American Sociological Association



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  1. Understanding Racial-ethnic Disparities in Health: Sociological Contributions

    This article provides an overview of the contribution of sociologists to the study of racial and ethnic inequalities in health in the United States. It argues that sociologists have made four principal contributions. First, they have challenged and problematized the biological understanding of race. Second, they have emphasized the primacy of social structure and context as determinants of racial differences in disease. Third, they have contributed to our understanding of the multiple ways in which racism affects health.

  2. Mechanisms Linking Social Ties and Support to Physical and Mental Health

    Over the past 30 years investigators have called repeatedly for research on the mechanisms through which social relationships and social support improve physical and psychological well-being, both directly and as stress buffers. I describe seven possible mechanisms: social influence/social comparison, social control, role-based purpose and meaning (mattering), self-esteem, sense of control, belonging and companionship, and perceived support availability. Stress-buffering processes also involve these mechanisms.

  3. Stress and Health: Major Findings and Policy Implications

    Forty decades of sociological stress research offer five major findings. First, when stressors (negative events, chronic strains, and traumas) are measured comprehensively, their damaging impacts on physical and mental health are substantial. Second, differential exposure to stressful experiences is a primary way that gender, racial-ethnic, marital status, and social class inequalities in physical and mental health are produced. Third, minority group members are additionally harmed by discrimination stress.

  4. Seeing Disorder: Neighborhood Stigma and the Social Construction of “Broken Windows”

    This article reveals the grounds on which individuals form perceptions of disorder. Integrating ideas about implicit bias and statistical discrimination with a theoretical framework on neighborhood racial stigma, our empirical test brings together personal interviews, census data, police records, and systematic social observations situated within some 500 block groups in Chicago. Observed disorder predicts perceived disorder, but racial and economic context matter more.

  5. 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.
  6. Exchange, Identity Verification, and Social Bonds

    Although evidence reveals that the social exchange process and identity verification process each can produce social bonds, researchers have yet to examine their conjoined effects. In this paper, we consider how exchange processes and identity processes separately and jointly shape the social bonds that emerge between actors. We do this with data from an experiment that introduces the fairness person identity (how people define themselves in terms of fairness) in a negotiated exchange context.
  7. The Spatial Proximity and Connectivity Method for Measuring and Analyzing Residential Segregation

    In recent years, there has been increasing attention focused on the spatial dimensions of residential segregation—from the spatial arrangement of segregated neighborhoods to the geographic scale or relative size of segregated areas. However, the methods used to measure segregation do not incorporate features of the built environment, such as the road connectivity between locations or the physical barriers that divide groups. This paper introduces the spatial proximity and connectivity (SPC) method for measuring and analyzing segregation.
  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. Rejoinder: Can We Weight Models by Their Probability of Being True?

    We thank the commenters for thoughtful, constructive engagement with our paper (this volume, pp. 1–33). Throughout this discussion, there is strong consensus that model robustness analysis is essential to sociological research methods in the twenty-first century. Indeed, both O’Brien (this volume, pp. 34–39) and Western (this volume, pp. 39–43) identify examples of sociological research that is plagued by uncertainty over modeling decisions and how those decisions can change the results and conclusions of the analyses.
  10. We Ran 9 Billion Regressions: Eliminating False Positives through Computational Model Robustness

    False positive findings are a growing problem in many research literatures. We argue that excessive false positives often stem from model uncertainty. There are many plausible ways of specifying a regression model, but researchers typically report only a few preferred estimates. This raises the concern that such research reveals only a small fraction of the possible results and may easily lead to nonrobust, false positive conclusions. It is often unclear how much the results are driven by model specification and how much the results would change if a different plausible model were used.