Using hierarchical linear modeling to investigate the moderating influence of leadership climate

By M. Gavin, David Hofmann. 2002

204 citations recorded by the source corpus.

Subfields: Management Information Systems, Management Science and Operations Research, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/a87580e363ad0c2fc9cfe70ef0e75808ddd8b3a5

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In Management Information Systems

In Management Science and Operations Research

In Organizational Behavior and Human Resource Management

Citations in the corpus

When Employees Are Out of Step with Coworkers: How Job Satisfaction Trajectory and Dispersion Influence Individual- and Unit-Level Voluntary Turnover (2012, methodology): “…any differences in the results between different centering methods, we also tested Hypotheses 2a and 3 using grand mean centering; consistently with Gavin and Hofmann (2002), we found the HGLM coefficients for the cross-level moderating effects were virtually identical. tion trajectory are…”
Transformational leadership and commitment: A multilevel analysis of group-level influences and mediating processes (2010, background): “As stated earlier, perceptions of a leader within a group should cluster for several reasons (Ehrhart, 2004; Gavin & Hofmann, 2002; Griffin &Mathieu, 1997).”
Leadership and individual differences: At the cusp of a renaissance (2012, background): “Because individuals are usually nested under higher-level entities (e.g., followers under leaders, leaders under organizations, etc.) researchers often estimate what are known as HLM-type models (cf. Gavin & Hofmann, 2002; Hofmann, 1997) like random-effects/intercepts models.”
A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data (2020, background): “The phenomenon of hierarchical levels of grouped data occurs widely, and HLM models are frequently used in the social work (Gavin, 2002), education (Bock, 2014), and geoscience (Banerjee et al., 2014).”
Developing and Leveraging Human Capital Resource to Promote Service Quality (2016, methodology): “…before testing these cross-level effects, we estimated a null model where we partitioned the variance in senior frontline–rated service quality into its within and between components as a necessary precondition when testing variables at different levels (gavin & Hofmann, 2002; Hofmann, 1997).”