Bias in cross-sectional analyses of longitudinal mediation
Bias in cross-sectional analyses of longitudinal mediation
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DOI：
10.1037/1082-989x.12.1.23
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发表时间：
2007-03-01
影响因子：
7
通讯作者：
Cole, David A.
中科院分区：
文献类型：
--
作者：
Maxwell, Scott E.;Cole, David A.
大多数调解的实证检验利用横截面数据，尽管调解的因果过程，随着时间的推移展开。作者考虑了在两种不同的变化模型中的任何一种下发生纵向中介的可能性：（a）自回归模型或（B）随机效应模型。对于这两个模型，作者证明，即使在调解完成的理想条件下，横截面调解方法通常也会产生纵向参数的严重偏倚估计。在变量M完全介导X对Y的影响的纵向模型中，X对Y的直接影响、X通过M对Y的间接影响以及M介导的总影响比例的横截面估计往往具有高度误导性。
Most empirical tests of mediation utilize cross-sectional data despite the fact that mediation consists of causal processes that unfold over time. The authors considered the possibility that longitudinal mediation might occur under either of two different models of change: (a) an autoregressive model or (b) a random effects model. For both models, the authors demonstrated that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters even under the ideal conditions when mediation is complete. In longitudinal models where variable M completely mediates the effect of X on Y, cross-sectional estimates of the direct effect of X on Y, the indirect effect of X on Y through M, and the proportion of the total effect mediated by M are often highly misleading.