Advances in Configural Frequency Analysis

Using real-world data examples, this authoritative book shows how to use the latest configural frequency analysis (CFA) techniques to analyze categorical data. Some of the techniques are presented here for the first time. In contrast to methods that focus on relationships among variables, such as log-linear modeling, CFA allows researchers to evaluate differences and change at the level of individual cells in a table. Illustrated are ways to identify and test for cell configurations that are either consistent with or contrary to hypothesized patterns (the types and antitypes of CFA); control for potential covariates that might influence observed results; develop innovative prediction models; address questions of moderation and mediation; and analyze intensive longitudinal data. The book also describes free software applications for executing CFA.

Alexander von Eye is Professor of Psychology at Michigan State University. He develops, studies, and applies methods for the analysis of categorical data (in particular, configural frequency analysis and log-linear modeling) and longitudinal data. He also works on and with classification methods and conducts simulation studies. Dr. von Eye has published over 350 articles in methodological, statistical, psychological, and developmental journals, and he is the (co)author or (co)editor of 18 books. He is a Fellow of the American Psychological Association and the American Psychological Society, and he was visiting professor of statistics, psychology, human development, and education at a number of universities in Austria and Germany, as well as at Penn State. Patrick Mair is Assistant Professor in the Institute for Statistics and Mathematics, WU Vienna University of Economics and Business. He was a visiting scholar at the University of California, Los Angeles. Dr. Mair's research focuses on computational/applied statistics and psychometrics, including methodological developments as well as corresponding implementations in the statistical computing environment R. His publications appear in journals of applied and computational statistics. Eun-Young Mun is Assistant Professor of Psychology at Rutgers, The State University of New Jersey. Her research aims to better understand how alcohol and drug use behaviors develop over time, and to delineate mechanisms of behavior change in order to develop effective prevention and intervention approaches, especially for adolescents and emerging adults. She is also interested in extending existing research methodology by integrating and synthesizing distinctive methods together--in particular, pattern-oriented and person-oriented longitudinal research methods--and by disseminating applications. She is coauthor of Analyzing Rater Agreement and publishes articles in developmental, clinical, and methodological journals.