Latent variable models: an introduction to factor, path, and structural analysis
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.
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Fitting path models
Varieties of path and structural modelsl
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assumed Bentler BMDP calculation causal Chapter column compound paths confirmatory factor analysis correlation matrix covariance matrix criteria cross-validation degrees of freedom diagonal matrix downstream variables eigenvalues eigenvectors equal error example exploratory factor analysis factor extraction factor intercorrelations factor scores factor solution genetic given goodness-of-fit groups implied indicators input inverse involving IPSOL iterative latent variable models least squares LISREL matrix multiplication maximum likelihood measurement model methods model of Fig model-fitting multiple number of factors Oblimin observed correlations observed variables obtained orthogonal parameters path coefficients path diagram path equations path model path representation pattern coefficients principal factor problem procedure Quartimax rawscore regression relationships represent rescaling residual variances rotation sample scale scree shown solved source variables specific SPSS standard deviations statistical structural equation structural model subroutine Table trait transformation trial values twins unknowns variance-covariance matrix Varimax vector x2 test yield zero