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Multilevel methods allow researchers to investigate relationships that expand across levels (e.g., individuals, teams, and organizations). The popularity of these methods for studying organizational phenomena has increased in recent decades. Methodologists have examined how these methods work under different conditions, providing an empirical base for making sound decisions when using these methods. In this article, we provide recommendations, tools, resources, and a checklist that can be useful for scholars involved in conducting or assessing multilevel studies. The focus of our article is on two-level designs, in which Level-1 entities are neatly nested within Level-2 entities, and top-down effects are estimated. However, some of our recommendations are also applicable to more complex multilevel designs.

Considering the motivational path of the Job Demands-Resources (JD-R) model, this study investigates a multistep process by which contextual job resources might have an indirect effect on idea implementation. Among 187 shop floor employees of a manufacturing company, we found support for a model whereby job control has an indirect effect, over a 5-month time frame, on idea implementation through employee work engagement and personal initiative. In addition, we found that the indirect effect of job control on idea implementation is stronger when employees have a higher conformity orientation. Support was not found for the hypothesized indirect effect of coworker social support on idea implementation. Our findings support the usefulness of the JD-R theory for understanding how increased idea implementation occurs at work, especially when the additional factors of personal initiative and employee conformity orientation are integrated into the model.

Research on faultlines—hypothetical dividing lines splitting a team into homogeneous subgroups based on team members' attributes—has produced several meta-analyses, reviews, and algorithms for faultline and subgroup detection. To help navigate this complexity, we summarize the current theories underlying faultline and subgroup research. We also compare the two most recent algorithms for computational faultline/subgroup detection, offer a guideline for choosing adequate algorithms, and recommend measure combinations for future research. We further review empirical faultlines and subgroup research and show that different contextual factors exhibit a strong influence on the effects of faultlines and subgroups. We discuss the need for further theorization on faultlines that does not rely on attribute salience, which considers the number of aligning attributes and the consequences of faultlines at the subgroup level. We conclude considering new potential applications of the faultline construct.