Latent variable models comprise an important branch of multivariate analysis. In particular, they are used to create a framework for reducing data from large scale statistical enquiries to manageable proportions. Factor analysis, a key latent variable model, has been effectively applied to problems in sociology, geology, chemistry, and psychology. This book provides a latent-variable framework for psychometricians and statisticians in their specification and use of suitable research models. Several chapters explore fields where the data are categorical and the latent variables are metrical.

Latent Variable Models and Factor Analysis (Griffin's Statistical Monographs and Courses)

Science & Math

Stuart, Alan

Bartholomew, D.J.

Mathematics

Oxford University Press

<p>Latent variables, variables that cannot be observed directly, have numerous applications, particularly in psychometrics and sociology, and this new edition provides a comprehensive and unified treatment. Extensively revised and including much recent work, the book develops a framework for handling latent variables with standard statistical methods. It includes an introductory chapter, followed by a careful development of the General Linear Latent Variable Model (GLLVM). A large class of latent variable models turns out to be special cases of GLLVM, and the book devotes a number of chapters to specific models. The treatment aims to be mathematically complete and emphasizes questions of precision and goodness of fit. The book features numerous examples and concludes with a critique of some of the popular and widely used linear models.</p>

Latent Variable Models and Factor Analysis (Kendall's Library of Statistics)

Politics & Social Sciences

Knott, M.

Social Sciences

Hodder Education Publishers

Stochastic Models for Social Processes (Chemical Analysis)

John Wiley & Sons, Ltd.

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