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  1. Computation and the Sociological Imagination

    Computational sociology leverages new tools and data sources to expand the scope and scale of sociological inquiry. It’s opening up an exciting frontier for sociologists of every stripe—from theorists and ethnographers to experimentalists and survey researchers. It expands the sociological imagination.

  2. Review Essay: See It with Figures

    The short story is that Kieran Healy’s Data Visualization: A Practical Introduction is a gentle introduction to the effective display of social science data using the R package ggplot2. It is beautifully put together, achingly clear, and effective.
  3. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings

    We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture.
  4. Assessing Differences between Nested and Cross-Classified Hierarchical Models

    Sociological Methodology, Volume 49, Issue 1, Page 220-257, August 2019.
  5. Review Essay: Back to the Future

    In one of my undergraduate courses, I show students a photo of Paul Lazarsfeld and Frank Stanton. Of course, neither social scientist is familiar to them, but I argue to my students that Lazarsfeld had a bigger impact on the daily practice of sociology than any member of the Marx/Weber/Durkheim triumvirate they study in classical theory.

  6. A General Framework for Comparing Predictions and Marginal Effects across Models

    Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects because they are in the natural metric of the dependent variable and they avoid identification problems when comparing regression coefficients across logit and probit models.
  7. Visualizing Police Exposure by Race, Gender, and Age in New York City

    This figure depicts the disparities in average police stops in New York City from 2004 to 2012, disaggregated by race, gender, and age. Composed of six bar charts, each graph in the figure provides data for a particular population at the intersection of race and gender, focusing on black, white, and Hispanic men and women. Each graph also has a comparative backdrop of the data on police stops for black males.
  8. Textual Spanning: Finding Discursive Holes in Text Networks

    We propose a measure of discursive holes well suited for the unique properties of text networks built from document similarity matrices considered as dense weighted graphs. In this measure, which we call textual spanning, documents similar to documents dissimilar from one another receive a high score, and documents similar to documents similar to one another receive a low score. After offering a simulation-based validation, we test the measure on an empirical document similarity matrix based on a preestimated topic-model probability distribution.
  9. Bribery in Sub-Saharan Africa: The Mediating Effects of Institutional Development and Trust

    The issue of bribery raises questions about the implications of institutional development and trust in the raw material industry. This paper uses theories of institutionalism and trust to explore path dependence arguments seeking to explain the resource curse puzzle. Institutional development and trust are examined as potential mediators linking mineral extraction/processing to bribery in sub-Saharan African countries.

  10. Asymmetric Fixed-effects Models for Panel Data

    Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement.