Main effects analysis in clinical research: statistical guidelines for disaggregating treatment groups.
Main effects analysis in clinical research: statistical guidelines for disaggregating treatment groups.
复制标题
临床研究中的主效应分析：分解治疗组的统计指南。
DOI：
10.1037//0022-006x.59.5.745
复制
发表时间：
1991
影响因子：
5.9
通讯作者：
Howard,KI
中科院分区：
文献类型：
--
作者：
Lyons,JS;Howard,KI
治疗结果研究一般依靠主效应方差分析(ANOVA)来确定治疗是否有差异有效。作为Bryk和Sw，RaudenBush(1988)发展了一种在方差异质性条件下解聚处理组的决策策略。然而，即使没有违反这一假设，也有理由考虑分解主要影响。分解的潜在统计意义可以被证明是依赖测量的可靠性的函数。有了这种可靠性，残差方差可以被划分为系统(个体差异)分量和随机误差分量。然后，就可以计算这些方差的比率的F检验。当该F具有统计意义并且细胞内系统方差占总方差的比例很大时，应进行分解以搜索重要的个体或治疗差异变量(即相互作用)。
Treatment outcome research generally relies on main effects analysis of variance (ANOVA) to determine whether treatments are differentially effective. AS Bryk and SW Raudenbush (1988) developed a decision strategy for disaggregating treatment groups under conditions of heterogeneity of variance. There is, however, reason to consider disaggregating main effects even when this assumption is not violated. The potential statistical significance of disaggregation can be shown to be a function of the reliability of the dependent measure. With this reliability, residual variance can be partitioned into a systematic (individual differences) component and a random error component. It is then possible to calculate an F test of the ratio of these variances. When this F is statistically significant and the proportion of within-cell systematic variance to total variance is large, disaggregation should be undertaken to search for important individual or treatment difference variables (ie, interactions).(PsycINFO Database Record (c) 2016 APA, all rights reserved)