Partisan Bias and Its Discontents.

Baron and Jost (this issue, p. 292) present three critiques of our meta-analysis demonstrating similar levels of partisan bias in liberals and conservatives: (a) that the studies we examined were biased toward finding symmetrical bias among liberals and conservatives, (b) that the studies we examined do not measure partisan bias but rather rational Bayesian updating, and (c) that social psychology is not biased in favor of liberals but rather toward creating false equivalencies. We respond in turn that (a) the included studies covered a wide variety of issues at the core of contemporary political conflict and fairly compared bias by establishing conditions under which both liberals and conservatives would have similar motivations and opportunities to demonstrate bias; (b) we carefully selected studies that were least vulnerable to Bayesian counterexplanation, and most scientists and laypeople consider these studies demonstrations of bias; and (c) there is reason to be vigilant about liberal bias in social psychology, but this does not preclude concerns about other possible biases, all of which threaten good science. We close with recommendations for future research and urge researchers to move beyond broad generalizations of political differences that are insensitive to time and context.

At Least Bias Is Bipartisan: A Meta-Analytic Comparison of Partisan Bias in Liberals and Conservatives.

Both liberals and conservatives accuse their political opponents of partisan bias, but is there empirical evidence that one side of the political aisle is indeed more biased than the other? To address this question, we meta-analyzed the results of 51 experimental studies, involving over 18,000 participants, that examined one form of partisan bias-the tendency to evaluate otherwise identical information more favorably when it supports one's political beliefs or allegiances than when it challenges those beliefs or allegiances. Two hypotheses based on previous literature were tested: an asymmetry hypothesis (predicting greater partisan bias in conservatives than in liberals) and a symmetry hypothesis (predicting equal levels of partisan bias in liberals and conservatives). Mean overall partisan bias was robust ( r = .245), and there was strong support for the symmetry hypothesis: Liberals ( r = .235) and conservatives ( r = .255) showed no difference in mean levels of bias across studies. Moderator analyses reveal this pattern to be consistent across a number of different methodological variations and political topics. Implications of the current findings for the ongoing ideological symmetry debate and the role of partisan bias in scientific discourse and political conflict are discussed.

A practical guide to big data research in psychology.

The massive volume of data that now covers a wide variety of human behaviors offers researchers in psychology an unprecedented opportunity to conduct innovative theory- and data-driven field research. This article is a practical guide to conducting big data research, covering data management, acquisition, processing, and analytics (including key supervised and unsupervised learning data mining methods). It is accompanied by walkthrough tutorials on data acquisition, text analysis with latent Dirichlet allocation topic modeling, and classification with support vector machines. Big data practitioners in academia, industry, and the community have built a comprehensive base of tools and knowledge that makes big data research accessible to researchers in a broad range of fields. However, big data research does require knowledge of software programming and a different analytical mindset. For those willing to acquire the requisite skills, innovative analyses of unexpected or previously untapped data sources can offer fresh ways to develop, test, and extend theories. When conducted with care and respect, big data research can become an essential complement to traditional research. (PsycINFO Database Record

Tweeting negative emotion: An investigation of Twitter data in the aftermath of violence on college campuses.

Studying communities impacted by traumatic events is often costly, requires swift action to enter the field when disaster strikes, and may be invasive for some traumatized respondents. Typically, individuals are studied after the traumatic event with no baseline data against which to compare their postdisaster responses. Given these challenges, we used longitudinal Twitter data across 3 case studies to examine the impact of violence near or on college campuses in the communities of Isla Vista, CA, Flagstaff, AZ, and Roseburg, OR, compared with control communities, between 2014 and 2015. To identify users likely to live in each community, we sought Twitter accounts local to those communities and downloaded tweets of their respective followers. Tweets were then coded for the presence of event-related negative emotion words using a computerized text analysis method (Linguistic Inquiry and Word Count, LIWC). In Case Study 1, we observed an increase in postevent negative emotion expression among sampled followers after mass violence, and show how patterns of response appear differently based on the timeframe under scrutiny. In Case Study 2, we replicate the pattern of results among users in the control group from Case Study 1 after a campus shooting in that community killed 1 student. In Case Study 3, we replicate this pattern in another group of Twitter users likely to live in a community affected by a mass shooting. We discuss conducting trauma-related research using Twitter data and provide guidance to researchers interested in using Twitter to answer their own research questions in this domain. (PsycINFO Database Record

Conservatives report, but liberals display, greater happiness.

Research suggesting that political conservatives are happier than political liberals has relied exclusively on self-report measures of subjective well-being. We show that this finding is fully mediated by conservatives' self-enhancing style of self-report (study 1; N = 1433) and then describe three studies drawing from "big data" sources to assess liberal-conservative differences in happiness-related behavior (studies 2 to 4; N = 4936). Relative to conservatives, liberals more frequently used positive emotional language in their speech and smiled more intensely and genuinely in photographs. Our results were consistent across large samples of online survey takers, U.S. politicians, Twitter users, and LinkedIn users. Our findings illustrate the nuanced relationship between political ideology, self-enhancement, and happiness and illuminate the contradictory ways that happiness differences can manifest across behavior and self-reports.

Political bias is tenacious.

Duarte et al. are right to worry about political bias in social psychology but they underestimate the ease of correcting it. Both liberals and conservatives show partisan bias that often worsens with cognitive sophistication. More non-liberals in social psychology is unlikely to speed our convergence upon the truth, although it may broaden the questions we ask and the data we collect.

Reducing implicit racial preferences: I. A comparative investigation of 17 interventions.

Many methods for reducing implicit prejudice have been identified, but little is known about their relative effectiveness. We held a research contest to experimentally compare interventions for reducing the expression of implicit racial prejudice. Teams submitted 17 interventions that were tested an average of 3.70 times each in 4 studies (total N = 17,021), with rules for revising interventions between studies. Eight of 17 interventions were effective at reducing implicit preferences for Whites compared with Blacks, particularly ones that provided experience with counterstereotypical exemplars, used evaluative conditioning methods, and provided strategies to override biases. The other 9 interventions were ineffective, particularly ones that engaged participants with others' perspectives, asked participants to consider egalitarian values, or induced a positive emotion. The most potent interventions were ones that invoked high self-involvement or linked Black people with positivity and White people with negativity. No intervention consistently reduced explicit racial preferences. Furthermore, intervention effectiveness only weakly extended to implicit preferences for Asians and Hispanics.

Effects of symptom presentation order on perceived disease risk.

People are quick to perceive meaningful patterns in the co-occurrence of events. We report two studies exploring the effects of streaks in symptom checklists on perceived personal disease risk. In the context of these studies, a streak is a sequence of consecutive items on a list that share the characteristic of being either general or specific. We identify a psychological mechanism underlying the effect of streaks in a list of symptoms and show that the effect of streaks on perceived risk varies with the length of the symptom list. Our findings reveal a tendency to infer meaning from streaks in medical and health decision making. Participants perceived a higher personal risk of having an illness when presented with a checklist in which common symptoms were grouped together than when presented with a checklist in which these same symptoms were separated by rare symptoms. This research demonstrates that something as arbitrary as the order in which symptoms are presented in a checklist can affect perceived risk of disease.