Home Articles Reader Opinion Editorial Book Reviews Discussion Writers Guide About TCRecord
transparent 13
Topics
Discussion
Announcements
 

Data, Dyads, and Dynamics: Exploring Data Use and Social Networks in Educational Improvement


by Alan J. Daly - 2012

Background: In the past decade, there has been an increasing national policy push for educators to systematically collect, interpret, and use data for instructional decision making. The assumption by the federal government is that having data systems will be enough to prompt the use of data for a wide range of decision making. These policies rely on inducements to inspire local level changes; however, they leave the processes related to data use largely undefined.

Objective: In this article, I argue that many of the studies on data use either invoke or directly assess network-related concepts and, as such, network theory and analysis provides a useful analytic and complementary framework and methods for examining the social infrastructure in the use of data for educational improvement.

Research Design: This article reports on a literature review of the data use and social network literatures and the utility of intersecting both literatures for studies on data use for educational improvement.

Conclusions: Many data use studies report that the interpretation and use of data takes place both within and between individuals who, through social interaction, are both co-constructing and making sense of data and their use. Given the increasing role of social relationships in data use studies better theorizing and understanding the dynamics of social influence and processes on the interpretation and use of data is needed. Social network theory and analysis offers a useful conceptual framework and accompanying methods for describing and analyzing the structure of a social system in an effort to understand how social relationships support and constrain the interpretation and use of data in educational improvement.

In the past decade, there has been an increasing national push for educators to systematically collect, interpret, and use data for instructional decision making. These efforts have been codified and emphasized in federal-level policies and programs such as No Child Left Behind and, most recently, the American Recovery and Reinvestment Act of 2009. However, the ultimate success of data use for educational improvement may depend on how states and local education agencies build capacity. States play a key role in determining accountability measures, and district offices are critical in mediating these policies through selecting data, developing knowledge, and supporting the use of data (Coburn, Touré, & Yamashita, 2009; Datnow, Park, & Wohlstetter, 2007; Hamilton et al., 2007; Ikemoto & Marsh, 2007). In addition, school-level development and individual capacity building are also important, with educational leaders and teachers playing key roles in disseminating data, directing new learning efforts, and aligning the use of data to existing efforts (Datnow & Park, 2009; Kerr, Marsh, Ikemoto, Darilek, & Barney, 2006; Knapp, Copland, & Swinerton, 2007). This suggests that the interpretation and use of data for improvement take place both within the individual and between educational actors who, through social interaction, co-construct and make sense of data and their use (Coburn, 2001, 2005; Datnow, Hubbard, & Mehan, 2002; Parise & Spillane, 2010; Spillane, Reiser, & Reimer, 2002). Social processes, therefore, may play a key role in the selection, interpretation, and use of data.


Social network theory may provide insight into how the social processes involved in data use stretch across individuals and levels of the educational system. Generally speaking, social network theory is concerned with the pattern of social ties that exists between actors in a social network (Scott, 2000). A social network perspective entails a move from a primary focus on the individual and the attributes of that actor to understanding the more dynamic supports and constraints of the larger social infrastructure (Borgatti & Foster, 2003; Cross, Borgatti, & Parker, 2002; Wellman & Berkowitz, 1998). Social network studies in education (e.g., Anderson, 2010; Coburn & Russell, 2008; Cole & Weinbaum, 2010; Daly, 2010; Frank, 2009; Frank, Zhao, Penuel, Ellefson, & Porter, 2011; Levine & Marcus, 2010; Penuel, Riel, Krause, & Frank, 2009; Spillane, Hunt, & Healey, 2009), as in other fields, primarily focus on how the constellation of relationships in networks may facilitate and constrain the flow of “relational resources” (attitudes, beliefs, knowledge, materials, and so on), as well as providing insight into how individuals gain access to, are influenced by, and leverage these resources (Degenne & Forsé, 1999). The network perspective does not supplant the importance of individual attributes in understanding the selection, interpretation, and use of data, but rather offers a complementary perspective and set of methods for better understanding the dynamic influence of social processes.


In this article, I argue that social network theory and analysis offers a conceptual framework and accompanying methods for describing and analyzing the structure of a social system in an effort to understand how relationships support and constrain the interpretation and use of data. A growing number of data use studies imply network-related concepts and the influence of social processes (e.g., Brunner et al., 2005; Burch, 2006; Coburn, 2010; Coburn & Russell, 2008; Daly, 2010; Diamond & Cooper, 2007; Firestone & González, 2007; Honig, 2006; Kerr et al., 2006; McLaughlin, 1990; Popham, 2008; Portz, 1996; Spillane et al., 2002; Ritter & Boruch, 1999; Young, 2006), suggesting that this set of ideas offers a powerful way to understand aspects of the data use phenomenon. Yet, few draw on social network theory and methods explicitly. Here, I offer a way to bring social network theory more fully into studies of data use by offering core concepts of social network theory that illuminate different sets of social influence processes related to data use. In making my argument, I first provide a brief overview of social network theory and analysis. I then discuss five themes as related to data use and show how drawing on social network theory and analysis can more fully enhance our understanding of those themes. I close with recommendations for promising areas for future research.


APPLYING SOCIAL NETWORK THEORY AND ANALYSIS


Rather than trying to understand the interpretation and use of data based on the attributes of an educator (gender, years of experience, training, education, beliefs, and so on), network theorists focus on the influence and outcome of an actor’s “position” vis–à-vis social ties with others, as well as the overall social structure of a network (Borgatti & Ofem, 2010). As Borgatti (2003) offered, network theory represents a paradigmatic shift from “theoretical constructs from monadic variables (attributes of individuals) to dyadic variables (attributes of pair of individuals)” (p. 2). In many cases, social network theorists suggest that the underlying social structure determines the type, access, and flow of resources to actors in the network, leading some scholars to suggest that the old adage “It is not what you know, but who you know” is more accurately, “Who you know defines what you know” (Cross & Parker, 2004; Newman, Barabasi, & Watts, 2006; Wasserman & Faust, 1998).


The foundational element in social network theory is the social ties between actors. The pattern of ties across a network creates an overall social structure that can support and constrain the access, variety, and use of resources. Given the importance of the “tie” in social network theory, it is useful in understanding the nuances in the term to draw on a typology of ties suggested by Borgatti, Mehra, Brass, and Labianca (2009), which includes social relations, interactions, and flows. In this typology, social relations are the foundation on which most social networks studies are based. Social relations can take the form of kinship (e.g., mother), other roles (e.g., colleague of, boss of ), affective (e.g., likes, hates), and cognitive (e.g., knows, knows about), and can exist on multiple intersecting levels (multiplex relations). For example, a colleague can both know about and dislike a boss. Interactions, on the other hand, are the individual events that may be quantified and are facilitated by, and may occur within, a social relationship and vice versa. For instance, second-grade teachers (social relation) may collaborate twice a month (interaction). Flows represent both tangible (assessment rubrics, score sheets, test scores, and so on) and intangible (information, beliefs, knowledge, interpretations, practices, and so on) resources that are transmitted through these interactions. Building on the previous example, second-grade teachers (social relation) may collaborate twice a month (interaction) in regard to the interpretation and use of data (flows).


The network data from social relations, interactions, and flows of resources between actors can be collected through interviews, focus groups, archives, and survey methods (Scott, 2000). These data can then be analyzed through a growing and specialized set of qualitative and quantitative approaches, including descriptive, inferential, and statistical modeling (Jackson, 2009). However, like other theoretical and methodological paradigms, several assumptions undergird social network theory and resulting research (Degenne & Forsé, 1999). First, actors in a social network are assumed to be interdependent rather than independent (Degenne & Forsé, 1999; Wasserman & Faust, 1998). Second, relationships are regarded as conduits for the exchange or flow of resources (Burt, 1982, 1997; Kilduff & Tsai, 2003; Powell, Koput, & Smith-Doerr, 1996). Third, the structure of a network has influence on the resources that flow to and from an actor (Borgatti & Foster, 2003). Fourth, patterns of relationships, captured by social networks, may present dynamic tensions because these patterns can act as both opportunities and constraints for individual and collective action (Brass & Burkhardt, 1993; Burt, 1982; Gulati, 1995).


As has been suggested, existing studies of data use invoke a number of themes related to network theory to explain key data use1 processes such as the role of district and site leaders in supporting a data oriented culture (Wayman & Stringfield, 2006); the use of intermediaries in developing capacity and brokering skills (Atteberry & Bryk, 2010; Marsh, McCombs, & Martorell, 2010); the nested and interdependent nature of data use in a coherent system (Datnow et al., 2007; Halverson, Grigg, Prichett, & Thomas, 2007; Kerr et al., 2006; Marsh, Pane, & Hamilton, 2006; Supovitz & Klein, 2003; Young, 2006); and the presence of organizational structures and opportunities to collaborate in a high-trust environment (Confrey & Makar, 2005; Copland, 2003; Datnow et al., 2007; Halverson et al., 2007; Hammerman & Rubin, 2002; Ikemoto & Marsh, 2007; Wayman & Stringfield, 2006).2 I selected these themes based on both their prevalence in studies of data use and that they invoke social network theory. This intersection between the literatures represents a potentially fertile ground to further conceptualize and examine the use of data in educational improvement. In organizing the themes, I begin with the role of social position, move to small groups and then out to larger interdependent systems, and close with the quality of social relationships. These broad themes from data use studies are captured in five distinct sections: formal and informal position, boundary spanners and brokers, subgroups, nested relationships, and the quality of relationships. It should be noted that although these themes are presented separately, the data use and network literatures suggest the presence of an interrelated and often interdependent relationship.


FORMAL AND INFORMAL POSITIONS IN STUDIES OF DATA USE


The importance of leadership in driving data use is suggested across multiple studies (Boudett, City, & Murnane, 2007; Chen, Heritage, & Lee, 2005; Datnow et al., 2007; Earl & Katz, 2002; Symonds, 2004; Wayman, 2005; Wayman & Stringfield, 2006). District and site leaders who occupy key positions in both modeling and supporting the use of data are more likely to build a strong culture for the use of data in improving instruction (Marsh et al., 2006; Mieles & Foley, 2005). These “data leaders” set clear expectations, provide time for collaboration, support professional development, and align resources to ensure the coherent use of data across a system (Halverson et al., 2007; Wayman, Cho, & Johnston, 2007; Young, 2006). However, many studies suggest that leaders may not have the skill sets to model and enact the leadership necessary to support data use, and as such, the effective use of data may falter (Wayman et al., 2007). One assumption in many data use studies is that successful data use leadership is primarily driven by the “data skills” of the leader—as opposed to being associated with the constellation of social relationships surrounding that leader. Although skill sets are clearly important, if a leader is socially marginalized by staff, the activities of that leader may be regarded as less influential, leaving others who may not occupy formal leadership positions to either take up, or alternatively cast off, this potentially valuable role (Camburn, Rowan, & Taylor, 2003).


This is not to suggest that in the data use literature, “leadership” is only defined through formal positions; in fact, many studies indicate the importance of a more distributed approach to data use (Copland, 2003; Wayman & Stringfield, 2006). Leaders who are able to effectively distribute leadership and practices among teachers, as well as create organizational structures such as data teams, seem to be able to better foster a school culture that embraces the use of data in making instructional decisions (Cromey & Hanson, 2000; Halverson et al., 2007; Lachat & Smith, 2005; Marsh et al., 2006; Wayman, 2005; Wayman, Snodgrass Rangel, Jimerson, & Cho, 2010). Therefore, although leaders may be formally well positioned and skilled to support data use, they must also simultaneously develop the necessary social relations in enacting the use of data as well as creating opportunities for others to occupy more informal leadership positions.


