Hardcovere-bookprint + e-book

Hardcover~~$66.00~~ **$56.10**

orderApril 20, 2010

ISBN 9781606237199

Price: 306 Pages

Size: 7" x 10"

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.

This title is part of the Methodology in the Social Sciences Series, edited by Todd D. Little, PhD.

“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.”

“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.”

“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.”

“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.”

“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.”

“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.”

1.1 Questions That CFA Can Answer

1.2 The Five Steps of CFA

1.3 Introduction to CFA: An Overview

1.4 Chapter Summary

**2. Configural Analysis of Rater Agreement**

2.1 Rater Agreement CFA

2.2 Data Examples

2.3 Chapter Summary

**3. Structural Zeros in CFA**

3.1 Blanking Out Structural Zeros

3.2 Structural Zeros by Design

3.2.1 Polynomials and the Method of Differences

3.2.2 Identifying Zeros That Are Structural by Design

3.3 Chapter Summary

**4. Covariates in CFA**

4.1 CFA and Covariates

4.2 Chapter Summary

**5. Configural Prediction Models**

5.1 Logistic Regression and Prediction CFA

5.1.1 Logistic Regression

5.1.2 Prediction CFA

5.1.3 Comparing Logistic Regression and P-CFA Models

5.2 Predicting an End Point

5.3 Predicting a Trajectory

5.4 Graphical Presentation of Results of P-CFA Models

5.5 Chapter Summary

**6. Configural Mediator Models**

6.1 Logistic Regression plus Mediation

6.2 CFA-Based Mediation Analysis

6.3 Configural Chain Models

6.4 Chapter Summary

**7. Auto-Association CFA**

7.1 A-CFA without Covariates

7.2 A-CFA with Covariates

7.2.1 A-CFA with Covariates I: Types and Antitypes Reflect Any of the Possible Relationships between Two or More Series of Measures

7.2.2 A-CFA with Covariates II: Types and Antitypes Reflect Only Relationships between the Series of Measures and the Covariate

7.3 Chapter Summary

**8. Configural Moderator Models**

8.1 Configural Moderator Analysis: Base Models with and without Moderator

8.2 Longitudinal Configural Moderator Analysis under Consideration of Auto-Associations

8.3 Configural Moderator Analysis as *n*-Group Comparison

8.4 Moderated Mediation

8.5 Graphical Representation of Configural Moderator Results

8.6 Chapter Summary

**9. The Validity of CFA Types and Antitypes**

9.1 Validity in CFA

9.2 Chapter Summary

**10. Functional CFA**

10.1 F-CFA I: An Alternative Approach to Exploratory CFA (Sequential Identification of Types and Antitypes)

10.1.1 Kieser and Victor's Alternative, Sequential CFA: Focus on Model Fit

10.1.2 von Eye and Mair's Sequential CFA: Focus on Residuals

10.2 Special Case: One Dichotomous Variable

10.3 F-CFA II: Explaining Types and Antitypes

10.3.1 Explaining Types and Antitypes: The Ascending, Inclusive Strategy

10.3.2 Explaining Types and Antitypes: The Descending, Exclusive Strategy

10.4 Chapter Summary

**11. CFAof Intensive Categorical Longitudinal Data**

11.1 CFA of Runs

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