The application of social network analysis methodologies is relatively new for mainstream evaluation and has yet to be fully explored in this discipline. This chapter discusses how and why SNA is appropriate for evaluation practice.
Social network theory stands apart from other social science theory because it focuses on the social context and behavior of relationships between actors rather than on the rational choices individual actors make, as seen in disciplines such as economics and the social and decision sciences. Traditional social sciences do not consider the existence of these social aspects of relationships as data. Even organizational sociology, which one might consider a more socially oriented field, disregards social ties and primarily studies individual characteristics of groups (Kilduff and Tsai, 2003).Social network analysis (SNA) applications have had three main and parallel influences beginning in the 1930s. The first was sociometric analysis, which used graph theory methods. The second was a mathematical approach taken up first by Kurt Lewin and later by Harvard researchers, which laid the foundation for the analysis of social networks. The Harvard analysis introduced the notion of cliques, which operationalized social structures. No longer was network analysis merely descriptive in nature. The third influence came from the Manchester anthropologists who looked at the structure of community relations in villages. All traditions were brought together, again at Harvard, in the 1960s and 1970s when contemporary SNA was developed (Kilduff and Tsai, 2003).Both the identification and the analysis of the structure of relationships within groups have been the subject of inquiry since the early 1930s. In the 1930s, German Gestalt theorists in psychology came to the United States to work. The most notable was Jacob Moreno (1934), who investigated how NEW DIRECTIONS FOR EVALUATION, no. 107, Fall 2005
This chapter provides an overview of the methodology of social network analysis (SNA) and a framework for understanding the next four chapters, which are case studies illustrating the application of SNA. 3 Exploring and Understanding Relationships Maryann M. DurlandAlthough the process of doing social network analysis (SNA) is similar to traditional research and evaluation design, it is in the details that the two traditions diverge. This chapter describes two areas of differences: (1) the framework for doing SNA and (2) data collection, analysis, and specific measures. SNA looks and sounds different from traditional methodologies (Hanneman, 2001). Two examples are the array of data and the language of analysis. At the data level, conventional data consist of columns of measurements on an attribute variable and rows of cases or subjects. This data analysis array is rectangular. The language of conventional analysis consists of terms such as scores, comparison of how subjects are similar or dissimilar across variables or how the variables are similar or dissimilar across the subjects. Network data have a square array. The rows, as in conventional data, are subjects or actors. The columns are the same set of actors. The measurement in each cell is the measure of the existence or degree of a relationship between the two actors. Though network analysts make row and column comparisons similar to conventional analysis, what is different is the holistic analysis of the data. The language of SNA includes terms such as density, cliques, and block models.SNA is about relationships and how to measure them. Results are in the form of both numerical data and a visual image called a sociogram or social map.
There has been a dramatic shift in attitude among organizations regarding the probabilities of crisis occurring. Once crises were considered the domain of the contingency management team that sought the fastest means to recovery, now the entire organization is compelled to take steps intended to mitigate conditions leading to a crisis. In this paper, the authors consider the organization's ‘first responders’ i.e., those who become involuntarily placed in the decision making process because they are the first to become aware of the conditions which indicate impending crisis simply because they are ‘on scene.’ As agents of the organization, these persons will make initial decisions well before the implementation of any formal contingency plan and because their decisions will be based on incomplete assumptions, they are likely to be in error. The impact of these initial crisis‐agent responses can cause irreparable damage to the organization, to the individuals within the organization, and to the surrounding environment. This tendency toward error is referred to as the initial crisis‐agent response impact syndrome: ICARIS. Exercising a program that prepares all employees for the initial decisions that need to be made at the moment of crisis can mitigate problems related to this issue.
Social network analysis (SNA) is not a new methodology, but its application to evaluation practice is new. Ultimately the most important question for evaluation practitioners is: How does SNA add value to evaluation practice? The application of SNA within academia has a long history, but its use within other fields is just beginning. The transition of the use of SNA from a more academic and theoretical focus to a more practical application means that the challenges of learning and understanding the methodology are at an innovative and potentially labor-intensive stage. Including the application of SNA to evaluation requires a risk taker, typical of first innovators. But there are currently examples of applications that can be adapted to evaluation practice, and because SNA can range from the simple to the complex in terms of theory alignment and measures used, the opportunities for implementing SNA within an evaluation design are broad.In Chapter Four, Kochan and Teddlie used SNA to highlight how faculty characteristics affected communication patterns. Organizational division or unity based on the characteristics of members of the organization is not unique to schools. In Chapter Six, Birk illustrated the use of SNA for understanding the capacity of an organization to carry out its goals. This type of application is not unique to scientific communities. Any organization or program that has specific activities, events, or projects that require specific expertise to implement faces the same questions.
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