Table Of ContentComposite-Based Structural Equation Modeling
Methodology in the Social Sciences
David A. Kenny, Founding Editor
Todd D. Little, Series Editor
www.guilford.com/MSS
This series provides applied researchers and students with analysis and research design books that
emphasize the use of methods to answer research questions. Rather than emphasizing statistical
theory, each volume in the series illustrates when a technique should (and should not) be used and
how the output from available software programs should (and should not) be interpreted. Common
pitfalls as well as areas of further development are clearly articulated.
RECENT VOLUMES
PRINCIPLES AND PRACTICE OF STRUCTURAL EQUATION MODELING,
FOURTH EDITION
Rex B. Kline
HYPOTHESIS TESTING AND MODEL SELECTION IN THE SOCIAL SCIENCES
David L. Weakliem
REGRESSION ANALYSIS AND LINEAR MODELS: CONCEPTS, APPLICATIONS,
AND IMPLEMENTATION
Richard B. Darlington and Andrew F. Hayes
GROWTH MODELING: STRUCTURAL EQUATION
AND MULTILEVEL MODELING APPROACHES
Kevin J. Grimm, Nilam Ram, and Ryne Estabrook
PSYCHOMETRIC METHODS: THEORY INTO PRACTICE
Larry R. Price
INTRODUCTION TO MEDIATION, MODERATION, AND CONDITIONAL PROCESS ANALYSIS:
A REGRESSION-BASED APPROACH, SECOND EDITION
Andrew F. Hayes
MEASUREMENT THEORY AND APPLICATIONS FOR THE SOCIAL SCIENCES
Deborah L. Bandalos
CONDUCTING PERSONAL NETWORK RESEARCH: A PRACTICAL GUIDE
Christopher McCarty, Miranda J. Lubbers, Raffaele Vacca, and José Luis Molina
QUASI-EXPERIMENTATION: A GUIDE TO DESIGN AND ANALYSIS
Charles S. Reichardt
THEORY CONSTRUCTION AND MODEL-BUILDING SKILLS: A PRACTICAL GUIDE
FOR SOCIAL SCIENTISTS, SECOND EDITION
James Jaccard and Jacob Jacoby
LONGITUDINAL STRUCTURAL EQUATION MODELING WITH Mplus:
A LATENT STATE–TRAIT PERSPECTIVE
Christian Geiser
COMPOSITE-BASED STRUCTURAL EQUATION MODELING:
ANALYZING LATENT AND EMERGENT VARIABLES
Jörg Henseler
Composite-Based
Structural Equation Modeling
Analyzing Latent and Emergent Variables
..........................................................................
Jörg Henseler
Series Editor’s Note by Todd D. Little
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Series Editor’s Note
An analyst’s toolbox can’t be too broad. Bringing Jörg Henseler’s book
on composite-based structural equation modeling (SEM) to Guilford’s
Methodology in the Social Sciences series adds a critical tool to our incred-
ible collection of analytic resources. I have been extremely pleased with
each contribution to the series and Jörg’s is no exception. As someone who
loves SEM in general, I was blown away with what composite-based SEM
adds and broadens in terms of analytic dexterity. Jörg’s expertise is both
broad and deep and, coupled with his incredible ability to make the new
concepts accessible, is truly an extraordinary gift. The culmination of this
gift is Composite-Based Structural Equation Modeling: Analyzing Latent and
Emergent Variables.
So, what does Jörg bring to our collective toolbox? As intimated in the
title of his book, SEM is a factor-based modeling approach to identify
latent variables from common variance among a set of indicators. Jörg’s
coverage of this approach is both refreshing and quite useful to help
contrast the benefits of the composite-based approach to SEM that focuses
on variance-based decompositions using partial least squares estimation
to identify emergent variables. Emergent variables are useful in a number
of contexts where latent variables may not be optimal.
