Johnson-Neyman type technique in hierarchical linear models
Johnson-Neyman type technique in hierarchical linear models
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DOI：
10.3102/10769986030003233
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发表时间：
2005-09-01
影响因子：
2.4
通讯作者：
Maier, KS
中科院分区：
文献类型：
--
作者：
Miyazaki, Y;Maier, KS
在分层线性模型中，我们经常发现在聚类水平上的组指标变量是回归斜率的显著预测因子。在这种情况下，结果和关键自变量之间的平均关系在组与组之间是不同的。在这些情况下，一个问题，如“什么范围的自变量是差异的结果变量统计学显着组之间？”自然产生。Johnson-Neyman（J-N）技术在协方差分析（ANCOVA）设置中回答了这类问题。在分层建模上下文中，不能应用ANCOVA中广泛使用的F检验，因为违反了聚类单位内方差齐性的假设。相反，近似Wald测试调用可用于确定显著性区域。为了说明J-N技术在分层线性建模中的应用，提供了一个来自教育研究的例子。
In hierarchical linear models we often find that group indicator variables at the cluster level are significant predictors for the regression slopes. When this is the case, the average relationship between the outcome and a key independent variable are different from group to group. In these settings, a question such as "what range of the independent variable is the difference in the outcome variable statistically significant among groups?" naturally arises. The Johnson-Neyman (J-N) technique answers this kind of question in the analysis of covariance (ANCOVA) settings. In the hierarchical modeling context, the F test, which is widely used in ANCOVA, cannot be applied because the assumption of homogeneity of variance within cluster units is violated. Instead, the approximate Wald test call be used to determine the region of significance. To illustrate the application of the J-N technique in the context of hierarchical linear modeling, an example from research ill education is provided.