Su-Young Kim
Biographic Data
| ID | 8867124 |
|---|---|
| NAME | Su-Young Kim |
| GIVEN NAMES | Su-Young |
| FAMILY NAME | Kim |
| SIGNATURE | KIM S |
| AFFILIATIONS | Ewha Womans University |
| ORCID | 0000-0002-1195-6126 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Principles and Procedures for Generating Non-Normal Data Using Mixture Models
Evaluating the performance of statistical procedures under varying degrees of multivariate non-normality has become increasingly important in methodological research. While various data generation methods have been introduced for this purpose, they tend to generate non-normal data with limited forms based primarily on skewness. This restricts their ability to produce data that reflect the diverse characteristics of non-normality observed in empir…
Evaluating Statistical Power, Type I Error, and Sample Size Requirements for Representative Moderated Mediation Models
The moderated mediation models simultaneously account for both the causal process linking an independent variable to a dependent variable and the contextual variables that influence the strength and direction of this process. Despite their extensive use, relatively few studies have examined which testing methods are most appropriate or what sample sizes ensure valid inference. This study compares the power and Type I error rates of the index of m…
Research on the Evaluation and Optimization of Street Quality in Cultural Attractions Based on Spatial Data
Historic and cultural scenic spots are concentrated spaces that hold historic and cultural value for a city, and their streets form the foundation of their scenery. Therefore, the street quality of historic and cultural scenic spots plays an important role in promoting the cultural and economic development of a city. We evaluate the development potential of road quality in historic and cultural scenic spots by using spatial data and the analytic …
Sample Size Requirements for Simple and Complex Mediation Models
Mediation models have been widely used in many disciplines to better understand the underlying processes between independent and dependent variables. Despite their popularity and importance, the appropriate sample sizes for estimating those models are not well known. Although several approaches (such as Monte Carlo methods) exist, applied researchers tend to use insufficient sample sizes to estimate their models of interest, which might result in…
Performance of Second-Order Latent Growth Model Under Partial Longitudinal Measurement Invariance
Second-order latent growth models (SLGMs) have recently been highlighted over the traditional first-order latent growth model. Although SLGMs can show several intuitive strengths, the model has remained less understood due to the issue of scaling-related misspecification. As one source of model misspecification, scaling could influence the estimation of SLGM. Since the impact could differ depending on which scaling method is employed, selecting a…
A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome
Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a…
Single and Multiple Ability Estimation in the SEM Framework
Latent variable models with many categorical items and multiple latent constructs result in many dimensions of numerical integration, and the traditional frequentist estimation approach, such as maximum likelihood (ML), tends to fail due to model complexity. In such cases, Bayesian estimation with diffuse priors can be used as a viable alternative to ML estimation. The present study compares the performance of Bayesian estimation to ML estimation…
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Single and Multiple Ability Estimation in the SEM Framework
Latent variable models with many categorical items and multiple latent constructs result in many dimensions of numerical integration, and the traditional frequentist estimation approach, such as maximum likelihood (ML), tends to fail due to model complexity. In such cases, Bayesian estimation with diffuse priors can be used as a viable alternative to ML estimation. The present study compares the performance of Bayesian estimation to ML estimation…
A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome
Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a…
Performance of Second-Order Latent Growth Model Under Partial Longitudinal Measurement Invariance
Second-order latent growth models (SLGMs) have recently been highlighted over the traditional first-order latent growth model. Although SLGMs can show several intuitive strengths, the model has remained less understood due to the issue of scaling-related misspecification. As one source of model misspecification, scaling could influence the estimation of SLGM. Since the impact could differ depending on which scaling method is employed, selecting a…
Sample Size Requirements for Simple and Complex Mediation Models
Mediation models have been widely used in many disciplines to better understand the underlying processes between independent and dependent variables. Despite their popularity and importance, the appropriate sample sizes for estimating those models are not well known. Although several approaches (such as Monte Carlo methods) exist, applied researchers tend to use insufficient sample sizes to estimate their models of interest, which might result in…
Research on the Evaluation and Optimization of Street Quality in Cultural Attractions Based on Spatial Data
Historic and cultural scenic spots are concentrated spaces that hold historic and cultural value for a city, and their streets form the foundation of their scenery. Therefore, the street quality of historic and cultural scenic spots plays an important role in promoting the cultural and economic development of a city. We evaluate the development potential of road quality in historic and cultural scenic spots by using spatial data and the analytic …
Principles and Procedures for Generating Non-Normal Data Using Mixture Models
Evaluating the performance of statistical procedures under varying degrees of multivariate non-normality has become increasingly important in methodological research. While various data generation methods have been introduced for this purpose, they tend to generate non-normal data with limited forms based primarily on skewness. This restricts their ability to produce data that reflect the diverse characteristics of non-normality observed in empir…
Evaluating Statistical Power, Type I Error, and Sample Size Requirements for Representative Moderated Mediation Models
The moderated mediation models simultaneously account for both the causal process linking an independent variable to a dependent variable and the contextual variables that influence the strength and direction of this process. Despite their extensive use, relatively few studies have examined which testing methods are most appropriate or what sample sizes ensure valid inference. This study compares the power and Type I error rates of the index of m…
Statistical Methods and Bayesian Inference (4 works) · Computer Science (3 works) · Econometrics (3 works) · Mathematics (3 works) · Statistics (3 works) · Advanced Causal Inference Techniques (2 works) · Bayes estimator (2 works) · Bayesian inference (2 works) · Bayesian probability (2 works) · Covariate (2 works)