This chapter provides a basic framework for researchers interested in reporting the results of their PLS analyses. It discusses key differences between PLS and covariance-based SEM (CBSEM) and explains why PLS is often used. The chapter includes two examples from Information Systems research: one with reflective indicators (mode A) and another with formative indicators (mode B). The paper highlights that while CBSEM focuses on model fit through chi-square and goodness-of-fit indices, PLS does not. Instead, PLS emphasizes the variances of dependent variables. The chapter also discusses methodological distinctions between PLS and CBSEM, such as the emphasis on covariance explanation, soft distributional assumptions, exploratory nature, modeling formative indicators, higher-order models, model complexity, sample size requirements, parameter estimation accuracy, prediction focus, determinate scores, and ease of model specification. It argues that PLS is often complementary to CBSEM and may be more suitable depending on the research context and objectives. Researchers using PLS should justify their choice by explaining the rationale for using PLS and why goodness-of-fit measures may not be as important. The chapter aims to help researchers better understand and report PLS analyses.This chapter provides a basic framework for researchers interested in reporting the results of their PLS analyses. It discusses key differences between PLS and covariance-based SEM (CBSEM) and explains why PLS is often used. The chapter includes two examples from Information Systems research: one with reflective indicators (mode A) and another with formative indicators (mode B). The paper highlights that while CBSEM focuses on model fit through chi-square and goodness-of-fit indices, PLS does not. Instead, PLS emphasizes the variances of dependent variables. The chapter also discusses methodological distinctions between PLS and CBSEM, such as the emphasis on covariance explanation, soft distributional assumptions, exploratory nature, modeling formative indicators, higher-order models, model complexity, sample size requirements, parameter estimation accuracy, prediction focus, determinate scores, and ease of model specification. It argues that PLS is often complementary to CBSEM and may be more suitable depending on the research context and objectives. Researchers using PLS should justify their choice by explaining the rationale for using PLS and why goodness-of-fit measures may not be as important. The chapter aims to help researchers better understand and report PLS analyses.