Social network theory also highlights the importance of “position.” However, position in a network sense refers to an actor’s location in a social structure. Network theorists argue that this social position affects access to resources and outcomes for both an actor and potentially the larger network. An individual’s position in a social network is determined by the constellation and quality of outgoing and incoming social ties that surround that actor. This pattern of ties creates a social structure that supports and constrains an individual’s access to resources in a network. In making the idea of a “network” and “social position,” as well as subsequent social network concepts, more concrete, I offer a graphic illustration of an adapted data use network from a fictional school named Elsinore (see Figure 1).3 Figure 1 represents the fictional social network of Elsinore school that was adapted to reflect whom educators turn to for expertise regarding data use. The nodes represent the nicknames of individual educators in Elsinore and are sized by the number of incoming and outgoing relationships (bigger nodes reflect more incoming and outgoing ties); lines represent the reported exchange of expertise around data use between educators in Elsinore; arrows represent the direction of those expertise ties; and larger circles indicate subgroups.


Figure 1. Data use expertise network in Elsinore school


[39_16811.htm_g/00001.jpg]

Note. Adapted from Shakespeare’s Hamlet, and Moretti (2011).


As is evident in Figure 1, individual educators in Elsinore receive and send differing amounts of expertise related to data use. For example, Hamlet has relatively more incoming and outgoing expertise ties than, for example, 2nd Gravedigger, who only has one incoming expertise relationship (hence the difference in node size). It is the constellation of social relationships within this data use relationship that determines the various informal social positions of educators within the Elsinore network. It is important to note that the informal social positions of educators in this expertise network may or may not be the same as an individual’s formal leadership role. For example, Claudius, who is a central actor in a social sense in the exchange of expertise, may not necessarily play a formal leadership role (e.g., principal) in the school. Although there are a number of social structural positions that an actor may occupy, two contrasting positions, central and peripheral, are often discussed in the data use and network literatures.


CENTRAL ACTORS


A person who occupies a central position in a social structural sense is one who receives a higher proportion of interactions than others in the network (Scott, 2000). In Figure 1, Hamlet and Claudius occupy more central positions in the data use expertise network. Actors who are in a central position are considered points of intersection at which the individual may disproportionately amass resources, such as expertise, and as such may have undue influence over the way in which that resource is disseminated across a system (Kilduff & Krackhardt, 2008; Raider & Krackhardt, 2001). Research suggests that being central in a social network provides an individual increased effect over the network because of access to multiple actors and the potential to create new linkages (Baker & Iyer, 1992; Balkundi & Harrison, 2006; Stuart, 1998; Tsai, 2001). By occupying a more central position in the flow of resources (in this case, expertise around data use), a central individual has easier access to resource flows from the larger social network (Adler & Kwon, 2002). Moreover, having more relationships may increase an actor’s opportunities to access unique information (Balkundi & Kilduff, 2005; Krackhardt, 1996). Access to novel and diverse resources provides a central actor with the possibility to guide, control, and broker the flow of resources within a group (Burt, 2005; Kilduff & Krackhardt, 2008). Network studies related to the use of data also support these findings, suggesting that an individual’s network position, attitude, or approach to data use—especially if that individual is of high status and well connected—can influence others as well as the flow of resources within a group (Cole & Weinbaum, 2007, 2010).


Occupying a central position in a social network may offer an individual potential in the form of status, power, and influence (Brass, 1984; Friedkin, 2004), but it may also burden the actor with too many relationships to maintain (Burt, 1992). In general, too many ties may be disadvantageous because these relationships may drain an actor’s own resources (Balkundi & Harrison, 2006). This may especially be the case in more affective relationships, which may require additional effort to maintain (Boyd & Taylor, 1998). Central actors, who are also in formal leadership roles, may find it socially challenging to withhold resources, reprimand team members with whom they have deep affective relations, or even make difficult decisions that might have negative consequences for an individual or group (Hughes, Ginnett, & Curphy, 1999; Taylor, Hanlon, & Boyd, 1992). Moreover, relationship patterns may also constrain actor behavior to a distinct role as defined by those relationships (Krackhardt, 1999). Along the same lines, it might be difficult for an actor embedded in a network of many personal relationships to oppose general opinions and interpretations of core organizational goals given the social pressures that can result from multiple relationships (Krackhardt & Kilduff, 1990).


PERIPHERAL ACTORS


In contrast, actors may also occupy peripheral or even isolated positions in a social network structure (e.g., Cornelius and Voltemand in Figure 1). These individuals typically receive less interaction, and, as such, fewer resources may come their way (Wasserman & Faust, 1998). These peripheral individuals may miss an opportunity to benefit from the resources, such as expertise, held by those in more central positions because they lack the necessary relationships (Borgatti & Everett, 1999; Cross & Parker, 2004). Because these peripheral individuals have fewer social ties, they tend to possess less influence over the larger network; as such, their perspectives may not be readily spread, as was suggested in a study of high school departments (Lima, 2003, 2007). In addition, it may also take longer, in a social sense, for resources to reach these peripheral actors, thus creating lag time in moving resources throughout a system (Borgatti, Jones, & Everett, 1998; Cross et al., 2002; Cummings & Cross, 2003; Fernandez & Gould, 1994). Moreover, because it may require more time for resources to reach these peripheral actors, a degradation of the original message could result (Cross et al., 2002; Cummings & Cross, 2003; Fernandez & Gould, 1994). This idea is similar to the game of telephone, in which the last one to receive the information often hears a distortion of the original message. Moreover, less central actors usually receive only the resources deemed necessary by those in more central positions (Borgatti & Cross, 2003; Burt, 2000), potentially inhibiting their overall perspective of an initiative and the organizational goals.


Because peripheral actors typically have few ties to other actors, they are also dependent on their limited relationships for resource flow, as exemplified by Reynaldo in Figure 1. This reduces the variety of resources to which a peripheral actor is exposed and essentially creates the condition whereby the “connecting” actor effectively “controls” the resource that flows to the peripheral individual. Moreover, if the connector is moved out of the network, the peripheral actor may become isolated. Isolated individuals, as identified by Gentleman B in Figure 1, do not, in a network sense, have the opportunity to provide (or receive) resources such as expertise, and it may be difficult to leverage their knowledge to support the goals of the larger organization, as was suggested in a study of elementary school teachers (Bakkenes, De Brabander, & Imants, 1999). Moreover, in terms of a sense of belonging, isolated individuals may be professionally disconnected and require differentiated levels of support to reconnect them to the larger system (Bakkenes et al., 1999). However, it should also be noted that although peripheral or isolated actors have limited opportunities because of fewer ties, they also have fewer social constraints on their actions because their lack of ties suggests reduced social influence from, and responsibility to, others. This lack of constraint may allow for resistance to schoolwide initiatives or, in contrast, allow these more peripheral actors opportunities to develop “disruptive innovations” that often occur on the periphery of a network (Christensen, Johnson, & Horn, 2008).


Although the research on data use suggests the importance of the formal role of leadership, in reality, leaders may occupy different, perhaps less central, positions in an informal network; as such, they may have limited influence over a data use initiative taking hold (Camburn et al., 2003; Halverson et al., 2007). Moreover, a leader may be central in one type of relationship, such as source of data, but peripheral in another, such as advice around interpretation (Spillane, Healey, & Kim, 2010). As such, network theory may provide a more nuanced understanding of the social position a leader occupies and the subsequent differentiated influence of that actor’s position on data use initiatives. Future data use studies using network analysis could also explore the dynamic and shifting nature of a principal’s social position in a network. Perhaps early on in a data use initiative, a leader plays a more central role and then slowly moves out of that position, providing opportunities for the distribution of leadership and practices related to data use. Exploring the trajectory and diffusion of an initiative while simultaneously tracking the degree to which a leader’s position and influence in a social network changes represents a potentially interesting and useful intersection of data use and network analysis. Moreover, at an individual level, the personal (ego) networks of leaders in multiple settings could be compared to determine if the constellation of relationships surrounding a leader is associated with outcomes related to a data use initiative. A similar line of inquiry could be perused on the ego networks of teachers, exploring how those relationships change over time regarding access to expertise in response to a reform (Coburn, Choi, & Mata, 2010).


In many data use studies, actors are often identified and studied based on the formal positions they hold in a system, such as a principal or lead teacher. However, this common approach may lead to a different set of conclusions than one may obtain from examining those actors who are regarded, in a social sense, as occupying more “informal” and central leadership roles. Because few data use studies have examined informal leaders in a system and the ways in which those socially central actors influence data use, a careful analysis of the resources that flow to and from these actors may be very revealing. This analysis could also compare the formal system of position and title with the informal social structure to examine alignment and congruence. Creating network maps, as exemplified in Figure 1, to locate individuals in social space may provide an opportunity to identify individuals who may or may not be in “formal” positions yet have significant influence over data use initiatives. Moreover, researchers could sample interviewees based on network position (Daly & Finnigan, 2009, 2010; Finnigan & Daly, 2010). For example, an interviewee, perhaps a formal leader of a data team, may indicate that he or she is well informed and connected to what is occurring in the larger system. However, even though this individual may hold a formal leadership position and perceive a level of “connectedness,” he or she may well be peripheral in the social network and as such have a different vantage point on actual use of data across the school.


A well-positioned leader in the use of data is important. However, as a recent social network study suggests, if those relations become too attached to a specific individual, such as a principal, the school may be negatively impacted when that leader departs because those relationships are also likely to leave (Hite, Hite, Mugimu, & Nsubuga, 2010). Conversely, a new leader who enters a system may also bring his or her social connections, which may provide new useful access to resources about data use; alternatively, those same ties may inhibit productive work because they constrain a leader to previous sources of knowledge (Hite et al., 2010). Examining the social ties connected to the exit and entrance of leaders and their effect on the use of data appears to be an important and understudied topic in the data use literature. Better understanding of both the positive benefits of leaders in central positions and the “costs” of occupying those social positions to both the individual and larger system in terms of data use represents a potentially rich addition to the data use literature.


BOUNDARY SPANNING AND BROKERING IN STUDIES OF DATA USE


In addition to the role of leadership, studies of data use have frequently identified the key position of boundary spanners in brokering access to data. These studies highlight the role of a range of different actors—the district office, intermediary agencies, individuals such as coaches—in bringing data, information, and support for data use to schools. For example, several studies provide evidence that district offices play a key role, providing access to data as well as technical supports to school sites (Ikemoto & Marsh, 2007; Lachat & Smith, 2005). District offices can also play a key boundary-spanning role by clearly articulating and supporting the development of shared understanding and alignment with respect to goals and practices, enabling a more coherent system around data to develop (Kerr et al., 2006; Supovitz & Klein, 2003; Wayman et al., 2007; Wohlstetter, Datnow, & Park, 2008; Young, 2006). Case studies of data use suggest that in creating a more coherent system, district office culture and knowledge related to the use of data may also have substantial influence on the practices of principals in the interpretation and use of data (Firestone & González, 2007; Louis, Febey, & Schroeder, 2005; Louis, Leithwood, Wahlstrom, & Anderson, 2010). For example, because standardized data do not usually come in manageable formats, district leaders may “repackage” the data for school consumption. However, in repackaging the data, studies suggest that leaders often do so in “simple” terms that align with their previous knowledge and beliefs as to what is important and valued (Coburn, Honig, & Stein, 2009; Honig, 2003; Spillane, 2000). In this sense, the use of data goes through a filtering process at the district office before it is brokered out to the schools (Weick, 1985). This filtering process may support and constrain the interpretation and use of data at the site level.


