Sy‐miin Chow
Datos Biográficos
| ID | 1463392 |
|---|---|
| NOMBRE | Sy‐miin Chow |
| NOMBRES | Sy‐miin |
| APELLIDO | Chow |
| FIRMA | CHOW S M |
| AFILIACIONES | Pennsylvania State University |
| ORCID | 0000-0003-1938-027X |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 40 |
| TOTAL DE CITAS | 8 |
| TOTAL COMO AUTOR | 40 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2004 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 2 |
Simultaneous detection of gradual and abrupt structural changes in Bayesian longitudinal modelling using entropy and model fit measures
Although individuals may exhibit both gradual and abrupt changes in their dynamic properties as shaped by both slowly accumulating influences and acute events, existing statistical frameworks offer limited capacity for the simultaneous detection and representation of these distinct change patterns. We propose a Bayesian regime‐switching (RS) modelling framework and an entropy measure adapted from the frequentist framework to facilitate simultaneo…
When Deep Learning Outperforms Conventional Models in Long-Term Forecasting
Forecasting long-term health trajectories from intensive longitudinal data (ILD) is essential for timely interventions. Growth Curve Models (GCMs) provide a conventional multilevel framework that o
Game, Set, and Match
Behavioral interventions targeting physical activity and/or healthy eating are often used for weight regulation, and adaptive intervention designs that personalize the dosage based on individuals' responses to the intervention components may optimize impact. However, little research has examined how best to design/optimize non-pharmacological adaptive weight regulation trials, and decisions regarding selection/use of control groups in these trial…
Examining the Reciprocal Relationship Between Social Media Use and Perceived Social Support Among Adolescents
Adolescents commonly use smartphone social media apps, which can affect their perceived social support (PSS). However, study results on social media’s effect on PSS differ, because they employ a self-reported time in social media use and concentrate only on between-person differences. They often neglect the social-anxiety level, which might be important. Our study investigated whether the within-person day-to-day changes in the time spent in two …
Multiple Imputation with Factor Scores
full-information maximum likelihood) under different conditions. Across conditions, we found MI-based methods overall outperformed the LD; the MI-FS approach yielded lower root mean square errors (RMSEs) and higher coverage rates for auto-regression (AR) parameters compared to MI-MV; and the PMI-MV and MI-MV approaches yielded higher coverage rates for most parameters except AR parameters compared to MI-FS. These approaches were also compared usi…
Measurement Model Misspecification in Dynamic Structural Equation Models
Dynamic Structural Equation Models (DSEMs) integrate multilevel modeling, time series analysis, and structural equation modeling within a Bayesian estimation framework, offering a versatile tool for analyzing intensive longitudinal data (ILD). However, the impact of measurement structure misspecification in DSEMs, especially under varying reliability conditions and model complexities, remains underexplored. Our Monte Carlo simulation revealed tha…
Couple synchrony in physical activity
BACKGROUND: Physical activity (PA) is crucial for managing osteoarthritis (OA) symptoms, but maintaining an active lifestyle remains challenging. Given the influence of spouses on each other's health behaviors, PA synchrony-the concurrent PA engagement between partners-may enhance PA levels and emotional well-being in individuals with knee osteoarthritis (IKOAs). PURPOSE: We investigate whether daily PA synchrony in couples is associated with PA …
From Behavioral Genetics to Idiographic Science
This special issue is a collection of papers inspired by Dr. Molenaar's work and innovations - a tribute to his passion for advancing science and his ability to ignite a spark of creativity and innovation in multiple generations of scientists. Following Dr. Molenaar's creative breadth, the papers address a wide variety of topics - sharing of new methodological developments, ideas, and findings in idiographic science, study of intraindividual vari…
Evaluating Discrete Time Methods for Subgrouping Continuous Processes
Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework—such as the vector autoregression (VAR)—have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-b…
Subgrouping with Chain Graphical VAR Models
Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique …
Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates
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…
A Growth of Hierarchical Autoregression Model for Capturing Individual Differences in Changes of Dynamic Characteristics of Psychological Processes
Several methodological innovations have been advanced in the past decades that combine growth curve models (GCMs) with models of autoregressive (AR) processes. However, most of these approaches do not effectively capitalize on known (e.g., study design-related) information to structure the growth curves into meaningful between- and within-phase changes, while simultaneously accommodating interindividual differences in these intraindividual change…
