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Social Networks among Teachers

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Published: Mon, 12 Feb 2018

CHAPTER 1

The Social Fabric of Elementary School Teams:

How Network Content Shapes Social Networks [1]

ABSTRACT

Background. Social networks among teachers are receiving increased attention as a vehicle to support the implementation of educational innovations, foster teacher development, and ultimately, improve school achievement. While researchers are currently studying a variety of teacher network types for their impact on educational policy implementation and practice, knowledge on how various types of networks are interrelated is limited. Moreover, studies that examine the dimensionality that may underlie various types of social networks in schools are scarce.

Purpose. The goal of this chapter was to increase our understanding of how network content shapes social network structure in elementary school teams. The study examines the extent to which various work-related (instrumental) and personal (expressive) social networks among educators are related. In addition, we explore a typology of social networks in schools and investigate whether the common distinction between instrumental and expressive social networks could be validated in the context of elementary school teams.

Method. Social network data were collected among 775 educators from 53 elementary schools in a large educational system in the Netherlands. The interrelatedness of seven social networks was assessed using the Quadratic Assignment Procedure (QAP) correlations. Multidimensional Scaling (MDS) was used to discern underlying dimensions that may explain the observed similarities. Finally, we describe and visualize the seven networks in an exemplary sample school.

Conclusions. Findings suggest small to moderate similarity between the social networks under study. Results support the distinction between instrumental and expressive networks in school teams and suggest a second dimension of mutual in(ter)dependence to explain differences in social relationships between educators.

The social fabric of elementary school teams; How network content shapes social networks

INTRODUCTION

The rapidly growing interest in social networks can be characterized as one of the major trends in social science research. According to scientific databases (ERIC, Picarta, and Web of Science), the number of publications in social sciences using the word ‘social network(s)’ in the title, keywords, or abstract, has increased exponentially over the last two decades (Borgatti & Foster, 2003) (see 1). Evidence of this trend in education is exhibited by an increasing number of articles focusing on the intersection of social networks and education in a growing variety of settings and areas of emphasis. The thesis that ‘relationships matter’ is currently inspiring educational researchers around the world to study social networks in school teams (Daly, in press; Daly & Finnigan, 2009; Daly et al., in press; McCormick, Fox, Carmichael, & Procter, in press; Penuel, Riel, Krause, & Frank, 2009) (see also 1). An important prerequisite for gaining insights in the potential of social networks for schools is the emergence of social network studies that provide a deepened understanding of the structure and content of teachers’ professional relationships (Coburn & Russell, 2008).

Social network scholars emphasize that social networks are shaped by the content or purpose of the social resources that are exchanged in the network (Burt, 1992; Coleman, 1990; Lin, 2001; Putnam, 2000; Scott, 2000; Wasserman & Faust, 1997). Studies suggest that the distribution of resources in a network may depend on the content of the network (Haines & Hurlbert, 1992; Raider & Burt, 1996). For instance, a social network that is maintained for the purpose of exchanging work related knowledge and expertise may look significantly different from a social network that is created for personal support. Even though both social networks contain social resources that may be accessed and leveraged, both networks may be shaped quite differently. Several scholars have therefore voiced the need to examine multiple relationships simultaneously (Friedkin, 2004; Ibarra & Andrews, 1993; McPherson, Smith-Lovin, & Cook, 2001; Mehra, Kilduff, & Brass, 1998; Monge & Contractor, 2003; Pustejovsky & Spillane, 2009; Wasserman & Faust, 1997). Yet, few studies have been conducted into the ways in which social networks are shaped differently depending on the content of their ties (Hite, Williams, & Baugh, 2005; Moolenaar, Daly, & Sleegers, in press).

The goal of this chapter is to examine the extent to which multiple social networks among educators are shaped differently depending on their content. We will address this goal by exploring the similarity between multiple social networks in school teams and working towards a typology of social networks in school teams according to underlying dimensions. Our enquiry is guided by social network theory and the social network concept of ‘network multiplexity’. In short, network multiplexity is concerned with the ‘overlap’ between social networks that transfer different content among the same individuals. With this chapter, we aim to contribute to recent knowledge on the nature of social networks in school teams by comparing and contrasting different networks (e.g., friendship, advice) in 53 Dutch elementary schools located in a single district. We will start with an overview of social network theory and network multiplexity as these provide the conceptual background to the study.

