Advances in Configural Frequency Analysis

A state-of-the-art tour of the newest methods for an important approach to hypothesis testing in contingency table analysis. Clearly written and loaded with excellent examples, this book takes the reader through cross-sectional and longitudinal models, including interesting approaches to mediator and moderator analysis and auto-association modeling. The authors' practical approach allows the researcher to immediately implement these very advanced models.--Michael J. Rovine, PhD, Department of Human Development and Family Studies, The Pennsylvania State University A very user-friendly book that offers practical examples of many advanced topics for those interested in a new person-oriented data analytic approach. I appreciated how the same data examples were used in different chapters in order to illustrate the different types of questions that can be answered through configural frequency analysis. This helps the reader stay focused on the material at hand and reinforces the general applicability of the method to a wide variety of research questions.--Michael J. Cleveland, PhD, Methodology Center, The Pennsylvania State University This book offers an outstanding presentation of advances in configural frequency analysis. In particular, the chapters on methods for the investigation of mediation, moderation, and longitudinal data will be very useful to researchers. These new approaches to configural analysis represent a valuable approach to answering and generating new research questions. A strength of the book is that many real data sets and examples are provided. This is a good book for categorical data analysis courses and an important reference for researchers applying the method.--David P. MacKinnon, PhD, Department of Psychology, Arizona State University A strong resource for researchers interested in the application of innovative statistical methods to handle categorical data. The authors describe steps for using state-of-the-art statistical methods, writing in a manner that facilitates analysis and understanding of complex statistical concepts. Included are examples showing how to apply configural frequency analysis to handle categorical data using longitudinal and factorial designs.--Mildred M. Maldonado-Molina, PhD, Department of Epidemiology and Health Policy Research, University of Florida There is a wealth of operational detail about the statistical base of configural frequency analysis and computational logistics. Other strengths of the book are the use of program output to illustrate points and the helpful chapter summaries.--Paula S. Nurius, MSW, PhD, School of Social Work, University of Washington - I was pleasantly delighted to find a thoroughly described statistical method written by experts in the field who know how to connect this technique to social science research....The statistical procedures and concepts are thoroughly described via equations and mathematical representations. --PsycCRITIQUES, 4/22/2010

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.

_x000D_ _x000D_ 1. Introduction _x000D_ 1.1 Questions That CFA Can Answer _x000D_ 1.2 The Five Steps of CFA _x000D_ 1.3 Introduction to CFA: An Overview _x000D_ 1.4 Chapter Summary _x000D_ 2. Configural Analysis of Rater Agreement _x000D_ 2.1 Rater Agreement CFA _x000D_ 2.2 Data Examples _x000D_ 2.3 Chapter Summary _x000D_ 3. Structural Zeros in CFA _x000D_ 3.1 Blanking Out Structural Zeros _x000D_ 3.2 Structural Zeros by Design _x000D_ 3.2.1 Polynomials and the Method of Differences _x000D_ 3.2.2 Identifying Zeros That Are Structural by Design _x000D_ 3.3 Chapter Summary _x000D_ 4. Covariates in CFA _x000D_ 4.1 CFA and Covariates _x000D_ 4.2 Chapter Summary _x000D_ 5. Configural Prediction Models _x000D_ 5.1 Logistic Regression and Prediction CFA _x000D_ 5.1.1 Logistic Regression _x000D_ 5.1.2 Prediction CFA _x000D_ 5.1.3 Comparing Logistic Regression and P-CFA Models _x000D_ 5.2 Predicting an End Point _x000D_ 5.3 Predicting a Trajectory _x000D_ 5.4 Graphical Presentation of Results of P-CFA Models _x000D_ 5.5 Chapter Summary _x000D_ 6. Configural Mediator Models _x000D_ 6.1 Logistic Regression plus Mediation _x000D_ 6.2 CFA-Based Mediation Analysis _x000D_ 6.3 Configural Chain Models _x000D_ 6.4 Chapter Summary _x000D_ 7. Auto-Association CFA _x000D_ 7.1 A-CFA without Covariates _x000D_ 7.2 A-CFA with Covariates _x000D_ 7.2.1 A-CFA with Covariates I: Types and Antitypes Reflect Any of the Possible Relationships between Two or More Series of Measures _x000D_ 7.2.2 A-CFA with Covariates II: Types and Antitypes Reflect Only Relationships between the Series of Measures and the Covariate _x000D_ 7.3 Chapter Summary _x000D_ 8. Configural Moderator Models _x000D_ 8.1 Configural Moderator Analysis: Base Models with and without Moderator _x000D_ 8.2 Longitudinal Configural Moderator Analysis under Consideration of Auto-Associations _x000D_ 8.3 Configural Moderator Analysis as n-Group Comparison _x000D_ 8.4 Moderated Mediation _x000D_ 8.5 Graphical Representation of Configural Moderator Results _x000D_ 8.6 Chapter Summary _x000D_ 9. The Validity of CFA Types and Antitypes _x000D_ 9.1 Validity in CFA _x000D_ 9.2 Chapter Summary _x000D_ 10. Functional CFA _x000D_ 10.1 F-CFA I: An Alternative Approach to Exploratory CFA (Sequential Identification of Types and Antitypes) _x000D_ 10.1.1 Kieser and Victor's Alternative, Sequential CFA: Focus on Model Fit _x000D_ 10.1.2 von Eye and Mair's Sequential CFA: Focus on Residuals _x000D_ 10.2 Special Case: One Dichotomous Variable _x000D_ 10.3 F-CFA II: Explaining Types and Antitypes _x000D_ 10.3.1 Explaining Types and Antitypes: The Ascending, Inclusive Strategy _x000D_ 10.3.2 Explaining Types and Antitypes: The Descending, Exclusive Strategy _x000D_ 10.4 Chapter Summary _x000D_ 11. CFA of Intensive Categorical Longitudinal Data _x000D_ 11.1 C