The insidious effects of failing to include design-driven correlated residuals in latent-variable covariance structure analysis
The insidious effects of failing to include design-driven correlated residuals in latent-variable covariance structure analysis
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DOI：
10.1037/1082-989x.12.4.381
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发表时间：
2007-12-01
影响因子：
7
通讯作者：
Steiger, James H.
中科院分区：
文献类型：
--
作者：
Cole, David A.;Ciesla, Jeffrey A.;Steiger, James H.
在实践中，在潜变量模型中包含相关残差即使不是一种彻头彻尾的作弊形式，也通常被视为一种统计花招。因此，研究人员倾向于在模型中只允许与数据良好拟合所需的相关残差。当前的文章表明，这种策略导致残差相关性的包含不足，而残差相关性在测量理论和研究设计的基础上是完全合理的。在许多设计中，缺乏这种相关性不会严重损害模型的拟合度；然而，如果不包含它们，可能会改变提取的潜在变量的含义，并产生潜在的误导性结果。建议包括（a）当测量理论暗示存在共享方法方差时，返回完整的多特征多方法设计；（b）当相关残差反映研究设计的预期特征时，放弃对相关残差的邪恶但必要的态度。
In practice, the inclusion of correlated residuals in latent-variable models is often regarded as a statistical sleight of hand, if not an outright form of cheating. Consequently, researchers have tended to allow only as many correlated residuals in their models as are needed to obtain a good fit to the data. The current article demonstrates that this strategy leads to the underinclusion of residual correlations that are completely justified on the basis of measurement theory and research design. In many designs, the absence of such correlations will not substantially harm the fit of the model; however, failure to include them can change the meaning of the extracted latent variables and generate potentially misleading results. Recommendations include (a) returning to the full multitrait-multimethod design when measurement theory implies the existence of shared method variance and (b) abandoning the evil-but-necessary attitude toward correlated residuals when they reflect intended features of the research design.