The present research investigated the moderating role of diversity beliefs with the aim of reconciling inconsistent findings regarding the impact of group boundary permeability on attitudes toward outgroup. In Study 1, all variables were measured with self‐report scales completed by Chinese participants. In Study 2, diversity beliefs were manipulated by randomly assigning Chinese participants to a high or low diversity belief condition. In Study 3, we replicated the moderating model with American participants. Results of all three studies indicated that diversity beliefs moderated the relationship between group boundary permeability and attitudes toward outgroup. Individuals with high diversity beliefs held more positive attitudes toward the outgroup when the group boundary was permeable (vs. impermeable). Conversely, individuals with low diversity beliefs held more negative attitudes toward the outgroup when the group boundary was permeable (vs. impermeable). These findings suggest that when the inflow of the outgroup members is inevitable, attitudes toward the outgroup may be effectively improved by increasing diversity beliefs. 相似文献
Traditional mediation analysis assumes that a study population is homogeneous and the mediation effect is constant over time, which may not hold in some applications. Motivated by smoking cessation data, we propose a latent class dynamic mediation model that explicitly accounts for the fact that the study population may consist of different subgroups and the mediation effect may vary over time. We use a proportional odds model to accommodate the subject heterogeneities and identify latent subgroups. Conditional on the subgroups, we employ a Bayesian hierarchical nonparametric time-varying coefficient model to capture the time-varying mediation process, while allowing each subgroup to have its individual dynamic mediation process. A simulation study shows that the proposed method has good performance in estimating the mediation effect. We illustrate the proposed methodology by applying it to analyze smoking cessation data.