Description: General Linear Model methods are the most widely used in data analysis in applied empirical research. Still, there exists no compact text that can be used in statistics courses and as a guide in data analysis. This volume fills this void by introducing the General Linear Model (GLM), whose basic concept is that an observed variable can be explained from weighted independent variables plus an additive error term that reflects imperfections of the model and measurement error. It also covers multivariate regression, analysis of variance, analysis under consideration of covariates, variable selection methods, symmetric regression, and the recently developed methods of recursive partitioning and direction dependence analysis. Each method is formally derived and embedded in the GLM, and characteristics of these methods are highlighted. Real-world data examples illustrate the application of each of these methods, and it is shown how results can be interpreted.
Brief description: Alexander von Eye is Professor Emeritus of Psychology at Michigan State University, USA. He received his Ph.D. in Psychology from the University of Trier, Germany, in 1976. He is known for his work on statistical modeling, categorical data analysis, and person-oriented research. Recognitions include an honorary professorship of the Technical University of Berlin, fellow status of the APA and the APS, and he was named Accredited Professional Statistician(TM) (PSTAT(TM)) of the American Statistical Association. He authored, among others, texts on Configural Frequency Analysis, and he edited, among others, books on Statistics and Causality (2016) and on direction dependence (2021). His over 400 articles appeared in the premier journals of the field, including, for instance, Psychological Methods, Multivariate Behavioral Research, Child Development, Development and Psychopathology, the Journal of Person-Oriented Research, the American Statistician, and the Journal of Applied Statistics.
Review Quotes: 'This book provides a thorough overview of regression analysis and the analysis of variance and covariance, foundational research methods in social and behavioral sciences. Dr. von Eye and Wiedermann, the authors, have decades of experience training graduate students on these methods and conducting research. Each chapter has a specific learning objective and methodically progresses toward more complex subjects. In addition, the latest methodological developments in causal inference and computationally intensive approaches are well integrated, which should greatly interest any social and behavioral scientists who want to stay abreast of the current state-of-the-art methods. This advanced graduate-level textbook is well-organized, up-to-date, and in-depth while still being understandable, with data examples and key takeaways. As someone involved in training graduate students in social and behavioral sciences, I am excited to use and recommend this book.' Eun-Young Mun, University of North Texas, USA