Studying Between-Subject Differences in Trends and Dynamics: Introducing the Random Coefficients Continuous-Time Latent Curve Model with Structured Residuals

IF 3.2 2区心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-05-03 DOI:10.1080/10705511.2023.2192889

Julian F. Lohmann, Steffen Zitzmann, Martin Hecht

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引用次数: 1

Abstract

The recently proposed continuous-time latent curve model with structured residuals (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle unequally spaced measurement occasions and describes processes independently of the length of the time intervals used in a given study. Third, it is a hierarchical model. Thus, multiple subjects can be analyzed simultaneously. However, subjects might also differ in dynamics and trends. Therefore, in the present paper, we extend the CT-LCM-SR to capture these differences as well. This newly proposed random coefficients continuous-time latent curve model with structured residuals (RC-CT-LCM-SR) is introduced theoretically and technically. Additionally, we provide an illustrative example with data from the Health and Retirement Study (HRS), and we show how the RC-CT-LCM-SR can be used to study multiple sources of between-subject differences over time.

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研究受试者之间的趋势和动态差异:引入带有结构化残差的随机系数连续时间潜在曲线模型

最近提出的带有结构化残差的连续时间潜在曲线模型(CT-LCM-SR)解决了行为科学中纵向数据分析的几个挑战。首先，它提供了有关过程趋势和动态的信息。其次，使用连续时间框架，CT-LCM-SR可以处理不等间隔的测量场合，并独立于给定研究中使用的时间间隔长度描述过程。第三，它是一个分层模型。因此，可以同时分析多个主题。然而，科目在动态和趋势上也可能有所不同。因此，在本文中，我们扩展了CT-LCM-SR来捕捉这些差异。从理论上和技术上介绍了新提出的随机系数连续时间结构残差潜曲线模型(RC-CT-LCM-SR)。此外，我们提供了一个来自健康与退休研究(HRS)的数据的说明性示例，并展示了如何使用RC-CT-LCM-SR来研究随时间变化的受试者之间差异的多个来源。

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来源期刊

Structural Equation Modeling: A Multidisciplinary Journal 数学-数学跨学科应用

CiteScore

8.70

自引率

11.70%

发文量

审稿时长

>12 weeks

期刊介绍： Structural Equation Modeling: A Multidisciplinary Journal publishes refereed scholarly work from all academic disciplines interested in structural equation modeling. These disciplines include, but are not limited to, psychology, medicine, sociology, education, political science, economics, management, and business/marketing. Theoretical articles address new developments; applied articles deal with innovative structural equation modeling applications; the Teacher’s Corner provides instructional modules on aspects of structural equation modeling; book and software reviews examine new modeling information and techniques; and advertising alerts readers to new products. Comments on technical or substantive issues addressed in articles or reviews published in the journal are encouraged; comments are reviewed, and authors of the original works are invited to respond.