Structural Equation Modeling (SEM) and regression are two statistical techniques used in research. This paper compares and contrasts these techniques to help researchers choose the appropriate method for their studies. The authors present a running example using the Technology Acceptance Model (TAM) to illustrate the differences between SEM and regression. They also discuss the two main types of SEM: covariance-based SEM and partial least squares (PLS) SEM. The paper provides guidelines for when to use SEM versus regression and offers heuristics and thresholds to guide research practice. It also discusses the extent to which research practices align with these guidelines. The paper is written for readers with basic knowledge of multivariate statistics and does not assume prior familiarity with SEM tools like LISREL or PLS. The authors conclude that SEM is more suitable for modeling complex processes and that regression is better for predictive applications. The paper also highlights the importance of measurement validity and reliability in research.Structural Equation Modeling (SEM) and regression are two statistical techniques used in research. This paper compares and contrasts these techniques to help researchers choose the appropriate method for their studies. The authors present a running example using the Technology Acceptance Model (TAM) to illustrate the differences between SEM and regression. They also discuss the two main types of SEM: covariance-based SEM and partial least squares (PLS) SEM. The paper provides guidelines for when to use SEM versus regression and offers heuristics and thresholds to guide research practice. It also discusses the extent to which research practices align with these guidelines. The paper is written for readers with basic knowledge of multivariate statistics and does not assume prior familiarity with SEM tools like LISREL or PLS. The authors conclude that SEM is more suitable for modeling complex processes and that regression is better for predictive applications. The paper also highlights the importance of measurement validity and reliability in research.