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  1. Race Differences in Linking Family Formation Transitions to Women’s Mortality

    We examine how the timing and sequencing of first marriage and childbirth are related to mortality for a cohort of 4,988 white and black women born between 1922 and 1937 from the National Longitudinal Survey of Mature Women. We use Cox proportional hazard models to estimate race differences in the association between family formation transitions and mortality. Although we find no relationships between marital histories and longevity, we do find that having children, the timing of first birth, and the sequencing of childbirth and marriage are associated with mortality.
  2. Multigenerational Attainments, Race, and Mortality Risk among Silent Generation Women

    This study extends health disparities research by examining racial differences in the relationships between multigenerational attainments and mortality risk among “Silent Generation” women. An emerging literature suggests that the socioeconomic attainments of adjacent generations, one’s parents and adult children, provide an array of life-extending resources in old age.
  3. 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.
  4. The Emergence of Statistical Objectivity: Changing Ideas of Epistemic Vice and Virtue in Science

    The meaning of objectivity in any specific setting reflects historically situated understandings of both science and self. Recently, various scientific fields have confronted growing mistrust about the replicability of findings, and statistical techniques have been deployed to articulate a “crisis of false positives.” In response, epistemic activists have invoked a decidedly economic understanding of scientists’ selves. This has prompted a scientific social movement of proposed reforms, including regulating disclosure of “backstage” research details and enhancing incentives for replication.
  5. Estimating Heterogeneous Treatment Effects with Observational Data

    Individuals differ not only in their background characteristics but also in how they respond to a particular treatment, intervention, or stimulation. In particular, treatment effects may vary systematically by the propensity for treatment. In this paper, we discuss a practical approach to studying heterogeneous treatment effects as a function of the treatment propensity, under the same assumption commonly underlying regression analysis: ignorability.

  6. 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.

  7. 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.
  8. 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.
  9. The Problem of Underdetermination in Model Selection

    Conventional model selection evaluates models on their ability to represent data accurately, ignoring their dependence on theoretical and methodological assumptions. Drawing on the concept of underdetermination from the philosophy of science, the author argues that uncritical use of methodological assumptions can pose a problem for effective inference. By ignoring the plausibility of assumptions, existing techniques select models that are poor representations of theory and are thus suboptimal for inference.
  10. Comment: The Inferential Information Criterion from a Bayesian Point of View

    As Michael Schultz notes in his very interesting paper (this volume, pp. 52–87), standard model selection criteria, such as the Akaike information criterion (AIC; Akaike 1974), the Bayesian information criterion (BIC; Schwarz 1978), and the minimum description length principle (MDL; Rissanen 1978), are purely empirical criteria in the sense that the score a model receives does not depend on how well the model coheres with background theory. This is unsatisfying because we would like our models to be theoretically plausible, not just empirically successful.