Includes bibliographies and index.
|Statement||Karl G. Jöreskog, Dag Sörbom ; with an introd. by William W. Cooley ; edited by Jay Magidson.|
|Contributions||Sörbom, Dag, joint author., Magidson, Jay.|
|LC Classifications||HA33 .J62|
|The Physical Object|
|Pagination||xxviii, 242 p. :|
|Number of Pages||242|
|LC Control Number||79052433|
Additional Physical Format: Online version: Jöreskog, K.G. Advances in factor analysis and structural equation models. Cambridge, Mass.: Abt Books, © ISBN: OCLC Number: Notes: Reprint. Originally published: Cambridge, Mass.: Abt Books, © Advances in Factor Analysis and Structural Equation Models 作者: Karl G. Jöreskog / Dag Sörbom / Jay Magidson 出版社: Cambridge, Massachusetts: Abt Books 副标题: with an introduction by William W. Cooley. edited by Jay Magidson 出版年: 页数: 装帧: 精装 ISBN: Exploratory and confirmatory factor analyses revealed a 7-factors solution, corresponding to the following subscales: Relationships to Others, New Possibilities, Personal Strength, Appreciation of Life, Spiritual Changes, Generativity, and Openness.
Advances in factor analysis and structural equation models,Abt Books, Massachusetts, has been cited by the following article: Article. Improving Accuracy of Educational Research Conclusions by Using Lisrel. Awaluddin Tjalla 1. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics Specification, Estimation and Testing,” in Advances in Factor Analysis and Structural Equation Models, Jöreskog, Karl G. and Sörbom, Dag, eds. Cambridge, Massachusetts: ABT Books, Cited by: Because it is a variable reduction procedure, principal component analysis is similar in many respects to exploratory factor analysis. In fact, the steps followed when conducting a principal component analysis are virtually identical to those followed when conducting an exploratory factor analysis. confirmatory factor analysis (CFA) models. In structural equation modeling, the confirmatory factor model is imposed on the data. In this case, the purpose of structural equation modeling is twofold. First, it aims to obtain estimates of the parameters of the model, i.e. the factor loadings, the variances and covariances of the factor, and theFile Size: KB.
This book includes chapters on major aspects of the structural equation modeling approach to research design and data analysis. It targets graduate students and seasoned researchers in the social and behavioral sciences who wish to understand the basic concepts and issues associated with the structural equation modeling approach and applications to research problems. Structural equation modeling (SEM) has advanced considerably in the social sciences. The direction of advances has varied by the substantive problems faced by individual disciplines. For example, path analysis developed to model inheritance in population genetics, and later to model status attainment in sociology. Factor analysis developed in. Structural equation modeling (SEM) is a widely applied and useful tool for project management scholars. In this Thoughtlet article, we critically reflect on the measurement philosophy underlying. Structural-equation modeling is an extension of factor analysis and is a methodology designed primarily to test substantive theory from empirical data. For example, a theory may suggest that certain mental traits do not affect other traits and that certain variables do not load on certain factors, and that structural equation modeling can be.