We studied three models for longitudinal mediation analysis: the autoregressive mediation model (AMM) using composite scores (uncorrected composite model, UCM), AMM using composite scores with correction for measurement error (corrected composite model, CCM), and AMM using latent variables with multiple indicators (latent variable model, LVM). Under the condition of unidimensional measurement model, we showed analytically that UCM yielded asymptotically biased direct and indirect effect estimates when composite reliabilities of observed variables were less than 1, and had unbiased estimates only under stringent and unlikely conditions. Further, CCM yielded asymptotically unbiased effect estimates when the sums of loadings for items measuring a latent variable were invariant over time. We verified conclusions from the analytical study regarding parameter estimation accuracy via a simulation study. Specifically, under different levels of measurement invariance, sample sizes, numbers of time points, and reliabilities, CCM and LVM had reasonably accurate direct and indirect effect estimates and good coverage rates in general. On the other hand, UCM was not recommended given inaccurate effect estimates and/or low coverage of true parameters across our considered conditions. In addition, CCM was much simpler in model structure and less sensitive to sample sizes in comparison with LVM in terms of model chi-square test and fit indexes. An empirical study was conducted for illustration. Mplus code for fitting the three models is provided. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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使用综合评分和潜在变量的自回归中介模型：比较和建议。

我们研究了三种用于纵向中介分析的模型：使用复合评分的自回归中介模型（AMM）（未校正的复合模型，UCM），使用复合评分的AMM和测量误差校正（校正的复合模型，CCM）以及使用具有多个指标（潜在变量模型，LVM）。在一维测量模型的条件下，我们分析地表明，当观测变量的复合可靠性小于1时，UCM产生渐近有偏的直接和间接影响估计，并且仅在严格且不太可能的条件下才具有无偏估计。此外，当测量潜在变量的项目的负荷总和随时间变化时，CCM得出渐近无偏效果估计。我们通过仿真研究验证了分析研究中有关参数估计精度的结论。具体而言，在不同程度的测量不变性，样本量，时间点数量和可靠性下，CCM和LVM总体上具有合理准确的直接和间接影响估计以及良好的覆盖率。另一方面，由于在我们考虑的条件下效果估计不准确和/或真实参数覆盖率较低，不建议使用UCM。此外，在模型卡方检验和拟合指数方面，与LVM相比，CCM的模型结构简单得多，对样本量的敏感性较低。进行了实证研究以说明。提供了适用于这三种模型的Mplus代码。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。