Myeongsun Yoon
Biographic Data
| ID | 6605680 |
|---|---|
| NAME | Myeongsun Yoon |
| GIVEN NAMES | Myeongsun |
| FAMILY NAME | Yoon |
| SIGNATURE | YOON M |
| AFFILIATIONS | Texas A&M University |
| ORCID | 0000-0001-7876-4826 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Investigating science identity classifications of rural high school students
The construct of science identity has been gaining attention across various domains in the educational arena. To date, however, no studies have used a person-centered quantitative approach within science identity research that is based upon traditional identity theory. Hence, there is an absence of a theoretical underpinning of the number of science identity classes that might be derived from using person-centered techniques or descriptive dialog…
Model-Selection-Based Approaches to Identifying the Optimal Number of Factors in Multilevel Exploratory Factor Analysis
This study examined the accuracy of commonly used model fit indexes in identifying number of factors in multilevel exploratory factor analysis using Monte Carlo simulations. Multilevel data were generated according to different scenarios of factor structures: cluster numbers, cluster sizes, and intraclass correlation coefficient (ICC) conditions. The results showed that when using the model-based approach, most of the commonly used fit indexes co…
Testing Factorial Invariance With Unbalanced Samples
In testing the factorial invariance of a measure across groups, the groups are often of different sizes. Large imbalances in group size might affect the results of factorial invariance studies and lead to incorrect conclusions of invariance because the fit function in multiple-group factor analysis includes a weighting by group sample size. The implication is that violations of invariance might not be detected if the sample sizes of the 2 groups …
Exploring the various interpretations of “test bias”
Test bias is a hotly debated topic in society, especially as it relates to diverse groups of examinees who often score low on standardized tests. However, the phrase "test bias" has a multitude of interpretations that many people are not aware of. In this article, we explain five different meanings of "test bias" and summarize the empirical and theoretical evidence related to each interpretation. The five meanings are as follows: (a) mean group d…
Testing Measurement Invariance Using MIMIC
Multiple-indicators multiple-causes (MIMIC) modeling is often used to test a latent group mean difference while assuming the equivalence of factor loadings and intercepts over groups. However, this study demonstrated that MIMIC was insensitive to the presence of factor loading noninvariance, which implies that factor loading invariance should be tested through other measurement invariance testing techniques. MIMIC modeling is also used for measur…
Testing Measurement Invariance
This study investigated two major approaches in testing measurement invariance for ordinal measures: multiple-group categorical confirmatory factor analysis (MCCFA) and item response theory (IRT). Unlike the ordinary linear factor analysis, MCCFA can appropriately model the ordered-categorical measures with a threshold structure. A simulation study under various conditions was conducted for the comparison of MCCFA and IRT with respect to the powe…
Exploring the various interpretations of “test bias”
Test bias is a hotly debated topic in society, especially as it relates to diverse groups of examinees who often score low on standardized tests. However, the phrase "test bias" has a multitude of interpretations that many people are not aware of. In this article, we explain five different meanings of "test bias" and summarize the empirical and theoretical evidence related to each interpretation. The five meanings are as follows: (a) mean group d…
Testing Measurement Invariance
This study investigated two major approaches in testing measurement invariance for ordinal measures: multiple-group categorical confirmatory factor analysis (MCCFA) and item response theory (IRT). Unlike the ordinary linear factor analysis, MCCFA can appropriately model the ordered-categorical measures with a threshold structure. A simulation study under various conditions was conducted for the comparison of MCCFA and IRT with respect to the powe…
Testing Measurement Invariance Using MIMIC
Multiple-indicators multiple-causes (MIMIC) modeling is often used to test a latent group mean difference while assuming the equivalence of factor loadings and intercepts over groups. However, this study demonstrated that MIMIC was insensitive to the presence of factor loading noninvariance, which implies that factor loading invariance should be tested through other measurement invariance testing techniques. MIMIC modeling is also used for measur…
Exploring the various interpretations of “test bias”
Test bias is a hotly debated topic in society, especially as it relates to diverse groups of examinees who often score low on standardized tests. However, the phrase "test bias" has a multitude of interpretations that many people are not aware of. In this article, we explain five different meanings of "test bias" and summarize the empirical and theoretical evidence related to each interpretation. The five meanings are as follows: (a) mean group d…
Testing Factorial Invariance With Unbalanced Samples
In testing the factorial invariance of a measure across groups, the groups are often of different sizes. Large imbalances in group size might affect the results of factorial invariance studies and lead to incorrect conclusions of invariance because the fit function in multiple-group factor analysis includes a weighting by group sample size. The implication is that violations of invariance might not be detected if the sample sizes of the 2 groups …
Model-Selection-Based Approaches to Identifying the Optimal Number of Factors in Multilevel Exploratory Factor Analysis
This study examined the accuracy of commonly used model fit indexes in identifying number of factors in multilevel exploratory factor analysis using Monte Carlo simulations. Multilevel data were generated according to different scenarios of factor structures: cluster numbers, cluster sizes, and intraclass correlation coefficient (ICC) conditions. The results showed that when using the model-based approach, most of the commonly used fit indexes co…
Investigating science identity classifications of rural high school students
The construct of science identity has been gaining attention across various domains in the educational arena. To date, however, no studies have used a person-centered quantitative approach within science identity research that is based upon traditional identity theory. Hence, there is an absence of a theoretical underpinning of the number of science identity classes that might be derived from using person-centered techniques or descriptive dialog…
Mathematics (5 works) · Psychometric Methodologies and Testing (5 works) · Advanced Statistical Modeling Techniques (4 works) · Statistics (4 works) · Structural equation modeling (4 works) · Confirmatory factor analysis (3 works) · Measurement invariance (3 works) · Psychology (3 works) · Psychometrics (3 works) · Advanced Statistical Methods and Models (2 works)