Framing morality policy issues: state legislative debates on abortion restrictions

Policy Sciences - Scholars of “morality policies” have often assumed a signature characteristic of such policies is that advocates will frame them as clashes between fundamental moral...     

The Dud Effect: Adding Highly Dissimilar Fillers Increases Confidence in Lineup Identifications

Recent research in decision-making has demonstrated the “dud-alternative effect”—the tendency to become more confident that a chosen response option is correct if it is surrounded by implausible response options (Windschitl & Chambers, J Exp Psychol 30:198–215, 2004). This finding may be applicable to a lineup task: The presence of duds (i.e., highly dissimilar fillers) may increase a witness’s confidence that an identified (non-dud) lineup member is the criminal. Four studies (N = 665) demonstrate that the mere presence of highly dissimilar fillers inflates witnesses’ confidence in a mistaken identification (Studies 1–4), provides evidence that this confidence inflation is due to the duds inflating the perceived similarity of the other lineup members to the criminal (Studies 2, 3), and delineates some conditions under which the effect holds (Studies 3, 4). The addition of highly dissimilar lineup members, far from being inert, as is often implicitly assumed, can bias witnesses’ confidence reports.     

Regulating the Influence Game in Australia

In this research note we describe the introduction and evolution of Australia's approach to regulating lobbyists. We examine how the lobbying regulations in place across Australia today compare with each other, as well as with lobbying regulations in some other countries around the world. The regulations in each Australian jurisdiction are classified according to their characteristics, providing insights into their commonalities and differences.     

Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

Kosuke Imai Department of Politics, Princeton University, Princeton, NJ 08544 e-mail: kimai{at}princeton.edu Gary King Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138 Elizabeth A. Stuart Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health, 624 North Broadway, Room 804, Baltimore, MD 21205 e-mail: estuart{at}jhsph.edu e-mail: king{at}harvard.edu (corresponding author) Although published works rarely include causal estimates frommore than a few model specifications, authors usually choosethe presented estimates from numerous trial runs readers neversee. Given the often large variation in estimates across choicesof control variables, functional forms, and other modeling assumptions,how can researchers ensure that the few estimates presentedare accurate or representative? How do readers know that publicationsare not merely demonstrations that it is possible to find aspecification that fits the author's favorite hypothesis? Andhow do we evaluate or even define statistical properties likeunbiasedness or mean squared error when no unique model or estimatoreven exists? Matching methods, which offer the promise of causalinference with fewer assumptions, constitute one possible wayforward, but crucial results in this fast-growing methodologicalliterature are often grossly misinterpreted. We explain howto avoid these misinterpretations and propose a unified approachthat makes it possible for researchers to preprocess data withmatching (such as with the easy-to-use software we offer) andthen to apply the best parametric techniques they would haveused anyway. This procedure makes parametric models producemore accurate and considerably less model-dependent causal inferences. Authors' note: Our thanks to Dan Carpenter and Jeff Koch fordata; Alberto Abadie, Neal Beck, Sam Cook, Alexis Diamond, BenHansen, Guido Imbens, Olivia Lau, Gabe Lenz, Paul Rosenbaum,Don Rubin, and Jas Sekhon for many helpful comments; and theNational Institutes of Aging (P01 AG17625-01), the NationalInstitute of Mental Health (MH066247), the National ScienceFoundation (SES-0318275, IIS-9874747, SES-0550873), and thePrinceton University Committee on Research in the Humanitiesand Social Sciences for research support. Software to implementthe methods in this paper is available at http://GKing.Harvard.edu/matchitand a replication data file is available as Ho et al. (2006).