A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Gu, Fei; Preacher, Kristopher J.; Wu, Wei; Yung, Yiu-Fai

doi:10.1080/00273171.2013.866537

Cited by 10 publications

(4 citation statements)

References 18 publications

(19 reference statements)

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“…This kind of clustering may lead some to think SSM may be related to multilevel models — and they are. Some multilevel SEMs can be fit as SSMs (Gu, Preacher, Wu, & Yung, 2014). …”

Section: Expectation Functionsmentioning

confidence: 99%

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OpenMx 2.0: Extended Structural Equation and Statistical Modeling

et al. 2015

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The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly-written CSOLNP. Entire new methodologies such as Item Factor analysis (IRT) and State-space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

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“…This kind of clustering may lead some to think SSM may be related to multilevel models — and they are. Some multilevel SEMs can be fit as SSMs (Gu, Preacher, Wu, & Yung, 2014). …”

Section: Expectation Functionsmentioning

confidence: 99%

“…Some simple multivariate multilevel models can be estimated with OpenMx 2.0 by using the state space expectation function (as in Gu et al, 2014). But the OpenMx development team is working towards a much more general solution that would accommodate cross-classified models as well as large and complex data.…”

Section: Planned Developmentsmentioning

confidence: 99%

OpenMx 2.0: Extended Structural Equation and Statistical Modeling

et al. 2015

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“…SSMs were originally developed in the field of mechanical and electrical engineering with the purpose of detecting, predicting, and separating random signals from noise (Kalman, 1960;Kalman & Bucy, 1961). In recent years, they have been introduced to the study of dynamics in psychological research (Chow et al, 2010;Gu et al, 2014;Hunter, 2018;Ji & Chow, 2019;Oud & Jansen, 2000). In the context of Cohort effects in maturation speed in ALDs -12 psychological processes, this approach allows detecting temporal dynamics, predicting future states of the system, and separating the latent relevant process from measurement error.…”

Section: Modeling Change In Continuous Time: State-space Modelsmentioning

confidence: 99%

A Dynamic Approach to Control for Cohort Differences in Maturation Speed Using Accelerated Longitudinal Designs

Cáncer

Estrada

Ferrer

2023

Structural Equation Modeling: A Multidisciplinary Journal

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Accelerated longitudinal designs (ALD) allow studying developmental processes usually spanning multiple years in a much shorter time framework by including participants from different age cohorts, which are assumed to share the same population parameters. However, different cohorts may have been exposed to dissimilar contextual factors, resulting in different developmental trajectories. If such differences are not accounted for, the generating process will not be adequately characterized. In this paper, we propose a continuous-time latent change score model as an approach to capture cohort differences affecting the speed of maturation of psychological processes in ALDs. This approach fills an important gap in the literature because, until now, no method existed for this goal. Using a Monte-Carlo simulation study, we show that the proposed model detects cohort differences adequately, regardless of their size in the population. Our proposed model can help developmental researchers control for cohort effects in the context of ALDs.

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“…The transition matrix reflects the effect that the current state vector has on a predicted future state vector. The state space modeling framework has been expanded to have applications in mediation analysis (Gu, Preacher, & Ferrer, 2014), multilevel regression models, and multilevel confirmatory factor models (Gu, Preacher, Wu, & Yung, 2014).…”

Section: State-space Modelingmentioning

confidence: 99%

Viii. The Past, Present, and Future of Developmental Methodology

Little¹,

Wang²,

Gorrall³

2017

Monographs Society Res Child

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This chapter selectively reviews the evolution of quantitative practices in the field of developmental methodology. The chapter begins with an overview of the past in developmental methodology, discussing the implementation and dissemination of latent variable modeling and, in particular, longitudinal structural equation modeling. It then turns to the present state of developmental methodology, highlighting current methodological advances in the field. Additionally, this section summarizes ample quantitative resources, ranging from key quantitative methods journal articles to the various quantitative methods training programs and institutes. The chapter concludes with the future of developmental methodology and puts forth seven future innovations in the field. The innovations discussed span the topics of measurement, modeling, temporal design, and planned missing data designs. Lastly, the chapter closes with a brief overview of advanced modeling techniques such as continuous time models, state space models, and the application of Bayesian estimation in the field of developmental methodology.

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A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Cited by 10 publications

References 18 publications

OpenMx 2.0: Extended Structural Equation and Statistical Modeling

OpenMx 2.0: Extended Structural Equation and Statistical Modeling

A Dynamic Approach to Control for Cohort Differences in Maturation Speed Using Accelerated Longitudinal Designs

Viii. The Past, Present, and Future of Developmental Methodology

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