Multiplicity control in structural equation modeling
Multiplicity control in structural equation modeling
复制标题
DOI：
10.1207/s15328007sem1401_5
复制
发表时间：
2007-01-01
影响因子：
6
通讯作者：
Cribbie, Robert A.
中科院分区：
文献类型：
--
作者：
Cribbie, Robert A.
在评估模型中多个参数的统计显著性时，进行结构方程建模分析的研究人员很少（如果有的话）控制类型I错误的膨胀概率。在本研究中，比较了家庭智能和错误发现率控制程序的I型错误控制率、功率和真实模型率与不施加多重性控制时的比率。结果表明，在没有多重性控制的情况下，I型错误率会严重膨胀，而且家族错误控制程序非常保守，检测真实关系的能力很小。错误发现率控制程序提供了无多重性控制和严格的家族错误控制之间的折衷，并且大样本量提供了对模型中所有参数做出正确推断的高概率。
Researchers conducting structural equation modeling analyses rarely, if ever, control for the inflated probability of Type I errors when evaluating the statistical significance of multiple parameters in a model. In this study, the Type I error control, power and true model rates of famsilywise and false discovery rate controlling procedures were compared with rates when no multiplicity control was imposed. The results indicate that Type I error rates become severely inflated with no multiplicity control, but also that familywise error controlling procedures were extremely conservative and had very little power for detecting true relations. False discovery rate controlling procedures provided a compromise between no multiplicity control and strict familywise error control and with large sample sizes provided a high probability of making correct inferences regarding all the parameters in the model.