皇家华人

Tavinder K. Ark earned her PhD in Measurement, Evaluation, and Research Methodology from the University of British Columbia, focusing on Generalizability Theory, Covariance Matrix Modeling, and Structural Equation Modeling under the mentorship of Dr. Bruno D. Zumbo. She completed her BSc and MSc in Psychology at McMaster University, where she explored the impact of analytic and holistic reasoning on diagnostic accuracy and bias in the context of medical education. After completing her PhD, Dr. Ark continued to develop her expertise in medical education as a Research Associate at New York University School of Medicine, collaborating with Dr. Adina L. Kalet. She later served as the Data Analytic Director at the Human Early Learning Partnership and Senior Data Scientist at Population Data BC, both within the School of Population and Public Health at the University of British Columbia. In these roles, she specialized in building and implementing data infrastructure systems for managing large-scale research data, developing data linkage algorithms, and conducting research on the connections between social-emotional well-being, health, and educational outcomes. Her work also included running complex analyses such as latent profile analysis and employing natural language processing techniques for text analysis.

Dr. Ark’s research and professional responsibilities involve advising on study design, psychometrics, and data system development to facilitate frictionless data use for research and curriculum evaluation. She collaborates with various researchers to explore the relationships between health and well-being, professional identity formation, and clinical competence. Her extensive training encompasses inferential statistics, psychometrics (including validity analyses and generalizability theory), complex survey techniques (such as bootstrapping and longitudinal data linkage), and advanced modeling techniques, including factor analysis, multilevel modeling, and Structural Equation Modeling (SEM). She also has expertise in natural language processing and implementing computational algorithms, including machine learning techniques. Dr. Ark’s current research interests include: - understanding the growth trajectories of different learners, - studying effective data visualization methods to enhance student learning and curriculum evaluation - developing and validating  and - studying the transition to residency through initiatives like .