Coaches and data specialists can also play useful boundary-spanning roles and broker support for data use within schools and across districts (Marsh et al., 2010; U.S. Department of Education [U.S. DOE], 2010). Network studies on data use and coaching suggest that a coach can play a significant role in the uptake and diffusion of information (Atteberry & Bryk, 2010; Marsh et al., 2010). More specifically, coaches may provide schoolwide support regarding the interpretation and use of data and the development of actionable instructional strategies (Marsh et al., 2010; Young, 2006). However, a coach’s ability to move information and strategies may be dependent on whether the coach has adequate social ties to diffuse resources throughout a system; absent those relationships, the expertise and knowledge of the coach may remain personal assets (Atteberry & Bryk, 2010). In addition, as coaches first enter a system, the “base-state” of social relations prior to the entry of that coach may also influence not only the ability of that coach to diffuse information but also the depth and success of a data initiative taking hold (Atteberry & Bryk, 2010). Because the role of a coach is typically focused on an individual, the coach may also create a “bottleneck” in terms of the flow of information—which may inhibit the independent use of data—and an overreliance on one individual (Boudett & Moody, 2005; Lachat & Smith, 2005; Wayman & Cho, 2008; Young, 2006).


Research on data use suggests that intermediary agencies can also play important boundary-spanning and brokering roles in the interpretation and use of data, as well as build the capacity of district and site personnel (Coburn, Touré, & Yamashita, 2009; Earl & Katz, 2002). These intermediaries may include organizations external to the district and school, such as universities, consultants, and private providers, which have also been described as having significant influence over reform (Ansell, Reckhow, & Kelly, 2009; Earl & Katz, 2002; Ikemoto & Marsh, 2007). Similar to the districts’ role in brokering data, which may pass through various “filters,” it is conceivable that these intermediary organizations also have “filters” and motivations that may not be readily transparent to the recipients of the support. Existing studies of data use suggest that attention to boundary spanners and brokers is important if we are to understand how data use processes unfold in schools and could be enhanced by drawing on social network theory and analysis.


Social network theory suggests that actors or groups that diffuse resources between otherwise disconnected individuals play an important role in the structure of a network. In social network theory and analysis, the concept of boundary spanners and brokers is often examined through an actor’s “betweenness.” Betweenness is assessed by how often an actor is positioned “in between” two people in the network who themselves are disconnected (Wasserman & Faust, 1998). In Figure 1, Horatio is considered to be “between” Sailor and Ambassadors as well as “between” Francisco and Ambassadors in the exchange of expertise around data use. Betweenness has been argued to support the flow of resources in a social network by creating bridging ties between disconnected actors (Burt, 1992). An individual is considered a bridge, in a social sense, when that actor “bridges” a structural hole, which refers to the gap between otherwise disconnected individuals or groups (Scott, 2000). Structural holes are the result of weaker (or absent) connections between individuals or groups in a social system (Burt, 2000). Research on structural holes focuses on the importance of individuals who connect otherwise disconnected others and who have the opportunity to “broker” resources between individuals or groups (Moolenaar, Daly, & Sleegers, 2011). Actors, such as Horatio, who bridge structural holes in a network occupy a position that may benefit the actor personally in terms of access to resource diversity or may provide benefits for the overall system in terms of connecting otherwise disconnected others (Burt, 1992; Obstfeld, 2005; Thornton, 1999). In addition, occupying such a brokering position offers potential social control over tasks and activities as the broker connects both sides of the social gap (Burt, 2000).


 Those who are in a position to span structural holes—in the case of data use, individuals in districts, intermediary organizations, and coaches—often have increased influence and power within a system because of the social control over resources. Because these individuals link otherwise disconnected others, they typically have access to a wider network of resources. Moreover, because they occupy a “coordinating” position between otherwise disconnected individuals or groups, they are in a structural position to “determine” who receives what particular resource and in what form (Ahuja, 2000). In this sense, boundary spanners may filter, distort, or hoard resources, which may provide benefit in the form of control or power to the broker but may simultaneously inhibit overall individual and organizational performance (Baker & Iyer, 1992; Burt, 1992). Social network methods can be used to identify the betweenness of an actor to better understand the degree to which an individual plays a boundary-spanning role. Once identified, more qualitative methods can be employed to understand the nature and quality of exchanges between boundary spanners and the individuals they connect.


Employing the concepts of brokers and boundary spanners, and the methodological techniques of social network analysis can enhance studies of data use in several ways. First, it may offer insight into power dynamics not necessarily based on formal position, but in the exchange and control of resources as described in the previous paragraph. Second, social network analysis could illuminate which actors and organizations are actually playing boundary-spanning and brokering roles. For example, observers point to the increased prominence of intermediary organizations in data use processes and outcomes, but little research has investigated how these intermediaries fit into the overall system of data use directly. It would be useful to also explore through a network lens how a district and intermediary organizations develop ties and how those connections facilitate and inhibit the interpretation, diffusion, and use of data. Moreover, network analysis could offer insight into the social diffusion and brokering process of data use through a system. Better understanding who in a system is sharing and moving data, and, as such, who may play a “filtering” role, may provide insight as to what practices are taken up and by whom, and how the original data may have changed during this process. Third, scholars could use social network analysis to investigate the relative effectiveness of different kinds of boundary spanners and brokers. For example, the data use literature suggests that an effective coach needs to be actively involved with many others in the school in providing support. The degree to which a coach is central or plays a brokering role in a data use network could be examined using network analysis. Analyzing the social position of the coach in a network and relating that social position to some measure of effectiveness represents an underexamined area in studies of data use.


SUBGROUPS AND STUDIES OF DATA USE


Moving beyond individual position, the importance of social interactions in small groups as reported in the data use literature typically examines how using data to make instructional decisions occurs at the department or grade level (Datnow et al., 2007; Diamond & Cooper, 2007; Grossman & Stodolsky, 1995; Lima, 2007; Sherer & Spillane, 2010; Wayman & Stringfield, 2006). These groups can develop strong norms of practice that can influence the type of data that may be sought as well as the construction of meaning about those data (Coburn & Talbert, 2006; Daly, 2010; Weinbaum, Cole, Weiss, & Supovitz, 2008; Young, 2006). This suggests that the effective use of data is a social and group phenomenon that may ultimately build the individual and group capacity for the work of improvement (Boudett et al., 2007; Earl & Katz, 2006). Collaborating with colleagues in generating knowledge relevant to student learning has been identified in a number of studies related to data use as a capacity-building approach (Datnow et al., 2007; Lachat & Smith, 2005; Kerr et al., 2006; Schildkamp & Kuiper, 2010; Wayman & Stringfield, 2006). Collaborative data teams are likely to implement and maintain change in practices if their work is conducted in a high-trust environment in which members perceive the interdependent nature of their group activities (Borko, 2004; Desimone, Porter, Garet, Yoon, & Birman, 2002; Wayman, Brewer, & Stringfield, 2009; Wei, Darling-Hammond, Andree, Richardson, & Orphanos, 2009).


The importance of collaborative group work related to data use has been suggested in a number of case studies across different contexts and levels. For example, in a case study of urban high schools, Lachat and Smith (2005) found that involving teachers in collaboratively analyzing data was important in improvement. Datnow et al. (2007) found that sharing data use strategies and connecting across schools could support better outcomes. Young (2006), who conducted case studies of grade levels working on data, found that cohesive grade levels with norms that supported the use of data and joint work were better able to use data for instructional decision making. While exploring data use in professional learning communities, Supovitz, Merrill, and Conger (2010) found that collaboration among members within these professional communities generally fostered deeper data use. In their case study, Lachat and Smith (2005) offered, “The activities of the data teams were central to increasing communication among school staff about the trends and issues shown in the data” (p. 344). In this sense, the collaborative work of subgroups not only influences and builds the capacity of the individual team member but also can spread beyond the immediate group out to the larger system (Feldman & Tung, 2001). Despite the promise of collaboration, without the organizational structures and resources for its support, it is unlikely that effective use of data will take place with any systematic and coherent regularity (Choppin, 2002; Cromey & Hanson, 2000; U.S. DOE, 2010). However, not all collaboration is beneficial; even with supports and resources in place, the misinterpretation or misuse of data can occur and be maintained through established patterns of interaction and limited expertise (Confrey & Makar, 2005; Young, 2006).


As in the data literature, subgroups in network theory are also influential in norm development and sense making of group members (Frank & Zhao, 2005). In Figure 1, subgroups are identified with a group of educators within a circle. A subgroup structure within a network consists of three or more individuals (actors) who are tied to one another more than to others in the larger network (Scott, 2000; Wasserman & Faust, 1998). A dense or cohesive subgroup—as is displayed in Figure 1 between the Queen, Ophelia, Laertes, and Polonius in the upper circle—supports the transfer of tacit or complex information (Ghoshal, Korine, & Szulanski, 1994; Hansen, 1999, 2002; Krackhardt, 1992; Reagans & McEvily, 2003; Szulanski, 1996; Uzzi, 1996, 1997), collaborative problem solving (Uzzi, 1997), and the development of coordinated and innovative solutions (Uzzi, 1997). These dense ties within and across subgroups provide stable predictable relations through which resources may flow (Ghoshal et al., 1994; Song, Nerur, & Teng, 2007; Tsai & Ghoshal, 1998). Presumably, this is the case because these dense trusting relationships may offer the opportunity for the “seeker” and “source” to expend the necessary effort to ensure that the seeker both understands the knowledge and can put it to use (Bryk & Schneider, 2002; Cross, 2001; Hansen, 1999; Krackhardt, 1992). These cohesive subgroup structures can also shape norms and attitudes and filter information within the group (Cole & Weinbaum, 2007; Daly, 2010; Frank & Zhao, 2005). However, these same subgroups can be susceptible to both structural and normative change if a highly central actor leaves and the remaining ties are not dense enough to support the same level of interaction (Useem, Christman, Gold, & Simon, 1997).


Dense ties within subgroups can assist actors in coordinating systemic action and the movement of resources because members have direct access to the flows of other subgroup members (Reagans & McEvily, 2003; Yayavaram & Ahuja, 2008). On the other hand, dense ties within subgroups are also likely to represent redundant information between these actors, which may reduce the overall access to novel resources and reinforce existing approaches (Frank, Zhao, & Borman, 2004). Moreover, strong normative ties within subgroups may support the development of unique subgroup interaction patterns and group members may select, interpret, and use data in ways particular to that subgroup (Frank & Zhao, 2005), which may make overall coherence difficult and the exchange of resources between subgroups less efficient (Tsai & Ghoshal, 1998). In this sense, dense ties may also result in groups holding and defending approaches that may not benefit the overall organization.


A sparse subgroup, as shown in the lower circle in Figure 1, is one with few ties between actors (Scott, 2000). Sparse ties within and across subgroups are considered important because they are likely to be the source of nonredundant, novel information, whereas dense ties tend to be those with others who possess and disseminate information the seeker already knows (Granovetter, 1973, 1982). Moreover, sparse ties have been associated with the diffusion of ideas (Rogers, 1995), as well as technical advice (Constant, Kiesler, & Sproull, 1996). However, relational structures composed of primarily sparse links, although perhaps effective at transmitting innovation, may limit support from members in advancing complex initiatives (Hakkarainen, Palonen, Paavola, & Lehtinen, 2004). Sparse ties between subgroups can also play important roles within networks, and some research suggests that overall network performance is associated with overlap between subgroups (Provan & Sebastian, 1998). Sparse ties between cohesive subgroups represent the potential bridging of structural holes and the opportunity for novel information to move between subgroups (Hansen, 1999; Yayavaram & Ahuja, 2008).


