Post-racial America?: racialization and polarization of policy-related judgments following the 2008 U.S. presidential election

The promise of a post-racial America signaled by the 2008 election of President Obama has gone unfulfilled. Using representative samples of the American electorate, Study 1 confirmed that those with stronger explicit and implicit anti-Black attitudes before the 2008 election voiced more negative policy-related judgments in July 2009 (racialization hypothesis). Study 2 demonstrated that the difference in policy-related judgments between high-prejudice and low-prejudice respondents was increasing over time between May 2009 and July 2010 (polarization hypothesis). Both the racialization and polarization of policy-related judgments were mediated by more negative evaluations of Obama. Study 3 suggested that the particular pattern of mediation may be unique to the Obama administration. Particularly noteworthy is that the measure of policy-related judgments used refers to issues (e.g., the economy, health care) that naively should be uninfluenced by racial attitudes. These findings suggest that racial attitudes continue to play a substantial role in today's political climate

“It’s Part of My Responsibility to Help”: Developing a Measure of Motivations for Extrinsic Emotion Regulation

Introduction A growing field of research has emerged to examine the processes by which people manage their own emotions as well as the emotions of others during social interactions, a set of phenomena broadly known as interpersonal emotion regulation (IER). Within this broad category, extrinsic emotion regulation (EER) refers specifically to the processes by which an individual targets and attempts to regulate the emotions of others (Zaki & Williams, 2013). Recent work by Netzer et al. (2015) has explored the emotion-related goals people have when engaging in EER, suggesting that both hedonic and instrumental goals may motivate these regulation attempts. We know that people can employ a variety of motives during EER attempts. But, which ones do they actually use in practice? And, how can we measure individual differences in one’s EER motivational tendencies? The current study aims to develop a better understanding of the answers to these questions through thematic analyses of participant narratives

Racial Attitudes Predicted Changes in Ostensibly Race‐Neutral Political Attitudes Under the Obama Administration

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136427/1/pops12315_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136427/2/pops12315.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136427/3/pops12315-sup-0001-suppinfo01.pd

Well-Being Correlates of Perceived Positivity Resonance: Evidence from Trait and Episode-Level Assessments

Positivity resonance is a type of interpersonal connection characterized by shared positivity, mutual care and concern, and behavioral and biological synchrony. Perceived positivity resonance is hypothesized to be associated with well-being. In three studies (N = 175; N = 120; N = 173), perceived positivity resonance was assessed at the trait level (Study 1) or the episode level, using the Day Reconstruction Method (Studies 2 and 3). Primary analyses reveal that perceived positivity resonance is associated with flourishing mental health, depressive symptoms, loneliness, and illness symptoms. These associations largely remain statistically significant when controlling for daily pleasant emotions or social interaction more generally. Ancillary analyses in Studies 2 and 3 support the construct validity of the episode-level assessment of perceived positivity resonance. The overall pattern of results is consistent with Positivity Resonance Theory. Discussion centers on avenues for future research and the need for behavioral interventions

Results of logistic regression analyses predicting voting behavior from explicit and implicit candidate preference and confidence.

<p>Predicting votes for Mr. Obama (1) versus Mr. McCain (0) from explicit and implicit preference for Mr. Obama (versus Mr. McCain) and their interaction with confidence. Controlling for date of attitude measures administration. Model 1 examines explicit candidate attitudes separately (<i>N</i> = 2,058). Model 2 examines implicit candidate attitudes separately (<i>N</i> = 2,013). Model 3 examines both attitude measures simultaneously (<i>N</i> = 2,013). CCC: correctly classified cases; <i>B</i>: regression weight <i>B</i> (log odds); <i>SE</i>: standard error of the regression weight <i>B</i>; Wald: Wald test statistic; OR: Odds ratio. Relative amount by which the odds increase (OR >1.0) or decrease (OR <1.0) when the value of the predictor is increased by 1 SD.</p

Critique of the Bias-of-Crowds Model Simply Restates the Model: Reply to Connor and Evers (2020).

Millions of people have taken an implicit test of racial bias, and the majority have displayed a preference for White people over Black people. What does it mean? The most common interpretation is that those who show such a preference are biased people and that they have an attitude, whether they explicitly acknowledge it or not, that favors White people over Black people. An alternative interpretation is that when people display a racial bias on an implicit test, it reflects the social environment they are in. It could be both. The lesson drawn from this research is important, because it bears not only on scientific theories of prejudice and discrimination but also on policy decisions about the best ways to eliminate racial disparities. Do we target individuals and try to change their attitudes? Or do we focus on social environments and the systems that impersonally preserve inequality

Results of logistic regression analyses predicting voting behavior from explicit and implicit prejudice and confidence.

<p>Predicting votes for Mr. Obama (1) versus Mr. McCain (0) from explicit and implicit prejudice toward Blacks and their interactions with confidence. Controlling for date of implicit attitude measure administration. Model 1 examines explicit prejudice separately (<i>N</i> = 2,056). Model 2 examines implicit prejudice separately (<i>N</i> = 2,024). Model 3 examines both prejudice measures simultaneously (<i>N</i> = 2,024). CCC: correctly classified cases; <i>B</i>: regression weight <i>B</i> (log odds); <i>SE</i>: standard error of the regression weight <i>B</i>; Wald: Wald test statistic; OR: Odds ratio. Relative amount by which the odds increase (OR >1.0) or decrease (OR <1.0) when the value of the predictor is increased by 1 SD.</p

Simple effects estimates for explicit and implicit candidate attitudes at each level of confidence.

<p>Simple effects tests for predicted values of explicit and implicit candidate attitudes at each level of confidence. Corresponds to Model 3 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085680#pone-0085680-t001" target="_blank">Table 1</a>. <i>B</i>: regression weight <i>B</i> (log odds); OR: Odds ratio. Relative amount by which the odds increase (OR >1.0) or decrease (OR <1.0) when the value of the predictor is increased by 1 SD; 95% CI: 95% confidence interval for the odds ratio. Intervals that do not contain 1 are considered significant at <i>p</i><.05.</p

Simple slopes relating candidate attitudes to voting for respondents at each of the five levels of confidence.

<p>Probability of voting for Mr. Obama (1) versus Mr. McCain (0) as a function of candidate preference, confidence, and their interaction. Panel A: The association between explicit candidate preference and voting was moderated by confidence. Panel B: The association between implicit candidate preference and voting was not moderated by confidence.</p