Least squares analyses (e.g., ANOVAs, linear regressions) of hierarchical data leads to Type-I error rates that depart severely from the nominal Type-I error rate assumed. Thus, when least squares methods are used to analyze hierarchical data coming from designs in which some groups are assigned to the treatment condition, and others to the control condition (i.e., the widely used “groups nested under treatment” experimental design), the Type-I error rate is seriously inflated, leading too often to the incorrect rejection of the null hypothesis (i.e., the incorrect conclusion of an effect of the treatment). To highlight the severity of the problem, we present simulations showing how the Type-I error rate is affected under different conditions of intraclass correlation and sample size. For all simulations the Type-I error rate after application of the popular Kish (1965) correction is also considered, and the limitations of this correction technique discussed. We conclude with suggestions on how one should collect and analyze data bearing a hierarchical structure.

We propose that to understand how rejection perceptions affect immigrants' acculturation orientations, we need to take account of perceptions of rejection and group identification with both the host society and the country of origin. In line with previous work, we found among Romanians and Moroccan immigrants in France that perceived French rejection directly affected French identification and acculturation orientations. In addition, perceived rejection by the country of origin (Romanians and Moroccans in the country of origin) negatively affected immigrants' identification with this group. In turn, identification with the country of origin positively predicted endorsement of integration and separation orientations, and negatively predicted endorsement of assimilation. Overall, results suggest that identification with the country of origin is an additional important factor in determining acculturation decisions.

No abstract

International audienceWe examined whether increasing individuals' perceived variability of an out-group reduces prejudice and discrimination toward its members. In a series of 4 laboratory and field experiments, we attracted participants' attention to either the homogeneity or the heterogeneity of members of an out-group, and then measured their attitudes or behaviors. Perceived variability was manipulated by making subgroups salient, by portraying the out-group members as having diverse opinions, by making salient that out-group members have different characteristics, or by asking participants to think about differences among out-group members. Prejudice and discrimination were measured in terms of self-reported attitudes, distribution of rewards, helping an out-group confederate, and evaluation of an out-group candidate in a simulated hiring decision. In all experiments, perceived variability decreased prejudice and discrimination. This effect may be due to the fact that perceived variability decreases the role of group membership in the production of attitudes and behaviors toward other individuals

No abstract