Hyeri Hong
Biographic Data
| ID | 6100239 |
|---|---|
| NAME | Hyeri Hong |
| GIVEN NAMES | Hyeri |
| FAMILY NAME | Hong |
| SIGNATURE | HONG H |
| AFFILIATIONS | California State University, Fresno |
| ORCID | 0000-0002-7576-2574 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
A Comprehensive Multivariate Generalizability Theory-Based Analysis of Responses to the Ipip-NEO-120
The 120-item International Personality Item Pool-NEO questionnaire (IPIP-NEO-120) was recently developed to measure the same Big Five global domain and nested facet constructs included in the 240-item Revised NEO Personality Inventory (NEO PI-R) but more efficiently and at no cost to users. We summarize evidence of reliability and validity for IPIP-NEO-120 scores reported in previous research and further evaluate the psychometric properties of th…
Estimating Item Wording Effects in Self-Report Measures with Generalizability Theory-Based SEMs: Illustrations Using the Self-Description Questionnaire-III
Handling item wording effects within Likert-style, self-report questionnaires has long been a challenge when measuring psychological traits. When testing models for such traits, wording effects are commonly addressed by correlating uniquenesses for negatively and positively phrased items or including separate uncorrelated method factors for each effect. However, the magnitude of wording effects is rarely considered in such analyses or distinguish…
Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks
We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the Big Five Inventory (BFI-2) revealed that the lavaan SEM package in R produced results virtually identic…
Benefits of Doing Generalizability Theory Analyses within Structural Equation Modeling Frameworks: Illustrations Using the Rosenberg Self-Esteem Scale
Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutorial, we review basic concepts for conducting univariate and multivariate GT analyses and demonstrate advantages of doing such analyses within SEM frameworks using multi-occasion data from the Rosenberg Self-Esteem Scale. We show how GT-SEMs can reproduc…
Analyzing and Comparing Univariate, Multivariate, and Bifactor Generalizability Theory Designs for Hierarchically Structured Personality Traits
We demonstrate how to use structural equation models to represent generalizability theory-based univariate, multivariate, and bifactor model designs. Analyses encompassed multi-occasion data obtained from the recently expanded form of the Big Five Inventory (BFI-2) that measures the broad personality domain constructs Agreeableness, Conscientiousness, Extraversion, Negative Emotionality, and Open-Mindedness along with three nested subdomain facet…
Differential effects of state testing policies and school characteristics on social studies educators’ gate-keeping autonomy: A multilevel model
Using data on secondary school social studies teachers (n = 6,702) from the Survey of the Status of Social Studies (S4), a multilevel model, and an Item Response Theory (IRT) analysis, this study examined the associations of state testing policy and school characteristics on secondary social studies teachers’ instructional autonomy as it relates to various state testing policies. Through multi-level analysis, this study suggests several key concl…
Differential effects of state testing policies and school characteristics on social studies educators’ gate-keeping autonomy: A multilevel model
Using data on secondary school social studies teachers (n = 6,702) from the Survey of the Status of Social Studies (S4), a multilevel model, and an Item Response Theory (IRT) analysis, this study examined the associations of state testing policy and school characteristics on secondary social studies teachers’ instructional autonomy as it relates to various state testing policies. Through multi-level analysis, this study suggests several key concl…
Differential effects of state testing policies and school characteristics on social studies educators’ gate-keeping autonomy: A multilevel model
Using data on secondary school social studies teachers (n = 6,702) from the Survey of the Status of Social Studies (S4), a multilevel model, and an Item Response Theory (IRT) analysis, this study examined the associations of state testing policy and school characteristics on secondary social studies teachers’ instructional autonomy as it relates to various state testing policies. Through multi-level analysis, this study suggests several key concl…
Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks
We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the Big Five Inventory (BFI-2) revealed that the lavaan SEM package in R produced results virtually identic…
Benefits of Doing Generalizability Theory Analyses within Structural Equation Modeling Frameworks: Illustrations Using the Rosenberg Self-Esteem Scale
Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutorial, we review basic concepts for conducting univariate and multivariate GT analyses and demonstrate advantages of doing such analyses within SEM frameworks using multi-occasion data from the Rosenberg Self-Esteem Scale. We show how GT-SEMs can reproduc…
Analyzing and Comparing Univariate, Multivariate, and Bifactor Generalizability Theory Designs for Hierarchically Structured Personality Traits
We demonstrate how to use structural equation models to represent generalizability theory-based univariate, multivariate, and bifactor model designs. Analyses encompassed multi-occasion data obtained from the recently expanded form of the Big Five Inventory (BFI-2) that measures the broad personality domain constructs Agreeableness, Conscientiousness, Extraversion, Negative Emotionality, and Open-Mindedness along with three nested subdomain facet…
A Comprehensive Multivariate Generalizability Theory-Based Analysis of Responses to the Ipip-NEO-120
The 120-item International Personality Item Pool-NEO questionnaire (IPIP-NEO-120) was recently developed to measure the same Big Five global domain and nested facet constructs included in the 240-item Revised NEO Personality Inventory (NEO PI-R) but more efficiently and at no cost to users. We summarize evidence of reliability and validity for IPIP-NEO-120 scores reported in previous research and further evaluate the psychometric properties of th…
Estimating Item Wording Effects in Self-Report Measures with Generalizability Theory-Based SEMs: Illustrations Using the Self-Description Questionnaire-III
Handling item wording effects within Likert-style, self-report questionnaires has long been a challenge when measuring psychological traits. When testing models for such traits, wording effects are commonly addressed by correlating uniquenesses for negatively and positively phrased items or including separate uncorrelated method factors for each effect. However, the magnitude of wording effects is rarely considered in such analyses or distinguish…
Generalizability theory (5 works) · Psychology (4 works) · Psychometric Methodologies and Testing (4 works) · Structural equation modeling (4 works) · Mathematics (3 works) · Multivariate statistics (3 works) · Personality Traits and Psychology (3 works) · Statistics (3 works) · Advanced Statistical Modeling Techniques (2 works) · Computer Science (2 works)