Partial least squares structural equation modeling (PLS-SEM) is a popular method for estimating complex path models with latent variables and their relationships. This chapter introduces the fundamentals of measurement and structural theory, explaining how to specify and estimate path models using PLS-SEM. It also provides an overview of complementary analytical techniques and illustrates the concepts through an application of the PLS-SEM method to a corporate reputation model using SmartPLS 3 software. Keywords: Partial least squares · PLS · PLS path modeling PLS-SEM · SEM · Variance-based structural equation modeling The chapter covers the principles of structural equation modeling, path models with latent variables, structural theory, measurement theory, path model estimation with PLS-SEM, and the evaluation of PLS-SEM results. It includes a research application on a corporate reputation model, with data, model estimation, and results evaluation. The chapter concludes with cross-references and references. PLS-SEM was developed from Wold's efforts in the 1970s and 1980s, aiming to create models for social sciences where "soft models and soft data" are common. It differs from covariance-based SEM in that it is a variance-based approach, focusing on the relationships between variables rather than the distributional assumptions. PLS-SEM is widely used in various disciplines, including accounting, management, hospitality, international management, operations management, management information systems, and marketing. It is particularly suitable for complex models with latent variables and is favored for its flexibility and robustness in handling non-normal data and small sample sizes.Partial least squares structural equation modeling (PLS-SEM) is a popular method for estimating complex path models with latent variables and their relationships. This chapter introduces the fundamentals of measurement and structural theory, explaining how to specify and estimate path models using PLS-SEM. It also provides an overview of complementary analytical techniques and illustrates the concepts through an application of the PLS-SEM method to a corporate reputation model using SmartPLS 3 software. Keywords: Partial least squares · PLS · PLS path modeling PLS-SEM · SEM · Variance-based structural equation modeling The chapter covers the principles of structural equation modeling, path models with latent variables, structural theory, measurement theory, path model estimation with PLS-SEM, and the evaluation of PLS-SEM results. It includes a research application on a corporate reputation model, with data, model estimation, and results evaluation. The chapter concludes with cross-references and references. PLS-SEM was developed from Wold's efforts in the 1970s and 1980s, aiming to create models for social sciences where "soft models and soft data" are common. It differs from covariance-based SEM in that it is a variance-based approach, focusing on the relationships between variables rather than the distributional assumptions. PLS-SEM is widely used in various disciplines, including accounting, management, hospitality, international management, operations management, management information systems, and marketing. It is particularly suitable for complex models with latent variables and is favored for its flexibility and robustness in handling non-normal data and small sample sizes.