American Sociological Association



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  1. How I Learned To Stop Worrying and Love the IRB

    Pulling back the veil on Institutional Review Boards.

  2. My Debt to the Nepali People

    A researcher on ethnographic dharma after disaster in Nepal.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Can Non-full-probability Internet Surveys Yield Useful Data? A Comparison with Full-probability Face-to-face Surveys in the Domain of Race and Social Inequality Attitudes

    The authors investigate the potential utility of Web-based surveys of non-full-probabilistically sampled respondents for social science research. Specifically, they compare demographic, attitude response, and multivariate model results produced by two distinct survey modalities: the traditional full-probability sample face-to-face survey and the non-full-probability Web survey.

  10. Decomposition of Gender or Racial Inequality with Endogenous Intervening Covariates: An Extension of the DiNardo-Fortin-Lemieux Method

    This paper begins by clarifying that propensity-score weighting in the DiNardo-Fortin-Lemieux (DFL) decomposition analysis—unlike propensity-score weighting in Rubin’s causal model, in which confounding covariates can be endogenous—may generate biased estimates for the decomposition of inequality into "direct" and "indirect" components when intervening variables are endogenous.