Creating and supporting opportunities for subgroups to exchange resources has the potential to develop novel information that benefits not only the subgroup but also the larger system in which the cluster resides (Frank & Zhao, 2005). Lateral ties between groups may increase a subgroup’s absorptive capacity—defined as a group’s ability to assimilate and replicate new information from external sources (Cohen & Levinthal, 1990). This idea is exemplified in the sharing of knowledge between districts and sites, as suggested by Honig (2006). Previous research has indicated that the ability of a subgroup to absorb information is directly related to its output of information to other groups (Balkundi & Harrison, 2006). This input-output relationship creates a reciprocal process that is facilitated by ties within and between subgroups. Therefore, investing in a subgroup’s ability to take in information from other subgroups may lead that group to output additional new information back into the larger organization. For example, as suggested by Datnow et al. (2007), ties between schools within a district offered avenues for the exchange of effective data practices.


The growing importance of social interaction and data use in groups suggests a rich area to be examined through the application of social network theory and analysis. Although at times presented as an individual endeavor, the interpretation and use of data do not exist in a vacuum; rather, the literature suggests that there are periods of individual as well as collective interactive sense-making processes at work. More carefully deconstructing the periods and processes both within and between the individual and group may shed more light on the use and interpretation of data. In addition, better understanding the role of sparse and dense ties within and between subgroups and the relationship with data use would be a useful application of network theory and methods. Because the work of densely connected groups appears to be associated with data use, scholars could measure the ties within groups and examine the strength of those relationships to identified outcomes. Early evidence related to grade-level ties and academic performance suggests that dense ties are predictive of increased student achievement (Pil & Leana, 2009). However, as Young (2006), Daly, Moolenaar, Bolivar, and Burke (2010), and others have suggested, even within one school, there may be significant variation in the number and quality of ties between grade-level members, suggesting an uneven distribution of social resources, which may also have an impact on the interpretation and use of data. Further, some dense connections may support the continuation of poor practice; therefore, understanding both the frequency and depth of interactions is an important addition to future data use studies (Coburn & Russell, 2008). Drawing on social network analysis may provide some additional descriptive and explanatory insight as to the depth of exchanges in a group, which tends to be missing in many studies of data use.


The meaning and use of data may well be determined, therefore, not just by individuals or dyads, but also by small, well-connected groups of educators. In this sense, data use may well be both a “local” artifact, created and belonging to the subgroup, and a larger network phenomenon because it is shared between groups, both of which may be explored through social network analysis. Examining the when, how, and why regarding data use at different levels—including individual, dyad, small group, and network—would add to our understanding of data use from a more social perspective. A recent network study by Frank and colleagues (2011) suggests that high depth implementation of reform is associated with different levels of collegial interaction. This work indicates that just noting the presence or absence of collaboration is not a nuanced enough frame for understanding the complexity of data use in small groups. Studies in different contexts that examine the density and strength of ties in comparison with the depth of data use both within and across individuals, subgroups, and networks would fill an existing gap in the data use literature.


NESTED RELATIONSHIPS IN STUDIES OF DATA USE


Although individual position and subgroups are important in the use of data, the literature also suggests that activity around data takes place at multiple, interdependent levels (Brunner et al., 2005; Coburn & Talbert, 2006; Hamilton, Halverson, Jackson, Mandinach, Supovitz, & Wayman, 2009). This more integrated view of data use suggests the embedded nature of the work with educators nested in grade levels, schools, districts, and state and federal policy contexts (Young, 2006). These studies further suggest that without a coherent approach to data use, a fragmented system may result, producing competing goals and a lack of consistent and effective data use (Agullard & Goughnour, 2006; Coburn, Honig, & Stein, 2009; Wayman & Cho, 2008). For example, a case study by Wayman and colleagues (2007) offered that to effectively use data, districts need to establish shared definitions of teaching and learning throughout the system. Moreover, this common language regarding data and instruction could be established through a series of guiding questions posed at all levels of the district.


Research on data use underscores the need to attend to the interdependencies between individuals and levels in developing the capacities to engage in accurate analysis and interpretation of data as well as development of goals (Confrey & Makar, 2005; Kerr et al., 2006; Marsh et al., 2006; Supovitz & Klein, 2003; Young, 2006). Datnow and coauthors (2007) illustrated this point in case studies of eight high-performing urban schools. Results from their work suggest the importance of shared and interdependent goals and practices across a system, which may ultimately enable educators to be more explicit in their expectations regarding the use of data. These case studies offer that the development of goal-setting decisions was most effective when it involved educators from across all levels (classroom, school, district), rather than primarily relying on intuition or isolated individual action to be the primary determinant of outcomes and practices. Recent work also indicates that goal-setting and decision-making processes around data may be influenced by the quantity and quality of social ties between leaders across a district (Daly & Finnigan, in press). Although a decision or shared goal may be developed at the district office or school, central office and site administrators may lack the necessary social connections to transmit those decisions, which may undermine the development of shared understanding and sets of common practices (Daly & Finnigan, 2011).


The idea of interdependency is also central to social network theory and analysis and is often referred to as “social embeddedness” (Granovetter, 1985; Gulati, 1998; Jones, Hesterly, & Borgatti, 1997; Uzzi, 1996, 1997). Social embeddedness, in a general network sense, refers to the nested nature of relations in a social structure. In a social network, individuals are embedded within dyadic relationships, and those dyadic relationships are nested in larger subgroups of three or more actors who eventually form a larger social network.


Social embeddedness is exemplified by the overall structure of relationships in Figure 1, with the Queen being more socially embedded than, for example, Gentleman B. Even a social network itself is embedded in a larger social structure—for instance, an organization, a community, or a country. Social embeddedness also implies that changes in relationships (formation, dissolution) at a “lower” level (e.g., the dyadic level) will have consequences for the “higher” order level (e.g., subgroup and overall network). These structural changes are consequential from a network standpoint because the overall structure of a network influences an actor’s ability to access and diffuse resources (Scott, 2000). As such, the significance of a dyadic relation extends beyond the two actors into a system of interdependent connections (Burt, 2000; Degenne & Forsé, 1999).


Although the data use literature does suggest the importance of a more systemic, calibrated (Wayman & Cho, 2008), inquiry-based, and embedded approach to the interpretation and use of data at multiple levels (Copland, 2003; Halverson et al., 2007), often data use and interpretation are left to an individual or pair of educators. As such, individual cognitions, beliefs, and experiences may primarily guide practice and inhibit a coherent and congruent approach to the use of data across a school or district (Hill, Rowan, & Ball, 2005; Supovitz & Klein, 2003). Better coordination of the interpretation and use of data across individuals, dyads, and interdependent levels is more likely through intentional and strategic planning for how complementary uses of data can coexist, co-occur, and be calibrated across a system (Coburn & Talbert, 2006; Hamilton et al., 2009; Honig, 2006; Supovitz & Klein, 2003; Wayman & Stringfield, 2006). Network sociograms (as presented in Figure 1) as well as associated measures such as the number of ties (density); pattern of incoming and outgoing ties (centrality); number and type of brokering ties (betweenness); and mutual relationships (reciprocity) can offer insight into how those changes may be coordinated and leveraged to achieve desired outcomes through careful mapping and analysis of ties.


Because the interpretation and use of data occur at multiple nested levels, network theory provides a lens and methods to examine the degree of interdependent interactions between and within these vertical and horizontal levels and how those interactions may affect outcomes on data use. For example, understanding the interpretation and data use in a school may also require examining the relationships, interactions, and flows between teachers, grade levels, schools, and district offices, as well as intermediary organizations. Moreover, the interdependent nature of relationships also suggests that social ties can cut across recognized formal structures, such as grade levels; therefore, only examining data use at the grade level may underestimate the social influence processes that may well extend beyond that formal structure. Often formal and informal lines may cross (McFarland, 2005) with actors who have both evaluative and friendship ties, as may be the case in a friendship relationship between a principal and teacher. These “multiplex” relationships suggest another layer of embeddedness between actors; these different relationships may have potentially conflicting norms of interaction. Hence, understanding the influence of both dyadic relations and the larger network structures on actors may be particularly important because this underlying social infrastructure may be leveraged to better create, use, and diffuse data (Cross et al., 2002). In addition, almost absent in the data use literature is the effect of teacher interactions and data use strategies on the social structure between teachers and students or even between the students themselves. This is not a minor point given that students are also part of the embedded system and the likely recipients of strategies that may arise from the use of data (McFarland, 2004, 2006). Considering how various data use approaches (e.g., sharing test results with students) may affect the social relationships with and between students seems an important line of inquiry in studies of data use.


QUALITY OF RELATIONSHIPS IN STUDIES OF DATA USE


The presence of relationships is important in studies of data use, but the literature also suggests that the quality of those social ties is equally important in creating a “safe” space for educators to mutually and rigorously examine practice (Chen et al., 2005; Holcomb, 2001; Keeney, 1998; Lachat & Smith, 2005; Symonds, 2004; Tschannen-Moran, 2004). Developing a collaborative environment that provides for the reciprocal sharing of results and strategies with colleagues (Forman, 2007; Young, 2006) may enhance the effective use of data for improvement. This suggests the importance of developing a data culture based on trust and reciprocity for the more effective use of data (Bryk & Schneider, 2002; Halverson et al., 2007). Trust seems to provide the foundation for educators to be vulnerable with one another and potentially open up practice in a spirit of inquiry (Copland, 2003; Daly & Chrispeels, 2008; Heritage & Yeagley, 2005; Lachat & Smith, 2005; Tschannen-Moran, 2004). In support of this point, a recent study regarding data use by the U.S. Department of Education (2010) stated, “Mutual trust may prove to be the glue needed to hold together the district and school practices that involve using data to improve instruction and achievement” (p. 49).


The quality of the ties between actors is also a key element in network theory. One way the “quality” of social interactions can be indicated in network terms is through a reciprocated relationship. A reciprocated tie is one in which actors have a symmetric or mutual tie, as shown in Figure 1 between Hamlet and Horatio, who in this example share expertise regarding data use. Reciprocated relationships provide opportunities for mutual exchange of resources and the creation of norms between actors (Morrison, 2002). These mutual relationships may provide opportunity for deeper exchanges because these reciprocated ties have potential to become imbued with trust, value, and legitimacy and be valuable in the learning process (Honig & Ikemoto, 2008). These same reciprocated relationships have also been suggested to support the transmission of complex knowledge and mutual problem solving (Hansen, 2002; Uzzi, 1996, 1997), all of which may be supportive of data use (Datnow et al., 2007; Wayman et al., 2007).


Collaborative interactions can support the exchange of expertise and practices by enabling reciprocated relationships, as is suggested in network studies of elementary schools (Baker-Doyle & Yoon, 2010; Coburn & Russell, 2008; Daly & Finnigan, 2010; Moolenaar, 2010; Penuel et al., 2010). A study by Cross (2001) suggested that even when individuals have ample amounts of available information, they still tend to seek data from trusted colleagues with whom they have a reciprocated relationship rather than from those individuals who are recognized as experts. The research related to teacher practices offers that unless the norms of mutual sharing are present, interactions may remain on a more superficial “contrived collegiality” level rather than moving to in-depth exchanges (Hargreaves, 1994; Little, 2007). Further, when reciprocated social ties are imbued with support, openness, and trust, educators are more likely to engage in joint collaborative work, exchange complex tacit information, and support risk-tolerant climates and innovation (Daly et al., 2010; Ikemoto & Marsh, 2007; Moolenaar, 2010; Moolenaar, Daly, & Sleegers, 2010; Young, 2008).


Research also suggests that reciprocal relations between individuals are associated with initiating and sustaining change efforts (Daly & Finnigan, 2011; McGrath & Krackhardt, 2003; Tenkasi & Chesmore, 2003). Reciprocal ties, in a social network sense, signal much stronger relations then unidirectional ties (Coleman, 1988). As such, some scholars argue that organizational change is more likely to occur through reciprocal interactions of individuals (Mohrman, Tenkasi, & Mohrman, 2003). This may be due to the fact that when individuals become reciprocally connected to larger institutional goals and pressures, these pressures may translate to beliefs, values, and action that may support a more coherent approach to the use of data (Coburn & Talbert, 2006). Mutual interaction with others may provide opportunities for making tacit knowledge explicit, as well as providing for the co-construction of meaning and sense-making in an effort to develop a shared understanding about the use of data (Bidwell, 2001; Coburn, 2001; Datnow & Park, 2009; Diamond, 2006; Spillane, 1999).


