Soo‐yong Lee
Biographic Data
| ID | 4462006 |
|---|---|
| NAME | Soo‐yong Lee |
| GIVEN NAMES | Soo‐yong |
| FAMILY NAME | Lee |
| SIGNATURE | LEE S Y |
| AFFILIATIONS | University of Wisconsin–Madison |
| ORCID | 0000-0002-7964-4508 |
| VERIFIED | Yes |
| TOTAL WORKS | 12 |
| TOTAL CITATIONS | 10 |
| AUTHOR COUNT | 12 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
A Preliminary Test of the Comprehensive Model of Post‐Dissolution Distress: Predicting Initial and Over Time Changes in Distress Following Romantic Dissolution
This study provided a preliminary test of the Comprehensive Model of Post‐Dissolution Distress with longitudinal data. The model includes distal (static; e.g., breakup controllability, relational anxiety) and proximal (time‐varying; e.g., desiring reconciliation, coping, quality of alternatives) factors in predicting both initial distress and change in distress over time. Adults who had experienced a breakup in the past 30 days completed an initi…
Tutorial for Bayesian Multilevel Structural Equation Modeling Using Blimp
This tutorial paper introduces the implementation of Multilevel Structural Equation Modeling (MSEM) using Blimp, a free, user-friendly, and flexible software for Bayesian estimation. Aimed at readers with some familiarity with MSEM and Bayesian methods, the tutorial walks through the specifications of a basic multilevel model in Blimp, progressively incorporating latent variables, random slopes, contextual effects, and moderated mediation effects…
Development of a Method for Handling Doubly-Censored Data in a Latent Growth Curve Modeling Framework
This study addresses the challenge of doubly-censoring effects in longitudinal data structures, particularly within latent growth curve models (LGCMs). Censoring can severely bias estimates and inferences, distorting the relationships between growth factors and covariates. To combat this issue, this study introduces the Generalized Tobit estimator (GBIT), an advancement of the conventional Tobit model, designed to handle mixed censoring effects i…
Mediation Analysis with an Event-History Mediator: An Empirical Data Illustration and Simulation Study
Investigating Latent Interaction Effects in Multiple-Group Analysis in the Structural Equation Modeling Framework
This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits. Our simulation study results demonstrated the superiority of the Latent Moderated Structural Equations …
Model Estimation Approaches for Fully-Latent Principal Stratification with Small Samples
Discrete-Time Survival Analysis Incorporating Time Structure in Developmental Research
Application of Associative Discrete-Time Survival Analysis Using Latent Transition Specification
This article demonstrates an associative latent transition-based model for analyzing the association between two discrete-time survival analysis (DTSA) models. DTSA allows social and behavioral researchers to investigate the qualitative change in event occurrence. Typically, DTSA is applied to a single survival process, where the hazard of an event occurrence is estimated across time. However, when researchers have hypotheses concerning the relat…
Parallel Process Latent Growth Modeling with Multivariate Confounders/Suppressors
A parallel process growth model is often limited to the developments of two main trends due to model complexity. Two major parallel processes are only modeled to examine how the changes in two longitudinal processes might be related without considering other growth models possibly related to the two main processes. However, the exclusion of a third LGM risks introducing bias into key associations while capturing the relations between only two gro…
Comparison of Three Approaches to Class Enumeration in Growth Mixture Modeling when Time Structures are Variant Across Latent Classes
In conventional approaches to Growth Mixture Modeling (GMM), a trajectory is first estimated using latent growth curve modeling that serves as a baseline trajectory for the GMM. In this approach, time structures are held invariant across latent classes when identifying the number of latent classes. However, this popular way of conducting GMM could undermine a proper estimation, especially under the condition where a distinct trajectory exists for…
DIF Detection With Zero-Inflation Under the Factor Mixture Modeling Framework
Response data containing an excessive number of zeros are referred to as zero-inflated data. When differential item functioning (DIF) detection is of interest, zero-inflation can attenuate DIF effects in the total sample and lead to underdetection of DIF items. The current study presents a DIF detection procedure for response data with excess zeros due to the existence of unobserved heterogeneous subgroups. The suggested procedure utilizes the fa…
Acculturation trajectories differ by youth age at arrival and time in residency among Latino immigrant families in a US emerging immigrant context
