This study investigates the performance of various missing data treatments in Partial Least Squares Structural Equation Modeling (PLS-SEM) through Monte Carlo simulations. The authors compare four methods: listwise deletion (complete case analysis), mean imputation, regression imputation, and Expectation-Maximization (EM) algorithm. The simulations are based on two prominent social science models: the European Customer Satisfaction Index (ECSI) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The study evaluates the impact of these methods on measurement model quality, bias in estimating structural model parameters, and model accuracy. The results show that regression imputation outperforms other methods in recovering model parameters and improving parameter estimate precision. The study recommends the widespread adoption of regression and EM imputation for handling missing data in PLS-SEM studies, highlighting their effectiveness in enhancing data quality and model fit.This study investigates the performance of various missing data treatments in Partial Least Squares Structural Equation Modeling (PLS-SEM) through Monte Carlo simulations. The authors compare four methods: listwise deletion (complete case analysis), mean imputation, regression imputation, and Expectation-Maximization (EM) algorithm. The simulations are based on two prominent social science models: the European Customer Satisfaction Index (ECSI) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The study evaluates the impact of these methods on measurement model quality, bias in estimating structural model parameters, and model accuracy. The results show that regression imputation outperforms other methods in recovering model parameters and improving parameter estimate precision. The study recommends the widespread adoption of regression and EM imputation for handling missing data in PLS-SEM studies, highlighting their effectiveness in enhancing data quality and model fit.