In reporting Implicit Association Test (IAT) results, researchers have most often used scoring conventions described in the first publication of the IAT (A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1998). Demonstration IATs available on the Internet have produced large data sets that were used in the current article to evaluate alternative scoring procedures. Candidate new algorithms were examined in terms of their (a) correlations with parallel self-report measures, (b) resistance to an artifact associated with speed of responding, (c) internal consistency, (d) sensitivity to known influences on IAT measures, and (e) resistance to known procedural influences. The best-performing measure incorporates data from the IAT's practice trials, uses a metric that is calibrated by each respondent's latency variability, and includes a latency penalty for errors. This new algorithm strongly outperforms the earlier (conventional) procedure.

It has been claimed and demonstrated that many (and possibly most) of the conclusions drawn from biomedi-cal research are probably false 1. A central cause for this important problem is that researchers must publish in order to succeed, and publishing is a highly competitive enterprise, with certain kinds of findings more likely to be published than others. Research that produces novel results, statistically significant results (that is, typically p < 0.05) and seemingly 'clean' results is more likely to be published 2,3. As a consequence, researchers have strong incentives to engage in research practices that make their findings publishable quickly, even if those practices reduce the likelihood that the findings reflect a true (that is, non-null) effect 4. Such practices include using flexible study designs and flexible statistical analyses and running small studies with low statistical power 1,5. A simulation of genetic association studies showed that a typical dataset would generate at least one false positive result almost 97% of the time 6 , and two efforts to replicate promising findings in biomedicine reveal replication rates of 25% or less 7,8. Given that these publishing biases are pervasive across scientific practice, it is possible that false positives heavily contaminate the neuroscience literature as well, and this problem may affect at least as much, if not even more so, the most prominent journals 9,10. Here, we focus on one major aspect of the problem: low statistical power. The relationship between study power and the veracity of the resulting finding is under-appreciated. Low statistical power (because of low sample size of studies, small effects or both) negatively affects the likelihood that a nominally statistically significant finding actually reflects a true effect. We discuss the problems that arise when low-powered research designs are pervasive. In general, these problems can be divided into two categories. The first concerns problems that are mathematically expected to arise even if the research conducted is otherwise perfect: in other words, when there are no biases that tend to create statistically significant (that is, 'positive') results that are spurious. The second category concerns problems that reflect biases that tend to co-occur with studies of low power or that become worse in small, underpowered studies. We next empirically show that statistical power is typically low in the field of neuroscience by using evidence from a range of subfields within the neuroscience literature. We illustrate that low statistical power is an endemic problem in neuroscience and discuss the implications of this for interpreting the results of individual studies. Low power in the absence of other biases Three main problems contribute to producing unreliable findings in studies with low power, even when all other research practices are ideal. They are: the low probability of finding true effects; the low positive predictive value (PPV; see BOX 1 for definitions of key statistical terms) when an eff...

How and why do moral judgments vary across the political spectrum? To test moral foundations theory (J. Haidt & J. Graham, 2007; J. Haidt & C. Joseph, 2004), the authors developed several ways to measure people's use of 5 sets of moral intuitions: Harm/care, Fairness/reciprocity, Ingroup/loyalty, Authority/respect, and Purity/sanctity. Across 4 studies using multiple methods, liberals consistently showed greater endorsement and use of the Harm/care and Fairness/reciprocity foundations compared to the other 3 foundations, whereas conservatives endorsed and used the 5 foundations more equally. This difference was observed in abstract assessments of the moral relevance of foundation-related concerns such as violence or loyalty (Study 1), moral judgments of statements and scenarios (Study 2), "sacredness" reactions to taboo trade-offs (Study 3), and use of foundation-related words in the moral texts of religious sermons (Study 4). These findings help to illuminate the nature and intractability of moral disagreements in the American "culture war."

The moral domain is broader than the empathy and justice concerns assessed by existing measures of moral competence, and it is not just a subset of the values assessed by value inventories. To fill the need for reliable and theoretically-grounded measurement of the full range of moral concerns, we developed the Moral Foundations Questionnaire (MFQ) based on a theoretical model of five universally available (but variably developed) sets of moral intuitions: Harm/care, Fairness/reciprocity, Ingroup/loyalty, Authority/respect, and Purity/sanctity. We present evidence for the internal and external validity of the scale and the model, and in doing so present new findings about morality: 1. Comparative model fitting of confirmatory factor analyses provides empirical justification for a five-factor structure of moral concerns. 2. Convergent/discriminant validity evidence suggests that moral concerns predict personality features and social group attitudes not previously considered morally relevant. 3. We establish pragmatic validity of the measure in providing new knowledge and research opportunities concerning demographic and cultural differences in moral intuitions. These analyses provide evidence for the usefulness of Moral Foundations Theory in simultaneously increasing the scope and sharpening the resolution of psychological views of morality.

Most theories in social and political psychology stress self-interest, intergroup conflict, ethnocentrism, homophily, ingroup bias, outgroup antipathy, dominance, and resistance. System justification theory is influenced by these perspectives-including social identity and social dominance theories-but it departs from them in several respects. Advocates of system justification theory argue that (a) there is a general ideological motive to justify the existing social order, (b) this motive is at least partially responsible for the internalization of inferiority among members of disadvantaged groups, (c) it is observed most readily at an implicit, nonconscious level of awareness and (d) paradoxically, it is sometimes strongest among those who are most harmed by the status quo. This article reviews and integrates 10 years of research on 20 hypotheses derived from a system justification perspective, focusing on the phenomenon of implicit outgroup favoritism among members of disadvantaged groups (including African Americans, the elderly, and gays/lesbians) and its relation to political ideology (especially liberalism-conservatism).KEY WORDS: ideology, system justification, intergroup relations, implicit bias There is a cluster of related theories that are by now so prevalent in social science that they strike the contemporary reader as self-evidently true. Although these theories are by no means indistinguishable, they share a set of common features, including the tenets that groups serve their own interests, develop ideolo-