This book provides an introduction to the theories, methods, and applications that constitute the social network perspective. Unlike more general texts, this title is designed for those current and aspiring educational researchers learning how to study, conceptualize, and analyze social networks. The author's main intent is to encourage you to consider the social network perspective in light of your emerging research interests and evaluate how well this perspective illuminates the social complexities surrounding educational phenomena. Whether your interests lie in examining a peer's influence on students' achievement, the relationship between social support and teacher retention, or how the pattern of relations among parents contributes to schools' norms, the tools introduced in this book will provide you with a slightly different take on these and other phenomena. Unlike other approaches, this perspective accounts for the importance of relationships within formal structures, and the informal patterns of interaction that emerge, sustain, or recede. Relying on diverse examples drawn from the educational research literature, this book makes explicit how the theories and methods associated with social network analysis can be used to better describe and explain the social complexities surrounding varied educational phenomena.

An Introduction to Statistical Inference With Network Data

Objectives

In this chapter, you will learn about the difference between the mathematical and statistical approaches to social network analysis. In Part II, many social network measures for concepts related to both complete and ego networks were presented. These measures reflect a mathematical approach that focuses on what a network of actors “looks like.” This approach does not, however, consider whether a certain configuration is predictable and normal, how one relation is associated with another relation among the same set of actors, or whether one network structure could be thought of as “better” than another. To attend to these important issues, in this chapter, you will learn about the ways in which statistical inference has evolved in order ...

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