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  1. Digital Punishment's Tangled Web

    Americans love crime. The criminal justice system is fetishized in popular culture and news media. We watch the news and scour the Internet to assess our own moral compass, take cues from others' digressions, and bear witness to justice and punishment. Historically, we learned about crime through news media and fiction. The Internet has dramatically changed this landscape: for the first time, mug shots and jailhouse rosters are available with a click.

  2. 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.
  3. 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.
  4. 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.

  5. 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.
  6. 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.
  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. 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.

  9. 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.

  10. 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.