship structures which
are analyzed with social network analysis methods.
Social Network Analysis methods can be divided into two main headings:
Visual methods and numerical methods. Visual methods are about the visualization
of network data. Graphical views of network data are drawn by using algorithms
such as Kamada-Kawai and Fruchterman Reingold algorithms. Then visual views of
networks are interpreted. Interpretations are mainly focused on whether there exist
periphery nodes or not and whether sparse or dense connections exist or not. And,
also central nodes are tried to be determined. Numerical methods are about the
calculation of some numeric characteristics of network. These characteristics can be
calculated individually (node by node) or collectively (group-whole network). Some
of the individual statistics are: Indegree centrality, closeness centrality, betweenness
centrality, clustering coefficients and constraint. Some of the group statistics are
density, average degree and clustering coefficient. Conventionally, all calculated
statistics are interpreted.
Network data is other than conventional data. Such data are obtained with
single item questions that ask a respondent to enumerate those individuals with
whom he or she has direct ties of a specific kind. These data are obtained from
diverse sources such as surveys, questionnaires, archival, diaries, electronic traces,
observation, informants and experiments (Marsden, 1990: 440). Surveys and
questionnaires are the predominant research methods that are widely used in most
of the social network studies. In these studies, generally social network data is
analyzed with social network software such as PAJEK or UCINET.
In the context above, social network applications are performed under five
main headings (Vera and Schupp, 2006):
1.
2.
3.
4.
5.
The structure and functioning of organizations
Genealogies of knowledge
Social capital and communities
Diffusion studies
Network intervention and regulation
A topical area of interest is the role of social networks in the creation of social
capital (Sandars, 2005: 5). The network perspective of social capital and its relation
with individual performance is noteworthy in the literature. A considerable body of
knowledge exists that examines the role of social capital plays in the success of
individuals and organizations (Aslam and et all, 2013). Some sample researches are
as follows: Baldwin, Bedell and Johnson (1997) designed an empirical analysis to
measure the social networks of students and the networks’ relationships to
performance outcomes. Results of their study indicate that centrality affected student
grades. Burt, Hogarth and Michaud (2000) found positive association for both French
and American managers between performance and social capital of a network.
Mehra, Kilduff and Brass (2001) tested how network position related to work
performance. Researchers concluded that centrality in social networks predict
individuals’ work place performance. Sparrowe and et all (2001) found that
individual job performance was positively related to centrality in advice networks.
Ahuja, Galletta and Carley (2003) performed social network analysis on e-mail
samples and found that centrality is a direct predictor of performance than the
individual characteristics. Yang and Tang (2003) investigated the effects of social
networks on students’ performance. Results showed that network variables are
positively related to student performance. Lamertz (2005) examined various
relationships between network variables and performance by using regression
analysis. Researcher’s one of the key finding was that the performance of a good
colleague behavior was related to betweenness centrality in the work network.
Some more recent sample researches are as follows: Plagens, (2011) explained
that scholars seeking to understand why some students and schools perform better
than others. Researcher underlined that the social capital might be part of
explanation. Abbasi, Chung and Hossain (2012) proposed social networks based
model for exploring scholars’ collaboration network properties associated with their
research performance. They found that research performance of scholars’ is
significantly correlated with scholars’ network measures. Aslam and et all (2013)
examined the relationship between social capital and knowledge sharing, and how
knowledge sharing impacts academic performance. Researchers performed multiple
linear regression analysis. Research results show that the relationship between
knowledge sharing and academic performance is negative. Henttonen, Janhonen and
Johanson (2013) investigated how team’s social network relationships affect its
performance and researchers found positive impact. Liu (2013) contributed to the
development of a conceptual theoretical model for explaining the interrelationships
among mechanisms of social capital and organizational creativity performance with
his study. Li, Liao and Yen (2013) explained that the contribution of their study is to
define indicators of social capital such as degree centrality, closeness centrality,
betweenness centrality and etc. Research results showed that betweenness centrality
plays the most important role in taking advantage of non-redundant resources in a
co-authorship network. Gonzalez, Claro and Palmatier (2014) investigated the effects
of relationship managers’ social networks on sales performance. The empirical
results show that social capital enhances performance.
