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A comparative analysis of multivariate approaches for data analysis in management sciences


A comparative analysis of multivariate approaches for data analysis in management sciences

Name and surname of author:

Rizwan Raheem Ahmed, Dalia Streimikiene, Justas Streimikis, Indre Siksnelyte-Butkiene

Early Access publication date:
23.01.2024
Keywords:
Partial least square-SEM (PLS-SEM), covariance-based-SEM (CB-SEM), SEM-based multivariate approach, multiple manifest variables, PLS-SEM vs. CB-SEM modeling
DOI (& full text):
Anotation:
The researchers use the SEM-based multivariate approach to analyze the data in different fields, including management sciences and economics. Partial least square structural equation modeling (PLS-SEM) and covariance-based structural equation modeling (CB-SEM) are powerful data analysis techniques. This paper aims to compare both models, their efficiencies and deficiencies, methodologies, procedures, and how to employ the models. The outcomes of this paper exhibited that the PLS-SEM is a technique that combines the strengths of structural equation modeling and partial least squares. It is imperative to know that the PLS-SEM is a powerful technique that can handle measurement error at the highest levels, trim and unbalanced datasets, and latent variables. It is beneficial for analyzing relationships among latent constructs that may not be candidly witnessed and might not be applied in situations where traditional SEM would be infeasible. However, the CB-SEM approach is a procedure that pools the strengths of both structural equation modeling and confirmatory factor analysis. The CB-SEM is a dominant multivariate technique that can grip multiple groups and indicators; it is beneficial for analyzing relationships among latent variables and multiple manifest variables, which can be directly observed. The paper concluded that the PLS-SEM is a more suitable technique for analyzing relations among latent constructs, generally for a small dataset, and the measurement error is high. However, the CB-SEM is suitable for analyzing compound latent and manifest constructs, mainly when the goal is to generalize results to specific population subgroups. The PLS-SEM and CB-SEM have specific efficiencies and deficiencies that determine which technique to use depending on resource availability, the research question, the dataset, and the available time.
The researchers use the SEM-based multivariate approach to analyze the data in different fields, including management sciences and economics. Partial least square structural equation modeling (PLS-SEM) and covariance-based structural equation modeling (CB-SEM) are powerful data analysis techniques. This paper aims to compare both models, their efficiencies and deficiencies, methodologies, procedures, and how to employ the models. The outcomes of this paper exhibited that the PLS-SEM is a technique that combines the strengths of structural equation modeling and partial least squares. It is imperative to know that the PLS-SEM is a powerful technique that can handle measurement error at the highest levels, trim and unbalanced datasets, and latent variables. It is beneficial for analyzing relationships among latent constructs that may not be candidly witnessed and might not be applied in situations where traditional SEM would be infeasible. However, the CB-SEM approach is a procedure that pools the strengths of both structural equation modeling and confirmatory factor analysis. The CB-SEM is a dominant multivariate technique that can grip multiple groups and indicators; it is beneficial for analyzing relationships among latent variables and multiple manifest variables, which can be directly observed. The paper concluded that the PLS-SEM is a more suitable technique for analyzing relations among latent constructs, generally for a small dataset, and the measurement error is high. However, the CB-SEM is suitable for analyzing compound latent and manifest constructs, mainly when the goal is to generalize results to specific population subgroups. The PLS-SEM and CB-SEM have specific efficiencies and deficiencies that determine which technique to use depending on resource availability, the research question, the dataset, and the available time.
APA Style Citation:

Ahmed, R. R., Streimikiene, D., Streimikis, J., & Siksnelyte-Butkiene, I.
(2024). A comparative analysis of multivariate approaches for data analysis in management
sciences. E&M Economics and Management, Vol. ahead-of-print, (No. ahead-of-print). https://
doi.org/10.15240/tul/001/2024-5-001.

Ahmed, R. R., Streimikiene, D., Streimikis, J., & Siksnelyte-Butkiene, I. (2024). A comparative analysis of multivariate approaches for data analysis in management sciences. E&M Economics and Management, Vol. ahead-of-print, (No. ahead-of-print). https://doi.org/10.15240/tul/001/2024-5-001.


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