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  1. Biases of Online Political Polls: Who Participates?

    With a large portion of the population online and the high cost of phone-based surveys, querying people about their voter preference online can offer an affordable and timely alternative. However, given that there are biases in who adopts various sites and services that are often used as sampling frames (e.g., various social media), online political polls may not represent the views of the overall population. How are such polls biased? Who is most likely to participate in them?
  2. Where Are All of the Women? Untangling the Effects of Representation, Participation, and Preferences on Gender Differences in Political Press Coverage

    The author examines why female politicians continue to be underrepresented in the press by measuring how structural inequalities, engagement in traditional and disruptive dialogue, and gender preferences influence the amount of press coverage U.S. House representatives receive.
  3. 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.

  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. Discovering Race in a “Post-Racial” World: Teaching Race through Primetime Television

    Teaching students about race remains a challenging task for instructors, made even more difficult in the context of a growing “post-racial” discourse. Given this challenge, it is important for instructors to find engaging ways to help students understand the continuing significance of race and racial/ethnic inequality. In this article, we detail an exercise that asks students to analyze representations of race/ethnicity on network television for one week as a way of illustrating white dominance, white privilege, and racial inequality.

  6. Teaching Content Analysis through Harry Potter

    Content analysis is a valuable research tool for social scientists that unfortunately can prove challenging to teach to undergraduate students. Published classroom exercises designed to teach content analysis have thus far been predominantly envisioned as lengthy projects for upper-level courses. A brief and engaging exercise may be more beneficial for introductory social science courses in which less time can be allotted to any one topic, such as content analysis.

  7. 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.
  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. 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.
  10. 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.