Understanding Structural Equation Models: Models of Relationships Between Variables

Designed for graduate students, early-career researchers, and advanced undergraduates who wish to move beyond plug-and-play SEMs to a deeper, more philosophical and data-conscious understanding.

Phillip K. Wood is Professor of Psychological Sciences at the University of Missouri-Columbia, where he has taught graduate seminars in quantitative methods, including beginning and advanced structural equation modeling (SEM), for over 30 years

He earned his Ph.D. in Educational Psychology and Measurement from the University of Minnesota, and earlier degrees from the University of Iowa and Wartburg College.

Dr. Wood’s research spans advanced latent variable modeling techniques--particularly SEM, latent growth, growth-mixture models, state-trait modeling, longitudinal data analysis and models for longitudinally intensive data as applied to developmental processes, substance abuse within young adult populations and life-span development.

A strong advocate of methodological transparency and reproducibility, Wood maintains open-access resources, including SAS, Mplus, lavaan, and Onyx code, accessible through his university-hosted repositories

He regularly moderates the Transcontinental Karl Popper Conference, which explores philosophy of science in psychological research, highlighting his commitment to the interplay between methodological rigor and theoretical skepticism.

Combining decades of classroom instruction with cutting-edge research, Phillip Wood brings a practical, data-conscious perspective fueled by a belief that SEM should be inquisitive, skeptical, and disciplined--a perfect guide for readers navigating the complexities of latent variable modeling.