Using synthesis theory (instead of measurement theory), Jörg demon-
strates how emergent variables are a formidable tool to model forma-
tive concepts such as activities, capabilities, designs, indices, instruments,
mixes, norms, orientations, policies, practices, quality, skills, solutions,
strategies, systems, treatments, values, and the like. Consequently, this
book is of particular value for researchers in disciplines in which com-
posites are ubiquitous, such as in business (e.g., marketing), criminology,
education, ecology, sociology, political science, information systems, and
so on.
Jörg provides detailed tutorials using his easy-to-use software program,
ADANCO, and the R package, cSEM. There is also an excellent companion
website (see the box at the end of the table of contents) that includes the data
and syntax files for the diverse examples included in this book, along with
v
vi Series Editor’s Note
presentation slides that are very handy for developing course content on
composite-based modeling. One of the added values of Jörg’s integrative
work is how he has solved the conundrum integrating multiple interre-
lated composites into structural equation models. His work is not just an
introduction to composite-based modeling but includes many critical ad-
vanced concepts in the context of emergent variables, including mediation,
moderation, higher-order variables, multiple-group modeling, and nonlin-
ff
ear e ects, as well as confirmatory composite analysis (sets of interrelated
composites, which is the analogue to confirmatory factor analysis).
Jörg’s book is very refreshing to read and is extremely accessible for stu-
dents and professionals alike. I personally learned a tremendous amount
of wonderful information that I will integrate into my own research and
teaching going forward. As an old dog, I enjoyed learning new tricks with
the aid of Jörg, “the composite whisperer.” As always, enjoy!
TODDD.LITTLE
Isolatingatmy“Wit’sEnd”retreat
Lakeside,Montana
Preface
Structural equation modeling (SEM) has become an important element of
the methodological toolbox of researchers in social and business science.
Both its usefulness and its ease of use have led to its widespread adoption.
While, for instance, SEM’s capability to test entire theories, to distinguish
between substantial theory and auxiliary theory (i.e., construct operational-
ization), and to take into account and correct for measurement error largely
explains its usefulness, an engaged community (in particular, SEMNET)
as well as a rich body of literature have increased its ease of use.
Why another book on SEM? Given the plethora of established SEM
textbooks such as Acock (2013), Bollen (1989b), Brown (2014), Byrne (1998),
Hancock and Mueller (2013), Hayduk (1987), Hoyle (2012), Kaplan (2008),
Kline (2015), Loehlin and Beaujean (2017), Marcoulides and Moustaki
(2002), Mulaik (2009), Raykov and Marcoulides (2006), or Schumacker and
Lomax (2016), this question is obvious. The answer is simple, but also
somewhat surprising: They miss a valuable capability of SEM, namely the
possibility to model, estimate, and test composite models. Not that I’m the
first one to notice. Already more than 10 years ago, Grace (2006, p. 143)
noted, “Up to this present time, the emphasis in SEM has been on latent
variables as the means of conveying theoretical concepts. It is my view that
this is quite limiting.” Since then, nothing has changed. SEM is widely
understood as “factor-based SEM” (Rigdon, 2012), and all extant introduc-
tory SEM texts promulgate that the auxiliary theory has to be expressed by
common factor models. However, they neglect that while SEM can correct
for measurement error, it does not imply that it has to do that.
Alternatively, researchers can model constructs as composites, i.e., as
linear combinations of observed variables. While researchers in ecology,
business, design sciences, sensory science, and beyond have started to work
with composites, methodologists have left the central questions around
composites unanswered: Are composites really a model? How should one
specify composite models? When are composite models identified? How
can composite models be estimated? Do composite models require new
vii
viii Preface
tests of model fit? These are the questions that motivated the genesis of
thisbook.
ThepresentbookrethinksSEMwithregardtotheemployedauxiliary
theory and lays the focus on composites. To do so, it contains a couple of
chaptersandsectionsthatareunique;i.e.,theydonotexistinthisformin
anyothertextbookonSEM:
• ff
Chapter 1 presents a typology of SEM techniques that di erentiates
betweencomposite-basedandfactor-basedSEM,aswellasbetween
variance-basedandcovariance-basedSEM.
