Critique of a Published Latent Variable or SEM Study
Dr. Nancy Agens, Head,
Technical Operations, Statswork
info@statswork.com
I. INTRODUCTION
Structural equation modelling (SEM)
becomes a major statistical technique in
examining complex research problems in
marketing and international business. In
most research, the SEM uses covariance
based modelling and later few researchers
argued to use the partial least square
approach for SEM. In this blog, a critical
review of SEM technique presented in
Richter et al (2014) is discussed with
application to business sector.
Six journals related to the business
management and marketing have been
considered and the articles related to SEM
has been scrutinized for this purpose. After
the classification of methods used, it is
found that 379 articles used covariance
based SEM and 45 used partial least
square based SEM. Researchers often
interested in finding the same results by
using these both methods of Structural
equation modelling. However, the
consistency of the partial least square
method or the development of new
algorithm may satisfies this task fully and
yield same result as in covariance based
structural equation modelling.
Generally, the partial least square SEM is
useful to handle complex models and
provide better prediction with no demand
of the data. Thus, this article clearly
reviewed the methodology adopted either
CB SEM or PLS SEM and the purpose of
using the SEM model for better
understanding.
Lets look at few important factors which
differentiate the covariance based and
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partial least square SEM in modelling
purpose.
The covariance based SEM has a strong
theoretical background and it estimates the
model by minimizing the covariance
matrix of the theoretical model and the
model based on empirical covariance
matrix of the data. Further, it is used to
identify the extent of empirical fit towards
the theoretical model.
However, the partial least square SEM is a
discovery oriented approach, that is,
without having a prior model and testing
the same, PLS SEM acts as Predictive
Analysis from the latent variable score. In
addition. PLS SEM is suitable for
modelling complex business problems. In
covariance based SEM, the complexity of
the model influence the goodness-of-fit
statistics. For example, consider a chisquare test statistic, then if the
complexity of the model or the number of
parameters increases then the chi-square
value will get decreased. Hence, the result
will be either the correct model or the
highly fitted model because of the
complexity of the problem.
In the case of PLS SEM, the number of
parameters is not a problem (complexity)
until the sample size is sufficient. Also,
PLS SEM provides more appropriate
prediction than the maximum likelihood
estimation in CB SEM (Reinartz et al.
2009). Hence, it is important to decide
which approach is useful for the analysis
while carrying out the research. The
following table explains the number of
articles used covariance based and partial
least square SEM for the review purpose.
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Table 1: Number of Articles used Covariance Based and Partial Least Square
In addition, the review went deeper and
found that how many articles used
measurement model and the structural
model or the both for the analysis and a
proper justification of using the same.
From the results, it is found that only few
researchers justified the use of CB SEM is
that to test the theory using statistical
hypothesis testing and others are not and in
the case of PLS SEM most of the
researchers justified the usage of the
proposed model for analysis. Thus, it is
concluded that the PLS SEM might be a
better choice for conducting an analysis for
business and management. Furthermore,
the assumptions and the multicollinearity
factors in the data has been statistically
reviewed and provided a basic guidelines
for using the PLS structural equation
model and the tolerance level for various
factors such as VIF, reliability, validity,
etc.
In conclusion, the studies considered for
understanding the better method for
business problem found that the PLS SEM
is the best methodology than CB SEM
because often the business industry wants
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a predictive model to enhance their
business standard or in investments. Thus,
PLS SEM satisfies the needs and works
well for predicting complex problems even
for the small sample sizes.
Further, it is advised to make a critical
assessment of the methodology or an
analytical approach before making a
business decisions. If the objective is to
develop the Theoretical Framework, then
the PLS SEM is appropriate and the
characteristics such as sample size,
assumptions of the distribution, the type of
measurement should be considered as
secondary one.
To sum up, SEM approach provides a
better understanding of the complex
problems in the field of business and
marketing and allow us to use various
modelling approaches. In addition to it,
PLS SEM acts as an major tool for the
exploratory analysis and it outperformed
CB SEM in many cases. A proper
sampling methodology should be adopted
for the analysis purpose and sample size
and its measurement type is also plays a
major role in the inference. Further, there
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has been few researchers that provide new
algorithm to show both SEM models
performs equally well. But, using a proper
tool makes the inference valid than using
the new method which results equal in
both CB and PLS SEM. Also, there should
be chance of lack of robustness in using
the partial least square method than the
maximum likelihood method. The
researchers should take care of that issue
because if the data contains influential or
the multi collinearity is present in the data
then the results may lead to invalid
conclusion. Thus, usage of PLS SEM
becomes a valid methodology to
understand the relationship between the
international business and the marketing
strategies. In this blog, I have listed out
few critics of the latent variable models
with an application to the international
marketing and business. However, this
may not be the case if you take other field
of research. Thus, a proper guideline
should be considered before processing
any Structural Equation Modeling.
REFERENCES
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6.
N F Richter et al (2016). A critical look at the use of
SEM in business research. International Marketing
Review. 33, 376-404.
Bentler, P.M. and Huang, W. (2014), “On
components, latent variables, PLS and simple
methods: reactions to Ridgon’s rethinking of PLS”,
Long Range Planning, Vol. 47 No. 3, pp. 138-145
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2012),
“Partial least squares: the better approach to
structural equation modeling?”, Long Range
Planning, Vol. 45 Nos 5-6, pp. 312-319.
Michael O. Killian et al (2019). A Systematic
Review of Latent Variable Mixture Modeling
Research in Social Work Journals. LVMM
Systematic Review, 1-36.
Joseph F. Hair et al (2019). When to use and how to
report the results of PLS-SEM. EBR, 31, 1-24.
Khan, Gohar F., Marko Sarstedt, Wen-Lung Shiau,
Joseph F. Hair, Christian M. Ringle, and Martin
Fritze (2018). Methodological research on partial
least squares structural equation modeling (PLSSEM): An analysis based on social network
approaches. Internet Research, forthcoming
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