Unsupervised Model Construction in Continuous-Time
Many of the advancements reconciling individual- and group-level results have occurred in the context of a discrete-time modeling framework. Discrete-time models are intuitive and offer relatively simple interpretations for the resulting dynamic structures; however, they do not possess the flexibility of models fitted in the continuous-time framework. We introduce ct-gimme, a continuous-time extension of the group iterative multiple model estimat…
Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach
Recent advances in technology contribute to a fast-growing number of studies utilizing intensive longitudinal data, and call for more flexible methods to address the demands that come with them. One issue that arises from collecting longitudinal data from multiple units in time is nested data, where the variability observed in such data is a mixture of within-unit changes and between-unit differences. This article aims to provide a model-fitting …
On Subgrouping Continuous Processes in Discrete Time
Recent years have witnessed a boom in the application of dynamic network models such as vector autoregression (VAR) models and their various flavors to the study of human behavior and psychology. V
Automated measurement of infant and mother Duchenne facial expressions in the Face‐to‐Face/Still‐Face
Although still‐face effects are well‐studied, little is known about the degree to which the Face‐to‐Face/Still‐Face (FFSF) is associated with the production of intense affective displays. Duchenne smiling expresses more intense positive affect than non‐Duchenne smiling, while Duchenne cry‐faces express more intense negative affect than non‐Duchenne cry‐faces. Forty 4‐month‐old infants and their mothers completed the FFSF, and key affect‐indexing …
Fitting Multilevel Vector Autoregressive Models in Stan, Jags, and Mplus
The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though …
A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters
Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, …
Dynamics of learning
A Person- and Time-Varying Vector Autoregressive Model to Capture Interactive Infant-Mother Head Movement Dynamics
Head movement is an important but often overlooked component of emotion and social interaction. Examination of regularity and differences in head movements of infant-mother dyads over time and across dyads can shed light on whether and how mothers and infants alter their dynamics over the course of an interaction to adapt to each others. One way to study these emergent differences in dynamics is to allow parameters that govern the patterns of int…
Child Effects on Parental Negativity
This study examined two possible mechanisms, evocative gene–environment correlation and prenatal factors, in accounting for child effects on parental negativity. Participants included 561 children adopted at birth, and their adoptive parents and birth parents within a prospective longitudinal adoption study. Findings indicated child effects on parental negativity, such that toddlers’ negative reactivity at 18 months was positively associated with…
A Diagnostic Procedure for Detecting Outliers in Linear State–Space Models
Outliers can be more problematic in longitudinal data than in independent observations due to the correlated nature of such data. It is common practice to discard outliers as they are typically regarded as a nuisance or an aberration in the data. However, outliers can also convey meaningful information concerning potential model misspecification, and ways to modify and improve the model. Moreover, outliers that occur among the latent variables (i…
Exploring Sleep Dynamic of Mother-Infant Dyads Using a Regime-Switching Vector Autoregressive Model
"Exploring Sleep Dynamic of Mother-Infant Dyads Using a Regime-Switching Vector Autoregressive Model." Multivariate Behavioral Research, 55(1), pp. 150–151
Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models
A dynamical system is a system of variables that show some regularity in how they evolve over time. Change concepts described in most dynamical systems models are by no means novel to social and behavioral scientists, but most applications of dynamic modeling techniques in these disciplines are grounded on a narrow subset of-typically linear-theories of change. I provide practical guidelines, recommendations, and software code for exploring and f…
Representing Sudden Shifts in Intensive Dyadic Interaction Data Using Differential Equation Models with Regime Switching
A growing number of social scientists have turned to differential equations as a tool for capturing the dynamic interdependence among a system of variables. Current tools for fitting differential equation models do not provide a straightforward mechanism for diagnosing evidence for qualitative shifts in dynamics, nor do they provide ways of identifying the timing and possible determinants of such shifts. In this paper, we discuss regime-switching…