THEORETICAL FRAMEWORK

Social network theory

A growing body of educational research points to the potential of social networks to affect teachers’ instructional practice, and ultimately, benefit student achievement (Coburn & Russell, 2008; Daly et al., in press; Penuel, Frank, & Krause, 2007; Penuel & Riel, 2007). Building on social network theory, these studies examine the extent to which the pattern of relationships among teachers and the exchange of resources within these relationships may support or constrain school functioning and improvement. An important feature of social network theory is the focus on both the individual actors and the social relationships linking them (Wasserman & Galaskiewicz, 1994). Through social interaction among educators, social relationships develop into a patchwork of ties that knit the social fabric of school teams (Field, 2003; Putnam, 2000). Social network theory argues that the quality and denseness of this social fabric eventually determines the speed, direction and flow of resources through a social network (Burt, 1992). In turn, it is through the flow and use of social resources that collective action may be facilitated and organizational goals may be achieved (Lin, 2001; Lochner, Kawachi, & Kennedy, 1999). For instance, strong social relationships are suggested to facilitate joint problem solving, lower transaction costs, and support the exchange of complex, tacit knowledge among network members (Hansen, 1999; Putnam, 1993; Uzzi, 1997).

Studies into social networks among educators have focused on various types of social networks that connect teachers within and between schools, such as discussion about curricular issues (content, teaching materials, planning), communication around reform, seeking advice, and friendship among teachers (Coburn & Russell, 2008; Cole & Weinbaum, 2007; Daly & Finnigan, 2009, Hite, Williams, & Baugh, 2005; Pustejovsky & Spillane, 2009). While some studies focus on a single relationship (Coburn & Russell, 2008), others include and contrast multiple relationships (Cole & Weinbaum, 2007; Pustejovsky & Spillane, 2009), although not for the purpose of explicating their similarities or differences per se. Therefore, what is less clear is whether educators’ social networks are shaped by the content that defines their ties (Hite, Williams, Hilton, & Baugh, 2006; Podolny & Baron, 1997). Insights in the way network content shapes collegial relationships is important for understanding the extent to which teachers’ professional relationships may affect educational practice. As Little (1990) marks: ‘It is precisely such “content” that renders teachers’ collegial affinities consequential for pupils’. This insight can be provided by investigating network multiplexity and exploring a typology of social networks in school teams.

Network multiplexity

In social network terms, multiplex relationships are relationships that serve multiple interests or are characterized by a multiplicity of purposes (Gluckman, 1955, 1965). In other words, multiplexity focuses on the extent to which there is overlap between different social relationships, for instance advice and friendship. Many studies focus on multiplex exchanges within a single relationship, for instance, whether a relationship between two individuals is characterized by the exchange of both work related advice and friendship (De Klepper, Van de Bunt, Groenewegen, 2007; Hansen, Mors, & Lovas, 2005; Hite et al., 2006; Hite, Williams, & Baugh, 2005, Koehly & Pattison, 2005; Lazega & Pattison, 1999; Lomi, 2002). Less attention has been paid to the issue of multiplexity in regard to whole networks. To advance social network theory in this direction, this chapter therefore focuses on multiplexity of whole networks. Meaning, we will examine the overlap between whole networks among the same set of individuals that are characterized by a multiplicity of purposes. Multiplex relationships that serve multiple purposes are suggested to be stronger than relationships that only serve a single purpose, and individuals who are connected through multiplex networks will have greater success in accessing and mobilizing resources (Kapferer, 1969; Doreian, 1974). Multiplex, or multi-dimensional social networks have been studied outside education to validate name generator questions (Ruan, 1998), to examine the pattern of relationships among lawyers (Lazega & Pattison, 1999), to differentiate between different types of support networks (Bernard et al., 1990) and advice networks (Cross, Borgatti, & Parker, 2001). Yet, knowledge on the extent to which social networks in school teams can be differentiated is scarce.

