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An Introduction to Latent Variable Models

An Introduction to Latent Variable Models
  • Author: B. Everett
  • Publisher: Springer Science & Business Media
  • ISBN: 9789400955646
  • Category: Science
  • Page: 108
  • View: 723

Latent variable models are used in many areas of the social and behavioural sciences, and the increasing availability of computer packages for fitting such models is likely to increase their popularity. This book attempts to introduce such models to applied statisticians and research workers interested in exploring the structure of covari ance and correlation matrices in terms of a small number of unob servable constructs. The emphasis is on the practical application of the procedures rather than on detailed discussion of their mathe matical and statistical properties. It is assumed that the reader is familiar with the most commonly used statistical concepts and methods, particularly regression, and also has a fair knowledge of matrix algebra. My thanks are due to my colleagues Dr David Hand and Dr Graham Dunn for helpful comments on the book, to Mrs Bertha Lakey for her careful typing of a difficult manuscript and to Peter Cuttance for assistance with the LlSREL package. In addition the text clearly owes a great deal to the work on structural equation models published by Karl Joreskog, Dag Sorbom, Peter Bentler, Michael Browne and others.

More Books:

An Introduction to Latent Variable Models
Language: en
Pages: 108

An Introduction to Latent Variable Models

Authors: B. Everett
Categories: Science
Type: BOOK - Published: 2013-03-07 - Publisher: Springer Science & Business Media

Latent variable models are used in many areas of the social and behavioural sciences, and the increasing availability of computer packages for fitting such models is likely to increase their popularity. This book attempts to introduce such models to applied statisticians and research workers interested in exploring the structure of
Latent Variable Models
Language: en
Pages: 336

Latent Variable Models

Authors: John C. Loehlin
Categories: Business & Economics
Type: BOOK - Published: 2004-05-20 - Publisher: Psychology Press

This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary
Latent Variable Models
Language: en
Pages: 376

Latent Variable Models

Authors: John C. Loehlin, A. Alexander Beaujean
Categories: Psychology
Type: BOOK - Published: 2016-12-07 - Publisher: Taylor & Francis

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing path diagrams to explain the relationships in the models. This approach helps less mathematically-inclined readers to grasp the underlying relations among path analysis, factor analysis, and structural equation modeling, and to set
An Introduction to Latent Variable Growth Curve Modeling
Language: en
Pages: 261

An Introduction to Latent Variable Growth Curve Modeling

Authors: Terry E. Duncan, Susan C. Duncan, Lisa A. Strycker
Categories: Mathematics
Type: BOOK - Published: 2006 - Publisher: Psychology Press

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader's familiarity with analysis
An Introduction to Latent Variable Growth Curve Modeling
Language: en
Pages: 274

An Introduction to Latent Variable Growth Curve Modeling

Authors: Terry E. Duncan, Susan C. Duncan, Lisa A. Strycker
Categories: Business & Economics
Type: BOOK - Published: 2013-05-13 - Publisher: Routledge

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis

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