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＜電子ブック＞
Basic Principles of Structural Equation Modeling : An Introduction to LISREL and EQS / by Ralph O. Mueller
(Springer Texts in Statistics. ISSN:21974136)

版	1st ed. 1996.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	1996
本文言語	英語
大きさ	XXVIII, 232 p : online resource
著者標目	*Mueller, Ralph O author SpringerLink (Online service)
件　名	LCSH:Sociology -- Methodology  全ての件名で検索 LCSH:Probabilities FREE:Sociological Methods FREE:Probability Theory
一般注記	1 Linear Regression and Classical Path Analysis -- Overview and Key Points -- Linear Ordinary Least Squares Regression -- Classical Path Analysis -- Summary -- Exercises -- Recommended Readings -- 2 Confirmatory Factor Analysis -- Overview and Key Points -- Specification and Identification of a CFA Model -- Data-Model Fit -- Model Modification -- Validity and Reliability from a CFA Perspective -- Summary -- Exercises -- Recommended Readings -- 3 General Structural Equation Modeling -- Overview and Key Points -- Specification and Identification of a General Structural Equation Model -- The Direct, Indirect, and Total Structural Effect Components -- Parameter Estimation -- The Structural Equation Modeling Process: An Illustrated Review and Summary -- Conclusion -- Exercises -- Recommended Readings -- Appendix A -- The SIMPLIS Command Language -- Overview and Key Points -- Appendix B -- Location, Dispersion, and Association -- Overview and Key Points -- Statistical Expectation -- A Measure of a Distribution’s Location -- A Measure of a Distribution’s Dispersion -- A Measure of Association Between Two Variables -- Statistical Standardization -- Standardized Variables -- A Standardized Measure of Association Between Two Variables -- Recommended Readings -- Appendix C -- Matrix Algebra -- Overview and Key Points -- Some Basic Definitions -- Algebra with Matrices -- The Variance/Covariance Matrix -- Recommended Readings -- Appendix D -- Descriptive Statistics for the SES Analysis -- Appendix E -- References During the last two decades, structural equation modeling (SEM) has emerged as a powerful multivariate data analysis tool in social science research settings, especially in the fields of sociology, psychology, and education. Although its roots can be traced back to the first half of this century, when Spearman (1904) developed factor analysis and Wright (1934) introduced path analysis, it was not until the 1970s that the works by Karl Joreskog and his associates (e. g. , Joreskog, 1977; Joreskog and Van Thillo, 1973) began to make general SEM techniques accessible to the social and behavioral science research communities. Today, with the development and increasing avail­ ability of SEM computer programs, SEM has become a well-established and respected data analysis method, incorporating many of the traditional analysis techniques as special cases. State-of-the-art SEM software packages such as LISREL (Joreskog and Sorbom, 1993a,b) and EQS (Bentler, 1993; Bentler and Wu, 1993) handle a variety of ordinary least squares regression designs as well as complex structural equation models involving variables with arbitrary distributions. Unfortunately, many students and researchers hesitate to use SEM methods, perhaps due to the somewhat complex underlying statistical repre­ sentation and theory. In my opinion, social science students and researchers can benefit greatly from acquiring knowledge and skills in SEM since the methods-applied appropriately-can provide a bridge between the theo­ retical and empirical aspects of behavioral research HTTP:URL=https://doi.org/10.1007/978-1-4612-3974-1

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