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.