However, reciprocated ties may also constrain the behavior of actors who may perceive higher obligations to reciprocate with others with whom they have a mutual tie (Uzzi, 1997). In a related line of research, studies have documented that when such reciprocated ties are woven into a group, individual action may be more constrained by the norm of that group (Burt, 1992; Granovetter, 1973; Simmel, 1950). Over time, such reciprocated relations may limit the individual’s behaviors to share, seek, and exchange resources with others outside the group because the individual has to conform to the group norms in which she or he resides or risk being isolated (Krackhardt, 1999). For example, it may be more difficult to effectively address problems of data use practices within a grade level that has strong norms around not interacting with others outside their grade. Thus, although reciprocal relationships have been associated with increased opportunities to deepen knowledge and build communities of practice, they also represent a potentially constraining effect (Honig & Ikemoto, 2008; Kilduff & Tsai, 2003; Lave & Wenger, 1991; Wenger, 1998).


As the data use literature suggests, individuals play multiple roles in serving as a resource for data initiatives, such as data cleaning (Chen et al., 2005), support for overall data use across a system (Lachat & Smith, 2005; Wayman, Stringfield, & Yakimowski, 2004), and providing assistance in the analysis and interpretation of data (Young, 2006). These individuals represent potentially useful sources of expertise within a system. However, unless individuals have the opportunity to interact and develop reciprocated ties imbued with trust it is likely this expertise will remain an individual, as opposed to collective, asset (Brunner et al., 2005; Burch, 2006; Coburn et al., 2010; Diamond & Cooper, 2007; Parise & Spillane, 2010). Therefore, although many data use studies have implied the importance of reciprocated and trusting relationships, measuring the relational quality of exchanges between actors through network methods may provide for additional analytic purchase in understanding the use and depth of data for improvement. For example, do strong reciprocated relationships tend to provide opportunities for more in-depth analysis of data and development of strategies, or do those relationships tend to support a more “shorthand” type of interaction that inhibits more in-depth exchanges? More carefully measuring the quality of interactions under different conditions may provide for insight into organizational supports and constraints regarding the formation of reciprocated high-trust ties.


Future data use studies that draw on network methods could also explore who is “trusted” in a social system to provide useful informational regarding data. This would enable researchers to assess where “high trust” individuals are in the system—either through interview, survey, or observation—and examine the type and quality of data use resources they are diffusing. In addition, taking measurements of the levels of trust within grade levels and then comparing those measurements with the constellation and percentage of reciprocated ties around data use within a grade level may offer a more nuanced perspective of type and quality of outcomes that occur within that grade level. Data use researchers could also correlate different relations between sets of actors. For example, researchers could explore the degree to which actors who have a high percentage of reciprocated and high-trust ties also have relationships around advice seeking related to data use. Qualitative work that examines the quality of information that is shared between actors with stronger ties, possibly meaning those of high reciprocation and trust, as compared with those with less trust may suggest how school climates and cultures support and constrain the use of data. Last, network measures, such as reciprocated ties around best data use practices, may be regressed against other more traditional scales, such as trust, to predict the formation or dissolution of ties. Better understanding the “rules of social engagement/disengagement”—meaning how ties are formed or dissolved between actors over time—regarding consequential data use exchanges is both understudied in the data use literature and represents a significant potential contribution of network theory and analysis.


FUTURE DIRECTIONS


The increasing pressure for data use in education reform necessitates the development of robust theories and methodologies to better understand data use. In this article, I argue that social network theory and analysis offers a useful theoretical lens and set of methods to illuminate different sets of social influence processes on the use and interpretation of data that are often implicitly suggested, yet are not always well theorized and systematically explored in existing studies. In making this argument, I identified five themes from the data use literature that invoke social network theory: formal and informal position, boundary spanners and brokers, subgroups, nested relationships, and the quality of relationships. Throughout the article, I offered several avenues in the data use literature that could be further examined using social network theory and methods. In this final section, I reiterate some high-yield areas and offer some additional ideas for examination.


Because the study of data use is growing in education, there remains a need for basic mixed-methods empirical work in the field that explores the effect of overall network structure on the interpretation and use of data at multiple levels. Studies are needed that examine key attributes of actors and relate those attributes to position in the social network. For example, are teachers with expertise related to data use central in the network, and in what way does that centrality support and constrain the use and interpretation of data? In addition, better understanding of the impact of “local” dyadic, triadic, and subgroup relations on the larger systems and, in turn, the impact of larger network structures on subgroups is an equally rich area for exploration. Examining the intersection and congruence of formal and informal social systems and the effect of formal changes on informal relations related to data use is a particularly high-yield area of focus that combines the data use and network literatures. In a related but distinct vein, there is also an enormous gap in our understanding of the effects of data use strategies on the social structure between students and teachers as well as between students themselves, who are the likely recipients of many data use strategies. Future data use work that examines the social structure between students and teachers within a data use initiative represents a rich source of untapped data.


Comparative studies on the use of data through network structure of high- and low-performing systems may also provide useful information for better understanding the uptake of data use initiatives. Because there is increased pressure for the use of data systems, studies that track the implementation of data use systems over time and the effect of those systems on informal networks of relations may offer insight into the speed, depth, and diffusion of data generated from those systems and accompanying strategies. It is also important to note that although network theory and analysis provides insight into the ties between actors in a system, it provides less specific information about what actually “flows” within those relationships. Mixed-methods research that examines the structure of ties—with qualitative work around the specific content and depth of flows within those ties—is critical and offers promising directions for studies related to the interpretation and use of data.


Individuals, whether they are leaders in formal roles or school-level coaches, are often identified as potentially being influential in data use studies. The ability to better understand that influence, or lack thereof, may come through examining the congruence between formal roles and informal positions they occupy in a social network. Although one may have the “formal” authority to lead an initiative, the same individual may lack the “informal” social ties to actually enact that strategy. Similarly, although an individual may be formally identified as a “coach,” he or she may not possess the relationships to engage others in coaching activities. As such, intersecting the formal and informal roles in studies of data use may hold potential value. Along these lines, the data use literature suggests the potential of distributing leadership; therefore, network methods could be used to explore the degree to which leadership is stretched across multiple individuals and how that influence potentially flows through a social system. Drawing on network analysis, scholars could also examine the role that intermediaries (districts, coaches, consultants, and so on) play in filtering, hoarding, and brokering information related to the use of data. Finally, because many urban systems often have high turnover of leadership and staff, using longitudinal network methods to examine the effect of that transition on networks of data use represents an important extension of both the network and data use literatures.


Another promising area for exploration related to the interpretation and use of data is the examination of the interaction between social selection (network structure) and the social influence (behavior/attitude) (Frank, Kim, & Belman, 2010). The ability to better understand the differential impact of social selection and social influence on data use is a core and underexplored question that requires additional empirical work. This area is particularly ripe; correlations are often found between the behavior of an individual and the behavior of his or her peers. Such associations are often attributed to peer influence (social influence effect); the possibility that individuals may choose friends who are similar to themselves in terms of some behavior (social selection effect) is often less explored. This line of inquiry could be further enhanced by examining the longitudinal formation of ties related to data use and untangling whether tie formation/dissolution was due more to the effect of social selection (developing reciprocal ties) or social influence (trust). Moreover, one could test a variety of related questions such as: To what degree are high-trust individuals more likely to form reciprocated ties? In what ways do reciprocated ties support trusting relationships? What are the combined influential effects on data use of attributes/attitudes/behaviors and network position? Disentangling and combining these influences may provide insight into the development of a data use culture.


Because network data are interdependent and violate the assumptions of independence in most statistical tests, advanced statistics and modeling have been introduced and continue to flourish in network theory and analysis. Advanced social network models that consider hypothesis-driven research questions about how networks form and what effects networks have on outcomes, such as the use of data, would make unique and important contributions to our understanding of data use. For example, researchers could explore such questions as: What is the immediate and longitudinal impact of dyadic, triadic, and clique formations on the interpretation of data? What network structures predict more effective interpretation and use of data within and between schools, in district offices, and between districts? How modifiable are social structures to policy or data initiatives? These models are potentially robust and statistically complex, and they offer some particularly rich areas for the quantitative and longitudinal analysis of network data.


Given that data use and educational improvement are ultimately conducted by and through individuals in formal and informal social systems, social network theory and analysis offers a useful and complementary conceptual framework for understanding and exploring the influence of social infrastructure on the use of data. Moreover, the network perspective also provides a growing and robust set of qualitative and quantitative methods to explore the access, diffusion, modification, control, and potential leverage points of data use within educational systems. However, perhaps the greatest promise of intersecting the data use and network literatures around educational improvement is in the combined potential to help us better understand the interpretations we make, the initiatives we enact, the outcomes we realize, and the data-filled social worlds we inhabit.


Acknowledgments


I am indebted to the anonymous reviewers, guest editors, Vicki Park, and Yi-Hwa Liou for their insightful and instructive comments on the development and refinement of this manuscript.


Notes


1. The literature also suggests that data may be used for a variety of tasks, including instructional decisions (Brunner et al., 2005; Forman, 2007; Wayman & Stringfield, 2006); refining instructional methods (Halverson, Prichett, & Watson, 2007; Supovitz & Klein, 2003); adapting curriculum (Marsh et al., 2006; Kerr et al. 2006); and informing parents and tracking accountability measures (Means, Gallagher, & Padilla, 2007). Data have also been used for what has been described as “gaming” the system, such as identifying a group of students to focus on in boosting overall school scores (Booher-Jennings, 2005; Marsh et al., 2006).

2. Researchers have also noted a number of additional themes that are also important in studies of data use. These themes may, but do not as readily, invoke social network concepts, including quantity and quality of data presentation (Celio & Harvey, 2005; Mandinach & Honey, 2008); timely access to data and data systems that incorporate a “user friendly” interface (Choppin, 2002; Means et al., 2007; Snipes, Doolittle, & Herlihy, 2002; Stringfield, Wayman, & Yakimowski-Srebnick, 2005); presence of “valid and reliable” data (Feldman & Tung, 2001; Herman & Gribbons, 2001; Ikemoto & Marsh, 2007; Ingram, Louis & Schroeder, 2004; Kerr et al., 2006); quantitative and data literacy skills (Confrey & Makar, 2005; Makar & Confrey, 2002; Supovitz & Klein, 2003; Wayman & Stringfield, 2006); and availability of professional development (Confrey, Makar, Kazak, 2004; Cromey & Hanson, 2000; Feldman & Tung, 2001; Symonds, 2004).

3. Network adapted from Shakespeare’s Hamlet and Moretti (2011). In this network, I substitute the “ally” relationship from the play Hamlet to represent the exchange of expertise around data use.


References


Adler, P. S., & Kwon, S. W. (2002). Social capital: Prospects for a new concept. Academy of Management Review, 27(1), 17–40.


Agullard, K., & Goughnour, D. S. (2006). Central office inquiry: Assessing organization, roles, and actions to support school improvement. San Francisco, CA: WestEd.


Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.


Anderson, L. (2010). Embedded, emboldened, and (net)working for change: Support-seeking and teacher agency in urban, high-needs schools. Harvard Educational Review, 80(4), 541–572.


Ansell, C., Reckhow, S., & Kelly, A. (2009). How to reform a reform coalition: Outreach, agenda expansion, and brokerage in urban school reform. Policy Studies Journal, 37(4), 717–743.


