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  1. Cabdrivers and Their Fares: Temporal Structures of a Linking Ecology

    The author argues that behind the apparent randomness of interactions between cabdrivers and their fares in Warsaw is a temporal structure. To capture this temporal structure, the author introduces the notion of a linking ecology. He argues that the Warsaw taxi market is a linking ecology, which is structured by religious time, state time, and family time. The author then focuses on waiting time, arguing that it too structures the interactions between cabdrivers and their fares.

  2. 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.

  3. Do Readers Judge Books by Author Gender? Results from a Randomized Experiment

    We run a randomized experiment to examine gender discrimination in book purchasing with 2,544 subjects on Amazon’s Mechanical Turk. We manipulate author gender and book genre in a factorial design to study consumer preferences for male versus female versus androgynous authorship. Despite previous findings in the literature showing gender discrimination in book publishing and in evaluations of work, respondents expressed no gender preference across a variety of measures, including quality, interest, and the amount they were willing to pay to purchase the book.

  4. Algorithmic Control in Platform Food Delivery Work

    Building on an emerging literature concerning algorithmic management, this article analyzes the processes by which food delivery platforms control workers and uncovers variation in the extent to which such platforms constrain the freedoms—over schedules and activities—associated with gig work.
  5. 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.
  6. Getting the Within Estimator of Cross-Level Interactions in Multilevel Models with Pooled Cross-Sections: Why Country Dummies (Sometimes) Do Not Do the Job

    Multilevel models with persons nested in countries are increasingly popular in cross-country research. Recently, social scientists have started to analyze data with a three-level structure: persons at level 1, nested in year-specific country samples at level 2, nested in countries at level 3. By using a country fixed-effects estimator, or an alternative equivalent specification in a random-effects framework, this structure is increasingly used to estimate within-country effects in order to control for unobserved heterogeneity.
  7. Assessing Differences between Nested and Cross-Classified Hierarchical Models

    Sociological Methodology, Volume 49, Issue 1, Page 220-257, August 2019.
  8. Social Space Diffusion: Applications of a Latent Space Model to Diffusion with Uncertain Ties

    Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step toward integrating these two perspectives by introducing a social space diffusion model.
  9. No Longer Discrete: Modeling the Dynamics of Social Networks and Continuous Behavior

    The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale.
  10. 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.