The objective of this study is to analyze the network perspective of social capital
and its relationship with performance. Indicators of social capital in the context of
social network analysis that were used in this study are constraint, indegree
centrality, closeness centrality and betweenness centrality measures. And as a
measure of student’s performance the final grade of the course has taken into
consideration. Then, the proposed relationship between network perspective of
social capital and performance was examined. To do so, rest of the paper organized
as follows. Section 2 presents the network perspective of social capital in detail.
Network theories of social capital were discussed. The measurement of individual’s
social capital was described. Section 3 is composed of application and findings.
Section 4 covers the conclusion.
2. THE NETWORK PERSPECTIVE OF SOCIAL CAPITAL
Network perspective of social capital has defined in many ways in the
literature by various researchers. Some of the leading scientists’ definitions are as
follows: Lin (1999) defines network perspective of social capital as resources
embedded in a social structure which are accessed and/or mobilized in purposive
actions. Baker (2000) explains that social capital refers to the resources available in
and through personal and business networks. Burt (2005: 4) defines network
perspective of social capital as the advantage created by a person’s location in
structure relationships. Tindall and Wellman (2001) define network perspective of
social capital with the following words:
“When a people need help, they can buy it, trade of it, get it
from governments and charities, or obtain through social
capital: their useful interpersonal ties with friends, relatives,
neighbors and workmates.”
Social capital exists where people have an advantage because of their location
structure (Burt, 2004: 351). The premise behind the notion of social capital is rather
simple and straightforward: investment in social relations with expected returns
(Lin, 1999: 30).
Two elements are thought to be the focus point in the network perspective of
social capital. These are (Lin, 1999: 36): Locations and embedded resources. The
locations of individuals are treated as one of the key element of social capital. Some
location measures are bridge, density, size, closeness, betweenness and eigenvector.
These measures help to identify individual nodes’ locations and to determine how
close they are to the strategic locations in the social network. The embedded
resources simply refer to the wealth, power and status. These resources are reflected
in the contact’s occupation, authority position, industrial sector, or income (Lin, 1999:
36).
In the table below, it is seen that “Capital Theory” goes back to Marx.
Although, the scope of this study is out of the historical development of capital
concept, the table above gives researchers a summary view about where the social
capital stands in the related literature, who are the theorists and what are the
distinctive features of the theories of capital. Theories of social capital can be
summarized with the following table:
Table 1. Theories of Capital
Theorist
The
Classical
Theory
Marx
Explanation Social
relations:
Exploitation
by the
capitalist
The Neo-Capital Theories
Cultural
Social
Capital
Capital
Bourdieu
Lin, Burt,
Marsden,
Flap,
Coleman
Accumulation Reproduction
Access to
of surplus
of dominant
and use of
value laborer symbols and
resources
meanings
embedded
(values)
in social
Human
Capital
Schultz,
Becker
Bourdieu,
Coleman,
Putnam
Solidarity and
reproduction of
group
(bourgeoise)
of the
proletariat
Capital
Level of
Analysis
A. Part of
surplus
value
between the
use (in
consumption
market) and
he exchange
value (in
productionlabor
market) of
the
commodity.
B.
Investment
in the
production
and
circulation
of
commodities
Structural
(classes)
networks
Investment in
technical
skills and
knowledge
Internalization
or
misrecognition
of dominant
values
Investment Investment in
in social
mutual
networks
recognition and
acknowledgment
Individual
Individual/class Individual
Group/individual
Source: Lin (1999: 30).
Social capital is defined by Lin (1999) as the investment in social relations by
individuals through which they gain access to embedded resources to enhance
expected returns of instrumental or expressive actions. In this definition Lin (1999)
underlines that the instrumental returns are wealth, power and reputation and
expressive returns are physical health, mental health and life satisfaction.