Atteberry, A., & Bryk, A. S. (2010). Analyzing the role of social networks in school-based professional development initiatives. In A. J. Daly (Ed.), The ties of change: Social network theory and application in education (pp. 51–76). Cambridge, MA: Harvard Education Press.


Baker, W. E., & Iyer, A. (1992). Information networks and market behavior. Journal of Mathematical Sociology, 16, 305–332.


Baker-Doyle, K. J., & Yoon, S. A. (2010). Making expertise transparent: Using technology to strengthen social networks in teacher professional development. In A. J. Daly (Ed.), Social network theory and educational change (pp. 115–126). Cambridge, MA: Harvard Education Press.


Bakkenes, I., De Brabander, C., & Imants, J. (1999). Teacher isolation and communication network analysis in primary schools. Educational Administration Quarterly, 35, 166–202.


Balkundi, P., & Harrison, D. (2006). Ties, leaders, and time in teams: Strong inferences about network structures effects on team viability and performance. Academy of Management Journal, 49(1), 49–68.


Balkundi, P., & Kilduff, M. (2005). The ties that lead: A social network approach to leadership. Leadership Quarterly, 16, 941–961.


Bidwell, C. E. (2001). Analyzing schools as organizations: Long-term permanence and short-term change. Sociology of Education, 1(Extra Issue), 100–114.


Booher-Jennings, J. (2005). Below the bubble: “Educational triage” and the Texas accountability system. American Educational Research Journal, 42(2), 231–268.


Borgatti, S. P. (2003). The key player problem. In R. Breiger, K. Carley, & P. Pattison (Eds.), Dynamic social network modeling and analysis: Workshop summary and papers (pp. 241–252). Washington, DC: National Academy of Sciences Press.


Borgatti, S., & Cross, R. (2003). A relational view of information seeking and learning in social networks. Management Science, 49(4), 432–445.


Borgatti, S., & Everett, M. (1999). Models of core periphery structures. Social Networks, 21, 375–395.


Borgatti, S., & Foster, P. (2003). The network paradigm in organizational research: A review and typology. Journal of Management, 29(6), 991–1013.


Borgatti, S., Jones, C., & Everett, M. (1998). Network measures of social capital. Connections, 21, 27–36.


Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323, 892–895.


Borgatti, S. P., & Ofem, B. (2010). Overview: Social network theory and analysis. In A. J. Daly (Ed.), The ties of change: Social network theory and application in education (pp. 17–30). Cambridge, MA: Harvard Education Press.


Borko, H. (2004). Professional development and teacher learning: Mapping the terrain. Educational Researcher, 33(8), 3–15.


Boudett, K. P., City, E. A., & Murnane, R. J. (2007). Data wise: A step-by-step guide to using assessment results to improve teaching and learning. Cambridge, MA: Harvard Education Press.


Boudett, K. P., & Moody, L. (2005). Organizing for collaborative work. In K. P. Boudett, E. A. City, & R. J. Murnane (Eds.), Datawise: A step-by-step guide to using assessment results to improve teaching and learning (pp. 11–28). Cambridge, MA: Harvard Education Press.


Boyd, N. G., & Taylor, R. R. (1998). A developmental approach to the examination of friendship in leader follower relationships. Leadership Quarterly, 9, 1–25.


Brass, D. J. (1984). Being in the right place at the right time: A structural analysis of individual differences in an organization. Administration Science Quarterly, 29, 518–539.


Brass, D. J., & Burkhardt, M. E. (1993). Potential power and power use: An investigation of structure and behavior. Academy of Management Journal, 36(3), 441–470.


Brunner, C., Fasca, C., Heinze, J., Honey, M., Light, D., Mandinach, E., & Wexler, D. (2005). Linking data and learning: The Grow Net­work study. Journal of Education for Stu­dents Placed at Risk, 10(3), 241–267.


Bryk, A. S., & Schneider, B. (2002). Trust in schools: A core resource for school improvement. New York, NY: Russell Sage Foundation.


Burch, P. (2006). The new educational privatization: Educational contrasting and high stakes accountability. Teachers College Record, 108(12), 2582–2610.


Burt, R. S. (1982). Toward a structural theory of action. New York, NY: Academic Press.


Burt, R. S. (1992). Structural holes: The structure of competition. Cambridge, MA: Harvard University Press.


Burt, R. S. (1997). A note on social capital and network content. Social Networks, 19(4), 355–373.


Burt, R. S. (2000). The network structure of social capital. In R. I. Sutton & B. M. Staw (Eds.), Research in organizational behavior (pp. 1–83). Greenwich, CT: JAI Press.


Burt, R. S. (2005). Brokerage and closure: An introduction to social capital. Oxford, England: Oxford University Press.


Camburn, E., Rowan, B., & Taylor, J. (2003). Distributed leadership in schools: The case of elementary schools adopting comprehensive school reform models. Educational Evaluation and Policy Analysis, 25(4), 347–373.


Celio, M. B., & Harvey, J. (2005). Buried treasure: Developing an effective management guide from mountains of educational data. Seattle, WA: Center on Reinventing Public Education.


Chen, E., Heritage, M., & Lee, J. (2005). Identifying and monitoring students’ learning needs with technology. Journal of Education for Students Placed at Risk, 10(3), 309–332.


Choppin, J. (2002, April). Data use in prac­tice: Examples from the school level. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.


Christensen, C. M., Johnson, C., & Horn, M. (2008). Disrupting class: How disruptive innovation will change the way the world learns. New York, NY: McGraw-Hill.


Coburn, C. (2001). Collective sensemaking about reading: How teachers mediate reading policy in their professional communities. Educational Evaluation and Policy Analysis, 23(2), 145–170.


Coburn, C. E. (2005). Shaping teacher sensemaking: School leaders and the enactment of reading policy. Educational Policy, 19, 476–509.


Coburn, C. E. (2010). Partnership for District Reform: The challenges of evidence use in a major urban district. In C. E. Coburn & M. K. Stein (Eds.), Research and practice in education: Building alliances, bridging the divide (pp. 167–182). New York, NY: Rowman & Littlefield.


Coburn, C. E., Choi, L., & Mata, W. (2010). “I would go to her because her mind is math”: Network formation in the context of a district-based mathematics reform. In A. J. Daly (Ed.), Social network theory and educational change (pp. 77–95). Cambridge, MA: Harvard Education Press.


Coburn, C. E., Honig, M. I., & Stein, M. K. (2009). What’s the evidence on district’s use of evidence? In J. Bransford, D. J. Stipek, N. J. Vye, L. Gomez, & D. Lam (Eds.), Educational improvement: What makes it happen and why? (pp. 67–86). Cambridge, MA: Harvard Educational Press.


Coburn, C. E., & Russell, J. L. (2008). District policy and teachers’ social networks. Education Evaluation and Policy Analysis, 30(3), 203–235.


Coburn, C. E., & Talbert, J. E. (2006). Conceptions of evidence use in school districts: Mapping the terrain. American Journal of Education, 112(4), 469–495.


Coburn, C. E., Touré, J., & Yamashita, M. (2009). Evidence, interpretation, and persuasion: Instructional decision making in the district central office. Teachers College Record, 111(4), 1115–1161.


Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.


Cole, R. P., & Weinbaum, E. H. (2007, April). Chain reaction: How teacher communication networks change individual attitudes about reform. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL.


Cole, R. P., & Weinbaum, E. H. (2010). Changes in attitude: Peer influence in high school reform. In A. J. Daly (Ed.), Social network theory and educational change (pp. 77–96). Cambridge, MA: Harvard University Press


Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, 95–120.


Confrey, J., & Makar, K. (2005). Critiquing and improving the use of data from high-stakes tests with the aid of dynamic statistics software. In C. Dede, J. P. Honan, & L. C. Peters (Eds.), Scaling up success: Lessons learned from technology-based educational improvement (pp. 198–226). San Francisco, CA: Jossey-Bass.


Confrey, J., Makar, K., & Kazak, S. (2004). Undertaking data analysis of student outcomes as professional development for teachers. ZDM, 36(1), 1–9.


Constant, D., Kiesler, S., & Sproull, L. (1996). The kindness of strangers: On the usefulness of weak ties for technical advice. Organization Science, 7, 119–135.


Copland, M. A. (2003). Leadership of inquiry: Building and sustaining capacity for school improvement. Educational Evaluation and Policy Analysis, 25, 375–395.


Cromey, A., & Hanson, M. (2000). An ex­ploratory analysis of school-based stu­dent assessment systems. Oak Brook, IL: North Central Regional Educational Laboratory.


Cross, R. (2001). A relational view of information seeking: Tapping people and inanimate sources in intentional search. Paper presented at the annual meeting of the Academy of Management, Washington, D.C.


Cross, R., Borgatti, S., & Parker, A. (2002). Making invisible work visible: Using social network analysis to support strategic collaboration. California Management Review, 44(2), 25–46.


Cross, R., & Parker, A. (2004). The hidden power of social networks: Understanding how work really gets done in organizations. Cambridge, MA: Harvard Business School Press.


Cummings, J., & Cross, R. (2003). Structural properties of work groups and their consequences for performance. Social Networks, 25, 197–210.


Daly, A. J. (Ed.). (2010). Social network theory and educational change. Cambridge, MA: Harvard Education Press.


Daly, A. J., & Chrispeels, J. (2008). A question of trust: Predictive conditions for adaptive and technical leadership in educational contexts. Leadership and Policy in Schools, 7(1), 30–63.


Daly, A. J., & Finnigan, K. (2009). A bridge between worlds: Understanding network structure to understand change strategy. Journal of Educational Change, 11(2), 111–138.


Daly, A. J., & Finnigan, K. (2010). Understanding network structure to understand change strategy. Journal of Educational Change, 111, 111–138.


Daly, A. J., & Finnigan, K. (2011). The ebb and flow of social network ties between district leaders under high stakes accountability. American Education Research Journal, 48(1), 39–79.


Daly, A. J., & Finnigan, K. (in press). Exploring the space between: Social networks, trust, and urban school district leaders. Journal of School Leadership.


Daly, A. J., Moolenaar, N., Bolivar, J., & Burke, P. (2010). Relationships in reform: The role of teachers’ social networks. Journal of Educational Administration, 48(3), 20–49.


Datnow, A., Hubbard, L., & Mehan, H. (2002). Extending educational reform: From one school to many. London, England: Routledge/Falmer.


Datnow, A., & Park, V. (2009). Conceptualizing policy implementation: Large-scale reform in an era of complexity. In D. Plank, B. Schneider, & G. Sykes (Eds.), Handbook of education policy research (pp. 348–361). New York, NY: Routledge.


Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How high-performing school systems use data to improve instruction for elementary students. Los Angeles: University of Southern California, Rossier School of Education, Center on Educational Governance.


Degenne, A., & Forsé, M. (1999). Introducing social networks. London, England: Sage.


Desimone, L. M., Porter, A. C., Garet, M. S., Yoon, K. S., & Birman, B. F. (2002). Effects of professional development on teachers’ instruction: Results from a three-year longitudinal study. Educational Evaluation and Policy Analysis, 24(2), 81–112.


Diamond, J. B. (2006, August). Where the rubber meets the road: Rethinking the connection between high-stakes testing policy and classroom practice. Paper presented at the annual meeting of the American Sociological Association, Montreal, Quebec, Canada.


Diamond, J. B., & Cooper, K. (2007). The uses of testing data in urban elementary schools: Some lessons from Chicago. NSSE Yearbook, 106(1), 241–263.


Earl, L., & Katz, S. (2002). Leading schools in a data-rich world. In K. Leithwood, P. Hallinger, G. Furman, P. Gronn, J. MacBeath, B. Mulford, & K. Riley (Eds.), The second international handbook of educational leadership and administration (pp. 1003–1022). Dordrecht, The Netherlands: Kluwer.


