Various theoretical accounts suggest that within-person effects relating to everyday experiences (assessed, e.g., via experience sampling studies or daily diary studies) are a central element for understanding between-person differences in future outcomes. In this regard, it is often assumed that the within-person effect of a time-varying predictor X on a time-varying mediator M contributes to the long-term development in an outcome variable Y. In the present work, we demonstrate that traditional multilevel mediation approaches fall short in capturing the proposed mechanism, however. We suggest that a model in which between-person differences in the strength of within-person effects predict the outcome Y mediated via mean levels in M more adequately aligns with the presumed theoretical account that within-person effects shape between-person differences. Using simulated data, we show that the central parameters of this multilevel structural equation model can be recovered well in most of the investigated scenarios. Our approach has important implications for whether or not to control for mean levels in models with within-person effects as predictors. We illustrate the model using empirical data targeting the question if the within-person association of occurrence of daily stressors (X) with daily experiences of negative affect (M) longitudinally predicts between-person differences in change in depressive symptoms (Y). Implications for other multilevel designs and intervention studies are discussed.

中文翻译：

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人内效应如何塑造人与人之间的差异：多层次结构方程建模视角。

各种理论解释表明，与日常经历相关的个人内部效应（例如，通过经验抽样研究或日常日记研究评估）是理解未来结果中人与人之间差异的核心要素。在这方面，通常假设时变预测变量 X 对时变中介 M 的个人内部效应有助于结果变量 Y 的长期发展。在目前的工作中，我们证明了传统的然而，多层次调解方法在捕捉所提议的机制方面存在不足。我们建议一个模型，其中人内效应强度的人际差异预测通过 M 中的平均水平介导的结果 Y 更充分地与假设的理论解释一致，即人内效应塑造了人际差异。使用模拟数据，我们表明这种多级结构方程模型的中心参数可以在大多数研究场景中很好地恢复。我们的方法对于是否控制以人体内效应作为预测因子的模型中的平均水平具有重要意义。我们使用针对以下问题的经验数据来说明该模型，如果日常压力源 (X) 的发生与负面情绪 (M) 的日常经历之间的人内关联纵向预测抑郁症状 (Y) 变化的人际差异。