On August 21, 2010, one reader commented on
structural equation modeling (SEM) techniques as follow: “I don’t
really know enough about SEM, I know some, but I definitely think I
need to know more. Where is the best place to start for someone who
has a basic understanding, but wants to learn more?”
reader, the purpose of this post is to provide relevant information
on Structural Equation Modeling (SEM) to you. I also want to use this
opportunity to emphasize that SEM and other second generation data
analysis techniques are increasingly being applied in management
information systems (MIS) research and practice. I will be posting
additional materials on the use, usefulness, and ease of use of SEM
in MIS research and practice on my weblog.
SEM techniques are
important for MIS research and practice because they provide powerful
ways to address key IT research and practice problems such as
understanding IT usage, end-users satisfaction, IT strategic
alignment, IT project planning and implementation, knowledge
management projects, and so on. These techniques can be extremely
useful to IT executives who have IT substantive knowledge for
modeling, exploration, and interpretation of results.
Structural Equation Modeling
equation modeling (SEM) techniques refer to a range of multivariate
methods aimed at examining the underlying relationships (or
structure) among multiple predictor and criterion variables in a
model. A structural equation modeling process requires two steps:
(a) building and testing a measurement model, and (b) building and
testing a structural model. The measurement model serves to create a
structural model including paths representing the hypothesized
associations among the research constructs.
compared and contrasted SEM techniques to other multivariate
techniques such as multiple regression analysis, multivariate
analysis of variance (MANOVA), and canonical correlation analysis
(CCA). While multiple regression analysis assesses the relationship
between several independent (or predictor) variables and a dependent
(or criterion) variable, MANOVA assesses the relationship between two
or more dependent variables and classificatory variables or factors.
In contrast, SEM techniques represent only a single relationship
between the dependent and independent variables.
regression and MANOVA do not consider the association effects between
criterion and predictor variables that generally characterized human
and behavioral problems in management. These techniques are unable
to examine a series of dependence associations concurrently. In
contrast, SEM techniques can expand the statistical efficiency and
explanatory ability for model testing with a single comprehensive
framework. According to various scholars, SEM techniques have the
ability to (a) model relationships among multiple predictor and
criterion variables, (b) represent latent variables (or unobserved
concepts) in those relationships, and (c) account for measurement
error in the estimation process.
Dow et al. (2008) compared
three structural equation techniques: path analysis, item level, and
parcel. Dow and his colleagues noted that these techniques produced
similar structural results even though they differed in the degree
they fit the data and the degree of explained variance. Path
analysis is considered as an extension of the regression model with a
regression performed for each variable in the model as endogenous
variable on others indicated by the model as causal. Item level SEM
uses individual items as measured indicators for latent variables and
requires a large number of parameters to estimate the fit of the
model to the data. Unlike path analysis, this method can test
hypotheses regarding the structural (path) model and the measurement
relations. Parcel SEM used item parcels to conduct analyses with
latent variables. Partial sum of responses to individual items were
derived from the parcels derived from partial sum of responses to
individual items. Our research study, for example, used path analysis
to build and test a business-IT strategic model for multinational
Various vendors propose different SEM software
packages: Amos 5, EQS 6, LISREL 8, Mplus 3, PLS Graph 3, PROC CALIS,
and Mx. For our study, LISREL 8 was used for statistical analyses.
LISREL is a multivariate analytical software package intended for
standard and multilevel structural equation modeling techniques.
These methods accommodate different data types including complex
survey data on continuous variables and simple random sample data on
ordinal and continuous variables.
One can note an increased
use of structural equation modeling (SEM) techniques in the social
sciences and in information systems research. Prior research used
these techniques to address business-IT strategic alignment. Kearns
and Lederer (2003) used SEM to build and test a model that examined
the relationship between business-IT strategic alignment and
organizational strategies. Their research model contained 6
constructs, 26 items, general demographics, and 8 hypotheses. Kearns
and Sabherwal (2007) used SEM to create a structural model exploring
the linkage between IT strategic alignment and IT business value.
Their research model contained 9 constructs, 44 items, and 10
Earlier studies using SEM did not propose models
of business-IT strategic alignment for multinational corporations
(MNCs). Our research study (Nkoyock, Spiker, Schmidt, & Martin, 2010)
used SEM to analyze data collected through the stratified random
sampling of two groups of IT and business managers. Our study drew
upon SEM techniques to build and test a business-IT alignment model
for MNCs with 10 constructs, 53 items, and 11 hypotheses.
SEM Modeling Process
SEM modeling process
is an incremental approach specifying the procedures for testing a
model. SEM analyses require essentially a 2-step modeling process of
building and testing measurement and structural models: (a)
measurement model, and (b) structural model.
model serves to test the reliability and validity of the measures
based on the research model. This model postulates the relationship
between the measured items and the underlying constructs or factors.
The achievement of “best fitting” measurement model consists of
attaining a non-significant chi-square statistic and the recommended
values of other goodness-of-fit indices. The second step of the SEM
modeling process is the test of the structural model.
research study used the measurement model to create a structural
model, including paths representing the hypothesized associations
among research constructs.
The structural model is the path
model defining the relationships among the latent or unobserved
variables. The structural model specifies which latent variables
cause changes in the values of other latent variables. Various
scholars posited the achievement of “best fitting” structural model
requires the incorporation of theory, substantive knowledge, previous
experience, or other guidelines to discern which independent
variables predict each dependent variable.
In our research
study, the initial structural model included three latent exogenous
variables (organizational emphasis on knowledge management,
management of perceived environmental uncertainty, and management of
transnational IS strategies), seven latent endogenous variables
represented the remaining constructs, and the hypothesized direct
paths. The main outcome of our study is a theoretical and practical
perspective of business-IT strategic alignment for multinational
corporations. For more details about the results of this research
study, please visit www.nkoyock.net.
Dow, K. E., Jackson, C., Wong, J., &
Leitch, R. A. (2008). A comparison of structural equation modeling
approaches: The case of user acceptance of information systems.
Journal of Computer Information Systems, 48(4), 106-114.
Kearns, G. S., & Lederer, A. L. (2003). A resource-based view of
strategic IT alignment: How knowledge sharing creates competitive
advantage. Decision Sciences, 34(1), 1-29. doi:
Kearns, G. S., & Sabherwal, R. (2007).
Strategic alignment between business and information technology: A
knowledge-based view of behaviors, outcomes, and consequences.
Journal of Management Information Systems, 23(3), 129-162.
Nkoyock, A., Spiker B. K., Schmidt T., & Martin, C. (2010).
Business-IT strategic alignment for complex multinational
corporations: The case of the U.N. Secretariat. MIS
Quarterly, to be published soon.