Advances in configural frequency analysis
著者
書誌事項
Advances in configural frequency analysis
（Methodology in the social sciences）
Guilford Press, c2010
- : hardcover
大学図書館所蔵 件 / 全3件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
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
「Nielsen BookData」 より