There are two methodologies commonly used to measure access to social
capital: name generators and position generators (Lin, Fu and Hsung, 2008). Name
generator question generate a list of the contacts’ names. The question such as “If
you look back over the last six months, who are the four or five people with whom
you discussed matters important to you?” is asked to the respondents is to state the
names of people with whom the respondent has relations (Burt, 1997: 358-359). On
the other hand, position generator question is asked to respondents to indicate
contacts (those known on a first name basis), if any, in each of the position. The
question such as “Among your relatives, friends, or acquaintances, are there people
who have the following jobs? If so, what is his/her relationship to you? If you don’t
know anyone with these jobs, and if you need to find such a person for private help
or to ask about some problems, whom among those you know would you go
through to find such a person? Who would he/she be to you? What job does he/she
do?” is asked to the respondents (Lin, Fu and Hsung, 2008: 66).
Two main network theories of social capital were proposed in the literature.
These are strong and weak ties theory and structural hole theory. Social ties that are
embedded in a social structure may have closure1 and multiplexity2. Such social ties
are called strong ties (Koput, 2010: 20). The strength of a tie between two people is
the combination of (Koput, 2010: 21):




Frequency and duration of interactions;
Intensity of emotional attachment;
Level olf intimacy and closeness;
Volume exchanged.
A weak tie is a tie that is not active, not used very much, or not shared by others
in the network (Anklam, 2007: 76). It may reflect a casual acquaintance or past
connection. Anklam (2007) explains that external ties may be weak, but very
powerful. Weak ties provide access into other networks, where there may be
different ideas or access to different resources. Granovetter’s (1973) article is the first
study that emphasizes the importance of weak ties.
Individual actors have been portrayed as seeking to increase their social capital
by forging network ties that span between self-contained cliques. Structural hole
research focuses attention solely on the importance of bridging ties (Kilduff and Tsai,
2008: 57). A structural hole is a relationship of non-redundancy between two contacts
(Degenne and Forse, 1999: 118).
Primary indicator of social capital in a network is constraint. And, secondary
indicators of social capital in a network are respectively indegree centrality, closeness
centrality, and betweenness centrality. Constraint is computed with the following
formula (Burt, 1992: 54):
In this formula “
shows that contact j constrains contact i. So, the constraint of
contact i is computed in this formula.
1
is the proportional strength i’s relation with
Closure means that asocial structure resides within a closed loop (Koput, 2010: 18). In other words,
say you have two friends. If the know each other, then there is closure in your small network.
2
Multiplexity means that any given pair of partners will often have one type of tie (Koput, 2010: 20).
Dyadic relations and the overall social structure can be described as multiplexity.
j.
is is the proportional strength i’s relation with contact q.
is the proportional
strength q’s relation with j.
Degree Centrality is a count of the number of direct work relationships in
which an actor is involved (Lamertz, 2005: 91). It measures an individual’s centrality
according to the number of connections to others. Central individuals have strong
connections to other network members; peripheral individuals do not (Degenne and
Forse, 1999: 132). In directed networks, the number of incoming lines named as
indegree centrality and the number of outgoing lines named as outdegree centrality.
A geodesic is the shortest path length between two nodes of a network
diagram. Closeness of node “i” is measured as the sum of geodesics to all other
nodes (Degenne and Forse, 1999: 135).
Betweenness Centrality examines actor’s indirect relationships and captures a
position that locates the actor as functional link between others who have no direct
relationship with each other (Lamertz, 2005: 91).
3. APPLICATION AND FINDINGS
A research study was performed in the Department of Special Education at
the Faculty of Education of Uludağ University Bursa in 2013-2014 academic years. A
social network questionnaire (see Appendix 1) was conducted to the 48 freshmen.
These freshmen are newcomers to the University. So, they are the first year students
and they don’t know each other. Mentioned questionnaire was conducted to the
same students twice in different time points. First one was conducted on October
2013 which is in the beginning of first semester and the second one was conducted on
May 2014 which is in the end of second semester. With the gathered data in two
different time points, the students’ temporal interactions’ development via face to
face and via mobile phone were analyzed by using Social Network Analysis. The
expectation is the raise of the interactions. More importantly, depending upon the
objective of this study the network perspective of social capital and its relationship
with performance was researched by using Linear Multiple Regression Analysis. The
originality of this analysis lies behind the social capital variable that was derived
from the network relationships between students’ interactions.
