We propose a synthesis of two lines of sociological research on boundary spanning in cultural production and consumption. One, research on cultural omnivorousness, analyzes choice by heterogeneous audiences facing an array of crisp cultural offerings. The other, research on categories in markets, analyzes reactions by homogeneous audiences to objects that vary in the degree to which they conform to categorical codes. We develop a model of heterogeneous audiences evaluating objects that vary in typicality.
ASA speaks with sociologist Doug Hartmann at the 2016 ASA Annual Meeting on August, 2016, in Seattle, WA. Hartmann talks about what it means to “do sociology,” how he uses sociology in his work, highlights of his work in the field, the relevance of sociological work to society, and his advice to students interested in entering the field.
Nuance is not a virtue of good sociological theory. Although often demanded and superficially attractive, nuance inhibits the abstraction on which good theory depends. I describe three “nuance traps” common in sociology and show why they should be avoided on grounds of principle, aesthetics, and strategy. The argument is made without prejudice to the substantive heterogeneity of the discipline.
The present essay will take readers through the bookshelf of this sociologist of diagnosis. It will demonstrate the wide-reaching topics that I consider relevant to the sociologist who considers diagnosis as a social object and also as a point of convergence where doctor and lay person encounter one another, where authority is exercised, health care is organized, political priorities are established, and conflict is enacted.
Through an analysis of restaurant reviews, this paper examines the production and consumption of food, as well as ideas and symbols about food, within a gentrifying neighborhood, the Downtown Eastside in Vancouver. In particular, it analyzes how reviewers frame culinary “authenticity” and attach symbolic value to a low‐income area of the city, while often acknowledging an emerging civil discourse that sees gentrification as a problem.
Randomized experiments on a network often involve interference between connected units, namely, a situation in which an individual’s treatment can affect the response of another individual. Current approaches to deal with interference, in theory and in practice, often make restrictive assumptions on its structure—for instance, assuming that interference is local—even when using otherwise nonparametric inference strategies.
In his article, Michael Schultz examines the practice of model selection in sociological research. Model selection is often carried out by means of classical hypothesis tests. A fundamental problem with this practice is that these tests do not give a measure of evidence. For example, if we test the null hypothesis β = 0 against the alternative hypothesis β ≠ 0, what is the largest p value that can be regarded as strong evidence against the null hypothesis? What is the largest p value that can be regarded as any kind of evidence against the null hypothesis?
Conventional model selection evaluates models on their ability to represent data accurately, ignoring their dependence on theoretical and methodological assumptions. Drawing on the concept of underdetermination from the philosophy of science, the author argues that uncritical use of methodological assumptions can pose a problem for effective inference. By ignoring the plausibility of assumptions, existing techniques select models that are poor representations of theory and are thus suboptimal for inference.
Qualitative comparative analysis (QCA) appears to offer a systematic means for case-oriented analysis. The method not only offers to provide a standardized procedure for qualitative research but also serves, to some, as an instantiation of deterministic methods. Others, however, contest QCA because of its deterministic lineage. Multiple other issues surrounding QCA, such as its response to measurement error and its ability to ascertain asymmetric causality, are also matters of interest.
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.