For over three decades, methodologists have cautioned against the use of cross-sectional mediation analyses because they yield biased parameter estimates. Yet, cross-sectional mediation models persist in practice and sometimes represent the only analytic option. We propose a sensitivity analysis procedure to encourage a more principled use of cross-sectional mediation analysis, drawing inspiration from Gollob and Reichardt (1987, 1991). The procedure is based on the two-wave longitudinal mediation model and uses phantom variables for the baseline data. After a researcher provides ranges of possible values for cross-lagged, autoregressive, and baseline Y and M correlations among the phantom and observed variables, they can use the sensitivity analysis to identify longitudinal conditions in which conclusions from a cross-sectional model would differ most from a longitudinal model. To support the procedure, we first show that differences in sign and effect size of the b-path occur most often when the cross-sectional effect size of the b-path is small and the cross-lagged and the autoregressive correlations are equal or similar in magnitude. We then apply the procedure to cross-sectional analyses from real studies and compare the sensitivity analysis results to actual results from a longitudinal mediation analysis. While no statistical procedure can replace longitudinal data, these examples demonstrate that the sensitivity analysis can recover the effect that was actually observed in the longitudinal data if provided with the correct input information. Implications of the routine application of sensitivity analysis to temporal bias are discussed. R code for the procedure is provided in the online supplementary materials. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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横截面中介中时间偏差的敏感性分析。


三十多年来，方法学家一直警告不要使用横截面中介分析，因为它们会产生有偏差的参数估计。然而，横截面中介模型在实践中仍然存在，有时代表了唯一的分析选择。我们从 Gollob 和 Reichardt (1987, 1991) 中汲取灵感，提出了一种敏感性分析程序，以鼓励更原则性地使用横截面中介分析。该过程基于两波纵向中介模型，并使用虚拟变量作为基线数据。在研究人员提供虚拟变量和观测变量之间的交叉滞后、自回归以及基线 Y 和 M 相关性的可能值范围后，他们可以使用敏感性分析来识别纵向条件，在这些条件下，横截面模型的结论差异最大从纵向模型。为了支持该过程，我们首先表明，当 b 路径的横截面效应大小较小且交叉滞后和自回归相关性相等或相似时，b 路径的符号和效应大小差异最常发生在幅度上。然后，我们将该程序应用于实际研究的横断面分析，并将敏感性分析结果与纵向中介分析的实际结果进行比较。虽然没有任何统计程序可以取代纵向数据，但这些示例表明，如果提供正确的输入信息，敏感性分析可以恢复在纵向数据中实际观察到的效果。讨论了常规应用敏感性分析对时间偏差的影响。在线补充材料中提供了该过程的 R 代码。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。