Latent Variable Models

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A01=A. Alexander Beaujean
A01=Alexander Beaujean
A01=John C. Loehlin
advanced latent variable modeling guide
analysis
Author_A. Alexander Beaujean
Author_Alexander Beaujean
Author_John C. Loehlin
Category=PBT
composite reliability methods
confirmatory factor analysis
correlation
Correlation Matrix
diagram
Downstream Variables
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
equation
Exploratory Factor Analysis
Factor Pattern Matrix
Fit Indices
fitting
Kaiser Guttman Rule
Latent Curve Models
Latent Variable Model
Latent Variables
LISREL Matrice
LISREL software applications
matrix
measurement
Measurement Model
Minimum Average Partial Procedure
Model Fitting Programs
MZ Twin
Noncentrality Parameter
Observed Variables
path
Path Diagram
path diagram interpretation
Path Model
Population RMSEA
programs
Promax Solution
repeated measures analysis
Residual Variances
Schmid Leiman Transformation
structural
Structural Equation Analysis
structural modeling techniques
Vice Versa

Product details

  • ISBN 9781138916074
  • Weight: 566g
  • Dimensions: 156 x 234mm
  • Publication Date: 14 Feb 2017
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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 chapter’s 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 book’s 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.

John C. Loehlin is Professor Emeritus of Psychology and Computer Science at the

University of Texas at Austin. He received his PhD in Psychology from the University

of California (Berkeley).

A. Alexander Beaujean is an Associate Professor of Educational Psychology at

Baylor University. He received PhDs in Educational Psychology and School

Psychology from the University of Missouri.

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