The Relation Between Social Identities and Outgroup Hostility Among German Immigrant-Origin Citizens

One of the major drivers of societal conflict are the intergroup relations which rely mainly on social identity and which are rarely analyzed for immigrant groups. This article changes this point of view by investigating the extent to which national, ethnic, and religious identities relate to outgroup hostilities towards the majority of the German population, towards other immigrant groups, and towards Syrian refugees among immigrant-origin citizens. We employ a theoretical framework based on the social identity approach and use new representative survey data from 2017 for Germans of Turkish descent (N = 480) and Russian Germans (N = 471). Based on multivariate linear regression analysis, we show that ethnic identity has the strongest positive relation with outgroup hostilities, with the exception of the Russian-Germans' evaluation of the German majority population. National identity among Germans of Turkish descent lessens their hostility towards other immigrants. Our results show the importance of analyzing immigrant groups with different migration trajectories separately before making generalized claims. Not only are the identity relations different between an ingroup identification and various outgroup targets, but they are also different between the immigrant groups for the same ingroup identification and outgroup target.     

Priming productive study strategies with preparatory quizzes in an engineering course

One approach to encourage productive study strategies is to incorporate preparatory quizzes (or pre‐quizzes) in which students are required to submit answers to questions before the underlying material is covered in class. In the present study, students took an introductory mechanical engineering class that either included pre‐quizzes (treatment group) or did not (control group). Students in the treatment group visited the online textbook more often and earlier in advance of deadlines, indicating better management of their study time—behaviors that have been shown to be productive study strategies. They also performed better in the course, indicating that techniques intended to prime productive study strategies can pay off. Finally, measures of productive learning strategies correlated with measures of course performance for both groups. These findings support the pretesting principle, which holds that students study more effectively and learn better when they take practice tests before a lesson.     

Remembering changes: repetition effects in face recollection.

Two experiments examined effects of repetition and change on states of awareness in face recognition. Participants studied repeatedly presented photographs of faces, with the second presentation following either immediately after the first presentation (massed repetition) or following six intervening items (spaced repetition). To manipulate perceptual change, each repeated face was either identical or a mirror image of the first presentation. Subsequently, when recognising a face, participants indicated whether they consciously recollected its prior occurrence ("remembering") or recognised it on the basis of familiarity ("knowing"). Changes in appearance between repeated faces enhanced remember, but not know, responses, and these effects were accentuated for spaced, rather than massed, repetition. These findings suggest that distinctiveness of encoding supports the phenomenological experience of conscious remembering.     

On normal theory based inference for multilevel models with distributional violations

Data in social and behavioral sciences are often hierarchically organized though seldom normal, yet normal theory based inference procedures are routinely used for analyzing multilevel models. Based on this observation, simple adjustments to normal theory based results are proposed to minimize the consequences of violating normality assumptions. For characterizing the distribution of parameter estimates, sandwich-type covariance matrices are derived. Standard errors based on these covariance matrices remain consistent under distributional violations. Implications of various covariance estimators are also discussed. For evaluating the quality of a multilevel model, a rescaled statistic is given for both the hierarchical linear model and the hierarchical structural equation model. The rescaled statistic, improving the likelihood ratio statistic by estimating one extra parameter, approaches the same mean as its reference distribution. A simulation study with a 2-level factor model implies that the rescaled statistic is preferable.This research was supported by grants DA01070 and DA00017 from the National Institute on Drug Abuse and a University of North Texas faculty research grant. We would like to thank the Associate Editor and two reviewers for suggestions that helped to improve the paper.     

Concise formulas for the standard errors of component loading estimates

Concise formulas for the asymptotic standard errors of component loading estimates were derived. The formulas cover the cases of principal component analysis for unstandardized and standardized variables with orthogonal and oblique rotations. The formulas can be used under any distributions for observed variables as long as the asymptotic covariance matrix for sample covariances/correlations is available. The estimated standard errors in numerical examples were shown to be equivalent to those by the methods using information matrices.The author is indebted to anonymous reviewers for the corrections and suggestions on this study, which have led to improvements of earlier versions of this article.