3.1. Social Network Analysis
The academic year started on 16 th of September 2013. At the end of October,
social network questionnaire was conducted by getting permission from the related
Lecture. As is opposed to conventional data gathering methods, a single item
question is asked in conducting a social network analysis. In this study, following
social network question was asked to the same 48 registered students of the
Department of Special Education of Uludağ University in different two time points
(on October 2013 and May 2014):
“Think about your classmates. Then, please write the names of
maximum 10 students whom you talk and discuss about course
related issues at the past two weeks via face to face or mobile phone
communication.”
With the gathered data, at first social network data files were formed. Then
social network analysis was performed by using PAJEK social network package
programme. Similar to statistical analysis, graphical images and summary measures
are used in social network analysis in the scope of network analysis.
In the figures below, the students’ temporal interactions’ developments in two
different time points via face to face and via mobile phone were presented. In the
first figure, the data that was collected on October 2013 was visualized. And, in the
second following figure, the data that was collected on May 2014 was visualized.
Figure 1. Students’ Interactions’ Network via Face to Face and via Mobile Phone on October 2013
Figure 2. Students’ Interactions’ Network via Face to Face and via Mobile Phone on May 2014
Temmuz/July 2015 - Cilt/Vol:11 - Sayı/Num: 02
The temporal changes were observed in the visual representations of the social networks
above. In the beginning of first academic year, after having met one and half months
connections were started to seen. By looking at naked eye to Figure 1 above, the network
connections seem to be sparse on October 2013. In the end of first academic year, after
having met eight and half months connections were started to increase. By looking at naked
eye to Figure 2 above, the network connections seem to be dense on May 2014. These two
evaluations in two different time points indicate expectedly that the students’ temporal
interactions’ were increased as the time passes. Moreover, the connections were evolved
over time and three student groups are emerged on May 2014 (See three circles in Figure 2).
In the table below, group characteristics of the networks are seen:
Table 2. Comparisons of the Group Characteristics of the Networks
Statistics
Density
n
Average Degree
Clustering Coefficient
Time Point
October 2013
8
May 2014
8
4
0,0718
6,75
0,175
4
0,1245
11,95
0,287
Parallel to the visual observations, group characteristics of the networks also indicate
that students’ temporal interactions were increased as the time passes. The face to face and
mobile phone interactions density was increased from 0,0718 up to 0,1245. The average
degree of the interactions was increased from 6,75 up to 11,95. And, the clustering coefficient
of the overall network was increased from 0,175 up to 0,287. In the table below, individual
characteristics of the networks are seen:
Table 3. Individual Characteristics of the Networks
Indegree
Centrality
Node October
Closeness
Centrality
May October May
Betweenness
Centrality
October
Clustering
Coefficients
May
October
May
1
6
5
0,348148 0,460784 0,032196
0,081925
0,166667 0,261029
2
3
5
0,299363 0,391667 0,012478
0,01363
0,25
0,357143
3
10
15
0,401709 0,479592 0,102586
0,061486
0,1
0,301471
4
3
5
0,267045 0,34058
0,033326
0,003309
0,05
0,45
5
2
7
0,221698 0,431193 0,001595
0,035366
0,5
0,267857
6
3
8
0,244792 0,451923 0,060469
0,059292
0,05
0,2
7
4
10
0,333333 0,427273 0,056525
0,052877
0,285714 0,333333
8
1
9
0,235
