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In the organizational literature, the impact of group size on the magnitude of the group-level correlation has not been explicitly delineated, despite the fact that group sizes vary considerably in organizational research. This article discusses the relationship between group size, ICC(J) values, and the magnitude of the group-level correlation, and shows that group size and ICC(I) values are important because they influence the reliability of the aggregate variables. Based on this discussion, a correction for attenuation formula is proposed that permits one to estimate the magnitude of the actual group-level correlation corrected for the reliability of the aggregate variables. A simulation study demonstrates that the correction for attenuation formula provides accurate estimates of the actual group-level correlation under a wide range of conditions. Implications for multilevel analyses are discussed.

Organizational data are inherently nested; consequently, lower level data are typically influenced by higher level grouping factors. Stated another way, almost all lower level organizational data have some degree of nonindependence due to work group, geographic membership, and so on. Unaccounted-for nonindependence can be problematic because it affects standard error estimates used to determine statistical significance. Currently, researchers interested in modeling higher level variables routinely use multilevel modeling techniques to avoid well-known problems with Type I error rates. In this article, however, the authors examine how nonindependence affects statistical inferences in cases in which researchers are interested only in relationships among lower level variables. They show that ignoring nonindependence when modeling only lower level variables reduces power (increases Type II errors), and through simulations, the authors show where this loss of power is most pronounced.

In this article, the authors illustrate how random coefficient modeling can be used to develop growth models for the analysis of longitudinal data. In contrast to previous discussions of random coefficient models, this article provides step-by-step guidance using a model comparison framework. By approaching the modeling this way, the authors are able to build off a regression foundation and progressively estimate and evaluate more complex models. In the model comparison framework, the article illustrates the value of using likelihood tests to contrast alternative models (rather than the typical reliance on tests of significance involving individual parameters), and it provides code in the open-source language R to allow readers to replicate the results. The article concludes with practical guidelines for estimating growth models.

A substantial number of U.S. Army soldiers deployed to Haiti for Operation Uphold Democracy did not believe it was important that the U.S. military be involved in the operation (49%); did not believe that what the U.S. military was doing was important (38%); and did not believe in the overall value of the operation (43%). At the same time, a substantial number of soldiers had positive feelings about what they were doing in Haiti and the mission they were accomplishing. The primary focus of this investigation was to examine factors that were related to the wide variation in soldier reports of support for Operation Uphold Democracy. The results indicated that a combination of soldier characteristics (e.g., race, gender), unit characteristics (e.g., unit type), task characteristics (e.g., task significance), and operational characteristics (e.g., perceptions of public support) accounted for nearly 50% of the variance in soldier reports of support for the overall operation.

Organizational researchers routinely have access to repeated measures from numerous time periods punctuated by one or more discontinuities. Discontinuities may be planned, such as when a researcher introduces an unexpected change in the context of a skill acquisition task. Alternatively, discontinuities may be unplanned, such as when a natural disaster or economic event occurs during an ongoing data collection. In this article, we build off the basic discontinuous growth model and illustrate how alternative specifications of time-related variables allow one to examine relative versus absolute change in transition and post-transition slopes. Our examples focus on interpreting time-varying covariates in a variety of situations (multiple discontinuities, linear and quadratic models, and models where discontinuities occur at different times). We show that the ability to test relative and absolute differences provides a high degree of precision in terms of specifying and testing hypotheses.

The study of multilevel phenomena in organizations involves a complex interplay between methods and statistics on one hand and theory development on the other. In this introduction, the authors provide a short summary of the five articles in this feature topic and use them as a platform to discuss the broad need for work in the two areas of (a) multilevel construct validation and measurement and (b) statistical advances in variance decomposition. Within these two broad frameworks, the authors specifically discuss, first, the need to continue moving beyond notions of isomorphism in developing and testing aggregate-level constructs. Second, they discuss the potential value of using discontinuous growth models to understand transitions in longitudinal studies. Finally, they discuss some of the issues surrounding the ability to decompose variance in multilevel modeling of dichotomous and other nonnormal outcome data.

Scholars have been interested in the extent to which organizational phenomena generalize across levels of analysis for quite some time. However, theoretical frameworks for developing homologous multilevel theories (i.e., theories involving parallel relationships between parallel constructs at different levels of analysis) have yet to be developed, and current analytical tools for testing such theories and models are limited and inflexible. In this article, the authors first propose a typology of multilevel theories of homology that considers different stages of theory development and different levels of similarity in relationships across levels. Building on cross-validation principles, the authors then delineate and demonstrate a comprehensive and flexible statistical procedure for testing different multilevel theories of homology. Finally, the authors discuss implications for theory, research, and practice, as well as potential caveats of the new statistical tests.

"The goal of this volume is to guide the field of military psychology in the development of evidence-based support for service members. Many psychological studies have described the mental health toll of combat as a warning about its cost in terms of human suffering. It is amazing that fewer studies have focused on evidence-based attempts to prevent mental health problems and enhance service member well-being and resilience. This volume is designed to fill this gap. The authors in this volume represent perspectives from clinical and research psychologists, physicians, and sociologists, and although the focus is largely the United States and primarily the army, international perspectives from the United Kingdom and Canada are included as well. The authors are a unique group of specialists who, as clinicians and researchers, are addressing the challenge of sustaining service member mental health. These authors share the goal of developing and implementing evidence-based interventions. Using the perspective of an occupational health model, the chapters in this volume emphasize the way in which the military organization can moderate the impact of combat on service member mental health through individual screening, training, peer support, leadership, and organizational policies. The chapters range from clinically based reflections on how to manage service member mental health during deployment to proposals for reconceptualizing service delivery, the role of peers, and what it means to transition home. This volume emphasizes what is known--and not known--about evidence-based approaches for early interventions and mental health resilience training conducted with service members. Throughout, the authors, all specialists in the field of military mental health, consider both the positive and negative impact that combat can have on service members and their families. The chapters also establish an agenda for research designed to support and promote the well-being of service members and their families"--Introduction. (PsycINFO Database Record (c) 2010 APA, all rights reserved).