The effect of ideological identification on the endorsement of moral values depends on the target group

Research suggests that liberals and conservatives use different moral foundations to reason about moral issues (moral divide hypothesis). An alternative prediction is that observed ideological differences in moral foundations are instead driven by ingroup-versus-outgroup categorizations of competing political groups (political group conflict hypothesis). In two preregistered experiments (total N = 958), using experimentally manipulated measures of moral foundations, we test strong versions of both hypotheses and find partial support for both. Supporting the moral divide hypothesis, conservatives endorsed the binding foundations more strongly than liberals even when a moderate target group was explicitly specified. Supporting the political group conflict hypothesis, both conservatives and liberals endorsed moral foundations more when moral acts targeted ingroup versus outgroup members. These results have implications for improving measures of moral values and judgments and point to ways to enhance the effectiveness of strategies aimed at building bridges between people from different political camps

I know that I know nothing:Can puncturing the illusion of explanatory depth overcome the relationship between attitudinal dissimilarity and prejudice?

People are prejudiced towards groups they perceive as having a worldview dissimilar from their own. This link between perceived attitudinal dissimilarity and prejudice is so stable that it has been described as a psychological law (Byrne, 1969). The current research tests whether reducing people’s (over-)confidence in their own understanding of policies by puncturing their illusion of explanatory depth in the political domain will reduce the link between perceived attitudinal dissimilarity and prejudice. In an initial pre-registered experiment (N = 296), we did not find support for our hypothesis, but exploratory analyses indicated that the hypothesized effect occurred for political moderates (but not for people who identified as strong liberals/conservatives). However, despite successfully manipulating people’s understanding of policies, in the main study (N = 492) we did not replicate the result of the initial experiment. We suggest potential explanations for our results and discuss future directions for research on breaking the link between attitudinal dissimilarity and prejudice.Keywords: dissimilarity; prejudice; illusion of explanatory depth; political psychology; attitude

The JASP guidelines for conducting and reporting a Bayesian analysis

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general

The effect of ideological identification on the endorsement of moral values depends on the target group

Studied was how ideological identification was related to moral values and how this might change when the target group was changed

The effect of ideological identification on the endorsement of moral values depends on the target group

Studied was how ideological identification was related to moral values and how this might change when the target group was changed

A tutorial on conducting and interpreting a Bayesian ANOVA in JASP

Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using frequentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to the p-value. However, the broad adoption of Bayesian statistics - and Bayesian ANOVA in particular - is frustrated by the fact that Bayesian concepts are rarely taught in applied statistics courses. Consequently, practitioners may be unsure how to conduct a Bayesian ANOVA and interpret the results. Here we provide a guide for executing and interpreting a Bayesian ANOVA with JASP, an open-source statistical software program with a graphical user interface. We explain the key concepts of the Bayesian ANOVA using two empirical examples

The JASP guidelines for conducting and reporting a Bayesian analysis

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general