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Original Article | Volume 2 Issue 1 (Jan-Feb, 2025) | Pages 13 - 21
A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
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1
Associate Professor, Kirloskar Institute of Management, Yantrapur, Harihar 57760, Karnataka
2
Associate Professor, Department of Business Administration, University of the People, Pasadena, CA, USA - 91101
3
Associate Professor, Kirloskar Institute of Management, Yantrapur, Harihar 577601, Karnataka
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Abstract

The method Partial Least Squares Structural Equation Modeling (PLS-SEM) has received growing interest across recent years thanks to its adaptive features for managing complex models that contain latent variables. The modeling approach of PLS-SEM optimizes dependent variable variance while providing exceptional solutions in data fraught with sample size restrictions and distribution irregularities versus traditional covariance-based structural equation modeling. The primer establishes fundamentals of PLS-SEM through its applications while presenting the modeling process along with methodology and step-by-step methodologies. We discuss both PLS-SEM advantages and restrictions while using real-world research examples from social science and healthcare fields and marketing applications.

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