Heungsun Hwang
Biographic Data
| ID | 6466261 |
|---|---|
| NAME | Heungsun Hwang |
| GIVEN NAMES | Heungsun |
| FAMILY NAME | Hwang |
| SIGNATURE | HWANG H |
| AFFILIATIONS | McGill University |
| ORCID | 0000-0002-5057-7479 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 15 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2002 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Power Analysis in Generalized Structured Component Analysis
Generalized structured component analysis (GSCA) is a comprehensive method for component-based structural equation modeling that represents constructs as weighted composites of indicators. Despite its flexibility, GSCA has lacked a formal procedure for power analysis. To address this gap, we develop a Monte Carlo-based power analysis procedure for GSCA and implement it in GSCA Pro, free and user-friendly GSCA software. This tutorial introduces th…
Regularized Structural Equation Modeling with Both Factors and Components
Comparison of Component-Based Structural Equation Modeling Methods in Testing Component Interaction Effects
Evaluation of Generative Adversarial Imputation Nets’ Performance in Handling Missing Data in Structural Equation Modeling
GSCA Pro—Free Stand-Alone Software for Structural Equation Modeling
GSCA Pro is free, user-friendly software for generalized structured component analysis structural equation modeling (GSCA-SEM), which implements three statistical methods for estimating models with factors only, models with components only, and models with both factors and components.This tutorial aims to provide step-by-step illustrations of how to use the software to estimate such various models after briefly discussing model specification, est…
Deep Learning Generalized Structured Component Analysis
Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed variables for a component are nonlinearly related, often reducing the component’s predictive power. To a…
Structured Factor Analysis
Jöreskog’s covariance-based approach (JCA) has been considered a standard method for structural equation modeling. However, JCA is prone to the occurrence of improper solutions and cannot make probabilistic inferences about the true factor scores. To address the enduring issues of JCA, we propose a data matrix-based alternative, termed structured factor analysis (SFA). Given a data matrix of indicators, SFA begins by estimating both measurement m…
A Prediction-Oriented Specification Search Algorithm for Generalized Structured Component Analysis
Generalized structured component analysis (GSCA) is used for specifying and testing the relationships between observed variables and components. GSCA can perform model selection by comparing theoretically established models. In practice, however, theories may not always completely and unambiguously specify the relationships between variables in the model. In such situations, a specification search strategy allows for exploring potential relations…
Cutoff criteria for overall model fit indexes in generalized structured component analysis
Generalized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in fa…
Bayesian Extended Redundancy Analysis
Extended redundancy analysis (ERA) combines linear regression with dimension reduction to explore the directional relationships between multiple sets of predictors and outcome variables in a parsimonious manner. It aims to extract a component from each set of predictors in such a way that it accounts for the maximum variance of outcome variables. In this article, we extend ERA into the Bayesian framework, called Bayesian ERA (BERA). The advantage…
Out-of-bag Prediction Error
Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized …
Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis
Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way …
Simultaneous Two-Way Clustering of Multiple Correspondence Analysis
A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which k-means is applied twice to partition the object scores of respondents and the weights of variable categories. In this way, j…
Fuzzy Clusterwise Growth Curve Models via Generalized Estimating Equations
The growth curve model has been a useful tool for the analysis of repeated measures data. However, it is designed for an aggregate-sample analysis based on the assumption that the entire sample of respondents are from a single homogenous population. Thus, this method may not be suitable when heterogeneous subgroups exist in the population with qualitatively distinct patterns of trajectories. In this paper, the growth curve model is generalized to…
Generalized Constrained Canonical Correlation Analysis
A method for generalized constrained canonical correlation analysis (GCCANO) is proposed that incorporates external information on both rows and columns of data matrices. In this method each set of variables is first decomposed into the sum of several submatrices according to the external information, and then canonical correlation analysis is applied to pairs of derived submatrices, one from each set, to explore linear relationships between them…
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Generalized Constrained Canonical Correlation Analysis
A method for generalized constrained canonical correlation analysis (GCCANO) is proposed that incorporates external information on both rows and columns of data matrices. In this method each set of variables is first decomposed into the sum of several submatrices according to the external information, and then canonical correlation analysis is applied to pairs of derived submatrices, one from each set, to explore linear relationships between them…
Fuzzy Clusterwise Growth Curve Models via Generalized Estimating Equations
The growth curve model has been a useful tool for the analysis of repeated measures data. However, it is designed for an aggregate-sample analysis based on the assumption that the entire sample of respondents are from a single homogenous population. Thus, this method may not be suitable when heterogeneous subgroups exist in the population with qualitatively distinct patterns of trajectories. In this paper, the growth curve model is generalized to…
Simultaneous Two-Way Clustering of Multiple Correspondence Analysis
A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which k-means is applied twice to partition the object scores of respondents and the weights of variable categories. In this way, j…
Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis
Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way …
Out-of-bag Prediction Error
Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized …
Cutoff criteria for overall model fit indexes in generalized structured component analysis
Generalized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in fa…
Bayesian Extended Redundancy Analysis
Extended redundancy analysis (ERA) combines linear regression with dimension reduction to explore the directional relationships between multiple sets of predictors and outcome variables in a parsimonious manner. It aims to extract a component from each set of predictors in such a way that it accounts for the maximum variance of outcome variables. In this article, we extend ERA into the Bayesian framework, called Bayesian ERA (BERA). The advantage…
A Prediction-Oriented Specification Search Algorithm for Generalized Structured Component Analysis
Generalized structured component analysis (GSCA) is used for specifying and testing the relationships between observed variables and components. GSCA can perform model selection by comparing theoretically established models. In practice, however, theories may not always completely and unambiguously specify the relationships between variables in the model. In such situations, a specification search strategy allows for exploring potential relations…
Structured Factor Analysis
Jöreskog’s covariance-based approach (JCA) has been considered a standard method for structural equation modeling. However, JCA is prone to the occurrence of improper solutions and cannot make probabilistic inferences about the true factor scores. To address the enduring issues of JCA, we propose a data matrix-based alternative, termed structured factor analysis (SFA). Given a data matrix of indicators, SFA begins by estimating both measurement m…
GSCA Pro—Free Stand-Alone Software for Structural Equation Modeling
GSCA Pro is free, user-friendly software for generalized structured component analysis structural equation modeling (GSCA-SEM), which implements three statistical methods for estimating models with factors only, models with components only, and models with both factors and components.This tutorial aims to provide step-by-step illustrations of how to use the software to estimate such various models after briefly discussing model specification, est…
Deep Learning Generalized Structured Component Analysis
Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed variables for a component are nonlinearly related, often reducing the component’s predictive power. To a…
Regularized Structural Equation Modeling with Both Factors and Components
Comparison of Component-Based Structural Equation Modeling Methods in Testing Component Interaction Effects
Evaluation of Generative Adversarial Imputation Nets’ Performance in Handling Missing Data in Structural Equation Modeling
Power Analysis in Generalized Structured Component Analysis
Generalized structured component analysis (GSCA) is a comprehensive method for component-based structural equation modeling that represents constructs as weighted composites of indicators. Despite its flexibility, GSCA has lacked a formal procedure for power analysis. To address this gap, we develop a Monte Carlo-based power analysis procedure for GSCA and implement it in GSCA Pro, free and user-friendly GSCA software. This tutorial introduces th…
Computer Science (13 works) · Mathematics (11 works) · Statistics (8 works) · Machine learning (7 works) · Structural equation modeling (7 works) · Artificial Intelligence (6 works) · Data mining (6 works) · Econometrics (5 works) · Sensory Analysis and Statistical Methods (5 works) · Algorithm (4 works)