Latent Variable Models and Factor Analysis: A Unified Approach

Bartholomew, David J.; Knott, Martin; Moustaki, Irini

ISBN 10: 0470971924 / ISBN 13: 9780470971925

Editore: Wiley, 2011

Riguardo questo articolo

Codice articolo ABLIING23Feb2215580222261

Dati bibliografici

• Titolo: Latent Variable Models and Factor Analysis: A Unified Approach
• Editore: Wiley
• Data di pubblicazione: 2011
• Lingua: Inglese
• ISBN 10: 0470971924
• ISBN 13: 9780470971925
• Rilegatura: Rilegato
• Edizione: terza edizione
• Condizione: New

Descrizione articolo

Riassunto

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency.

This book:

• Provides a unified approach showing how such apparently diverse methods as Latent Class Analysis and Factor Analysis are actually members of the same family.
• Presents new material on ordered manifest variables, MCMC methods, non-linear models as well as a new chapter on related techniques for investigating dependency.
• Includes new sections on structural equation models (SEM) and Markov Chain Monte Carlo methods for parameter estimation, along with new illustrative examples.
• Looks at recent developments on goodness-of-fit test statistics and on non-linear models and models with mixed latent variables, both categorical and continuous.

No prior acquaintance with latent variable modelling is pre-supposed but a broad understanding of statistical theory will make it easier to see the approach in its proper perspective. Applied statisticians, psychometricians, medical statisticians, biostatisticians, economists and social science researchers will benefit from this book.

Informazioni sull?autore

David Bartholomew, Martin Knott and Irini Moustaki, Department of Statistics, The London School of Economics and Political Science, London, UK