Although missing data methods have advanced in recent years, methodologists have devoted less attention to multilevel data structures where observations at level-1 are nested within higher-order organizational units at level-2 (e.g., individuals within neighborhoods; repeated measures nested within individuals; students nested within classrooms). Joint modeling and chained equations imputation are the principal imputation frameworks for single-level data, and both have multilevel counterparts. These approaches differ algorithmically and in their functionality; both are appropriate for simple random intercept analyses with normally distributed data, but they differ beyond that. The purpose of this paper is to describe multilevel imputation strategies and evaluate their performance in a variety of common analysis models. Using multiple imputation theory and computer simulations, we derive 4 major conclusions: (a) joint modeling and chained equations imputation are appropriate for random intercept analyses; (b) the joint model is superior for analyses that posit different within- and between-cluster associations (e.g., a multilevel regression model that includes a level-1 predictor and its cluster means, a multilevel structural equation model with different path values at level-1 and level-2); (c) chained equations imputation provides a dramatic improvement over joint modeling in random slope analyses; and (d) a latent variable formulation for categorical variables is quite effective. We use a real data analysis to demonstrate multilevel imputation, and we suggest a number of avenues for future research. (PsycINFO Database Record

Measures of behavior in psychology and the behavioral sciences often take the form of count variables. Counts sum the number of discrete events in a fixed time period (e.g., 24 hours) and range upward from a minimum of zero. Traditional approaches to the analysis of count variables such as analysis of variance and multiple regression often do not give proper results.A variety of specialized approaches to count data that provide better answers have been developed, but they are not well known to most psychologists. In addition, group researchers working with data that are clustered face the

How might religion shape intergroup conflict? We tested whether religious infusion-the extent to which religious rituals and discourse permeate the everyday activities of groups and their members-moderated the effects of two factors known to increase intergroup conflict: competition for limited resources and incompatibility of values held by potentially conflicting groups. We used data from the Global Group Relations Project to investigate 194 groups (e.g., ethnic, religious, national) at 97 sites around the world. When religion was infused in group life, groups were especially prejudiced against those groups that held incompatible values, and they were likely to discriminate against such groups. Moreover, whereas disadvantaged groups with low levels of religious infusion typically avoided directing aggression against their resource-rich and powerful counterparts, disadvantaged groups with high levels of religious infusion directed significant aggression against them-despite the significant tangible costs to the disadvantaged groups potentially posed by enacting such aggression. This research suggests mechanisms through which religion may increase intergroup conflict and introduces an innovative method for performing nuanced, cross-societal research.

Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional distributions. In single-level multivariate normal data, these two approaches have been shown to be equivalent, but less is known about their similarities and differences with multilevel data. This study examined four multilevel multiple imputation approaches: JM approaches proposed by Schafer and Yucel and Asparouhov and Muthén and FCS methods described by van Buuren and Carpenter and Kenward. Analytic work and computer simulations showed that Asparouhov and Muthén and Carpenter and Kenward methods are most flexible, as they produce imputations that preserve distinct within- and between-cluster covariance structures. As such, these approaches are applicable to random intercept models that posit level-specific relations among variables (e.g., contextual effects analyses, multilevel structural equation models). In contrast, methods from Schafer and Yucel and van Buuren are more restrictive and impose implicit equality constraints on functions of the within- and between-cluster covariance matrices. The analytic work and simulations underscore the conclusion that researchers should not expect to obtain the same results from alternative imputation routines. Rather, it is important to choose an imputation method that partitions variation in a manner that is consistent with the analysis model of interest. A real data analysis example illustrates the various approaches.

Previous theorizing suggests that often-stigmatized individuals may be just as likely, if not more likely, than infrequently stigmatized individuals to protect self-regard by derogating members of low-status groups after receiving negative feedback from high-status others. Often-stigmatized individuals, however, can discount criticism from these high-status others as reflecting prejudice,