Latent state-trait (LST) models are increasingly applied in psychology. Although existing LST models offer many possibilities for analyzing variability and change, they do not allow researchers to relate time-varying or time-invariant covariates, or a combination of both, to loading, intercept, and factor variance parameters in LST models. We present a general framework for the inclusion of nominal and/or continuous time-varying and time-invariant covariates in LST models. The new framework builds on modern LST theory and Bayesian moderated nonlinear factor analysis and is termed moderated nonlinear LST (MN-LST) framework. The MN-LST framework offers new modeling possibilities and allows for a fine-grained analysis of trait change, person-by-situation interaction effects, as well as inter- or intraindividual variability. The new MN-LST approach is compared to alternative modeling strategies. The advantages of the MN-LST approach are illustrated in an empirical application examining dyadic coping in romantic relationships. Finally, the advantages and limitations of the approach are discussed, and practical recommendations are provided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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在潜在状态特质模型中包含时变和时不变协变量的通用框架。

潜在状态特质（LST）模型越来越多地应用于心理学。尽管现有的 LST 模型为分析变异性和变化提供了多种可能性，但它们不允许研究人员将时变或时不变协变量或两者的组合与 LST 模型中的加载、截距和因子方差参数相关联。我们提出了一个在 LST 模型中包含名义和/或连续时变和时不变协变量的通用框架。新框架建立在现代LST理论和贝叶斯调节非线性因子分析的基础上，被称为调节非线性LST（MN-LST）框架。MN-LST 框架提供了新的建模可能性，并允许对特征变化、个体与情境的交互效应以及个体间或个体内的变异性进行细粒度分析。将新的 MN-LST 方法与替代建模策略进行比较。MN-LST 方法的优点在检查浪漫关系中的二元应对的实证应用中得到了说明。最后，讨论了该方法的优点和局限性，并提供了实用建议。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。