von Eye and Wiedermann present the General Linear Model (GLM) and derivatives such as correlation, regression, analysis of variance, and direction dependence analysis in a compact format. Each method is illustrated using real-world data so that students, instructors, and data analysts can understand methods and procedures.
Foundations, Models, and Applications
This unique book provides a comprehensive and detailed coverage of configural frequency analysis (CFA), the most useful method of analysis of categorical data in person-oriented research. It presents the foundations, methods, and models of CFA and features numerous empirical data examples from a range of disciplines that can be reproduced by the readers. It also addresses computer applications, including relevant R packages and modules. Configural frequency analysis is a statistical method that allows the processing of important and interesting questions in categorical data. The perspective of CFA differs from the usual perspective of relations among variables; its focus is on patterns of variable categories that stand out with respect to specific hypotheses, and as such, CFA allows for testing numerous substantive hypotheses. The book describes the origins of CFA and their relation to chi-square analysis as well as the developments that are based on log-linear modeling. The models covered range from simple models of variable independence to complex models that are needed when causal hypotheses are tested. Empirical data examples are provided for each model. New models are introduced for person-oriented mediation analysis and locally optimized time series analysis, and new results concerning the characteristics of CFA methods are bolstered using Monte Carlo simulations. Primarily intended for researchers and students in the social and behavioral sciences, the book will also appeal to anyone who deals with categorical data from a person-centered perspective.
Person-oriented research is based on the fact that statements at the aggregate level rarely apply at the level of smaller numbers of cases or individuals. The first tenet of person-oriented research proposes that structure and development of behavior are, at least in part, specific to the individual. While aggregate-level statements may be correct, they cover only that portion of the overall variability that is shared by the aggregate. Larger, and more important portions of variability may remain unexplained. Recent research has already created many examples that show that individual characteristics may not be captured at all when results that were created at the aggregate level are used for description at the individual level. With this volume, we pursue three aims: provide an overview of theoretical and methodological developments of person-oriented research; present recent and new developments in the domain of statistical analysis of data from a person-oriented perspective; and, present empirical sample cases in which methodological implications of the relationship between aggregate-level and person-oriented research are defined and illustrated."