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  1. 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.

  2. The Role of Gender, Class, and Religion in Biracial Americans Racial Labeling Decisions

    Racial attachments are understood to be socially constructed and endogenous to gender, socioeconomic, and religious identities. Yet we know surprisingly little about the effect of such identities on the particular racial labels that individuals self-select. In this article, I investigate how social identities shape the racial labels chosen by biracial individuals in the United States, a rapidly growing population who have multiple labeling options.

  3. Exceptional Outgroup Stereotypes and White Racial Inequality Attitudes toward Asian Americans

    Stereotypes of outgroups help create social identificational boundaries for ingroups. When the ingroup is dominant, members employ individualist sentiments to justify their status. In this study, we build on advances in social psychological research that account for multiple outgroup stereotypes. We argue the Asian American model minority stereotype is analogous to the "cold but competent" position of perceptions toward Asians in Fiske’s stereotype content model.

  4. Race, Immigration, and Exogamy among the Native-born: Variation across Communities

    Did rising immigration levels change racial and ethnic exogamy patterns for young adults in the United States? Adding local demographics to Qian and Lichter’s national results, the authors examine the relationship between the sizes of the local immigrant populations in urban and rural areas and U.S.-born individuals’ exogamy patterns in heterosexual unions, controlling for the areas’ racial compositions.

  5. Socioeconomic Attainments of Japanese Brazilians and Japanese Americans

    This paper investigates the socioeconomic attainments of Japanese Brazilians and Japanese Americans. The findings indicate that Japanese Brazilians have higher levels of education and wages than white Brazilians, while Japanese Americans have higher levels of education and wages than white Americans. These results are inconsistent with a conventional "white supremacy" model that is popular in contemporary American sociology.

  6. Do Asian Americans Face Labor Market Discrimination? Accounting for the Cost of Living among Native-born Men and Women

    Being nonwhite, Asian Americans are an important case in understanding racial/ethnic inequality. Prior research has focused on native-born workers to reduce unobserved heterogeneity associated with immigrants. Native-born Asian American adults are concentrated, however, in areas with a high cost of living where wages tend to be higher. Regional location is thus said to inflate the wages of Asians. Given that many labor markets are national in scope with regional migration being common, current place of residence is unlikely to be a fully exogenous independent variable.
  7. Nonlinear Autoregressive Latent Trajectory Models

    Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories, the identification status of such models, approaches to comparing them with alternative models, and the interpretation of parameters have not been systematically assessed.
  8. 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.
  9. We Ran 9 Billion Regressions: Eliminating False Positives through Computational Model Robustness

    False positive findings are a growing problem in many research literatures. We argue that excessive false positives often stem from model uncertainty. There are many plausible ways of specifying a regression model, but researchers typically report only a few preferred estimates. This raises the concern that such research reveals only a small fraction of the possible results and may easily lead to nonrobust, false positive conclusions. It is often unclear how much the results are driven by model specification and how much the results would change if a different plausible model were used.
  10. 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.