Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators.

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-05-31 DOI:10.1080/00273171.2024.2347959
Young Won Cho, Sy-Miin Chow, Christina M Marini, Lynn M Martire
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引用次数: 0

Abstract

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

具有短时间序列和时变变量的多层次潜差结构方程模型:Frequentist and Bayesian Estimators: A Comparison of Frequentist and Bayesian Estimators.
使用微分方程进行连续时间建模是一种很有前途的技术,可用于对纵向数据的变化过程进行建模。在拟合这种模型的方法中,潜在微分结构方程建模(LDSEM)方法在结构方程建模(SEM)框架内定义了潜在的衍生变量,从而使研究人员能够利用 SEM 框架的优势来建立模型、进行估计、推理和比较。但仍有一些问题尚未解决,包括 LDSEM 的多层次变化在较短时间长度(如 14 个时间点)下的表现,尤其是在涉及耦合多变量过程和时变协变量时。此外,使用贝叶斯估计法来促进具有复杂和高维随机效应结构的多层次 LDSEM(M-LDSEM)模型估计的可能性尚未得到研究。我们进行了一系列蒙特卡罗模拟,评估了拟合 M-LDSEM 的三种可能方法,包括:频数主义单水平和双水平稳健估计法以及贝叶斯双水平估计法。我们的研究结果表明,贝叶斯方法优于其他频数法。时变协变量的影响得到了很好的恢复,耦合参数的偏差最小,特别是使用贝叶斯估计器的高阶导数信息。最后,我们提供了一个实证例子来说明该方法的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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