Life Course Socioeconomic Status, Daily Stressors, and Daily Well-Being
Childhood may be a sensitive period that has salient implications for day-to-day well-being later in life
Modeling Intraindividual Dynamics Using Stochastic Differential Equations
The results highlight the utility of SDE models for studying affect dynamics and informing theoretical predictions about how intraindividual dynamics change over the life course
General Slowing or Decreased Inhibition? Mathematical Models of Age Differences in Cognitive Functioning
Researchers have attempted to explain age-related decrements in cognitive performance in terms of reduced processing speed or decreased ability to inhibit irrelevant thoughts. We present these ideas in the context of a dynamic model derived from extensions of the classical predator-prey equation. Reduced processing speed among older adults is represented by use of delays in the dynamic model, whereas the interference imposed by distractors is cap…
Emotion as a Thermostat
The authors present in this study a damped oscillator model that provides a direct mathematical basis for testing the notion of emotion as a self-regulatory thermostat. Parameters from this model reflect individual differences in emotional lability and the ability to regulate emotion. The authors discuss concepts such as intensity, rate of change, and acceleration in the context of emotion, and they illustrate the strengths of this approach in co…
An Unscented Kalman Filter Approach to the Estimation of Nonlinear Dynamical Systems Models
In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways: (1) as a building block for approximating the log-likelihood of nonlinear state-space models and (2)…
Using Innovative Outliers to Detect Discrete Shifts in Dynamics in Group-Based State-Space Models
Outliers are typically regarded as data anomalies that should be discarded. However, dynamic or "innovative" outliers can be appropriately utilized to capture unusual but substantively meaningful shifts in a system's dynamics. We extend De Jong and Penzer's 1998 approach for representing outliers in single-subject state-space models to a group-based framework. The group-based model enables model predictions concerning the group as a whole while i…
Automated Measurement of Facial Expression in Infant–Mother Interaction
Automated facial measurement using computer vision has the potential to objectively document continuous changes in behavior. To examine emotional expression and communication, we used automated measurements to quantify smile strength, eye constriction, and mouth opening in two 6‐month‐old infant‐mother dyads who each engaged in a face‐to‐face interaction. Automated measurements showed high associations with anatomically based manual coding (concu…
Dynamic infant–parent affect coupling during the face-to-face/still-face
We examined dynamic infant-parent affect coupling using the Face-to-Face/Still-Face (FFSF). The sample included 20 infants whose older siblings had been diagnosed with Autism Spectrum Disorders (ASD-sibs) and 18 infants with comparison siblings (COMP-sibs). A series of mixed effects bivariate autoregressive models was used to represent the self-regulation and interactive dynamics of infants and parents during the FFSF. Significant bidirectional a…
Dynamic Factor Analysis Models With Time-Varying Parameters
Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor model with vector autoregressive relations and time-varying cross-regression parameters at the factor leve…
Regime-Switching Bivariate Dual Change Score Model
Mixture structural equation model with regime switching (MSEM-RS) provides one possible way of representing over-time heterogeneities in dynamic processes by allowing a system to manifest qualitatively or quantitatively distinct change processes conditional on the latent "regime" the system is in at a particular time point. Unlike standard mixture structural equation models such as growth mixture models, MSEM-RS allows individuals to transition b…
Longitudinal Multi-Trait-State-Method Model Using Ordinal Data
Multi-trait multi-method (MTMM) models provide a way to assess convergent and discriminant validity when multiple traits are measured by multiple methods. In recent years, longitudinal extensions of MTMM models have been proposed in the structural equation modeling framework to evaluate whether and how the trait as well as method factors change over time. We propose a novel longitudinal ordinal MTMM model that can be used to effectively distingui…
Bayesian Factor Analysis as a Variable-Selection Problem
Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the prese…
A Comparison of Two-Stage Approaches for Fitting Nonlinear Ordinary Differential Equation Models with Mixed Effects
Several approaches exist for estimating the derivatives of observed data for model exploration purposes, including functional data analysis (FDA; Ramsay & Silverman, 2005 Ramsay, J. O., & Silverman, B. W. (2005). Functional data analysis (2nd ed.). New York, NY: Springer-Verlag.[Crossref] , [Google Scholar]), generalized local linear approximation (GLLA; Boker, Deboeck, Edler, & Peel, 2010 Boker, S. M., Deboeck, P. R., Edler, C., & Peel, P. K. (2…