Towards a typology of social networks in school teams

Teacher-to-teacher exchange can be captured by a variety of references that all refer to some form of collegiality (Little, 1990; Rosenholtz, 1989), such as sharing, giving advice, discussing work, and collaborating. Little (1990) argues that these exchanges are not just a straightforward collection of activities, but rather ‘phenomenologically discrete forms that vary from one another in the degree to which they induce mutual obligation, expose the work of each person to the scrutiny of others, and call for, tolerate, or reward initiative in matters of curriculum and instruction’ (p. 512). Little (1990) places various collegial forms on a dimension of mutual interdependence, with storytelling as an example of collegiality that entails low mutual interdependence, and joint work as an example of collegiality that involves high interdependence. She poses that a shift on this dimension toward increased interdependence relates to changes in the frequency and intensity of teachers’ interactions and the likelihood of mutual influence. Moreover, increased interdependence poses rising demands for collective autonomy and teacher-to-teacher initiative (Little, 1990). While this dimension of mutual interdependence could serve as a valuable guide in typifying various forms of social relationships in school teams, it has not yet received much empirical attention. Given the popularity of social network studies in education, the question in which forms the amorphous concept of ‘collegiality’ permeates teachers’ daily practice is more relevant than ever before.

Another useful dimensionality of social relationships that has become common practice in social network research is the distinction between instrumental and expressive relationships (Ibarra, 1993, 1995). These distinct relationships are believed to provide different kinds of support and transfer unique knowledge and information (Erickson, 1988). Instrumental relationships encompass social interactions that are ultimately aimed at achieving organizational goals, such as work related advice or collaboration. Instrumental ties are believed to be ‘weak’ ties through which work related information and knowledge is exchanged between experts and people who seek information (Granovetter, 1973). Expressive relationships are formed through social interaction that is not directly aimed at work related issues, that often places the individual’s interest above that of the organization (Burt, 1997), and that is mostly characterized by an affective component, such as personal support and friendship. In general, expressive ties are believed to be stronger, more durable and trustworthy, and offer greater potential to exert social influence (Granovetter, 1973; Ibarra, 1993; Marsden, 1988; Uzzi, 1997).

Increased understanding of a typology of social networks in school teams is indicated as social network studies often examine various types of networks without specifically addressing differences between the social networks under investigation[2]. By exploring multiple social networks this chapter not only aims to deepen our insights in the social fabric of school teams, but also addresses the validity of the common instrumental-expressive distinction in the context of education. The boundaries between instrumental and expressive relationships are fuzzy and often tend to overlap (Borgatti & Foster, 2003). In addition, recent research has suggested that one type of relationship can in part determine or reinforce another type of relationship (Casciaro & Lobo, 2005). Since a systematic investigation of multiple networks in school teams is missing, this chapter is one of the earliest to explore a typology of social networks in school teams. In addition to advancing social network theory, the study thereby offers a unique insight in the social fabric of Dutch elementary schools.

METHOD

Context

We conducted a survey study at 53 elementary schools in south of The Netherlands. The schools resided under a single district board that provided the schools with IT, financial, and administrative support. The schools participated in the study as part of a district-wide school improvement program focused on school monitoring and teacher development. The 53 sample schools were located in rural as well as urban areas and served a student population ranging from 53 to 545 students in the age of 4 to 13. While the schools differed slightly regarding students’ SES and ethnicity, the schools’ student population can be considered as rather homogeneous in comparison to the Dutch average.

Sample

All principals and teachers were asked to participate in the survey study. A total of 51 principals and 775 teachers responded to this call, reflecting a return rate of 96.8 %. Of the sample, 72.9 % was female and 52.5 % worked full-time (32 hours or more). The age of educators in the sample ranged from 21 to 63 (M = 45.7, sd = 10.7). Additional sample characteristics are included Table 1 and 2.
Instruments

Social networks. To discern common types of interaction among teachers in elementary education, we interviewed seventeen elementary school teachers, two principals and one coach[3] who volunteered in reaction to a canvas call among the personal social network contacts of the principal researcher. We asked the educators to describe a regular work week and give examples of the types of social interaction they had with their colleagues. The hour-long interviews were audio-recorded and conducted using a semi-structured interview guide (Patton,

