Professor and University of Washington, United States
Dr. Oscar L. Olvera Astivia is an accomplished academic specializing in psychometrics and measurement methodology. Currently serving as an Assistant Professor in Measurement and Statistics at the University of Washington, Dr. Astivia’s research focuses on advanced statistical methods, including Monte Carlo simulations and psychometric analysis. His work is influential in understanding complex data structures and enhancing methodological rigor in educational and psychological research. With a Ph.D. from the University of British Columbia and a history of roles at leading institutions such as the University of South Florida and the University of British Columbia, Dr. Astivia has contributed significantly to the field through numerous publications, conference presentations, and invited talks. His research covers various topics, including psychometric evaluation, simulation studies, and the application of statistical methods in complex data analysis. Dr. Astivia’s expertise and innovative approaches have earned him recognition and respect in the academic community.
Profile
Dr. Oscar L. Olvera Astivia holds a Ph.D. in Measurement, Evaluation, and Research Methodology from the University of British Columbia (UBC), Vancouver, British Columbia, Canada, earned in April 2017. Under the mentorship of Dr. Bruno D. Zumbo, he focused his doctoral research on Monte Carlo simulation algorithms within psychometrics, contributing valuable insights into the field of educational and psychological measurement. Dr. Astivia also completed a Master of Arts in Measurement, Evaluation, and Research Methodology at UBC in June 2013, with a thesis on the estimation of the polychoric correlation coefficient using Markov Chain Monte Carlo methods. His academic journey began with a Bachelor of Arts in Mathematics from the University of the Fraser Valley, Abbotsford, British Columbia, in May 2008. This strong foundation in mathematics and research methodologies has significantly shaped Dr. Astivia’s career, leading to numerous contributions to the field of psychometrics and statistical analysis.
Dr. Oscar L. Olvera Astivia’s research interests are centered around the advancement of measurement and statistical methodologies, particularly within the field of psychometrics. His work primarily explores Monte Carlo simulation algorithms, a powerful tool in statistical modeling and research design. Dr. Astivia has made significant contributions to understanding complex statistical concepts such as the estimation of polychoric correlations, reliability estimation in non-normal data distributions, and the implications of heteroskedasticity in regression analyses. He is deeply invested in enhancing the accuracy and applicability of statistical methods in educational and psychological research. Moreover, Dr. Astivia is keen on improving data simulation techniques and addressing methodological challenges in multivariate analyses. His research not only pushes the boundaries of theoretical psychometrics but also provides practical solutions for researchers facing statistical challenges in real-world applications, ensuring that statistical practices remain robust and reliable across various disciplines.
Dr. Oscar L. Olvera Astivia is a distinguished researcher with a robust skill set in quantitative research methodologies, particularly in the fields of psychometrics, statistics, and educational measurement. His expertise lies in advanced statistical techniques, including Monte Carlo simulation algorithms, structural equation modeling, and multivariate data analysis. Dr. Astivia has made significant contributions to the development and application of these methods, with a keen focus on addressing complex statistical problems such as non-normality in data, heteroskedasticity, and multicollinearity. His research is characterized by a deep understanding of both theoretical and applied aspects of statistics, allowing him to develop innovative solutions to methodological challenges in social sciences. Additionally, Dr. Astivia’s proficiency in data analysis software and his ability to translate complex statistical concepts into practical research applications make him a valuable asset to the academic community and an influential figure in his field.
Awards and Recognition
Dr. Astivia’s achievements have been recognized through various academic appointments and the acceptance of his work in prestigious journals and conferences. His contributions to the field of psychometrics and educational measurement have positioned him as a leading researcher, and his work continues to influence both academic and applied research. Although specific awards are not mentioned in his profile, his recognition within the academic community is evident from his numerous publications and presentations.
Conclusion
Dr. Oscar L. Olvera Astivia is a highly accomplished researcher with significant contributions to the fields of psychometrics and educational measurement. His work has broad geographic and interdisciplinary impact, with applications in education, public health, and beyond. While his primary research does not focus on environmental health or infectious diseases, the statistical methods he has developed are highly relevant to these fields. Dr. Astivia’s collaborative efforts, applied research, and extensive publication record make him a strong candidate for the Research for Best Researcher Award.
Publications Top Notes
- A method to simulate multivariate outliers with known Mahalanobis distances for normal and non-normal data
- Authors: Olvera Astivia, O.L.
- Year: 2024
- Simultaneous estimation of the intermediate correlation matrix for arbitrary marginal densities
- Authors: Olvera Astivia, O.L., Kroc, E., Zumbo, B.D.
- Year: 2024
- Citations: 2
- Many nonnormalities, one simulation: Do different data generation algorithms affect study results?
- Authors: Fairchild, A.J., Yin, Y., Baraldi, A.N., Astivia, O.L.O., Shi, D.
- Year: 2024
- Citations: 2
- The Case for the Curve: Parametric Regression With Second- and Third-Order Polynomial Functions of Predictors Should Be Routine
- Authors: Kroc, E., Astivia, O.L.O.
- Year: 2023
- Theoretical considerations when simulating data from the g-and-h family of distributions
- Authors: Astivia, O.L.O., Edward, K.
- Year: 2022
- The Importance of Thinking Multivariately When Setting Subscale Cutoff Scores
- Authors: Kroc, E., Olvera Astivia, O.L.
- Year: 2022
- Citations: 3
- How to Think Clearly About the Central Limit Theorem
- Authors: Zhang, X., Astivia, O.L.O., Kroc, E., Zumbo, B.D.
- Year: 2022
- Citations: 7
- Psychometric Evaluation of the Korean Version of Hospital Survey on Patient Safety Culture
- Authors: Lee, S.E., Havaei, F., Astivia, O.L.O., Shin, J.A.
- Year: 2022
- Citations: 2
- The National Standard of Psychological Health and Safety in the Workplace: A Psychometric and Descriptive Study of the Nursing Workforce in British Columbia Hospitals
- Authors: Havaei, F., Park, M., Astivia, O.L.O.
- Year: 2021
- Citations: 6
- A Note on the General Solution to Completing Partially Specified Correlation Matrices
- Authors: Olvera Astivia, O.L.
- Year: 2021
- Citations: 1