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  1. 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.
  2. 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.
  3. 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.
  4. 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.

  5. Visualizing Africa’s Educational Gender Gap

    This figure depicts the gendered patterns of educational expansion across Africa. The horizontal axis displays educational access, and vertical lines represent educational gender gaps for 267 country-specific birth cohorts, representing adults born between 1941 and 1992 in 32 African countries. The gaps take on an almond shape. In early stages of educational expansion, boys enter school at higher rates than girls; female enrollment begins to catch up only when at least half of the cohort attends school.
  6. The Impact of Racial Diversity in the Classroom: Activating the Sociological Imagination

    Diverse college campuses have been conclusively associated with a variety of positive outcomes for all students. However, we still know very little empirically about how student diversity directly impacts the core task of the university: classroom learning. While students vary based on race along a broad spectrum of experiences and backgrounds, we have yet to establish how those varying backgrounds might impact the ways students engage with course material.

  7. Gender, Socioeconomic Status, and Diet Behaviors within Brazilian Families

    Existing literature documents the key role that parents play in transmitting diet behaviors to their children; however, less is known about differences by parent and child gender within families, especially with attention to household socioeconomic status (SES). We use nationally representative household data from Brazil and ask how parent-child associations of diet behavior differ by gender within lower- and higher-SES households.
  8. Text Analysis with JSTOR Archives

    I provide a visual representation of keyword trends and authorship for two flagship sociology journals using data from JSTOR’s Data for Research repository. While text data have accompanied the digital spread of information, it remains inaccessible to researchers unfamiliar with the required preprocessing. The visualization and accompanying code encourage widespread use of this source of data in the social sciences.

  9. Visualizing Belief in Meritocracy, 1930–2010

    In this figure I describe the long trend in popular belief in meritocracy across the Western world between 1930 and 2010. Studying trends in attitudes is limited by the paucity of survey data that can be compared across countries and over time. Here, I show how to complement survey waves with cohort-level data. Repeated surveys draw on a representative sample of the population to describe the typical beliefs held by citizens in a given country and period.
  10. The Paradox of Persistence: Explaining the Black-White Gap in Bachelor’s Degree Completion

    Bachelor’s degree (BA) completion is lower among black students than among white students. In this study, we use data from the Education Longitudinal Study of 2002 and the Integrated Postsecondary Education Data System, together with regression-based analytical techniques, to identify the primary sources of the BA completion gap. We find that black students’ lower academic and socioeconomic resources are the biggest drivers of the gap. However, we also find that black students are more likely to enroll in four-year colleges than are white students, given pre-college resources.