Critique of a published latent variable or SEM study – Statswork

Critique of a Published Latent Variable or SEM Study Dr. Nancy Agens, Head, Technical Operations, Statswork

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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.

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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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