Structural
Equation Modeling:
A Second Course
-- Gregory R.
Hancock & Ralph O. Mueller (Eds.)
"…an
essential reference for any applied
researcher
(student or faculty)
engaging in the practice of
structural equation
modeling."
-- Rachel T. Fouladi, Simon
Fraser University
"… a vital
contribution to the field of SEM beyond
the
introductory level."
-- Richard G. Lomax, University
of Alabama
Editors: Gregory
R. Hancock & Ralph O. Mueller
Formats:
Paperback and Hardback
Length:
448 pages
Pub. Date: January 2006
Publisher: Information Age
Publishing
Description:
This volume is intended to serve as a didactically-oriented resource
covering a broad range of advanced topics often not discussed in
introductory courses on structural equation modeling (SEM). Such topics
are important in furthering the understanding of foundations and
assumptions underlying SEM as well as in exploring SEM as a potential
tool to address new types of research questions that might not have
arisen during a first course. Chapters focus on the clear explanation
and application of topics, rather than on analytical derivations, and
contain syntax and partial output files from popular SEM software.
TABLE OF CONTENTS
1.
Introduction
Gregory
R.
Hancock, University of Maryland, College Park
Ralph
O.
Mueller, The George Washington University
Part I. Foundations
2.
The
Problem of Equivalent Structural Models
Scott
L.
Hershberger, California State University, Long Beach
3.
Formative
Measurement and Feedback Loops
Rex B.
Kline,
Concordia University
4.
Power Analysis In Covariance Structure
Modeling
Gregory
R.
Hancock, University of Maryland, College Park
Part II.
Extensions
5. Evaluating
Between-Group
Differences in Latent Variable Means
Marilyn S.
Thompson, Arizona State University
Samuel B.
Green, Arizona State University
6.
Using
Latent Growth Models to Evaluate Longitudinal Change
Gregory
R.
Hancock, University of Maryland, College Park
Frank R.
Lawrence, The Pennsylvania University
7. Mean and Covariance
Structure Mixture
Models
Phill
Gagné,
The Georgia State University
8.
Structural
Equation Models of Latent
Interaction and Quadratic Effects
Herbert
W.
Marsh, University of Western Sydney
Kit-Tai
Hau, The Chinese University of Hong Kong
Zhonglin Wen, South China Normal University
Part III.
Assumptions
9. Nonnormal and
Categorical Data in
Structural Equation Modeling
Sara J.
Finney, James Madison University
Christine
DiStefano, University of South
Carolina
10.
Analyzing Structural
Equation Models
with Missing Data
Craig
K.
Enders, Arizona State University
11.
Using
Multilevel Structural Equation Modeling
Techniques with Complex Sample Data
Laura
M.
Stapleton, University of Maryland, Baltimore County
12. The
Use of Monte Carlo Studies in Structural
Equation Modeling Research
Deborah L.
Bandalos, University of Georgia