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  1. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings

    We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture.
  2. Work–Family Conflict and Well-Being among German Couples: A Longitudinal and Dyadic Approach

    This study examines dual-earner couples to determine whether changes in work–family conflict predict changes in one’s own (i.e., actor effects) or partner’s (i.e., partner effects) health and well-being as well as gender differences in these relationships.
  3. Family Complexity into Adulthood: The Central Role of Mothers in Shaping Intergenerational Ties

    As a result of the divorce revolution, more children grow up in complex families. Yet, we know little about how family complexity affects relationships when children are adults and parents are ageing. In this article, we use unique survey data to test fundamental ideas about intergenerational ties: the role of biology, partnerships (marriage and cohabitation), residence, and selection.
  4. Sacred Alters: The Effects of Ego Network Structure on Religious and Political Beliefs

    Does who we know impact how strongly we believe? The claim seems reasonable, but research linking social network composition to political beliefs has produced conflicting results. We argue that methodological differences in measuring close ties can explain these inconsistencies and that work on the sacred umbrella provides a useful framework for moving forward. The sacred umbrella argues that when people close to you share your religious beliefs, you are shielded from doubt and uncertainty; perhaps the same mechanism also operates for political views.
  5. Uncertainty and Social Influence

    Much research documents that uncertainty is an important factor in the social influence process. We argue that there are two senses in which uncertainty plays a role. First, task uncertainty is a necessary but variable condition for social influence to occur. Second, uncertainty reduction is a mechanism producing social influence. We discuss how tasks can vary in the level of uncertainty they entail and how this impacts the mechanisms resulting in social influence. In this context, we predict that task uncertainty moderates social influence and that uncertainty reduction mediates it.
  6. Freedom and the Iranian Women’s Movement

    The women of Iran are intimately familiar with repression and segregation. Iran’s mandatory dress code—veiling—is but one of many restrictions that regulate and control women’s bodies and shape their sense of agency and freedom.
  7. Assessing Differences between Nested and Cross-Classified Hierarchical Models

    Sociological Methodology, Volume 49, Issue 1, Page 220-257, August 2019.
  8. Review Essay: Back to the Future

    In one of my undergraduate courses, I show students a photo of Paul Lazarsfeld and Frank Stanton. Of course, neither social scientist is familiar to them, but I argue to my students that Lazarsfeld had a bigger impact on the daily practice of sociology than any member of the Marx/Weber/Durkheim triumvirate they study in classical theory.

  9. Advancing Identity Theory: Examining the Relationship between Activated Identities and Behavior in Different Social Contexts

    This study advances identity theory by testing the impact of (moral) identity activation on behavior in different social contexts. At a large southwestern university, 343 undergraduate students completed a survey that measured meanings of their moral identity. Later they completed a laboratory task in which they were awarded more points than they deserved. Participants were given the opportunity to admit (or not admit) the improper point reward.

  10. A General Framework for Comparing Predictions and Marginal Effects across Models

    Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects because they are in the natural metric of the dependent variable and they avoid identification problems when comparing regression coefficients across logit and probit models.