Using Random Group Resampling in multilevel research - An example of the buffering effects of leadership climate

In this article, we provide a detailed example of how Random Group Resampling (RGR) can be used to empirically identify group effects with an example involving the buffering effects of leadership climate. RGR provides a tool for statistically determining whether group-level relationships are the result of true group phenomenon (group effects) or the result of aggregating individual data to the group level (grouping effects). Here, we present a group-level model of the stress-buffering hypothesis. Using this group-level perspective, we propose that the average perceptions of leadership climate within Army Companies will moderate the relationship between unit task significance and unit hostility. An unweighted group-means analysis revealed significant buffering effects. Following the unweighted group-means analysis, we used RGR to determine whether the significant interaction was a function of the aggregation process (grouping effects) or a function of the group-level properties of the data (group effects). The RGR analysis indicated that the interaction was related to the group-level proper-ties or the data, and was not merely a by-product of the aggregation process. We conclude by discussing the flexibility of RGR and use it to supplement a within and between analyses (WABAs). Published by Elsevier Science Inc.