Earl, L., & Katz, S. (2006). Leading schools in a data-rich world. Thousand Oaks, CA: Corwin Press.


Feldman, J., & Tung, R. (2001, April). Whole school reform: How schools use the data-based inquiry and decision making process. Paper presented at the annual meeting of the American Educational Research Association, Seattle, WA.


Fernandez, R. M., & Gould, R. V. (1994). A dilemma of state power: Brokerage and influence in the national health policy domain. American Journal of Sociology, 99, 1455–1491.


Finnigan, K. S., & Daly, A. J. (2010). Learning at a system level: Ties between principals of low performing schools and central office leaders. In A. J. Daly (Ed.), Social network theory and educational change (pp. 179–195). Cambridge, MA: Harvard Education Press.


Firestone, W. A., & González, R. A. (2007). Culture and processes affecting data use in schools. In P. A. Moss (Ed.), Evidence and decision making (pp. 132–154). Malden, MA: Blackwell.


Forman, M. L. (2007). Developing an action plan: Two Rivers Public Charter School fo­cuses on instruction. In K. P. Boudett & J. L. Steele (Eds.), Data wise in action: Stories of schools using data to improve teaching and learning (pp. 107–124). Cambridge, MA: Harvard Education Press.


Frank, K. A. (2009). Quasi-ties: Directing resources to members of a collective. American Behavioral Scientist, 52, 1613–1645.


Frank, K., Kim, C. M., & Belman, D. (2010). Utility theory, social networks, and teacher decision making: Modeling networks’ influences on teachers attitudes and practices. In A. J. Daly (Ed.), The ties of change: Social network theory and application in education (pp. 233–242). Cambridge, MA: Harvard Press.


Frank, K., & Zhao, Y. (2005). Subgroups as a meso-level entity in the social organization of schools. In L. Hedges & B. Schneider (Eds.), Social organization of schools (pp. 279–318). New York, NY: Sage.


Frank, K. A., Zhao, Y., & Borman, K. (2004). Social capital and the diffusion of innovations within organizations: Application to the implementation of computer technology in schools. Sociology of Education, 77(2), 148–171.


Frank, K. A., Zhao, Y., Penuel, W. R., Ellefson, N., & Porter, S. (2011). Focus, fiddle, and friends: Experiences that transform knowledge for the implementation of innovations. Sociology of Education, 84(2), 137–156.


Friedkin, N. E. (2004). Social cohesion. Annual Review of Sociology, 30, 409–425.


Ghoshal, S., Korine, H., & Szulanski, G. (1994). Interunit communication in multinational corporations. Management Science, 40(1), 96–110.


Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91, 481–510.


Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380.


Granovetter, M. S. (1982). The strength of weak ties: A network theory revisited. In P. V. Marsden & N. Lin (Eds.), Social structure and network analysis (pp. 105–130). Beverly Hills, CA: Sage.


Grossman, P. L., & Stodolsky, S. S. (1995). Content as context: The role of school subjects in secondary school teaching. Educational Researcher, 24(8), 5–11.


Gulati, R. (1995). Does familiarity breed trust? The implications of repeated ties for contractual choices in alliances. Academy of Management Journal, 35, 85–112.


Gulati, R. (1998). Alliances and networks. Strategic Management Journal, 19(4), 293–317.


Hakkarainen, K., Palonen, T., Paavola, S., & Lehtinen, E. (2004). Communities of networked expertise: Professional and educational perspectives. Amsterdam, The Netherlands: Elsevier.


Halverson, R., Grigg, J., Prichett, R., & Thomas, C. (2007). The new instructional leadership: Creating data-driven instructional systems in school. Journal of School Leadership, 17(2), 159–194.


Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/practiceguides/dddm_pg_092909.pdf.


Hamilton, L. S., Stecher, B. M., Marsh, J., McCombs, J. S., Robyn, A., & Russell, J. (2007). Implementing standards-based accountability under No Child Left Behind: Responses of superintendents, principals, and teachers in three states. Santa Monica, CA: RAND.


Hammerman, J. K., & Rubin, A. (2002). Visualizing a statistical world. Hands On!, 25(2).


Hansen, M. (2002). Knowledge networks: Explaining effective knowledge sharing in multiunit companies. Organization Science, 13(3), 232–248.


Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82–111.


Hargreaves, A. (1994). Changing teachers, changing times: Teachers’ work and culture in the postmodern age. New York, NY: Teachers College Press.


Heritage, M., & Yeagley, R. (2005). Data use and school improvement: Challenges and prospects. NSSE Yearbook, 104(2), 320–339.


Herman, J., & Gribbons, B. (2001). Lessons learned in using data to support school in­quiry and continuous improvement: Final report to the Stuart Foundation. Los Ange­les: University of California, Center for the Study of Evaluation.


Hill, H. C., Rowan, B., & Ball, D. L. (2005). Effects of teachers’ mathematical knowledge for teaching on student achievement. American Educational Research Journal, 42(2), 371–406.


Hite, J. M., Hite, S. J., Mugimu, C. B., & Nsubuga, Y. K. (2010). Strategic “co-opetition”: Headteacher networking in Uganda’s secondary schools. In A. J. Daly (Ed.), Social network theory and educational change (pp. 197–220). Cambridge, MA: Harvard Press.


Holcomb, E. L. (2001). Asking the right questions: Techniques for collaboration and school change (2nd ed.). Thousand Oaks, CA: Corwin Press.


Honig, M. I. (2003). Building policy from practice: District central office administrators’ roles and capacity for implementing collaborative education policy. Educational Administration Quarterly, 39, 292–338.


Honig, M. (2006). Street-level bureaucracy revisited: Frontline district central-office administrators as boundary spanners in education policy implementation. Educational Evaluation and Policy Analysis, 28(4), 357–383.


Honig, M. I., & Ikemoto, G. (2008). Adaptive assistance for learning improvement efforts: The case of the Institute for Learning. Peabody Journal of Education, 83(3), 328–363.


Hughes, R. L., Ginnett, R. C., & Curphy, G. J. (1999). Leadership: Enhancing the lessons of experience. New York, NY: McGraw-Hill.


Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the “data driven” mantra: Different conceptions of data-driven decision making. NSSE  Yearbook, 106(1), 105–131.


Ingram, D., Louis, K. S., & Schroeder, R. G. (2004). Accountability policies and teacher decision making: Barriers to the use of data to improve practice. Teachers College Record, 106, 1258–1287.


Jackson, M. O. (2009). An overview of social networks and economic applications. In J. Benhabib, A. Bisin, & M. O. Jackson (Eds.), The handbook of social economics (pp. 1–96). New York, NY: Elsevier Press.


Jones, C., Hesterly, W. S., & Borgatti, S. P. (1997). A general theory of network governance: Exchange conditions and social mechanisms. Academy of Management Journal, 22(4), 911–945.


Keeney, L. (1998). Using data for school improvement:  Report on the second practitioners’ conference for Annenberg Challenge sites. Tools for Accountability Project, Houston, TX. Retrieved from http://annenberginstitute.org/tools/using_data/using_data.pdf


Kerr, K. A., Marsh, J. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006). Strategies to promote data use for instructional improvement: Actions, outcomes, and lessons from three urban districts. American Journal of Education, 112(4), 496–520.


Kilduff, M., & Krackhardt, D. (2008). Interpersonal networks in organizations: Cognition, personality, dynamics, and culture: Structural analysis in the social sciences. Cambridge, England: Cambridge University Press.


Kilduff, M., & Tsai, W. (2003). Social networks and organizations. London, England: Sage.


Knapp, M., Copland, M., & Swinerton, J. (2007). Understanding the promise and dynamics of data-informed leadership. In P. A. Moss (Ed.), Evidence and decision making (pp. 74–104). Malden, MA: National Society for the Study of Education.


Krackhardt, D. (1992). The strength of strong ties: The importance of philos in organizations. In N. Nohria & R. Eccles (Eds.), Networks and organizations: Structure, form and action: (pp. 216–239). Boston, MA: Harvard Business School Press.


Krackhardt, D. (1996). Social networks and the liability of newness for managers. Journal of Organizational Behavior, 3, 159–173.


Krackhardt, D. (1999). The ties that torture: Simmelian tie analysis in organizations. In S. B. Bacharach, S. B. Andrews, & D. Knoke (Eds.), Research in the sociology of organizations (pp. 183–210). Stamford, CT: JAI Press.


Krackhardt, D., & Kilduff, M. (1990). Friendship patterns and culture: The control of organizational diversity. American Anthropologist, 92, 142–154.


Lachat, M. A., & Smith, S. (2005). Practices that support data use in urban high schools. Journal of Education for Students Placed at Risk, 10(3), 333–349.


Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, England: Cambridge University Press.


Levine, T. H., & Marcus, A. S. (2010). How the structure and focus of teachers’ collaborative activities facilitate and constrain teacher learning. Teaching and Teacher Education, 26(3), 389–398.


Lima, J. A. (2003). Trained for isolation: The impact of departmental cultures on student teachers’ views and practices of collaboration. Journal of Education for Teaching, 29(3), 197–218.


Lima, J. A. (2007). Teachers’ professional development in departmentalized, loosely coupled organizations: Lessons for school improvement from a case study of two curriculum departments. School Effectiveness and School Improvement, 18(3), 273–301.


Little, J. W. (2007). Teachers’ accounts of classroom experience as a resource for professional learning and instructional decision making. In P. A. Moss (Ed.), Evidence and decision making (pp. 217–240). Malden, MA: National Society for the Study of Education.


Louis, K. S., Febey, K., & Schroeder, R. (2005). State-mandated accountability in high schools: Teachers’ interpretations of a new era. Educational Evaluation and Policy Analysis, 27(2), 177–204.


Louis, K. S., Leithwood, K., Wahlstrom, K. L., & Anderson, S. E. (2010). Learning from leadership: Investigating the links to improved student learning. Retrieved from http://www.wallacefoundation.org/KnowledgeCenter/KnowledgeTopics/CurrentAreasofFocus/EducationLeadership/Documents/Learning-from-Leadership-Investigating-Links-Final-Report.pdf


Makar, K., & Confrey, J. (2002). Comparing two distributions: Investigating secondary teachers’ statistical thinking. Retrieved from http://www.stat.auckland.ac.nz/~iase/publications/1/10_18_ma.pdf


Mandinach, E. B., & Honey, M. (2008). Data-driven school improvement: Linking data and learning. New York, NY: Teachers College Press.


Marsh, J. A., McCombs, J. S., Martorell, F. (2010). How instructional coaches support data-driven decision making: Policy implementation and effects in Florida middle schools. Educational Policy, 24(6), 872–907.


Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven de­cision making in education: Evidence from recent RAND research (OP-170). Santa Monica, CA: RAND.


McFarland, D. A. (2004). Resistance as a social drama—A study of change-oriented encounters. American Journal of Sociology, 109(6), 1249–1318.


McFarland, D. A. (2005). Why work when you can play? Dynamics of formal and informal organization in classrooms. In L. Hedges & B. Schneider (Eds.), The social organization of schooling (pp. 147–174). New York, NY: Russell Sage Foundation.


McFarland, D. A. (2006). Curricular flows: Trajectories, turning points, and assignment criteria in high school math careers. Sociology of Education, 79, 177–205.


McGrath, C., & Krackhardt, D. (2003). Network conditions for organizational change. Journal of Applied Behavioral Science, 39(3), 324–336.


McLaughlin, M. W. (1990). The Rand change agent study revisited: Macro perspectives and micro realities. Educational Researcher, 19(9), 11–16.