0,439252 0,013454
0,065337
0,333333 0,233333
9
4
9
0,321918 0,423423 0,097812
0,020914
0,194444 0,466667
10
5
11
0,26257
0,456311 0,037746
0,041671
0,433333 0,418182
11
3
9
0,248677 0,447619 0,048906
0,019346
0,2
0,466667
12
2
7
0,217593 0,419643 0,020139
0,027843
0,3
0,411111
13
6
4
0,311258 0,38843
0,005738
0,160714 0,6
14
3
4
0,324138 0,358779 0,026955
0
0,3
15
6
10
0,338129 0,435185 0,05764
0,037146
0,263889 0,390909
16
2
2
0,261111 0,25
0
0
17
5
7
0,373016 0,415929 0,053732
0,020588
0,238095 0,444444
18
2
5
0,30719
0,328671 0,045088
0,081406
0,333333 0,25
19
2
7
0,21659
0,408696 0,018716
0,012714
0,166667 0,589286
20
1
6
0,2
0,385246 0,007344
0,031598
0,35
0,354545
21
4
3
0,324138 0,370079 0,068395
0,012785
0,2
0,380952
22
2
6
0,279762 0,431193 0,1177
0,062737
0,119048 0,25
23
2
6
0,25
0,041619
0,15
24
6
7
0,248677 0,405172 0,086584
0,015985
0,152778 0,527778
25
6
5
0,343066 0,408696 0,085085
0,025254
0,1
26
4
3
0,281437 0,356061 0,094606
0,02177
0,166667 0,166667
27
6
3
0,313333 0,391667 0,08995
0,009814
0,2
28
2
5
0,230392 0,353383 0,015253
0,019786
0,166667 0,180556
29
3
3
0,229268 0,348148 0,044794
0,006948
0,033333 0,433333
0,121916
0,007102
0,427273 0,02312
0,666667
1.000.000
0,194444
0,178571
0,166667
30
0
2
0
31
1
6
32
6
33
0,25
0
0,5
1.000.000
0,258242 0,405172 0,042942
0,042284
0,4
0,3
7
0,335714 0,435185 0,204502
0,069059
0,160714 0,174242
5
3
0,317568 0,356061 0,155167
0,009173
0,095238 0,15
34
5
7
0,295597 0,38843
0,017778
0,125
35
2
4
0,221698 0,358779 0
0,014801
1.000.000 0,236111
36
2
4
0,301282 0,34058
0,083196
0,017329
0,233333 0,066667
37
4
6
0,324138 0,385246 0,046907
0,026545
0,05
0,333333
38
3
11
0,326389 0,484536 0,066174
0,0754
0,3
0,212121
39
4
4
0,290123 0,345588 0,013644
0,003678
0,3
0,45
40
4
6
0,270115 0,38843
0,054094
0,093562
0,05
0,263889
41
3
5
0,283133 0,423423 0,002652
0,024377
0,166667 0,277778
42
0
3
0
0,391667 0
0,025239
1.000.000 0,2
43
0
0
0
0
0
0
0,166667
44
0
6
0
0,412281 0
0,11624
0
0,1375
45
3
5
0,303226 0,408696 0,076561
0,039554
0,1
0,196429
46
4
5
0,264045 0,394958 0,014274
0,064846
0,1
0,172727
47
8
7
0,270115 0,408696 0,089315
0,043406
0,118182 0,252747
48
0
0
0
0
0,25
0
0
0,089807
0
0
0,125
0,178571
Parallel to the visual findings and group characteristics, individual characteristics of
the networks indicate that students’ temporal interactions were increased. When these
interactions are increased, it is meaningful to research whether the network perspective of
social capital has effect on the students’ performance or not.
3.2. Linear Multiple Regression Analysis
In linear multiple regression analysis, the social network data that was gathered from the
same 48 students on May 2014 and the students’ “Mentally Handicapped and Education”
course final grades were used.
The dependent variable of the regression model is the score that was measured by the
Mentally Handicapped and Education course final grade for each student. A student’s final
grade is a score which is out of 100 points and is simply the sum of the fifty percent of the
midterm exam and fifty percent of the final exam. This final grade was treated as the
performance indicator of the students for the Mentally Handicapped and Education course.
The independent variables divide into following two groups: Network variables and control
variables.
Since the objective of this study is to analyze the network perspective of social capital
and its relationship with performance, the primary independent network variable is
constraint. Secondary independent network variables are indegree centrality, closeness
centrality, and betweenness centrality. All network variables were computed by using
PAJEK social network package programme. Following table presents the values of constraint
independent variable on May 2014.
