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  1. Study Finds EITC Bolsters Recipients’ Self-Respect While Helping Them Financially

    America's welfare state is quietly evolving from needs-based to an employment-based safety net that rewards working families and fuels dreams of a better life, indicates a new study led by a Michigan State University (MSU) scholar.

    The major reason: the little-known Earned Income Tax Credit (EITC), a $65 billion federal tax-relief program for poor, working families. The program has been expanded dramatically during the past 25 years, while cash welfare has been sharply curtailed.

  2. Many Religious People View Science Favorably, But Reject Certain Scientific Theories

    A new study finds that many U.S. adults — roughly one in five — are deeply religious, know a lot about science, and support many practical uses of science and technology in everyday life, but reject scientific explanations of creation and evolution.

  3. Lightness/Darkness of Skin Affects Male Immigrants' Likelihood of Gaining Employment

    Skin color is a significant factor in the probability of employment for male immigrants to the United States, according to a new study by two University of Kansas (KU) researchers.

  4. The IRL Fallacy

    Putting the lie to "digital dualism" in an essay on the inseparability of online and offline selves.

  5. Jishuku, Altruism, and Expatriate Emotion

    When a devastating earthquake and tsunami hit japan in 2011, the effects were felt by over a million expatriates worldwide.

  6. Immigration and Welfare Support in Germany

    In recent years, several international-comparative studies have analyzed the relationship between migration and native populations’ decreasing support for redistributive policies. However, these studies use cross-sectional designs and aggregate the number of foreign-born residents at the national level. Both aspects are theoretically and methodologically problematic. We address these shortcomings by investigating cross-sectional as well as longitudinal effects in the case of Germany, using a combination of individual- and regional-level data for several time points from 1994 to 2010.

  7. Childhood Disadvantage and Health Problems in Middle and Later Life: Early Imprints on Physical Health?

    Drawing from cumulative inequality theory, we examine the relationship between childhood disadvantage and health problems in adulthood. Using two waves of data from Midlife Development in the United States, we investigate whether childhood disadvantage is associated with adult disadvantage, including fewer social resources, and the effect of lifelong disadvantage on health problems measured at the baseline survey and a 10-year follow-up.

  8. Discrimination in Lending Markets: Status and the Intersections of Gender and Race

    Research documents that lenders discriminate between loan applicants in traditional and peer-to-peer lending markets, yet we lack knowledge about the mechanisms driving lenders’ behavior. I offer one possible mechanism: When lenders assess borrowers, they are implicitly guided by cultural stereotypes about the borrowers’ status. This systematically steers lenders toward funding higher status groups even when applicants have the same financial histories.

  9. Cancer Diagnosis and Mental Health among Older White Adults: Moderating Role for Social Networks?

    Cancer is a life-changing condition for many American seniors, and a growing body of literature is assessing the mental health implications of living with the disease. This article builds from the well-known buffering hypothesis with insights from recent cancer research to investigate whether social networks moderate the association between cancer and mental health for older men and women.

  10. An Introduction to the General Monotone Model with Application to Two Problematic Data Sets

    We argue that the mismatch between data and analytical methods, along with common practices for dealing with "messy" data, can lead to inaccurate conclusions. Specifically, using previously published data on racial bias and culture of honor, we show that manifest effects, and therefore theoretical conclusions, are highly dependent on how researchers decide to handle extreme scores and nonlinearities when data are analyzed with traditional approaches.