Probing interactions in fixed and multilevel regression: Inferential and graphical techniques
Probing interactions in fixed and multilevel regression: Inferential and graphical techniques
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DOI：
10.1207/s15327906mbr4003_5
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发表时间：
2005-01-01
影响因子：
3.8
通讯作者：
Curran, PJ
中科院分区：
文献类型：
--
作者：
Bauer, DJ;Curran, PJ
许多重要的研究假设涉及条件关系，其中一个预测因子的效果随另一个预测因子的值而变化。这种关系通常被评估为乘法交互作用，可以在固定和随机效应回归中进行测试。通常，这些相互作用的效果必须进一步探讨，以充分阐明条件关系的性质。探测相互作用的最常用方法是在预测因子的特定水平上测试简单斜率。一种更通用的方法是约翰逊-内曼（J-N）技术。然而，这种技术并没有被广泛使用，因为它目前仅限于固定效应回归中的连续相互作用的分类，并且尚未扩展到更广泛的随机效应回归模型。本文的目标是推广J-N技术，以允许对固定效应和随机效应回归中出现的各种相互作用进行检验。我们回顾现有的方法探测相互作用，阐述了扩展这些测试到更广泛的条件所需的解析表达式，并展示了J-N技术相对于简单的斜坡与三个经验的例子的优势。
Many important research hypotheses concern conditional relations in which the effect of one predictor varies with the value of another. Such relations are commonly evaluated as multiplicative interactions and can be tested in both fixed- and random-effects regression. Often, these interactive effects must be further probed to fully explicate the nature of the conditional relation. The most common method for probing interactions is to test simple slopes at specific levels of the predictors. A more general method is the Johnson-Neyman (J-N) technique. This technique is not widely used, however, because it is currently limited to categorical by continuous interactions in fixed-effects regression and has yet to be extended to the broader class of random-effects regression models. The goal of our article is to generalize the J-N technique to allow for tests of a variety of interactions that arise in both fixed- and random-effects regression. We review existing methods for probing interactions, explicate the analytic expressions needed to expand these tests to a wider set of conditions, and demonstrate the advantages of the J-N technique relative to simple slopes with three empirical examples.