(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions
The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may b…
Dynamical systems modeling of early childhood self-regulation
Self-regulation can be conceptualized in terms of dynamic tension between highly probable reactions (prepotent responses) and use of strategies that can modulate those reactions (executive processes). This study investigated the value of a dynamical systems approach to the study of early childhood self-regulation. Specifically, ordinary differential equations (ODEs) were used to model the interactive influences of 115 36-month-olds' executive pro…
Modeling Intraindividual Dynamics Using Stochastic Differential Equations
The results highlight the utility of SDE models for studying affect dynamics and informing theoretical predictions about how intraindividual dynamics change over the life course
Representing Sudden Shifts in Intensive Dyadic Interaction Data Using Differential Equation Models with Regime Switching
A growing number of social scientists have turned to differential equations as a tool for capturing the dynamic interdependence among a system of variables. Current tools for fitting differential equation models do not provide a straightforward mechanism for diagnosing evidence for qualitative shifts in dynamics, nor do they provide ways of identifying the timing and possible determinants of such shifts. In this paper, we discuss regime-switching…
Life Course Socioeconomic Status, Daily Stressors, and Daily Well-Being
Childhood may be a sensitive period that has salient implications for day-to-day well-being later in life
Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models
A dynamical system is a system of variables that show some regularity in how they evolve over time. Change concepts described in most dynamical systems models are by no means novel to social and behavioral scientists, but most applications of dynamic modeling techniques in these disciplines are grounded on a narrow subset of-typically linear-theories of change. I provide practical guidelines, recommendations, and software code for exploring and f…
Child Effects on Parental Negativity
This study examined two possible mechanisms, evocative gene–environment correlation and prenatal factors, in accounting for child effects on parental negativity. Participants included 561 children adopted at birth, and their adoptive parents and birth parents within a prospective longitudinal adoption study. Findings indicated child effects on parental negativity, such that toddlers’ negative reactivity at 18 months was positively associated with…
A Diagnostic Procedure for Detecting Outliers in Linear State–Space Models
Outliers can be more problematic in longitudinal data than in independent observations due to the correlated nature of such data. It is common practice to discard outliers as they are typically regarded as a nuisance or an aberration in the data. However, outliers can also convey meaningful information concerning potential model misspecification, and ways to modify and improve the model. Moreover, outliers that occur among the latent variables (i…
Exploring Sleep Dynamic of Mother-Infant Dyads Using a Regime-Switching Vector Autoregressive Model
"Exploring Sleep Dynamic of Mother-Infant Dyads Using a Regime-Switching Vector Autoregressive Model." Multivariate Behavioral Research, 55(1), pp. 150–151
Dynamics of learning
A Person- and Time-Varying Vector Autoregressive Model to Capture Interactive Infant-Mother Head Movement Dynamics
Head movement is an important but often overlooked component of emotion and social interaction. Examination of regularity and differences in head movements of infant-mother dyads over time and across dyads can shed light on whether and how mothers and infants alter their dynamics over the course of an interaction to adapt to each others. One way to study these emergent differences in dynamics is to allow parameters that govern the patterns of int…
Fitting Multilevel Vector Autoregressive Models in Stan, Jags, and Mplus
The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though …
A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters
Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, …
Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach
Recent advances in technology contribute to a fast-growing number of studies utilizing intensive longitudinal data, and call for more flexible methods to address the demands that come with them. One issue that arises from collecting longitudinal data from multiple units in time is nested data, where the variability observed in such data is a mixture of within-unit changes and between-unit differences. This article aims to provide a model-fitting …
Computer Science (30 obras) · Mathematics (23 obras) · Econometrics (19 obras) · Psychology (19 obras) · Mental Health Research Topics (17 obras) · Statistics (17 obras) · Artificial Intelligence (14 obras) · Machine learning (14 obras) · Artificial Intelligence (10 obras) · Autoregressive model (10 obras)