Direction Dependence Analysis

Wolfgang Wiedermann is Professor of Statistics, Measurement, and Evaluation in Education in the College of Education and Human Development at the University of Missouri, Columbia. He received his Ph.D. in Quantitative Psychology from the University of Klagenfurt, Austria. His work focuses on the development of methods for causal structure learning and causal inference, distributional regression, and methods for person-oriented research. He has co-authored books on the general linear model (in 2023) and Configural Frequency Analysis (in 2021) and edited volumes on direction dependence modeling (in 2020) and statistics and causality (in 2016). His work appears in journals such as Psychological Methods, Multivariate Behavioral Research, Behavior Research Methods, Prevention Science, Developmental Psychology, and Development and Psychopathology. Alexander von Eye, is Professor Emeritus of Psychology at Michigan State University. He received his Ph.D. in Psychology from the University of Trier, Germany, in 1976. His work focuses on categorical data analysis, methods of analysis of direction dependence hypotheses, person-oriented research, and human development. He authored texts on, e.g., Configural Frequency Analysis (with Wiedermann), and on log-linear modeling, and he edited, e.g., a book on statistics and causality (with Wiedermann). His over 400 articles appeared in the premier journals of the field, including Psychological Methods, Multivariate Behavioral Research, Child Development, the American Statistician, and the Journal of Applied Statistics.

'Will we ever find out which of the chicken or the egg came first? The answer may lie beyond the Gaussian world. This is the fascinating premise of this unique book, which distills 25 years of intensive research into the groundbreaking field of directional dependence analysis.' Yadolah Dodge, Professor Emeritus, University of Neuchâtel, and Valentin Rousson, Associate Professor, University of Lausanne

• 1. Introduction; 2. The linear regression model; 3. Asymmetry properties of distributions of observed variables; 4. Asymmetry properties of error distributions; 5. Independence properties of causes and errors; 6. Direction of dependence under latent confounding; 7. The integrated framework of Direction Dependence Analysis; 8. Stability and sensitivity analyses; 9. Extensions and applications; 10. Statistical software; 11. Concluding remarks.