Latent Variable Models and Factor Analysis: A Unified Approach

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.

Keywords: Applied Probability & Statistics, David Bartholomew, Martin Knott, Irini Moustaki, latent variable models, factor analysis, covariate effects, nonlinear terms, multiple population analysis, univariate and bivariate margins, structural equation models, SEM, Markov Chain Monte Carlo methods, Foundations of Factor Analysis, Stanley Mulaik, Generalized Latent Variable Modeling, Anders Skrondal, Sophia Rabe-Hesekth,