This paper aims to clarify the concept of moderator variables in marketing research by presenting a typology and a framework for identifying their presence and type. The authors define moderator variables as those that systematically modify the strength or form of the relationship between a predictor and a criterion variable. They introduce two types of moderator variables: homologizers, which influence the strength of the relationship, and pure or quasi moderator variables, which modify the form of the relationship. The paper discusses various methods for identifying moderator variables, including subgroup analysis and moderated regression analysis (MRA). Subgroup analysis involves splitting the sample into subgroups based on the hypothesized moderator and comparing regression coefficients, while MRA maintains the integrity of the sample and controls for the effects of the moderator. The authors propose a framework that combines both methods to determine the presence and type of moderator variables. The framework is validated using simulated data, demonstrating its effectiveness in identifying different types of moderator variables. The findings highlight the importance of correctly identifying moderator variables for accurate predictions and further research.This paper aims to clarify the concept of moderator variables in marketing research by presenting a typology and a framework for identifying their presence and type. The authors define moderator variables as those that systematically modify the strength or form of the relationship between a predictor and a criterion variable. They introduce two types of moderator variables: homologizers, which influence the strength of the relationship, and pure or quasi moderator variables, which modify the form of the relationship. The paper discusses various methods for identifying moderator variables, including subgroup analysis and moderated regression analysis (MRA). Subgroup analysis involves splitting the sample into subgroups based on the hypothesized moderator and comparing regression coefficients, while MRA maintains the integrity of the sample and controls for the effects of the moderator. The authors propose a framework that combines both methods to determine the presence and type of moderator variables. The framework is validated using simulated data, demonstrating its effectiveness in identifying different types of moderator variables. The findings highlight the importance of correctly identifying moderator variables for accurate predictions and further research.