Acculturation trajectories differ by youth age at arrival and time in residency among Latino immigrant families in a US emerging immigrant context
Comparison of Three Approaches to Class Enumeration in Growth Mixture Modeling when Time Structures are Variant Across Latent Classes
In conventional approaches to Growth Mixture Modeling (GMM), a trajectory is first estimated using latent growth curve modeling that serves as a baseline trajectory for the GMM. In this approach, time structures are held invariant across latent classes when identifying the number of latent classes. However, this popular way of conducting GMM could undermine a proper estimation, especially under the condition where a distinct trajectory exists for…
DIF Detection With Zero-Inflation Under the Factor Mixture Modeling Framework
Response data containing an excessive number of zeros are referred to as zero-inflated data. When differential item functioning (DIF) detection is of interest, zero-inflation can attenuate DIF effects in the total sample and lead to underdetection of DIF items. The current study presents a DIF detection procedure for response data with excess zeros due to the existence of unobserved heterogeneous subgroups. The suggested procedure utilizes the fa…
Application of Associative Discrete-Time Survival Analysis Using Latent Transition Specification
This article demonstrates an associative latent transition-based model for analyzing the association between two discrete-time survival analysis (DTSA) models. DTSA allows social and behavioral researchers to investigate the qualitative change in event occurrence. Typically, DTSA is applied to a single survival process, where the hazard of an event occurrence is estimated across time. However, when researchers have hypotheses concerning the relat…
Parallel Process Latent Growth Modeling with Multivariate Confounders/Suppressors
A parallel process growth model is often limited to the developments of two main trends due to model complexity. Two major parallel processes are only modeled to examine how the changes in two longitudinal processes might be related without considering other growth models possibly related to the two main processes. However, the exclusion of a third LGM risks introducing bias into key associations while capturing the relations between only two gro…
Investigating Latent Interaction Effects in Multiple-Group Analysis in the Structural Equation Modeling Framework
This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits. Our simulation study results demonstrated the superiority of the Latent Moderated Structural Equations …
Model Estimation Approaches for Fully-Latent Principal Stratification with Small Samples
Discrete-Time Survival Analysis Incorporating Time Structure in Developmental Research
Development of a Method for Handling Doubly-Censored Data in a Latent Growth Curve Modeling Framework
This study addresses the challenge of doubly-censoring effects in longitudinal data structures, particularly within latent growth curve models (LGCMs). Censoring can severely bias estimates and inferences, distorting the relationships between growth factors and covariates. To combat this issue, this study introduces the Generalized Tobit estimator (GBIT), an advancement of the conventional Tobit model, designed to handle mixed censoring effects i…
Mediation Analysis with an Event-History Mediator: An Empirical Data Illustration and Simulation Study
A Preliminary Test of the Comprehensive Model of Post‐Dissolution Distress: Predicting Initial and Over Time Changes in Distress Following Romantic Dissolution
This study provided a preliminary test of the Comprehensive Model of Post‐Dissolution Distress with longitudinal data. The model includes distal (static; e.g., breakup controllability, relational anxiety) and proximal (time‐varying; e.g., desiring reconciliation, coping, quality of alternatives) factors in predicting both initial distress and change in distress over time. Adults who had experienced a breakup in the past 30 days completed an initi…
Tutorial for Bayesian Multilevel Structural Equation Modeling Using Blimp
This tutorial paper introduces the implementation of Multilevel Structural Equation Modeling (MSEM) using Blimp, a free, user-friendly, and flexible software for Bayesian estimation. Aimed at readers with some familiarity with MSEM and Bayesian methods, the tutorial walks through the specifications of a basic multilevel model in Blimp, progressively incorporating latent variables, random slopes, contextual effects, and moderated mediation effects…
Computer Science (9 works) · Mathematics (7 works) · Econometrics (6 works) · Statistics (6 works) · Advanced Causal Inference Techniques (5 works) · Psychometric Methodologies and Testing (5 works) · Latent growth modeling (4 works) · Psychology (4 works) · Statistical Methods and Bayesian Inference (4 works) · Advanced Statistical Modeling Techniques (3 works)