Centering Decisions in Hierarchical Linear Models: Implications for Research in Organizations

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Abstract
Organizational researchers are increasingly interested in modeling the multilevel nature of organizational data. Although most organizational researchers have chosen to investigate these models using traditional Ordinary Least Squares approaches, hierarchical linear models (i.e., random coefficient models) recently have been receiving increased attention. One of the key questions in using hierarchical linear models is how a researcher chooses to scale the Level-1 independent variables (e.g., raw metric, grand mean centering, group mean centering), because it directly influences the interpretation of both the level-1 and level-2 parameters. Several scaling options are reviewed and discussed in light of four paradigms of multilevel/cross-level research in organizational science: incremental (i.e., group variables add incremental prediction to individual level outcomes over and above individual level predictors), mediational (i.e., the influence of group level variables on individual outcomes are mediated by individual perceptions), moderational (i.e., the relationship between two individual level variables is moderated by a group level variable), and separate (i.e., separate within group and between group models). The paper concludes with modeling recommendations for each of these paradigms and discusses the importance of matching the paradigm under which one is operating to the appropriate modeling strategy.

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