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A first course in structural equation modeling Tenko Raykov and George A. Marcoulides.

Por: Colaborador(es): Detalles de publicación: Mahwah, N.J. Lawrence Erlbaum Associates c2006.Edición: 2nd edDescripción: ix, 238 p. ill. 24 cm. 1 CD-ROM (4 3/4 in.)ISBN:
  • 0805855882
Tema(s): Clasificación CDD:
  • 519.535 R266f2 21
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Descripciones mejoradas de Syndetics:

In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one.

Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software.

Highlights of the Second Edition include:

* Review of latent change (growth) analysis models at an introductory level

* Coverage of the popular Mplus program

* Updated examples of LISREL and EQS

* A CD that contains all of the text's LISREL, EQS, and Mplus examples.

A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

ESTE LIBRO INCLUYE UN DISCO

Includes bibliographical references (p. 227-231) and indexes.

Tabla de contenidos provista por Syndetics

  • Preface
  • Fundamentals of Structural Equation Modeling
  • Getting to Know the EQS and LISREL Programs
  • Path Analysis
  • Confirmatory Factor Analysis
  • Structural Regression Models
  • Latent Change Analysis
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