PROFESSIONAL FILE ARTICLE 136 © Copyright 2015, Association for Institutional Research APPLICATIONS OF SOCIAL NETWORK ANALYSIS IN INSTITUTIONAL RESEARCH Ning Wang better served. Traditional descriptive publications, faculty collaboration and inferential statistics, from simple on research projects, peer influence About the Author frequencies and cross-tabulations, among students with specific ethnic Ning Wang is the Director of to the whole family of regressions, or social backgrounds, mentorship Institutional Research at University of to more-advanced techniques such between faculty members and California, San Francisco. as survival analysis and structural students, formation of learning equation modeling, have sufficiently communities among students with Abstract fulfilled a large part of IR’s analytical shared academic interests, and so forth. Social network analysis (SNA), with functionality. At the same time, the Relations also extend beyond people: its distinct perspective on studying large amount of data found in IR for example, majors within a discipline relations and its exceptional capability and the nature of IR research that are interrelated by overlapping course to visualize data, should be embraced emphasizes identification of patterns, offerings, colleges and universities are by institutional researchers as a predictions, and possible interventions, interrelated by students transferring promising new research methodology coupled with high-capacity software in and out, and states form a network complementary to inferential and such as SAS, have made exploratory through out-of-state student exploratory statistics. This article statistics a new frontier in IR. The recent enrollment. introduces SNA through discussion of interest in data mining and predictive three analytical studies on topics highly modeling exemplifies this shift. Such networks of relations are relevant to institutional research (IR): extensive in higher education, but few (1) double-majors, (2) gatekeeping However, a missing piece of IR analytics studies have addressed their dynamics courses, and (3) STEM pipeline leaking. is the study of relations. Traditional and implications, partly because of The unique approach of SNA in statistical methods assume the the methodological limitations of exploring, analyzing, and presenting observation independence—that is, inferential and exploratory statistics. data has great potential for advancing they assume that observations of a As Wasserman and Faust (1994) in their IR’s analytical capacity. study are not related to one another, classic book of social network analysis but rather can be independently (SNA) stated, “The focus on relations, INTRODUCTION examined by various internal and and the patterns of relations, requires external attributes (Chen & Zhu, a set of methods and analytic concepts Institutional research (IR) professionals 2001). The observations in higher that are distinct from the methods of frequently adopt new analytical tools education settings, however, often traditional statistics and data analysis” and research methodologies. This has are not independent. The activities (p. 3). The inadequate understanding allowed more sophisticated studies of higher education and the people of relations and interactions among to be carried out that better inform involved are relational and interactive the various entities in higher education institutions’ policy making, which leads in nature. Examples of these activities calls for the addition of network in the long term to students being include co-authorship of scholarly analysis into IR’s analytical paradigm. SPRING 2015 VOLUME | PAGE 1 At the intersection of inferential This article introduces SNA to the IR undirected graph, weight, modularity, statistics, exploratory statistics, and community. As a well-established and centrality. network analysis is data visualization— method that has been widely used in or the representation of data through social sciences, SNA can contribute Borrowed from graph theory, the graphical means. However, “data a great deal to IR with its unique interconnected objects in SNA visualization . involves more than perspective on relations and its power are represented by mathematical just representing data in a graphical in visual presentation. The following abstractions called vertices (more form (instead of using a table). The will (1) introduce basic concepts in commonly called nodes), while the information behind the data should SNA, (2) present three studies that links that connect some pairs of nodes also be revealed in a good display; used SNA, and (3) discuss issues key to are called edges. The number of edges the graphic should aid the readers or successfully applying SNA in IR. incident upon a node is defined as viewers in seeing the structure in the degree. Typically, a graph is depicted data” (Chen, Härdle, & Unwin, 2008, SOCIAL NETWORK in diagrammatic form as a set of dots p. 6). As the well-known statistician for the nodes, joined by lines or curves and pioneer in data visualization ANALYSIS AND ITS for the edges. When applied to a study, Edward Tufte stated, “At their best, BASIC CONCEPTS nodes represent the observations graphics are instruments for reasoning SNA is inherently an interdisciplinary of a study, and edges represent the about quantitative information. Of endeavor that uses social psychology, relations between the observations all methods for analyzing and sociology, statistics, and graph of a study. If the relations are initiated communicating statistical information, theory. Beginning in the 1970s, from certain observations to others, well-designed data graphics are usually the empirical study of various the edges would be represented the simplest and at the same time the networks has played an increasingly with arrows from the initiators to the most powerful” (Tufte, 2001, p. 13). important role in the social sciences. receivers, and the graph would be Among many of its applications, directed. Conversely, if the relations Founded on graph theory, network SNA has been used to understand between two observations are mutual, analysis is exceptionally well developed the diffusion of innovations, the the edge would be represented with in generating meaningful and communication of news, the spread a line segment connecting the two, intriguing visual representations of of diseases, the culture and structure and the graph would be undirected. data. While charts and graphs are of social organizations and business A graph is weighted if a value or a integral components of inferential and corporations, the formation of political weight is assigned to each edge. exploratory statistics, graphics is at the views and affiliations, and so forth Depending on the problem at hand, heart of network analysis. It is the way (Carrington, Scott, & Wasserman, such weights might represent a diverse that an underlying network structure 2005). More recently SNA has gained set of attributes of the relationship can be uncovered, while at the significant use in studying online (Hanneman & Riddle, 2005). same time providing the vocabulary communities and social media such as through which network properties Facebook and Twitter. For demonstration, Figure 1 is can be described. At a time when a weighted undirected graph effective communication of findings The complicated mathematical representing a hypothetical network to institutions’ administrators and background of SNA is beyond the of faculty collaboration. Nodes 1–10 other constituents is more important scope of this article. However, it would are faculty members. Edges exist than ever to further data-driven be helpful to explain in simple terms between those who collaborated and research-based policy making, several basic yet essential concepts on grant proposals, and weights on network analysis, with its expertise in used in the examples of analytical the edges denote the number of data visualization, can be especially works described in this paper: vertice grant proposals that the two faculty beneficial to IR. or node, edge, degree, directed and members submitted together. As seen PAGE 2 | SPRING 2015 VOLUME degree, closeness, and betweenness. Degree centrality is defined as the number of edges that a node has. The nodes having higher degrees are related to other nodes, and therefore are at positions in the network that are more central. Closeness centrality emphasizes the distance of a node to all other nodes in the network. Betweenness centrality focuses on the position of a node between pairs of nodes. The higher betweenness of a node means more nodes depend on it to make connections with other nodes. Centrality can be evaluated with a set of statistics, such as Freeman Degree Centrality, Geodesic Path Distances, Eigenvector Centrality, Hierarchical Reduction, and so forth (Hanneman & Riddle, 2005). This article does not attempt to elaborate on details of these statistics; the readers are encouraged to obtain more information (e.g., Figure 1. Demonstration of Basic Concepts in Social Network Analysis Carrington et al., 2005; Chen et al., 2008; Tufte, 1990, 2001; Wasserman & Faust, 1994). The output of the in the graph, Faculty 2 worked with measure is to compute the difference above-mentioned statistics for the Faculty 1 once, with Faculty 5 once, between the number of edges falling hypothetical network in Figure 1 is and with Faculty 3 three times on within groups and the expected provided in Table 1 (next page). grant proposals; the node representing number