Ejike J. Edeh
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Senior Graduate Assistant
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Ejike J. Edeh's research focuses on statistical modeling techniques, particularly structural equation modeling (SEM). His work investigates the sensitivity of fit indices in SEM to various factors, including model size, misspecification, and prior informativeness in Bayesian SEM. Edeh has also explored the application of text-to-speech technology among different student populations. He collaborates with researchers at the University of Arkansas at Fayetteville, including Xinya Liang and Wen‐Juo Lo, and at the University of Arkansas for Medical Sciences, including Ji Li.
Edeh's scholarship metrics include an h-index of 3, with 5 total publications and 274 total citations. His recent publications address the practical application of SEM and its theoretical underpinnings, with a workbook on Partial Least Squares SEM using R and several papers in 2025 examining Bayesian SEM fit indices and exploratory SEM.
Metrics
- h-index: 3
- Publications: 5
- Citations: 274
Positions
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Senior Graduate Assistant 2020–presentUniversity of Arkansas at Fayetteville College of Education and Health Professions ORCID
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University of Arkansas at Fayetteville publications 2022–2025ORCID
Selected Publications
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Sensitivity of Fit Indices to Model Complexity and Misspecification in Exploratory Structural Equation Modeling (2025)
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Correction: Probing beyond: The impact of model size and prior informativeness on Bayesian SEM fit indices (2025)
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Exploring Universal Text-to-Speech Use in Assessment Among Student Sub-Populations (2025)
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Probing beyond: The impact of model size and prior informativeness on Bayesian SEM fit indices (2025)
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Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook (2022)
Collaboration Network
Top Collaborators
- Probing beyond: The impact of model size and prior informativeness on Bayesian SEM fit indices
- Sensitivity of Fit Indices to Model Complexity and Misspecification in Exploratory Structural Equation Modeling
- Correction: Probing beyond: The impact of model size and prior informativeness on Bayesian SEM fit indices
- Probing beyond: The impact of model size and prior informativeness on Bayesian SEM fit indices
- Sensitivity of Fit Indices to Model Complexity and Misspecification in Exploratory Structural Equation Modeling
- Correction: Probing beyond: The impact of model size and prior informativeness on Bayesian SEM fit indices
- Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
- Sensitivity of Fit Indices to Model Complexity and Misspecification in Exploratory Structural Equation Modeling
- Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
- Exploring Universal Text-to-Speech Use in Assessment Among Student Sub-Populations
- Exploring Universal Text-to-Speech Use in Assessment Among Student Sub-Populations
- Sensitivity of Fit Indices to Model Complexity and Misspecification in Exploratory Structural Equation Modeling
- Sensitivity of Fit Indices to Model Complexity and Misspecification in Exploratory Structural Equation Modeling
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