Table 1. Sample demographics of schools and educators (N = 53, n = 775)

Individual level

Gender

Male

210 (27.1 %)

Female

565 (72.9 %)

Working hours

Part time (less than 32 hours)

368 (47.5 %)

Full time (32 hours or more)

407 (52.5 %)

Experience

1-3 years

152 (19.6 %)

at school

4-10 years

256 (33.0 %)

> 11 years

367 (47.4 %)

Grade level[4]

Lower grade (K – 2)

353 (45.4 %)

Upper grade (3 – 6)

422 (54.5 %)

School level

Team experience

6 months to 2 years

More than 2 years

20 (37.8 %)

33 (62.2 %)

Table 2. Sample demographics of schools and educators (N = 53, n = 775)

N

Min.

Max.

M

Sd

Individual level

Age

775

21

63

45.7

10.7

School level

Gender ratio[5]

53

57.0

100.0

76.8

10.7

Average age

53

35.4

52.8

45.3

3.7

Number of students

53

53

545

213

116.6

Team size

53

6

31

14.8

6.8

Socio-economic status (SES) [6]

53

0.4

47.3

7.9

9.5

1990; Spradley, 1980). We analyzed the interview data using a constant comparative analysis method (Boeije, 2002; Glaser & Strauss, 1967). We compared perspectives of educators with different formal roles and at different grade levels, grouped different forms of social interaction mentioned by the educators, and checked and rechecked emerging types of social interaction (Miles & Huberman, 1994). From this preliminary analysis, we deduced seven social networks that capture the forms of social interaction as described by the interviewed educators. As a member-check procedure (Miles & Huberman, 1994), these social networks were then shared with a new group of educators. This group comprised eleven principals and six teachers who formed a pilot sample to establish face validity of the social network questions. Based on their comments, slight adjustments were made that resulted in the final questions to assess social networks of educators in elementary school teams (see Table 3).

We include discussing work as social interaction concerning the discussion of work related issues. The nature of teaching requires the accumulation, transfer and exchange of ideas, experiences, expertise, and knowledge, all which can be shared through the discussing of work with colleagues (Monge & Contractor, 2003). Discussing work can be regarded a general form of resource exchange related to work and can pertain to various topics, such as instruction, planning, or use of teaching materials.

Collaboration refers to joint work among educators who are collectively responsible for the product of collaboration, and as such, collaborative relationships address collective action among teachers (Little, 1990). Interaction through collaboration may offer valuable opportunities for the exchange of knowledge and ideas, and the alignment of shared goals and expectations. Given the nature of schools as ‘loosely coupled’ systems (Weick, 1976) and the relative autonomy that teachers have in their classrooms (Lortie, 2002), collaboration in Dutch elementary schools often follows formal task hierarchy and is prescribed by formal roles, such as coaches or social support specialists. However, collaboration may also be voluntary, such as participating in a committee for a specific event.

Asking for advice is of interest to the study of teacher networks since receiving advice may be part of ongoing teacher development and may facilitate the adoption and implementation of reform and innovation in schools (Moolenaar, Daly & Sleegers, in press). Asking for advice addresses the issue of ‘who seeks out whom’ for work-related advice and thereby, in contrast to the previous types of instrumental interaction, implies an interdependence of knowledge, expertise, or information between the advice-seeker and the advice-giver. For the advice-giver, advice relationships are a powerful tool to gain social control as they convey information and disclose vulnerability and risk-taking on the part of the advice-seeker. Research has indicated than advice-seekers often seek advice from people with a higher status than the advice-seeker (Blau, 1964; Lazega & Van Duijn, 1997).

Table 3. The seven social network questions to assess social networks in Dutch elementary school teams

Social network questions (in Dutch)

Met welke collega’s kunt u goed over uw werk praten?

Met welke collega’s werkt u het liefst samen?

Aan welke collega’s vraagt u meestal advies over uw werk?

Met welke collega’s brengt u graag pauzes door?

Met welke collega’s heeft u wel eens meer persoonlijke gesprekken?

Met welke collega’s spreekt u wel eens buiten het werk?

