American Sociological Association

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  1. Ripples of Fear: The Diffusion of a Bank Panic

    Community reactions against organizations can be driven by negative information spread through a diffusion process that is distinct from the diffusion of organizational practices. Bank panics offer a classic example of selective diffusion of negative information. Bank panics involve widespread bank runs, although a low proportion of banks experience a run. We develop theory on how organizational similarity, community similarity, and network proximity create selective diffusion paths for resistance against organizations.

  2. The Contingent Value of Embeddedness: Self-affirming Social Environments, Network Density, and Well-being

    Social capital theorists claim that belonging to a densely knit social network creates a shared identity, mutually beneficial exchange, trust, and a sense of belonging in that group. Taken together with the empirical research on the importance of social support and social integration for individuals’ well-being, there is reason to expect that the density of one’s personal social network should be positively related to well-being.

  3. What Is Relational Structure? Introducing History to the Debates on the Relation between Fields and Social Networks

    In this article, I argue that the current views on the relation between fields and social networks are based on two false premises: first, that fields and social networks are mutually exclusive forms of relational structure, and second, that the objective form of relational structure is an a priori fact.

  4. Cohorts, ‘‘Siblings,’’ and Mentors: Organizational Structures and the Creation of Social Capital

    How can an organization help participants increase their social capital? Using data from an ethnographic study of Launch, an organization that prepares low-income students of color to attend elite boarding schools, I analyze how the organization’s structures not only generate social ties among students but also stratify those ties horizontally and vertically, thereby connecting students to a set of social contacts who occupy a range of hierarchical positions and who are able to provide access to resources that are beneficial in different contexts and at different times.

  5. The Connection between Neighboring and Volunteering

    Sociological theory predicts that volunteers are likely to be more socially integrated into their communities than nonvolunteers. In this study, we test this theory by examining three dimensions of relations to neighbors—contact, social engagement, and the perception that neighbors trust each other. We hypothesize reciprocal relations between volunteering and these three measures.

  6. Frame-Induced Group Polarization in Small Discussion Networks

    We present a novel explanation for the group polarization effect whereby discussion among like-minded individuals induces shifts toward the extreme. Our theory distinguishes between a quantitative policy under debate and the discussion’s rhetorical frame, such as the likelihood of an outcome. If policy and frame position are mathematically related so that frame position increases more slowly as the policy becomes more extreme, majority formation at the extreme is favored, thereby shifting consensus formation toward the extreme.
  7. Nonlinear Autoregressive Latent Trajectory Models

    Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories, the identification status of such models, approaches to comparing them with alternative models, and the interpretation of parameters have not been systematically assessed.
  8. Causal Inference with Networked Treatment Diffusion

    Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends).
  9. Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference

    Randomized experiments on a network often involve interference between connected units, namely, a situation in which an individual’s treatment can affect the response of another individual. Current approaches to deal with interference, in theory and in practice, often make restrictive assumptions on its structure—for instance, assuming that interference is local—even when using otherwise nonparametric inference strategies.
  10. Comment: Bayes, Model Uncertainty, and Learning from Data

    The problem of model uncertainty is a fundamental applied challenge in quantitative sociology. The authors’ language of false positives is reminiscent of Bonferroni adjustments and the frequentist analysis of multiple independent comparisons, but the distinct problem of model uncertainty has been fully formalized from a Bayesian perspective.