Centering decisions in hierarchical linear models: Implications for research in organizations
Centering decisions in hierarchical linear models: Implications for research in organizations
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DOI：
10.1016/s0149-2063(99)80077-4
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发表时间：
1998-01-01
影响因子：
13.5
通讯作者：
Gavin, MB
中科院分区：
文献类型：
--
作者：
Hofmann, DA;Gavin, MB
组织研究人员对组织数据的多层次性质建模越来越感兴趣。尽管大多数组织研究人员选择使用传统的普通最小二乘法来研究这些模型，但分层线性模型（即随机系数模型）最近受到了越来越多的关注。使用分层线性模型的关键问题之一是研究人员如何选择对第一层自变量进行缩放（例如，原始度量、总均值中心化、组均值中心化），因为这直接影响对第一层和第二层参数的解释。根据组织科学中多层次/跨层次研究的四种范式，对几种缩放选项进行了回顾和讨论：增量式（即组变量在个体层面预测变量之上对个体层面结果增加增量预测）、中介式（即组层面变量对个体结果的影响通过个体感知起中介作用）、调节式（即两个个体层面变量之间的关系受到一个组层面变量的调节）和分离式（即组内和组间分别建模）。本文针对每种范式提出了建模建议，并讨论了将所采用的范式与适当的建模策略相匹配的重要性。
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 (Le., 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.