The General Linear Model: A Primer

Alexander von Eye · Wolfgang Wiedermann

Jun 2023 · Cambridge University Press

About this ebook

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.

About the author

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 StatisticianTM (PSTATTM) 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.

Wolfgang Wiedermann is Associate Professor at the University of Missouri, USA. He received his Ph.D. in Quantitative Psychology from the University of Klagenfurt, Austria. His primary research interests include the development of methods for causal inference, methods to evaluate the causal direction of dependence, and methods for person-oriented research. He has edited books on new developments in statistical methods for dependent data analysis in the social and behavioral sciences (2015), advances in statistical methods for causal inference (2016) and causal direction of dependence (2021). His work appears in leading journals of the field, including Psychological Methods, Multivariate Behavioral Research, Behavior Research Methods, Developmental Psychology, and Development and Psychopathology. Recognitions include the Young Researcher Award 2021 by the Scandinavian Society for Person-Oriented Research.