•
In Chapter 2, this book dives particularly deep into the nature of
auxiliarytheories. Itdevelopssynthesistheoryasanauxiliarytheory
ofemergentvariablesandcontrastsitwiththewell-knownmeasure-
menttheoryoflatentvariables.
•
Section 3.2 shows how to specify composite models by means of
emergentandexcrescentvariables.
•
Chapter 4 covers the particular identification issues of composite
models.
•
AlandmarkiscertainlyChapter8,whichexplainsconfirmatorycom-
posite analysis (CCA). CCA was recently introduced by Schuberth,
Henseler,andDijkstra(2018a)inanalogytoitswell-knowncounter-
part,confirmatoryfactoranalysis(CFA).
•
In most chapters, readers will find software tutorials explaining the
useofADANCOandtheRpackagecSEM.
On the one hand, this book provides a full-fledged introduction to SEM,
and covers all basic steps: model specification, model identification, model
estimation, and model testing and assessment. In this way, the book is
much more similar to classical texts on SEM (in particular Kline, 2015) than
to textbooks dedicated to particular composite-based SEM techniques such
as partial least squares path modeling (e.g., Falk & Miller, 1992; Hair, Hult,
Ringle, & Sarstedt, 2016; Sanchez, 2013; Wong, 2019) or generalized struc-
tured component analysis (Hwang & Takane, 2014), and it can formidably
serve as a primer for novices in SEM. On the other hand, it is a valuable
reference for readers who already have a background in using SEM for
factor models and who would like to learn more about the use of SEM
for composite models. Finally, the book covers advanced topics that are
ff ff
useful for all analysts, for instance, moderating e ects, mediating e ects,
and higher-order constructs. Experienced users will find useful details,
extensions, and clarifications. Readers will, for instance, learn
Preface ix
•
whatcomposite-basedSEMisandwhichstepsitentails,
•
what types of research questions can be answered using composite-
basedSEM,
•
howstructuralequationmodelsshouldbespecifiedusingADANCO
andcSEM,
•
howtoreporttheresultsofcomposite-basedSEMandwheretofind
therelevantoutputinADANCOandcSEM,and
•
howtointerpretthefindings.
The book contains many visualizations, examples, and summary ta-
bles. Datasets are provided so that the reader can do the examples on his
or her own computer. The book is written at a level such that beginning
PhD students and advanced master’s students in management, marketing,
information systems research, ecology, psychology, sociology, communica-
tion science, and most other fields will be able to understand it without
problems. However, the audience for this monograph is by no means lim-
ited to researchers in the aforementioned disciplines. Rather, the book also
opens composite-based SEM’s application spectrum toward more design-
oriented disciplines, such as psychotherapy, tourism technology, human-
media interaction, and new technology research. In these disciplines, CCA
as implemented in ADANCO and cSEM has the potential to play a sim-
ilar role to the one CFA plays in the behavioral sciences.
Thank you very much to the highly professional team of The Guilford
Press, in particular C. Deborah Laughton, Laura Patchkofsky, Robert
Sebastiano, Juliet Simon, and Dazzia Szczepaniak, for their guidance
through the publishing process. Moreover, I would like to thank all the
people who supported me in one way or the other in writing this book, be it
by providing comments about the various chapters or by permitting me to
use materials of theirs. In particular, I would like to express my gratitude
to:
•
RichardP.Bagozzi,UniversityofMichigan,AnnArbor,MI,USA
•
JoseBenitez,ESCRennesSchoolofBusiness,France
•
Kenneth A. Bollen, University of North Carolina, Chapel Hill, NC,
USA
•
AnaCastillo,UniversityofGranada,Spain
•
GabrielCepeda,UniversityofSeville,Spain
•
NaveenDonthu,GeorgiaStateUniversity,Atlanta,GA,USA
•
GeorgFassott,UniversityofKaiserslautern,Germany
•
ToddLittle,TexasTechUniversity,Lubbock,TX,USA
•
CarolineLancelotMiltgen,AudenciaBusinessSchool,Nantes,France
