American Sociological Association

Search

Search

The search found 55 results in 0.029 seconds.

Search results

  1. Testing a Digital Inequality Model for Online Political Participation

    Increasing Internet use is changing the way individuals take part in society. However, a general mobilizing effect of the Internet on political participation has been difficult to demonstrate. This study takes a digital inequality perspective and analyzes the role of Internet expertise for the social structuration of online political participation. Analyses rely on two nationally representative surveys in Switzerland and use cluster analysis and structural equation modeling. A distinct group of political online participants emerged characterized by high education and income.
  2. ASA President Eduardo Bonilla-Silva Responds to Chief Justice John Roberts

    Last week, Supreme Court Chief Justice John Roberts, during oral arguments in the gerrymandering case Gill v Whitford, referred to social science as "sociological gobbledygook." ASA President Eduardo Bonilla-Silva has responded in a letter, the content of which is below. You can also download a .pdf of the letter here


    Dear Chief Justice John Roberts:

  3. U.S. has 5 percent of world's population, but had 31 percent of its public mass shooters from 1966-2012

    Despite having only about 5 percent of the world's population, the United States was the attack site for a disproportionate 31 percent of public mass shooters globally from 1966-2012, according to research presented at the 2015 Annual Meeting of the American Sociological Association (ASA).

  4. The Algorithmic Rise of the “Alt-Right”

    As with so many technologies, the Internet’s racism was programmed right in—and it’s quickly fueled the spread of White supremacist, xenophobic rhetoric throughout the western world.
  5. Searching for a Mate: The Rise of the Internet as a Social Intermediary

    This article explores how the efficiency of Internet search is changing the way Americans find romantic partners. We use a new data source, the How Couples Meet and Stay Together survey. Results show that for 60 years, family and grade school have been steadily declining in their influence over the dating market. In the past 15 years, the rise of the Internet has partly displaced not only family and school, but also neighborhood, friends, and the workplace as venues for meeting partners.

  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. Selling Feminism, Consuming Femininity

    For over half a century, magazines aimed at teens have been teaching girls how to inhabit stereotypical gender roles. Surprisingly, though the celebrities on the covers have changed, the messages have remained the same.

  8. 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.
  9. 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).
  10. Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference

    Randomized experiments on a network often involve interference between connected units, namely, a situation in which an individual’s treatment can affect the response of another individual. Current approaches to deal with interference, in theory and in practice, often make restrictive assumptions on its structure—for instance, assuming that interference is local—even when using otherwise nonparametric inference strategies.