Holtmann Jana, Koslowski Kenneth
Wilhelm-Wundt Institute for Psychology, Leipzig University, Neumarkt 9-19, 04109, Leipzig, Germany.
Behav Res Methods. 2025 Sep 5;57(10):277. doi: 10.3758/s13428-025-02694-3.
The study of time-dependent within-person dynamics has gained popularity in recent years through the use of multilevel (latent) time-series models. However, due to the complexity of the models, model applications are usually limited with respect to the inclusion of time-varying moderating factors on the longitudinal within-person relations between variables. That is, in common applications of multilevel time-series models, the within-person dynamics of constructs over time are regarded as being insensitive to changes in other time-varying factors or changes in contexts. We illustrate an extension of multilevel latent time-series models that incorporate latent interaction effects at the dynamic within-person level. We build on previous work that incorporated time-varying observed or latent moderator variables for the dynamic parameters in vector autoregressive models and provide a tutorial for the application of the models, implemented and estimated using Bayesian estimation via Markov chain Monte Carlo techniques. The models are illustrated by two empirical applications that investigate the temporal dynamics of negative affect, rumination, and mindful attention. The performance of different models with varying complexity is investigated via several simulation studies to provide recommendations regarding the models' applicability for applied researchers. Required sample sizes range between at least 25 time points for around 50 persons in the simplest fixed-effects models and at least 100 time points for at least 100 persons in random-effects factor models, depending on the expected effect sizes of the dynamic parameters.
近年来，通过使用多层次（潜在）时间序列模型，对随时间变化的个体内部动态的研究越来越受欢迎。然而，由于模型的复杂性，在纳入变量之间纵向个体内部关系的随时间变化的调节因素方面，模型应用通常受到限制。也就是说，在多层次时间序列模型的常见应用中，构念随时间的个体内部动态被认为对其他随时间变化的因素或情境变化不敏感。我们阐述了多层次潜在时间序列模型的一种扩展，该扩展在动态个体内部层面纳入了潜在交互效应。我们基于之前的工作，即在向量自回归模型中为动态参数纳入随时间变化的观测或潜在调节变量，并提供了该模型应用的教程，该模型通过马尔可夫链蒙特卡罗技术使用贝叶斯估计进行实现和估计。通过两个实证应用对模型进行了说明，这两个应用研究了消极情绪、沉思和正念注意力的时间动态。通过几项模拟研究调查了不同复杂程度模型的性能，以为应用研究人员提供有关模型适用性的建议。根据动态参数的预期效应大小，在最简单的固定效应模型中，所需样本量至少为约50人25个时间点，在随机效应因子模型中，至少为至少100人100个时间点。
