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  1. Elements of Professional Expertise: Understanding Relational and Substantive Expertise through Lawyers' Impact

    Lawyers keep the gates of public justice institutions, particularly through their roles in formal procedures like hearings and trials. Yet, it is not clear what lawyers do in such quintessentially legal settings: conclusions from past research are bedeviled by a lack of clear theory and inconsistencies in research design. Conceptualizing litigation work in terms of professional expertise, I conduct a theoretically grounded synthesis of the findings of extant studies of lawyers’ impact on civil case outcomes.

  2. Prepare for a Vote: Understanding the Proposed Revision to the ASA Code of Ethics

    At the 2014 Annual Meeting in San Francisco, Executive Officer Sally Hillsman, met with the Committee on Professional Ethics (COPE) and suggested that it was time to revise the Code of Ethics. Revisions were last made to the Code 20 years ago, and a great deal of change had taken place. Regulatory and technological advances have had striking impacts on the field. At the time, the Department of Health and Human Services was about to announce changes to The Common Rule, which governs the vast majority of human subjects research efforts.

  3. ASA Signs on to Letter Supporting Federal Data Sources

    The ASA signed on to a letter expressing our strong support for the critical Federal data sources that inform and strengthen our nation’s world-leading economic, educational, democratic and civic institutions and successes. Our Federal statistical and data systems provide information that is uniquely accurate, objective, relevant, timely, and accessible. 

  4. What’s the Harm? The Coverage of Ethics and Harm Avoidance in Research Methods Textbooks

    Methods textbooks play a role in socializing a new generation of researchers about ethical research. How do undergraduate social research methods textbooks portray harm, its prevalence, and ways to mitigate harm to participants? We conducted a content analysis of ethics chapters in the 18 highest-selling undergraduate textbooks used in sociology research methods courses in the United States and Canada in 2013. We found that experiments are portrayed as the research design most likely to harm participants.
  5. The Emergence of Statistical Objectivity: Changing Ideas of Epistemic Vice and Virtue in Science

    The meaning of objectivity in any specific setting reflects historically situated understandings of both science and self. Recently, various scientific fields have confronted growing mistrust about the replicability of findings, and statistical techniques have been deployed to articulate a “crisis of false positives.” In response, epistemic activists have invoked a decidedly economic understanding of scientists’ selves. This has prompted a scientific social movement of proposed reforms, including regulating disclosure of “backstage” research details and enhancing incentives for replication.
  6. What is Critical Realism? And Why Should You Care?

    Critical realism (CR) is a philosophical system developed by the Indo-British philosopher, Roy Bhaskar, in collaboration with a number of British social theorists, including Margaret Archer, Mervyn Hartwig, Tony Lawson, Alan Norrie, and Andrew Sayer. It has a journal, a book series, an association, an annual meeting and, in short, all the usual trappings of an intellectual movement. The movement is centered in the UK but has followers throughout Europe, Asia, the Americas, and the Antipodes.

  7. Why Liberals and Atheists Are More Intelligent

    The origin of values and preferences is an unresolved theoretical question in behavioral and social sciences.

  8. Estimating Income Statistics from Grouped Data: Mean-constrained Integration over Brackets

    Researchers studying income inequality, economic segregation, and other subjects must often rely on grouped data—that is, data in which thousands or millions of observations have been reduced to counts of units by specified income brackets.
  9. Comment: The Inferential Information Criterion from a Bayesian Point of View

    As Michael Schultz notes in his very interesting paper (this volume, pp. 52–87), standard model selection criteria, such as the Akaike information criterion (AIC; Akaike 1974), the Bayesian information criterion (BIC; Schwarz 1978), and the minimum description length principle (MDL; Rissanen 1978), are purely empirical criteria in the sense that the score a model receives does not depend on how well the model coheres with background theory. This is unsatisfying because we would like our models to be theoretically plausible, not just empirically successful.
  10. The Problem of Underdetermination in Model Selection

    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.