Mark Van Ryzin

Mark Van Ryzin profile picture
  • Title: Lecturer
  • Phone: 541-346-5065
  • Office: 215 Lokey Education Bldg

Biography

Dr. Van Ryzin’s research focuses on the social aspects of education, especially peer relations in middle and high school. He has conducted randomized trials of a variety of social-emotional learning (SEL) programs, including group-based collaborative approaches to learning. He has found that these group-based approaches to learning, when implemented properly, yield significant improvements to student behavior, social-emotional skills, and mental health, with much larger effects than most curriculum-based SEL programs. Dr. Van Ryzin has also studied a number of family-based and community prevention programs using both site-based and tele-health delivery mechanisms.

Dr. Van Ryzin teaches basic and advanced statistical methods, including correlation/regression, ANOVA, factor analysis, Structural Equation Modeling (SEM), Hierarchical Linear Modeling (HLM), and group-based trajectory modeling. He has also used meta-analysis, social network analysis, and machine learning.

Research

My core research interest is the development and integration of streamlined, efficient prevention strategies into large-scale delivery systems, particularly schools and primary care systems. I focus on how peer and family processes impact adolescent behavior and adjustment, and how these processes can be modified by prevention programming. I have both basic and applied aspects to my work, in which research findings on adolescent development can inform the refinement and optimization of prevention programs and, in turn, the implementation and evaluation of prevention programs can inform developmental theory. My work is framed by a number of social and developmental theories, including attachment theory, self-determination theory, contact theory, and theories of coercion and social learning. I have employed a variety of cutting-edge methods in my research, including non-parametric and mixture modeling, meta-analysis, technology-supported daily diary methods, and social network analysis.