Latent variable models : an introduction to factor, path, and structural analysis

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書誌事項

Latent variable models : an introduction to factor, path, and structural analysis

John C. Loehlin

Lawrence Erlbaum Associates, 1992

2nd ed

大学図書館所蔵 件 / 全24件

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注記

Includes bibliographical references (p. 270-285) and index

内容説明・目次

内容説明

This book provides an introduction to a rapidly-growing area in the social and behavioral sciences -- the modeling of systems in which one or more variables are hypothesized, but not directly observed. Providing a conceptually unified treatment of modeling of this type -- exploratory and confirmatory factor analysis, path analysis, and structural equation analysis -- it is intended to introduce these techniques to individuals who have had some exposure to statistical methods in general, but are beginners in this particular area. Using an inductive and informal approach, it emphasizes the use of path diagrams and a variety of concrete examples, and keeps the mathematics largely intuitive. Examples are drawn from a variety of fields, including psychometrics, sociology, psychology, education and behavior genetics. Although some introductory material is provided for LISREL, EQS, and CALIS, and for exploratory factor analysis programs in SAS, SPSS, and BMPD, the book is not closely tied to any one computer program or statistical package.

目次

Contents: Path Models in Factor, Path, and Structural Equation Analysis. Fitting Path Models. Varieties of Path and Structural Models-I. Varieties of Path and Structural Models-II. Exploratory Factor Analysis-I: Extracting the Factors. Exploratory Factor Analysis-II: Transforming the Factors to Simpler Structure. Elaborations and Extensions of Latent Variable Analysis. Appendices: Simple Matrix Operations. Derivation of Matrix Version of Path Equations. LISREL Matrices and Examples. Alpha and Canonical Factor Extraction-A Worked Example. Examples of Input for Factor Extraction by SPSS and SAS. Data Matrix for Thurstone's Box Problem. Table of Chi-Square. Noncentral Chi-Square for Estimating Power. Power of a Test of Poor Fit and Sample Sizes for Powers of .80. Answers to Exercises. References. Index.

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