This article provides a comprehensive guide to the use of Structural Equation Modeling (SEM) and regression techniques in Information Systems (IS) research. It begins by highlighting the growing interest in SEM techniques and their importance in IS research, emphasizing the need to compare different types of SEM techniques to select appropriate research designs. The article presents a running example using the Technology Acceptance Model (TAM) to analyze the same dataset through three statistical techniques: linear regression, LISREL, and Partial Least Squares (PLS). It then compares two classes of SEM: covariance-based SEM and partial-least-squares-based SEM, and discusses linear regression models. The article offers guidelines for when to use SEM techniques versus regression techniques, including heuristics and rule-of-thumb thresholds. It concludes with a discussion on the extent to which practice aligns with these guidelines. The tutorial includes straightforward examples, a glossary, and a structural model applying the TAM to e-commerce, demonstrating the differences between LISREL, PLS, and linear regression. The article also addresses the differences between first-generation and second-generation models, emphasizing the advantages of SEM in handling complex, multivariate data sets. It provides a detailed comparison of the two primary methods of SEM analysis—covariance analysis and partial least squares—and discusses the assumptions and algorithms used in each technique. The article concludes by summarizing the key points and providing a practical guide for researchers.This article provides a comprehensive guide to the use of Structural Equation Modeling (SEM) and regression techniques in Information Systems (IS) research. It begins by highlighting the growing interest in SEM techniques and their importance in IS research, emphasizing the need to compare different types of SEM techniques to select appropriate research designs. The article presents a running example using the Technology Acceptance Model (TAM) to analyze the same dataset through three statistical techniques: linear regression, LISREL, and Partial Least Squares (PLS). It then compares two classes of SEM: covariance-based SEM and partial-least-squares-based SEM, and discusses linear regression models. The article offers guidelines for when to use SEM techniques versus regression techniques, including heuristics and rule-of-thumb thresholds. It concludes with a discussion on the extent to which practice aligns with these guidelines. The tutorial includes straightforward examples, a glossary, and a structural model applying the TAM to e-commerce, demonstrating the differences between LISREL, PLS, and linear regression. The article also addresses the differences between first-generation and second-generation models, emphasizing the advantages of SEM in handling complex, multivariate data sets. It provides a detailed comparison of the two primary methods of SEM analysis—covariance analysis and partial least squares—and discusses the assumptions and algorithms used in each technique. The article concludes by summarizing the key points and providing a practical guide for researchers.