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  1. The IRL Fallacy

    Putting the lie to "digital dualism" in an essay on the inseparability of online and offline selves.

  2. 2015 Presidential Address: Sometimes the Social Becomes Personal: Gender, Class, and Sexualities

    All sociologists recognize that social constraints affect individuals’ outcomes. These effects are sometimes relatively direct. Other times constraints affect outcomes indirectly, first influencing individuals’ personal characteristics, which then affect their outcomes. In the latter case, the social becomes personal, and personal characteristics that are carried across situations (e.g., skills, habits, identities, worldviews, preferences, or values) affect individuals’ outcomes. I argue here for the importance of both direct and indirect effects of constraints on outcomes.

  3. Discrimination in Lending Markets: Status and the Intersections of Gender and Race

    Research documents that lenders discriminate between loan applicants in traditional and peer-to-peer lending markets, yet we lack knowledge about the mechanisms driving lenders’ behavior. I offer one possible mechanism: When lenders assess borrowers, they are implicitly guided by cultural stereotypes about the borrowers’ status. This systematically steers lenders toward funding higher status groups even when applicants have the same financial histories.

  4. The Contingent Value of Embeddedness: Self-affirming Social Environments, Network Density, and Well-being

    Social capital theorists claim that belonging to a densely knit social network creates a shared identity, mutually beneficial exchange, trust, and a sense of belonging in that group. Taken together with the empirical research on the importance of social support and social integration for individuals’ well-being, there is reason to expect that the density of one’s personal social network should be positively related to well-being.

  5. A "Global Interdependence" Approach to Multidimensional Sequence Analysis

    Although sequence analysis has now become a widespread approach in the social sciences, several strategies have been developed to handle the specific issue of multidimensional sequences. These strategies have distinct characteristics related to the way they explicitly emphasize multidimensionality, interdependence, and parsimony.

  6. A Progressive Supervised-learning Approach to Generating Rich Civil Strife Data

    "Big data" in the form of unstructured text pose challenges and opportunities to social scientists committed to advancing research frontiers. Because machine-based and human-centric approaches to content analysis have different strengths for extracting information from unstructured text, the authors argue for a collaborative, hybrid approach that combines their comparative advantages.

  7. A Design and a Model for Investigating the Heterogeneity of Context Effects in Public Opinion Surveys

    Context effects on survey response, caused by the unobserved interaction between beliefs stored in personal memory and triggers generated by the structure of the survey instrument, are a pervasive challenge to survey research. The authors argue that randomized survey experiments on representative samples, when paired with facilitative primes, can enable researchers to model selection into variable context effects, revealing heterogeneity at the population level.

  8. An Introduction to the General Monotone Model with Application to Two Problematic Data Sets

    We argue that the mismatch between data and analytical methods, along with common practices for dealing with "messy" data, can lead to inaccurate conclusions. Specifically, using previously published data on racial bias and culture of honor, we show that manifest effects, and therefore theoretical conclusions, are highly dependent on how researchers decide to handle extreme scores and nonlinearities when data are analyzed with traditional approaches.

  9. Beyond Text: Using Arrays to Represent and Analyze Ethnographic Data

    Recent methodological debates in sociology have focused on how data and analyses might be made more open and accessible, how the process of theorizing and knowledge production might be made more explicit, and how developing means of visualization can help address these issues. In ethnography, where scholars from various traditions do not necessarily share basic epistemological assumptions about the research enterprise with either their quantitative colleagues or one another, these issues are particularly complex.

  10. Shrinkage Estimation of Log-odds Ratios for Comparing Mobility Tables

    Statistical analysis of mobility tables has long played a pivotal role in comparative stratification research. This article proposes a shrinkage estimator of the log-odds ratio for comparing mobility tables. Building on an empirical Bayes framework, the shrinkage estimator improves estimation efficiency by "borrowing strength" across multiple tables while placing no restrictions on the pattern of association within tables.