This chapter aims to provide a basic framework for researchers interested in reporting the results of their Partial Least Squares (PLS) analyses. It begins by discussing the key differences and rationale for using PLS compared to Covariance-Based Structural Equation Modeling (CBSEM), emphasizing that PLS does not focus on accounting for measurement item covariances. The chapter includes two examples from Information Systems research: one with reflective indicators (mode A) and another with formative indicators (mode B). It highlights the need for researchers to justify their choice of PLS and explain why goodness-of-fit measures are not as prominent in PLS reports. The chapter also addresses the educational aspect of explaining the underlying principles of both CBSEM and PLS, suggesting that PLS can be complementary to CBSEM and may be more suitable for certain empirical contexts and objectives.This chapter aims to provide a basic framework for researchers interested in reporting the results of their Partial Least Squares (PLS) analyses. It begins by discussing the key differences and rationale for using PLS compared to Covariance-Based Structural Equation Modeling (CBSEM), emphasizing that PLS does not focus on accounting for measurement item covariances. The chapter includes two examples from Information Systems research: one with reflective indicators (mode A) and another with formative indicators (mode B). It highlights the need for researchers to justify their choice of PLS and explain why goodness-of-fit measures are not as prominent in PLS reports. The chapter also addresses the educational aspect of explaining the underlying principles of both CBSEM and PLS, suggesting that PLS can be complementary to CBSEM and may be more suitable for certain empirical contexts and objectives.