Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between-and within-level components of indirect effects. Moreover, these methods have each appeared in isolation, so a unified framework that integrates the existing methods, as well as new multilevel mediation models, is lacking. Here we show that a multilevel structural equation modeling (MSEM) paradigm can overcome these 2 limitations of mediation analysis with MLM. We present an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases. We use several applied examples and accompanying software code to illustrate the flexibility of this framework and to show that different substantive conclusions can be drawn using MSEM versus MLM. Researchers in behavioral, educational, and organizational research settings often are interested in testing mediation hypotheses with hierarchically clustered data. For example, Bacharach, Bamberger, and Doveh (2008) investigated the mediating role of distress in the relationship between the intensity of involvement in work-related incidents and problematic drinking behavior among firefighting personnel. They used data in which firefighters were nested within ladder companies and all three variables were assessed at the subject level. Using data from customer service engineers working in teams, Maynard, Mathieu, Marsh, and Ruddy (2007) found that team-level interpersonal processes mediate the relationship between team-level resistance to empowerment and individual job satisfaction. Both of these examples-and many others-involve data that vary both within and between higher level units. Traditional methods for assessing mediation (e.g., Baron & Kenny, 1986;MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002;MacKinnon, Warsi, & Dwyer, 1995) are inappropriate in these multilevel settings, primarily because the assumption of independence of observations is violated when clustered data are used, leading to downwardly biased standard errors if ordinary regression is used. For this reason, several methods have been proposed for addressing mediation hypotheses when the data are hierarchically organized.These recommended procedures for testing multilevel mediation have been developed and framed almost exclusively within the standard multilevel modeling (MLM) paradigm (for thorough treatments of MLM, see Bryk, 2002, andSnijders &Bosker, 1999) and implemented with commercially available MLM software, such as SAS PROC MIXED, HLM, or MLwiN. For example, some authors have discussed models in which the independent variable X, mediator M, and dependent variable Y all are measured at Level 1 of a two-level hierarchy (a 1-1-1 design, adopting notation proposed by Krull & MacKinnon, 2001), 1 and slopes either are fixed (Pituch, Whittaker, & Stapleton, 2005...

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A growing number of studies have examined the "sharedness" of leadership processes in teams (i.e., shared leadership, collective leadership, and distributed leadership). We meta-analytically cumulated 42 independent samples of shared leadership and examined its relationship to team effectiveness. Our findings reveal an overall positive relationship (ρ = .34). But perhaps more important, what is actually shared among members appears to matter with regard to team effectiveness. That is, shared traditional forms of leadership (e.g., initiating structure and consideration) show a lower relationship (ρ = .18) than either shared new-genre leadership (e.g., charismatic and transformational leadership; ρ = .34) or cumulative, overall shared leadership (ρ = .35). In addition, shared leadership tends to be more strongly related to team attitudinal outcomes and behavioral processes and emergent team states, compared with team performance. Moreover, the effects of shared leadership are stronger when the work of team members is more complex. Our findings further suggest that the referent used in measuring shared leadership does not influence its relationship with team effectiveness and that compared with vertical leadership, shared leadership shows unique effects in relation to team performance. In total, our study not only cumulates extant research on shared leadership but also provides directions for future research to move forward in the study of plural forms of leadership.

Testing multilevel mediation using hierarchical linear modeling (HLM) has gained tremendous popularity in recent years. However, potential confounding in multilevel mediation effect estimates can arise in these models when within-group effects differ from between-group effects. This study summarizes three types of HLM-based multilevel mediation models, and then explains that in two types of these models confounding can be produced and erroneous conclusions may be derived when using popularly recommended procedures. A Monte Carlo simulation study illustrates that these procedures can underestimate or overestimate true mediation effects. Recommendations are provided for appropriately testing multilevel mediation and for differentiating within-group versus between-group effects in multilevel settings.

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