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Basic Principles of Structural Equation Modeling

An Introduction to LISREL and EQS

  • Ralph O. Mueller

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Ralph O. Mueller
    Pages 1-61
  3. Ralph O. Mueller
    Pages 62-128
  4. Ralph O. Mueller
    Pages 129-178
  5. Back Matter
    Pages 179-231

About this book

Introduction

During the last two decades, structural equation modeling (SEM) has emerged as a powerful multivariate data analysis tool in social science research settings, especially in the fields of sociology, psychology, and education. Although its roots can be traced back to the first half of this century, when Spearman (1904) developed factor analysis and Wright (1934) introduced path analysis, it was not until the 1970s that the works by Karl Joreskog and his associates (e. g. , Joreskog, 1977; Joreskog and Van Thillo, 1973) began to make general SEM techniques accessible to the social and behavioral science research communities. Today, with the development and increasing avail­ ability of SEM computer programs, SEM has become a well-established and respected data analysis method, incorporating many of the traditional analysis techniques as special cases. State-of-the-art SEM software packages such as LISREL (Joreskog and Sorbom, 1993a,b) and EQS (Bentler, 1993; Bentler and Wu, 1993) handle a variety of ordinary least squares regression designs as well as complex structural equation models involving variables with arbitrary distributions. Unfortunately, many students and researchers hesitate to use SEM methods, perhaps due to the somewhat complex underlying statistical repre­ sentation and theory. In my opinion, social science students and researchers can benefit greatly from acquiring knowledge and skills in SEM since the methods-applied appropriately-can provide a bridge between the theo­ retical and empirical aspects of behavioral research.

Keywords

Covariance matrix Descriptive statistics Factor analysis Measure Statistical Method best fit data analysis linear regression psychology statistics structural equation modeling

Authors and affiliations

  • Ralph O. Mueller
    • 1
  1. 1.Department of Educational Leadership, Graduate School of Education and Human DevelopmentThe George Washington UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-3974-1
  • Copyright Information Springer-Verlag New York 1996
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8455-0
  • Online ISBN 978-1-4612-3974-1
  • Series Print ISSN 1431-875X
  • Buy this book on publisher's site