Welke collega’s beschouwt u als vrienden?

English equivalent of the original Dutch question

Whom do you turn to in order to discuss your work?

With whom do you like to collaborate the most?

Whom do you go to for work related advice?

With whom do you like to spend your breaks?

Whom do you go to for guidance on more personal matters?

Who do you sometimes speak outside work?

Who do you regard as a friend?

Network

Discussing work

Collaboration

Asking advice

Spending breaks

Personal guidance

Contact outside work

Friendship

The interviewed educators mentioned spending breaks as another important form of social interaction. During breaks, teachers may exchange many types of resources, both work related and personal. Relationships based on spending breaks may be seen as mostly expressive since, according to the interviewed educators, breaks imply ‘off the job’ moments in which teachers may discuss personal issues or social conversation more easily than during formal meetings.

Another social relationship among educators involves going to a colleague for personal guidance and to discuss personal matters. This form of interaction explicitly addresses the informal, personal nature of relationships. A relationship around personal guidance and the discussion of personal matters implies a certain level of trust between the people involved in the relationship. Such a personal bond is believed to be more strong and durable than work related relationships such as work related collaborative exchange (Granovetter, 1973). Whereas ‘spending breaks’ and ‘personal guidance’ may be described as ‘friendly’ relationships, the next two relationships tap into interaction that more specifically addresses ‘friendship’ (Kurth, 1970).

The next social relationship, according to the interviewed educators, entails having contact outside work. When teachers have frequent contact with one another outside school, this may indicate a relationship that is built on more personal grounds than work. Therefore, having contact outside work may be a good indicator of some sort of friendship or strong bond, even though both individuals may not define the relationship as a friendship relationship (Ibarra, 1992; Zagenczyk, Gibney, Murrell & Boss, 2008).

The final social relationship addresses friendship. Friendship is included in many social network studies as the prototypical expressive relationship (e.g., Cole & Weinbaum, 2007; Lazega & Pattison, 1999) as friendship expresses personal affect and social support (Gibbons, 2004). Individuals depend on friends for counseling and companionship (Krackhardt & Stern, 1988), and friendship ties facilitate open and honest communication that may boost organizational change (Gibbons, 2004).

These seven social network questions were included in a social network survey to assess social relationships among educators. Respondents were provided with a school specific appendix that contained the names of the school team members of their school, accompanied by a letter combination for each school team member (e.g., Mr. Jay Hoffer[7] = AB). They were asked to answer each social network question by writing down the letter combination(s) of the coworker(s) they would like to indicate as being a part of their social network as specified by the question. The number of colleagues a respondent could answer was unlimited.

Data analysis

Social network analysis. The data were examined using social network analysis. Social network analysis is a technique to systematically analyze patterns of relationships in order to understand how individual action is situated in structural configurations (Scott, 2000; Valente, 1995). We first constructed matrices for each network question for each school. The matrices were compiled following the same procedure, namely if educator i nominated educator j as an advice relationship, a 1 was entered in cell Xij. If educator i did not nominate educator j, a 0 was entered in cell Xij. This procedure resulted in an asymmetric matrix that summarized all directed relationships among the educators within a single school. To explore and describe the networks, several social network properties at both the individual and school level were calculated based on the matrices using software package Ucinet 6.0 (Borgatti, Everett, & Freeman, 2002; Borgatti, Jones & Everett 1998; Burt, 1983).

Individual level properties include raw and normalized scores for out-degree and in-degree, and ego-reciprocity. Out-degree depicts the number of people nominated by the respondent, and can therefore be interpreted as a measure of individual activity. In-degree represents the number of people by whom the respondent was nominated, and can be read as a measure of individual popularity.

The raw scores of in- and out-degree encompassed the actual number of educators that were named by the respondents. Because the average in-degree is the same as the average out-degree (each out-going relationship for one educator also implies an in-coming relationship for another educator), we only report the average in-/out-degree. The standard deviations of the out- and in-degrees reflect the variability among educators in the amount of out-going and in-coming relationships, and may thus be different for the out-degrees and in-degrees. For instance, educators may vary greatly in the number of relationships they indicate to have, but there may be less variability in the number of relationships that educators receive. The range of the average raw scores varies from 0 to 14.8 since this is the average team size of the sample schools. Besides these raw scores, we also report normalized scores for out-degree and in-degree to facilitate comparisons among schools with different team sizes.