Table 4. Constraint Values on May 2014
Node
Constraint
Node
Constraint
1
0,156946
25
0,199122
2
0,263984
26
0,220985
3
0,161339
27
0,305276
4
0,330337
28
0,18601
5
0,242441
29
0,315133
6
0,185566
30
0,953125
7
0,19806
31
0,221452
8
0,204622
32
0,144564
9
0,26832
33
0,285937
10
0,215799
34
0,178599
11
0,261319
35
0,191809
12
0,255042
36
0,215266
13
0,353485
37
0,224454
14
0,432215
38
0,190101
15
0,241604
39
0,367758
16
0,953125
40
0,226707
17
0,258079
41
0,198117
18
0,407932
42
0,253623
19
0,313358
43
0,303519
20
0,239376
44
0,117743
21
0,300576
45
0,224819
22
0,165348
46
0,1651
23
0,209772
47
0,183127
24
0,280208
48
0,192119
Control variables are gender, age and region, respectively. While gender and region
were measured as qualitative variables, age was measured as quantitative variable. Gender
was decoded with one and two (1=female, 2=male). Region represents the seven regions of
Turkey (1=Marmara Region, 2=Aegean Region, 3=Mediterranean, 4=Black Sea, 5=Central
Anatolia, 6=East Anatolia, 7=Southeastern Anatolia).
Descriptive statistics of the variables that were used in the estimation of the linear
multiple regression models are presented in the following two tables.
Table 5. Descriptive Statistics of the Quantitative Variables
Statistics
N
Mean
Std. Deviation
Min.
Max.
Score
48
63,04
16,940
29
99,5
Constraint
48
0,27
0,158
0,11
0,95
Indegree
48
5,77
2,868
0
15
Closeness
48
0,37
0,093
0,00
0,48
Betweenness
48
0,03
0,027
0,00
0,12
Age
48
19,81
1,232
19
26
Variables
Numbers of observations, mean, standard deviation, minimum and maximum
statistics are presented for each quantitative variables in the table above. For instance,
“Score” variable’s mean expresses that the Mentally Handicapped and Education course
students’ final grade average is 63,04. The minimum final grade score is 29 points out of 100
and the maximum final grade score is 99,5 out of hundred. For instance, “Age” variable’s
mean expresses that the average of the Mentally Handicapped and Education course
students’ age is 19,81. The youngest student is 19 years old and the oldest student is 26 years
old.
Table 6. Descriptive Statistics of the Qualitative Variables
Statistics
N
Mode
Min.
Max.
Gender
48
1
1
2
Region
48
2
1
7
Variables
Numbers of observations, mode, minimum and maximum statistics are presented for
each qualitative variables in the table above. For instance, “Gender” variable’s mode is 1. It
expresses that female genders occurs most of in the sample as is compared to the occurrence
of male gender. “Region” variable’s mode is 2. It expresses that the students whose regions
are from Aegean Region occurs most of in the sample as is compared to the occurrence of
other six regions. The correlation coefficients of the dependent variable and the indicators of
social capital network variables are shown in the table below:
Table 7. Pearson Correlations
Score
Score
Constraint
Indegree
Closeness
Betweenness
1
0,365
0,131
0,185
0,032
1
-0,386
-0,364
-0,445
1
0,720
0,489
1
0,472
Constraint
Indegree
Closeness
Betweenness
Sim.
1
Several linear regression analyses were performed. Indegree centrality, betweenness
centrality coefficient network measures were found insignificant in affecting students’
performance in various regression estimations. For the sake of simplicity, only the most
proper linear regression model’s results are given in the table below.
Table 8. Linear Multiple Regression Results
Unstandardized
Standardized
Coefficients
Coefficients
B
Std. Error
(Constant)
16,985
36,505
Constraint
38,727
14,601
Closeness
-12,793
Gender
Age
R Square
Adjusted R
Square
Collinearity Statistics
t
Sig.
