Researchers often combine longitudinal panel data analysis with tests of interactions (i.e., moderation). A popular example is the cross-lagged panel model (CLPM). However, interaction tests in CLPMs and related models require caution because stable (i.e., between-level, B) and dynamic (i.e., within-level, W) sources of variation are present in longitudinal data, which can conflate estimates of interaction effects. We address this by integrating literature on CLPMs, multilevel moderation, and latent interactions. Distinguishing stable B and dynamic W parts, we describe three types of interactions that are of interest to researchers: 1) purely dynamic or WxW; 2) cross-level or BxW; and 3) purely stable or BxB. We demonstrate estimating latent interaction effects in a CLPM using a Bayesian SEM in Mplus to apply relationships among work-family conflict and job satisfaction, using gender as a stable B variable. We support our approach via simulations, demonstrating that our proposed CLPM approach is superior to a traditional CLPMs that conflate B and W sources of variation. We describe higher-order nonlinearities as a possible extension, and we discuss limitations and future research directions.

研究人员经常将纵向面板数据分析与交互测试（即适度）结合起来。一个流行的例子是交叉滞后面板模型 (CLPM)。然而，CLPM 和相关模型中的交互测试需要谨慎，因为纵向数据中存在稳定（即水平之间，B）和动态（即，水平内，W）变异源，这可能会混淆交互效应的估计。我们通过整合关于 CLPM、多级调节和潜在交互的文献来解决这个问题。区分稳定的B和动态的W部分，我们描述了研究人员感兴趣的三种类型的相互作用：1）纯动态或WxW；2) 跨级或BxW; 和 3) 纯粹稳定或BxB。我们展示了使用 Mplus 中的贝叶斯 SEM 估计 CLPM 中的潜在交互效应，以应用工作-家庭冲突和工作满意度之间的关系，使用性别作为稳定的B变量。我们通过模拟支持我们的方法，证明我们提出的 CLPM 方法优于将B和W变异源混为一谈的传统 CLPM 。我们将高阶非线性描述为可能的扩展，并讨论了局限性和未来的研究方向。