The normalized scores can be interpreted as the percentage of relationships of the whole network that an educator maintains. The normalized out- and in-degrees range from 0 (the educator has no relationships) to 100 (the educator has a relationship with all of his/her team members). Again, the average percentage of out-going relationships is the same as the average percentage of in-coming relationships. The standard deviations of the normalized out- and in-degrees mirror the variability among educators in the percentage of relationships that are sent (out-going) or received (in-coming).

Ego-reciprocity is a measure of reciprocity at the individual level. Ego-reciprocity is calculated as the number of reciprocal relationships in which in educator is involved, divided by the total number of his/her relationships. Ego-reciprocity thus reflects the percentage of ties of an educator that is reciprocated. Ego-reciprocity ranges from 0 (none of the individual’s relationships are reciprocated) to 100 (all of the individual’s relationships are reciprocated).

At the school level, we calculated the network measures of density, reciprocity, and centralization. Density represents the concentration of relationships in a social network, and is calculated by dividing the number of observed relationships by the total number of possible relationships in a given network. This means that the greater the proportion of social relationships between school staff members, the more dense the social network. The density of a school’s network may range from 0 (there are no relationships in the school team) to 1 (all school team members have indicated to maintain a relationship with each other). The density of a network can be thought of as a measure of cohesion (Blau, 1977). A dense network is believed to be able to move resources more quickly than a network with fewer ties (Scott, 2000).

Reciprocity captures the extent to which the relationships in a social network are reciprocal, and is calculated as the number of reciprocal relationships in a team, divided by the total possible number of reciprocal relationships. Higher levels of reciprocity have been associated with complex knowledge exchange and higher organizational performance (Kilduff & Tsai, 2003). The reciprocity of a school’s network may range from 0 (none of the relationships in the school team are reciprocated or mutual) to 1 (all of the relationships in the school team are reciprocated or mutual).

In-centralization was included to examine the central tendency of the social networks. This measure assesses whether the relationships in a given network are evenly dispersed in a network, or whether the relationships are centralized around one (or a few) very central people, who receive many nominations. In-centralization is based on the variability of in-degrees within a given team. High in-centralization reflects a high variability in the school team between educators who are often nominated and educators who are seldom nominated. As such, centralization of a social network refers to the difference between one or a few highly central person(s) and other (more peripheral) people in the network. Centralization ranges from 0 (no variability – all members of the network are chosen for advice as frequently) to 1 (maximum variability – every educator in a network only nominates a single person in the network, while these educators themselves are not nominated at all). The more centralized the social network is, the more resources are disseminated by a single or a few influential people to the rest of the network. In contrast, relationships and resources in a decentralized social network are much more evenly shared among all school team members.

Examining multiplexity

To determine the similarity between the seven social networks within each school, we estimated a series of Quadratic Assignment Procedure (QAP) correlations in Ucinet (Borgatti, Everett, & Freeman, 2002; Hanneman & Riddle, 2005; Krackhardt, 1987). The QAP is a procedure to calculate correlations between social networks. When conducting social network research, statistical assumptions of independence are violated because relations between individuals are nested and embedded within the same network. Social network data are often interdependent, thus limiting the use of ‘conventional’ statistical techniques such as Pearson correlations. The QAP was designed as a variation on conventional correlational analyses for the use with social network data.

The QAP follows a specific process. First, a Pearson correlation coefficient is calculated for two corresponding cells of two matrices that contain network data. Then, it randomly permutes the rows and columns of one of the matrices hundreds of times (each time computing a new correlation coefficient), and compares the proportion of times that these random correlations are larger than or equal to the original observed correlation. A low proportion (p<.05) suggests a strong relationship between the matrices that is unlikely to have occurred by chance (Baker & Hubert, 1981). We calculated QAP correlations for the seven networks within each school, and then aggregated these correlations using matrix algebra to signify overall QAP correlations


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