Means, B., Gallagher, L., & Padilla, C. (2007). Teachers’ use of student data management systems to improve instruction. Report prepared for U.S. Department of Education, Office of Planning, Evaluation and Policy Development. Menlo Park, CA: SRI International.


Mieles, T., & Foley, E. (2005). From data to decisions: Lessons from school districts using data warehousing. Providence, RI: Annenberg.


Mohrman, S., Tenkasi, R., & Mohrman, A. (2003). The role of networks in fundamental organizational change. Journal of Applied Behavioral Science, 39(3), 301–323.


Moolenaar, N. M. (2010). Ties with potential: Nature, antecedents, and consequences of social networks in school teams. Unpublished doctoral dissertation.


Moolenaar, N. M., Daly, A. J., & Sleegers, P. J. (2010). Occupying the principal position: Examining relationships between transformational leadership, social network position, and schools’ innovative climate. Educational Administration Quarterly, 46(5), 623–670.


Moolenaar, N., Daly, A. J., & Sleegers, P. (2011). Ties with potential: Social network structure and innovation in Dutch schools. Teachers College Record, 113(9), 1983–2017.


Moretti, F. (2011). Library lab: Network theory, plot analysis. Palo Alto, CA: Stanford University.


Morrison, E. W. (2002). Newcomers’ relationships: The role of social network ties during socialization. Academy of Management Journal, 45(6), 1149–1160.


Newman, M. E. J., Barabasi, A. L., & Watts, D. J. (2006). The structure and dynamics of networks. Princeton, NJ: Princeton University Press.


Obstfeld, D. (2005). Social networks, the Tertius Iungens orientation, and involvement in innovation. Administrative Science Quarterly, 50, 100–130.


Parise, L., & Spillane, J. (2010). Teacher learning and instructional change: How formal and on-the-job learning opportunities predict change in elementary school teachers’ practice. Elementary School Journal, 110(3), 323–346.


Penuel, W. R., Riel, M., Joshi, A., Pearlman, L., Kim, C. M., & Frank, K. A. (2010). The alignment of the informal and formal organizational supports for reform: Implications for improving teaching in schools. Educational Administration Quarterly, 46(1), 57–95.


Penuel, W. R., Riel, M. R., Krause, A., & Frank, K. A. (2009). Analyzing teachers’ professional interactions in a school as social capital: A social network approach. Teachers College Record, 111(1), 124–163.


Pil, F., & Leana, C. (2009) Applying organization research to public school reform. Academy of Management Journal, 52(6), 1101–1124.


Popham, W. J. (2008). Transformative assessment. Alexandria, VA: Association for Supervision and Curriculum Development.


Portz, J. (1996). Problem definitions and policy agendas: Shaping the educational agenda in Boston. Policy Studies Journal, 24(3), 371–386.


Powell, W. W., Koput, K. IV., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 41, 116–145.


Provan, K. G., & Sebastian, J. (1998). Networks within networks: Service link overlap, organizational cliques, and network effectiveness. Academy of Management Journal, 41(4), 453–462.


Raider, H., & Krackhardt, D. (2001). Intraorganizational networks. In J. A. C. Baum (Ed.), Companion to organizations (pp. 58–74). Oxford, England: Blackwell.


Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: The effects of cohesion and range. Administrative Science Quarterly, 48(2), 240–267.


Ritter, G. W., & Boruch, R. F. (1999). The political and institutional origins of a randomized controlled trial on elementary and school class size. Educational Evaluation and Policy Analysis, 21, 111–126.


Rogers, E. (1995). Diffusion of innovations (4th ed.). New York, NY: Free Press.


Schildkamp, K., & Kuiper, W. (2010). Data informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26(3), 482–496.


Scott, J. (2000). Social network analysis (2nd ed.). London, England: Sage.


Sherer, J. Z., & Spillane, J. P. (2010). Constancy and change in work practice in schools: The role of organizational routines. Teachers College Record, 113(3), 611–657.


Simmel, G. (1950). The sociology of Georg Simmel (K. Wolff, Comp. & Trans). Glencoe, IL: Free Press.


Snipes, J., Doolittle, F., & Herlihy, C. (2002). Foundations for success: Case studies of how urban school systems improve student achievement. Washington, DC: MDRC and the Council of Great City Schools.


Song, S., Nerur, S., & Teng, J. (2007). An exploratory study on the roles of network structure and knowledge processing orientation in the work unit knowledge management. Advances in Information Systems, 38(2), 8–26.


Spillane, J. P. (1999). External reform initiatives and teachers’ efforts to reconstruct their practice: The mediating role of teachers’ zones of enactment. Journal of Curriculum Studies, 31(2), 143–175.


Spillane, J. P. (2000) A fifth-grade teacher’s reconstruction of mathematics and literacy teaching: Exploring interactions among identity, learning, and subject matter. Elementary School Journal, 100(4), 273–307.


Spillane, J. P., Healey, K., & Kim, C. M. (2010). Leading and managing instruction: Using social network analysis to explore formal and informal aspects of the elementary school organization. In A. J. Daly (Ed.), Social network theory and educational change (pp. 129–156). Cambridge, MA: Harvard University Press.


Spillane, J. P., Hunt, B., & Healey, K. (2009). Managing and leading elementary schools: Attending to the formal and informal organization. International Studies in Educational Administration, 37(1), 5–28.


Spillane, J. P., Reiser, B. J., & Reimer, T. (2002). Policy implementation and cognition: Reframing and refocusing implementation research. Review of Educational Research, 72(3), 387–431.


Stringfield, S., Wayman, J. C., & Yakimowski-Srebnick, M. E. (2005). Scaling up data use in classrooms, schools, and districts. In C. Dede, J. P. Honan, & L. C. Peters (Eds.), Scaling up success: Lessons learned from technology-based educational improvement (pp. 133–152). San Francisco, CA: Jossey-Bass.


Stuart, T. E. (1998). Network positions and propensities to collaborate: An investigation of strategic alliance formation in a high-technology industry. Strategic Management Journal, 43, 668–698.


Supovitz, J., & Klein, V. (2003). Mapping a course for improved student learning: How innovative schools use student performance data to guide improvement. Philadelphia, PA: Consortium for Policy Research in Education.


Supovitz, J. A., Merrill, E., & Conger, M. (2010, April). Productive teacher use of student performance and related data in professional learning communities. Paper presented at the annual meeting of the American Educational Research Association, Denver, CO.


Symonds, K. W. (2004). After the test: Closing the achievement gaps with data. Naperville, IL: Learning Point Associates. Retrieved from  http://www.racialequitytools.org/resourcefiles/symonds.pdf


Szulanski, G. (1996). Exploring stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17, 27–43.


Taylor, R. R., Hanlon, S. C., & Boyd, N. G. (1992). Can leaders and subordinates be friends? A classroom approach for addressing an important managerial dilemma. Journal of Management Education, 16, 39–55.


Tenkasi, R., & Chesmore, M. (2003). Social networks and planned organizational change. Journal of Applied Behavioral Science, 39(3), 281–300.


Thornton, P. H. (1999). The sociology of entrepreneurship. Annual Review of Sociology, 25, 19–46.


Tsai, W. (2001). Knowledge transfer in intraorganizational networks: Effects of network position and absorptive capacity on business unit innovation and performance. Academy of Management Journal, 44(5), 996–1004.


Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm networks. Academy of Management Journal, 41(4), 464–476.


Tschannen-Moran, M. (2004). Trust matters: Leadership for successful schools. San Francisco, CA: Jossey-Bass.


U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. (2010). Use of education data at the local level from accountability to instructional improvement (Contract No. ED-01-CO-0040). Washington, DC: Author.


Useem, E. L., Christman, J. B., Gold, E., & Simon, E. (1997). Reforming alone: Barriers to organizational learning in urban school change initiatives. Journal of Education for Students Placed at Risk, 2(1), 55–78.


Uzzi, B. (1996). The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American Sociological Review, 61, 674–698.


Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42(1), 35–67.


Wasserman, S., & Faust, K. (1998). Social network analysis: Methods and applications. New York, NY: Cambridge University Press.


Wayman, J. C. (2005). Involving teachers in data-driven decision making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Students Placed at Risk, 10(3), 295–308.


Wayman, J. C., Brewer, C., & Stringfield, S. (2009, April). Leadership for effective data use. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.


Wayman, J. C., & Cho, V. (2008). Preparing educators to effectively use student data systems. In T. J. Kowalski & T. J. Lasley (Eds.), Handbook on data-based decision-making in education (pp. 89–104). New York, NY: Routledge.


Wayman, J. C., Cho, V., & Johnston, M. T. (2007). The data-informed district: A district-wide evaluation of data use in the Natrona County School District. Austin: University of Texas.


Wayman, J. C., Snodgrass Rangel, V. W., Jimerson, J. B., & Cho, V., (2010). Improving data use in NISD: Becoming a Data-Informed District. Austin: University of Texas.


Wayman, J. C., & Stringfield, S. (2006). Technology-supported involvement of entire faculties in examination of student data for instructional improvement. American Journal of Education, 112(4), 549–571.


Wayman, J. C., Stringfield, S., & Yakimowski, M. (2004). Software enabling school improvement through the analysis of student data. Baltimore, MD: Johns Hopkins University Center for Research on the Education of Students Placed at Risk.


Wei, R. C., Darling-Hammond, L., Andree, A., Richardson, N., & Orphanos, S. (2009). Professional learning in the learning profession: A status report on teacher development in the United States and abroad. Dallas, TX: National Staff Development Council.


Weick, K. E. (1985). Cosmos vs. chaos: Sense and nonsense in electronic contexts. Organizational Dynamics, 14(2), 50–64.


Weinbaum, E. H., Cole, R. P., Weiss, M. J., & Supovitz, J. A. (2008). Going with the flow: Communication and reform in high schools. In J. A. Supovitz & E. H. Weinbaum (Eds.), The implementation gap (pp. 68–102). New York, NY: Teachers College Press.


Wellman, B., & Berkowitz, S. D. (1998). Social structures: A network approach. Cambridge, MA: Cambridge University Press.


Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. New York, NY: Cambridge University Press.


Wohlstetter, P., Datnow, A., & Park, V. (2008). Creating a system for data-driven decision making: Applying the principal-agent framework. School Effectiveness and School Improvement Journal, 19(3), 239–259.


Yayavaram, S., & Ahuja, G. (2008). Impact on the usefulness of inventions and knowledge-base malleability. Administrative Science Quarterly, 53( 2), 333–362.


Young, V. M. (2006). Teachers’ use of data: Loose coupling, agenda setting, and team norms. American Journal of Education, 112(4), 521–548.


Young, V. (2008). Supporting teachers’ use of data: The role of organization and policy. In E. B. Mandinach & M. Honey (Eds.), Data driven school improvement: Linking data and learning (pp. 87–106). New York, NY: Teachers College Press.





Cite This Article as: Teachers College Record Volume 114 Number 11, 2012, p. 1-38
https://www.tcrecord.org ID Number: 16811, Date Accessed: 10/21/2021 3:38:52 AM

Purchase Reprint Rights for this article or review
 
Article Tools

Related Media


Related Articles

Related Discussion
 
Post a Comment | Read All

About the Author
  • Alan Daly
    University of California, San Diego
    E-mail Author
    ALAN J. DALY is an associate professor of education at the University of California, San Diego. In addition to 15 years of public education experience as a teacher, psychologist, and administrator, Alan has also been the program director for the Center for Educational Leadership and Effective Schools at the University of California, Santa Barbara. Research interests include leadership, educational policy, district reform, and social network theory. His recent publications include an edited volume entitled Social Network Theory and Educational Change, published by Harvard Education Press.
 
Member Center
In Print
This Month's Issue

Submit
EMAIL

Twitter

RSS