You are here

Disable VAT on Taiwan

Unfortunately, as of 1 January 2020 SAGE Ltd is no longer able to support sales of electronically supplied services to Taiwan customers that are not Taiwan VAT registered. We apologise for any inconvenience. For more information or to place a print-only order, please contact uk.customerservices@sagepub.co.uk.

Structural Equation Modeling
Share
Share

Structural Equation Modeling
Foundations and Extensions

Second Edition


July 2008 | 272 pages | SAGE Publications, Inc
Using detailed, empirical examples, Structural Equation Modeling, Second Edition, presents a thorough and sophisticated treatment of the foundations of structural equation modeling (SEM). It also demonstrates how SEM can provide a unique lens on the problems social and behavioral scientists face.

Intended Audience

While the book assumes some knowledge and background in statistics, it guides readers through the foundations and critical assumptions of SEM in an easy-to-understand manner.

 
Preface to the Second Edition
 
1. Historical Foundations of Structural Equation Modeling for Continuous and Categorical Latent Variables
 
2. Path Analysis: Modeling Systems of Structural Equations Among Observed Variables
 
3. Factor Analysis
 
4. Structural Equation Models in Single and Multiple Groups
 
5. Statistical Assumptions Underlying Structural Equation Modeling
 
6. Evaluating and Modifying Structural Equation Models
 
7. Multilevel Structural Equation Modeling
 
8. Latent Growth Curve Modeling
 
9. Structural Models for Categorical and Continuous Latent Variables
 
10. Epilogue: Toward a New Approach to the Practice of Structural Equation Modeling

It is great book for long. data analysis, I will definitely use this book for my students.

Mr Fatih Koca
COLLEGE OF EDUCATION, TEXAS TECH
July 3, 2013

excellent companion to primary book selected.

Dr Ernesto Rosario-Hernandez
Psychology , Ponce School of Medicine
July 3, 2013

this book is too difficult for any students

Dr George Kodsy
information technology, college applied science
May 22, 2013

Easy to use text - provides the essentials for researchers who are not primarily methodologists, making it excellent for students.

Dr Scott Smith
Sociology Anthropology Dept, Oakland University
August 2, 2012

The text is very technical and difficult for the hons and M level students

Professor David Maree
Psychology , University of Pretoria
July 10, 2012

Good introductory and advanced text on SEM. Use of R for supplemental analyses is good choice.

Professor Joachim Kruse
Department of Psychology & Pedagogics, Ludwig Maximilian University of Munich
August 8, 2011

The students found it difficult to understand, it is suitable for PhD research or higher

Dr Ali Al-Sherbaz
Business & International Mgmt Division, Northampton University
August 2, 2011

Although the course will be held in English, students wanted some literature to be in German and the book "Strukturgleichungsmodellierung" by Weiber and Mühlhaus was chosen instead.

Mrs Carla Sánchez Aguilar
Faculty of Economics, Georg-August University of Göttingen
July 19, 2011

An excellent, accessible book. The structure is clear, the maths are well explained and progressed throughout the book. With the book it is possible to embark on SEM without floundering.

Dr Paula Kersten
School of Health Sciences, Southampton University
April 17, 2011

This is an excellent introduction into Structural Equation Modelling. I liked the historical approach detailing from which ideas SEM actually emerged, which helped in getting the overall idea. Moreover, having studied mathematics, I appreciated the connection that is established between the conceptual ideas behind and the mathematics of SEM. In the end the book helped me to understand SEM beyond the mere use of a software.

Professor Knut Neumann
Please select your department, University of Kiel
March 14, 2011
Key features
NEW TO THIS EDITION:
  • The foundations of SEM, including path analysis and factor analysis.
  • Traditional SEM for continuous latent variables, including latent growth curve modeling for continuous growth factors, and issues in testing assumptions of SEM.
  • SEM for categorical latent variables, including latent class analysis, Markov models (latent and mixed latent), and growth mixture modeling.
  • Philosophical issues in the practice of SEM, including the problem of causal inference.

Sage College Publishing

You can purchase or sample this product on our Sage College Publishing site:

Go To College Site

This title is also available on SAGE Research Methods, the ultimate digital methods library. If your library doesn’t have access, ask your librarian to start a trial.