書誌事項

Advances in configural frequency analysis

Alexander von Eye, Patrick Mair, Eun-Young Mun ; series editor's note by Todd D. Little

(Methodology in the social sciences)

Guilford Press, c2010

  • : hardcover

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注記

Includes bibliographical references (p. 288-298) and indexes

内容説明・目次

内容説明

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.

目次

_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

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