Data Analysis Using Partial Least Squares Structural Equation Modeling (PLS-SEM) in Conducting Quantitative Research

In contemporary research, Partial Least Squares Structural Equation Modeling (PLS-SEM) has emerged as a crucial statistical tool, particularly effective for analyzing complex structural models involving multiple constructs and indicators.This paper aims to elucidate the application of PLS-SEM in quantitative research, highlighting its advantages in extending theories and simultaneously estimating measurement and structural models.The methodological approach is divided into three primary stages: data screening and diagnostic tests, measurement model assessment, and structural model assessment.The data screening ensures dataset suitability by addressing missing data and outliers, while diagnostic tests fulfil normality, linearity, and multicollinearity assumptions.The measurement model assessment validates constructs through composite reliability and average variance extracted (AVE) metrics.The structural model assessment evaluates the significance and relevance of relationships between constructs, determines the coefficient of determination (R and adjusted R), assesses mediating effects, and analyzes the moderating variables.By detailing these methodological steps, the article provides a comprehensive guide for researchers aiming to employ PLS-SEM in their studies, emphasizing its rigour and practicality in handling complex theoretical models.