James Uanhoro
Biographic Data
| ID | 6653913 |
|---|---|
| NAME | James Uanhoro |
| GIVEN NAMES | James |
| FAMILY NAME | Uanhoro |
| SIGNATURE | UANHORO J |
| AFFILIATIONS | University of North Texas |
| ORCID | 0000-0002-4843-927X |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 11 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
A Hierarchical Ordinal Regression Model for Treatment-Reversal Designs with Application to Non-Overlap Effect Sizes
We present a hierarchical ordinal model for analyzing single-case designs (SCDs), with a focus on treatment-reversal designs. SCDs involve systematic measurement of outcomes for individual cases across different conditions or phases, aiming to establish causal relations between interventions and behavioral changes. While visual analysis is a common approach in SCDs, the field is increasingly adopting quantitative effect size metrics, such as non-…
A 5-year longitudinal study of bilinguals’ vocabulary growth and the role of the prekindergarten home language and literacy environments
Historical Measurement Information Can Be Used to Improve Estimation of Structural Parameters in Structural Equation Models With Small Samples
This study investigates the incorporation of historical measurement information into structural equation models (SEM) with small samples to enhance the estimation of structural parameters. Given the availability of published factor analysis results with loading estimates and standard errors for popular scales, researchers may use this historical information as informative priors in Bayesian SEM (BSEM). We focus on estimating the correlation betwe…
Bayesian Structural Equation Models of Correlation Matrices
We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean based on the transformed model-implied correlation matrix, and a covariance assumed to be of known form.…
Impostorism and Psychological Distress among College Students of Color
Modeling Misspecification as a Parameter in Bayesian Structural Equation Models
Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter—a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its own terms as a measure of absolute model fit and allows for comparing different models fit to the same …
Hierarchical Covariance Estimation Approach to Meta-Analytic Structural Equation Modeling
We present a fully Bayesian approach to meta-analytic SEM based on hierarchical modeling of sample covariance matrices. The approach allows for flexible models that would not be identified under a traditional maximum likelihood approach. The approach allows for the inclusion of moderators, produces a global fit index, and permits the investigation of local misspecification. Simulation-based calibration studies show that the Bayesian computation p…
The Influence of Context on the Abstraction Level of Children’s Conversations in the Preschool Classroom
Research Findings: Conversing abstract concepts boost children’s language learning. Despite the numerous studies on the linguistic environment of early childhood education settings (ECE), most of this work disregards contextual factors that may influence abstract conversations and omits characteristics of children’s verbal participation in these interactions. We examined how preschool classroom contexts influenced the abstraction level of childre…
Disentangling the discrimination and mental health relationship among diverse college students
The present study advances our understanding of the association between discrimination and negative mental health outcomes. African American participants with high shame-proneness were uniquely impacted by discrimination. Researchers, clinicians, and university officials are encouraged to develop culturally informed interventions and services to support this population. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
Investigation of the Effect of Badges in the Online Homework System for Undergraduate General Physics Course
Badges in education are an increasingly popular phenomenon, and a variety of questions exists as to the abilities and effectiveness of badges. In this study, the effect of digital badges within a Moodle-based online homework system was studied for an undergraduate general physics course at a large research-based university in northeast Taiwan. One hundred and sixty-two participants from two General Physics sessions were involved in this study and…
Using a Cross-Classified Multilevel Mediation Model (CC-M3) with Longitudinal Data Having Changes in Cluster Membership
Cluster memberships associated with the mediation effect are often changed due to the temporal distance between the cause-and-effect variables in longitudinal data. Nevertheless, current practices in multilevel mediation analysis mostly assume a purely hierarchical data structure. A Monte Carlo simulation study is conducted to examine the consequence of ignoring the changes in cluster memberships in multilevel mediation analysis. Results show tha…
No prominent works on this page.
Investigation of the Effect of Badges in the Online Homework System for Undergraduate General Physics Course
Badges in education are an increasingly popular phenomenon, and a variety of questions exists as to the abilities and effectiveness of badges. In this study, the effect of digital badges within a Moodle-based online homework system was studied for an undergraduate general physics course at a large research-based university in northeast Taiwan. One hundred and sixty-two participants from two General Physics sessions were involved in this study and…
Using a Cross-Classified Multilevel Mediation Model (CC-M3) with Longitudinal Data Having Changes in Cluster Membership
Cluster memberships associated with the mediation effect are often changed due to the temporal distance between the cause-and-effect variables in longitudinal data. Nevertheless, current practices in multilevel mediation analysis mostly assume a purely hierarchical data structure. A Monte Carlo simulation study is conducted to examine the consequence of ignoring the changes in cluster memberships in multilevel mediation analysis. Results show tha…
Hierarchical Covariance Estimation Approach to Meta-Analytic Structural Equation Modeling
We present a fully Bayesian approach to meta-analytic SEM based on hierarchical modeling of sample covariance matrices. The approach allows for flexible models that would not be identified under a traditional maximum likelihood approach. The approach allows for the inclusion of moderators, produces a global fit index, and permits the investigation of local misspecification. Simulation-based calibration studies show that the Bayesian computation p…
The Influence of Context on the Abstraction Level of Children’s Conversations in the Preschool Classroom
Research Findings: Conversing abstract concepts boost children’s language learning. Despite the numerous studies on the linguistic environment of early childhood education settings (ECE), most of this work disregards contextual factors that may influence abstract conversations and omits characteristics of children’s verbal participation in these interactions. We examined how preschool classroom contexts influenced the abstraction level of childre…
Disentangling the discrimination and mental health relationship among diverse college students
The present study advances our understanding of the association between discrimination and negative mental health outcomes. African American participants with high shame-proneness were uniquely impacted by discrimination. Researchers, clinicians, and university officials are encouraged to develop culturally informed interventions and services to support this population. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
Bayesian Structural Equation Models of Correlation Matrices
We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean based on the transformed model-implied correlation matrix, and a covariance assumed to be of known form.…
Impostorism and Psychological Distress among College Students of Color
Modeling Misspecification as a Parameter in Bayesian Structural Equation Models
Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter—a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its own terms as a measure of absolute model fit and allows for comparing different models fit to the same …
A 5-year longitudinal study of bilinguals’ vocabulary growth and the role of the prekindergarten home language and literacy environments
Historical Measurement Information Can Be Used to Improve Estimation of Structural Parameters in Structural Equation Models With Small Samples
This study investigates the incorporation of historical measurement information into structural equation models (SEM) with small samples to enhance the estimation of structural parameters. Given the availability of published factor analysis results with loading estimates and standard errors for popular scales, researchers may use this historical information as informative priors in Bayesian SEM (BSEM). We focus on estimating the correlation betwe…
A Hierarchical Ordinal Regression Model for Treatment-Reversal Designs with Application to Non-Overlap Effect Sizes
We present a hierarchical ordinal model for analyzing single-case designs (SCDs), with a focus on treatment-reversal designs. SCDs involve systematic measurement of outcomes for individual cases across different conditions or phases, aiming to establish causal relations between interventions and behavioral changes. While visual analysis is a common approach in SCDs, the field is increasingly adopting quantitative effect size metrics, such as non-…
Psychology (6 works) · Bayesian probability (5 works) · Computer Science (5 works) · Mathematics (5 works) · Statistics (5 works) · Econometrics (4 works) · Structural equation modeling (4 works) · Algorithm (3 works) · Artificial Intelligence (3 works) · Covariance (3 works)