Cumulative consequences of stigma: Possessing multiple concealable stigmatized identities is associated with worse quality of life

Large health disparities exist between stigmatized and nonstigmatized groups. In addition to experiencing and anticipating greater discrimination, members of stigmatized groups also tend to demonstrate greater ruminative tendencies in response, which may lead to these poor health outcomes. Even among stigmatized groups, differences in the visibility of stigma lead to different mechanisms through which stigma takes its toll. Previous work has primarily focused on the impact of belonging to a single marginalized group; however, people often belong to multiple marginalized groups, and this likely affects both their health outcomes and their anticipation of stigma. In the current study, we focused on individuals with concealable stigmatized identities (CSIs)—socially stigmatized identities that are not immediately apparent to others—and created a measure of concealable marginalization that captures multiple group memberships. We predicted that those possessing a greater number of CSIs would anticipate more stigma from others, and, in turn, ruminate more about the stigma, which would negatively impact the health. Surveying N = 288 adults with CSIs, we found that possessing a greater number of marginalized concealable identities predicted worse self-reported physical quality of life. These relationships were partially mediated by greater anticipated stigma and brooding rumination in regard to their CSI. This work illuminates a more complete picture of how living with CSIs can take its toll on health.     

Traditional and Rank-Based Tests for Ordered Alternatives in a Cluster Correlated Model

Psychometrika - Methods for the analysis of one-factor randomized groups designs with ordered treatments are well established, but they do not apply in the case of more complex experiments. This...     

BEWILDERMENT AND THEREAFTER: Some Reflections in Response to Lee Yearley

The following reflections were originally an oral response to issues raised in Lee Yearley's presentation in May 2009 at Harvard Divinity School. As written here, they follow upon his oral and now written comments, highlighting key issues and points for development, drawing on this respondent's expertise in comparative and Hindu studies.     

How to Argue Against Active Euthanasia

Most arguments against active euthanasia, as do most arguments in applied ethics generally, take place within the framework of what can broadly be referred to as a modern, as opposed to an ancient, approach to moral theory. In this paper, I argue that this fact works to the disadvantage of opponents of active euthanasia, and that if there is a successful argument against active euthanasia, it will be of the latter sort. In Part I, I attempt to clarify thedistinction between modern and ancient approaches with which I am concerned. In Part II, I attempt to show that any argument against active euthanasia that is of the first sort is bound to fail. In Part III, I propose an argument against active euthanasia of the second sort that I believe has a better chance for success. In Part IV, I consider some objections that can be raised against this argument and attempt to show how they can be overcome.     

Measurement Equivalence of 360°-Assessment Data: Are different raters rating the same constructs?

This study uses data collected using a 360°-assessment instrument to investigate the structural equivalence of 360°-assessment ratings, according to rater type, controlling for organizational level. Data from 533 managers and their raters were employed in the study, which used multi-group structural equations modeling. Issues central to the implementation and use of 360°-assessment data are also considered within the context of current research and practice.     

Response error and processing biases in confidence judgment

Previous studies showed that random error can explain overconfidence effects typically observed in the literature. One of these studies concluded that, after accounting for random error effects in the data, there is little support for cognitive‐processing biases in confidence elicitation. In this paper, we investigate more closely the random error explanation for overconfidence. We generated data from four models of confidence and then estimated the magnitude of random error in the data. Our results show that, in addition to the true magnitude of random error specified in the simulations, the error estimates are influenced by important cognitive‐processing biases in the confidence elicitation process. We found that random error in the response process can account for the degree of overconfidence found in calibration studies, even when that overconfidence is actually caused by other factors. Thus, the error models say little about whether cognitive biases are present in the confidence elicitation process. Copyright © 2008 John Wiley & Sons, Ltd.