Zachary F Fisher
Biographic Data
| ID | 4461995 |
|---|---|
| NAME | Zachary F Fisher |
| GIVEN NAMES | Zachary F |
| FAMILY NAME | Fisher |
| SIGNATURE | FISHER Z F |
| AFFILIATIONS | Pennsylvania State University |
| ORCID | 0000-0003-2744-5141 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Dynamic Fit Index Cutoffs for Time Series Network Models
In this study, we extend the dynamic fit index (DFI) developed by McNeish and Wolf to the context of time series analysis. DFI is a simulation-based method for deriving fit index cutoff values tailored to the specific model and data characteristics. Through simulations, we show that DFI cutoffs for detecting an omitted path in time series network models tend to be closer to exact fit than the popular benchmark values developed by Hu and Bentler. …
Penalized Subgrouping of Heterogeneous Time Series
Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterog…
Prospective associations between stressors and alcohol use from early adolescence to young adulthood in Mexican-origin youth in the United States
Stressors experienced across multiple domains (e.g., family and peers) may contribute to alcohol use trajectories; however, little is known about the longitudinal links between stressors and alcohol use among Latinx youth. Guided by prior work on stressors and alcohol use, the present study used longitudinal data to examine whether Mexican-origin adolescents' ( N = 674; 50% female; 28% Mexico born; 72% U.S. born) experiences of family and peer st…
Daily reciprocal relationships between affect, physical activity, and sleep in middle and later life
BACKGROUND: The daily dynamics among affect, physical activity, and sleep are often explored by taking a unidirectional approach. Yet, obtaining a comprehensive understanding of the reciprocal dynamics among affect and health behaviors is crucial for promoting daily well-being. PURPOSE: This study examined the reciprocal associations among affect, physical activity, and sleep in daily life in a U.S. national sample of mid- and later-life adults. …
Optimal Instrument Selection Using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators
Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, non-iterative estimator for latent variable models. Associated with this estimator are equation specific tests of model misspecification. One issue with equation specific tests is that they lack specificity, in that they indicate that some instruments are problematic without revealing which specific ones. Instruments that are po…
Contamination bias in the estimation of child maltreatment causal effects on adolescent internalizing and externalizing behavior problems
BACKGROUND: When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual-measurement strategy of detecting and controlling contamination impacts causal effect size estimates of chi…
Structured Estimation of Heterogeneous Time Series
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. introduced the multi-VAR approach for simultaneously estimating multiple-subject multivariate time series characterized by common and individualizing features using penalized estimation. This approach differs from many popular modeling approaches for multiple-subject time series in that qualitati…
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…
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…
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
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, …
Fifty years of structural equation modeling: A history of generalization, unification, and diffusion
A Limited Information Estimator for Dynamic Factor Models
Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor model…
Fifty years of structural equation modeling: A history of generalization, unification, and diffusion
Daily reciprocal relationships between affect, physical activity, and sleep in middle and later life
BACKGROUND: The daily dynamics among affect, physical activity, and sleep are often explored by taking a unidirectional approach. Yet, obtaining a comprehensive understanding of the reciprocal dynamics among affect and health behaviors is crucial for promoting daily well-being. PURPOSE: This study examined the reciprocal associations among affect, physical activity, and sleep in daily life in a U.S. national sample of mid- and later-life adults. …
A Limited Information Estimator for Dynamic Factor Models
Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor model…
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, …
Fifty years of structural equation modeling: A history of generalization, unification, and diffusion
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
Optimal Instrument Selection Using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators
Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, non-iterative estimator for latent variable models. Associated with this estimator are equation specific tests of model misspecification. One issue with equation specific tests is that they lack specificity, in that they indicate that some instruments are problematic without revealing which specific ones. Instruments that are po…
Contamination bias in the estimation of child maltreatment causal effects on adolescent internalizing and externalizing behavior problems
BACKGROUND: When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual-measurement strategy of detecting and controlling contamination impacts causal effect size estimates of chi…
Structured Estimation of Heterogeneous Time Series
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. introduced the multi-VAR approach for simultaneously estimating multiple-subject multivariate time series characterized by common and individualizing features using penalized estimation. This approach differs from many popular modeling approaches for multiple-subject time series in that qualitati…
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…
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…
Prospective associations between stressors and alcohol use from early adolescence to young adulthood in Mexican-origin youth in the United States
Stressors experienced across multiple domains (e.g., family and peers) may contribute to alcohol use trajectories; however, little is known about the longitudinal links between stressors and alcohol use among Latinx youth. Guided by prior work on stressors and alcohol use, the present study used longitudinal data to examine whether Mexican-origin adolescents' ( N = 674; 50% female; 28% Mexico born; 72% U.S. born) experiences of family and peer st…
Daily reciprocal relationships between affect, physical activity, and sleep in middle and later life
BACKGROUND: The daily dynamics among affect, physical activity, and sleep are often explored by taking a unidirectional approach. Yet, obtaining a comprehensive understanding of the reciprocal dynamics among affect and health behaviors is crucial for promoting daily well-being. PURPOSE: This study examined the reciprocal associations among affect, physical activity, and sleep in daily life in a U.S. national sample of mid- and later-life adults. …
Dynamic Fit Index Cutoffs for Time Series Network Models
In this study, we extend the dynamic fit index (DFI) developed by McNeish and Wolf to the context of time series analysis. DFI is a simulation-based method for deriving fit index cutoff values tailored to the specific model and data characteristics. Through simulations, we show that DFI cutoffs for detecting an omitted path in time series network models tend to be closer to exact fit than the popular benchmark values developed by Hu and Bentler. …
Penalized Subgrouping of Heterogeneous Time Series
Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterog…
Mental Health Research Topics (9 works) · Computer Science (8 works) · Econometrics (7 works) · Mathematics (7 works) · Psychology (6 works) · Statistics (6 works) · Artificial Intelligence (4 works) · Machine learning (4 works) · Series (stratigraphy) (4 works) · Complex Systems and Time Series Analysis (3 works)