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  1. Understanding Recent Growth Dynamics in Small Urban Places: The Case of New England

    This article utilizes recently published US Census data covering the pre‐and post‐Great Recession period (1990–2015) to identify key determinants of growth among small urban places in the New England Region. We find little evidence of random growth and robust evidence of convergence in growth, indicating that smaller urban areas tend to experience faster rates of growth than larger ones, over both the short and long term. Factors such as distance to large city areas and amenities are found to be particularly relevant to population growth rates.

  2. Is Urbanization Good for the Climate? A Cross-County Analysis of Impervious Surface, Affluence, and the Carbon Intensity of Well-Being

    We contribute to literature exploring the socioecological impact of urban development as a multidimensional project, one in which changes to landscape features complement changes in demographic and administrative measures to co-constitute the socioecological impact of urbanity. We use a random coefficients modeling approach to examine U.S. relationships between the intensity of impervious surface within a county, population density in impervious areas, and carbon intensity of well-being (CIWB)—here constructed using industrial emissions.

  3. 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.

  4. 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.
  5. 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.
  6. Equifinality and Pathways to Environmental Concern: A Fuzzy-Set Analysis

    Studying how people understand and develop concern for environmental problems is a key area of research within environmental sociology. Previous research shows that numerous social factors have measurable effects on environmental concern. However, results tend to be somewhat inconsistent across studies on this topic. One possible explanation for this is because these social factors are typically examined as independent from one another. However, these factors are interrelated in complex ways, as shown by research on the moderating effects of race and political ideology on education.
  7. Assessing Differences between Nested and Cross-Classified Hierarchical Models

    Sociological Methodology, Volume 49, Issue 1, Page 220-257, August 2019.
  8. 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.

  9. Cabdrivers and Their Fares: Temporal Structures of a Linking Ecology

    The author argues that behind the apparent randomness of interactions between cabdrivers and their fares in Warsaw is a temporal structure. To capture this temporal structure, the author introduces the notion of a linking ecology. He argues that the Warsaw taxi market is a linking ecology, which is structured by religious time, state time, and family time. The author then focuses on waiting time, arguing that it too structures the interactions between cabdrivers and their fares.

  10. 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.