American Sociological Association



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  1. 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.

  2. Qualitative Comparative Analysis in Critical Perspective

    Qualitative comparative analysis (QCA) appears to offer a systematic means for case-oriented analysis. The method not only offers to provide a standardized procedure for qualitative research but also serves, to some, as an instantiation of deterministic methods. Others, however, contest QCA because of its deterministic lineage. Multiple other issues surrounding QCA, such as its response to measurement error and its ability to ascertain asymmetric causality, are also matters of interest.

  3. Comparing Regression Coefficients Between Same-sample Nested Models Using Logit and Probit: A New Method

    Logit and probit models are widely used in empirical sociological research. However, the common practice of comparing the coefficients of a given variable across differently specified models fitted to the same sample does not warrant the same interpretation in logits and probits as in linear regression. Unlike linear models, the change in the coefficient of the variable of interest cannot be straightforwardly attributed to the inclusion of confounding variables. The reason for this is that the variance of the underlying latent variable is not identified and will differ between models.

  4. Creating an Age of Depression: The Social Construction and Consequences of the Major Depression Diagnosis

    One type of study in the sociology of mental health examines how social and cultural factors influence the creation and consequences of psychiatric diagnoses. Most studies of this kind focus on how diagnoses emerge from struggles among advocacy organizations, economic and political interest groups, and professionals.

  5. 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.

  6. The Public Stigma of Mental Illness What Do We Think; What Do We Know; What Can We Prove?

    By the 1990s, sociology faced a frustrating paradox. Classic work on mental illness stigma and labeling theory reinforced that the “mark” of mental illness created prejudice and discrimination for individuals and family members. Yet that foundation, coupled with deinstitutionalization of mental health care, produced contradictory responses. Claims that stigma was dissipating were made, while others argued that intervention efforts were needed to reduce stigma.

  7. 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.

  8. 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.
  9. 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.
  10. Comment: Some Challenges When Estimating the Impact of Model Uncertainty on Coefficient Instability

    I once had a colleague who knew that inequality was related to an important dependent variable. This colleague knew many other things, but I focus on inequality as an example. It was difficult for my colleague to know just how to operationalize inequality. Should it be the percentage of income held by the top 10 percent, top 5 percent, or top 1 percent of the population? Should it be based on the ratio of median black income to median white income, or should it be the log of that ratio? Should it be based on the Gini index, or perhaps the Theil index would be better?