Beta
Tolerance
VIF
0,465
0,644
0,363
2,652
0,011**
0,768
1,302
4,373
-0,382
-2,925
0,005*
0,844
1,184
47,518
24,675
0,262
1,926
0,061***
0,779
1,284
1,855
1,672
0,135
1,110
0,273
0,972
1,029
0,38
0,32
Note: * denotes the significance at 0.01 percent significance level.
** denotes the significance at 0.05 percent significance level.
F Statistic
6,648
Sig.
0,000
Durbin-Watson
1,904
*** denotes the significance at 0.10 percent significance level.
The above multiple regression result show that constraint, closeness centrality and
gender independent variables have effect on the dependent variable, performance. While the
parameters of these variables are statistically significant, age variable’s parameter was found
insignificant. It hasn’t got any effect on performance.
R Square value (0,38) can be interpreted as thirty eight percent of the variance in the
response variable (score) can be explained by the explanatory variables. Adjusted R Square
value (0,32) can be interpreted as thirty two percent of the variance in the response variable
can be explained by the explanatory variables. F statistic show that the proposed multiple
regression model with fits data well (Sig.=0,000=0,05).
Durbin Watson Statistic (1,904) is close to 2 which indicate that there is no first order
serial correlation. This is not surprising because as is well known serial correlation problem
arises mostly when it is studied with time series data.
When the collinearity statistics is considered, high tolerance values that are close to
“1” show that there is no multicollinearity problem. The small VIF values that are smaller
than “10” also confirm the tolerance values that there is no multicollinearity problem.
The regression results above show that gender has effect on performance. So, it
becomes meaningful to analyze which category of gender (female or male) is related with
higher performance values. To do so, quantitative score variable was transformed into
another variable. It was categorized as is shown in the table below:
Table 9. Crosstabulation of Gender and Categorical Scores
Scores
Total
1
2
3
4
5
(0-30,5)
(31-50,5)
(51-70,5)
(71-90,5)
(91-100)
Female
1
0
11
9
3
24
Male
1
7
13
3
0
24
2
7
24
12
3
48
GENDER
Total
Table 9 shows the crosstabulation of gender and categorical scores or final grades of
the students. When the number of students with the higher grades are compared, it is been
observed that female students’ is related with higher performance values.
4. CONCLUSION
The objective of this study is to analyze the network perspective of social capital and
its relationship with performance. On the basis of the sample results, it was found that the
primary independent network variable “social capital-constraint” has effect on the
dependent variable individual performance (student’s performance). This primary result is
consistent with the literature results. Especially, this result is consistent with Baldwin, Bedell
and Johnson (1997); Yang and Tang (2003) and Plagens (2011) results who investigated the
effects of social networks on students’ performance and found positive association.
Moreover, it is also consistent with the other research area results that are respectively job
performance (Sparrowe and et all, 2001; Ahuja, Galletta and Carley, 2003; Lamertz, 2005;
Henttonen, Janhonen and Johanson, 2013; Liu, 2013; Gonzalez, Claro and Palmatier, 2014),
academic performance (Abbasi, Chung and Hossain, 2012; Aslam and et all, 2013; Li, Liao
and Yen, 2013). Moreover, it is found that secondary network independent variable
“closeness centrality”, and a control variable “gender” has effect on the individual
performance of the students. As a result, it is found that students’ performances are directly
related to network perspective of social capital on the basis of sample results.
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Appendix 1. A social network questionnaire
Demografic Questions




Name/ Surname ………………………/……………………
Age.................
Gender
1)Female
2)Male
Region..............................
Social Network Question
 “Think about your classmates. Then, please write the names of maximum 10 students
whom you talk and discuss about course related issues at the past two weeks via face
to face or mobile phone communication.”
Name / Surname
1………………………………………………………………………………………
2………………………………………………………………………………………
3………………………………………………………………………………………
4………………………………………………………………………………………
5………………………………………………………………………………………
6………………………………………………………………………………………
7………………………………………………………………………………………
8………………………………………………………………………………………
9………………………………………………………………………………………
10………………………………………….…………………………………………

The Network Perspective of Social Capital and its

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