Latent Variable Models

An Introduction to Factor, Path, and Structural Equation Analysis, Fifth Edition

By: John C. Loehlin, Alexander Beaujean

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Hardcover | 6 January 2017 | Edition Number 5

At a Glance

Hardcover
392 Pages

Edition Type
New edition

Dimensions(cm)
23.39 x 15.6 x 2.24

Hardcover

RRP $357.00

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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 up and carry out such analyses. This revised and expanded fifth edition again contains key chapters on path analysis, structural equation models, and exploratory factor analysis. In addition, it contains new material on composite reliability, models with categorical data, the minimum average partial procedure, bi-factor models, and communicating about latent variable models.

The informal writing style and the numerous illustrative examples make the book accessible to readers of varying backgrounds. Notes at the end of each chapter expand the discussion and provide additional technical detail and references. Moreover, most chapters contain an extended example in which the authors work through one of the chapterâs examples in detail to aid readers in conducting similar analyses with their own data. The book and accompanying website provide all of the data for the bookâs examples as well as syntax from latent variable programs so readers can replicate the analyses. The book can be used with any of a variety of computer programs, but special attention is paid to LISREL and R.

An important resource for advanced students and researchers in numerous disciplines in the behavioral sciences, education, business, and health sciences, Latent Variable Models is a practical and readable reference for those seeking to understand or conduct an analysis using latent variables.

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