This paper addresses the confusion surrounding the definition and identification of moderator variables in marketing research. It presents a typology of moderator variables and a framework for identifying their presence and type. The framework includes two approaches: subgroup analysis and moderated regression analysis (MRA). The paper also uses simulated data to illustrate and validate the framework. Moderator variables are defined as variables that systematically modify the form and/or strength of the relationship between a predictor and criterion variable. There are two main types of moderator variables: homologizers, which influence the strength of the relationship through the error term, and pure or quasi moderators, which modify the form of the relationship. Homologizers are variables that do not interact with the predictor variable and are not significantly related to either the predictor or criterion variable. Pure moderators interact with the predictor variable and are not related to the criterion variable, while quasi moderators are related to the criterion variable and interact with the predictor variable. The paper discusses the differences between subgroup analysis and MRA in identifying moderator variables. Subgroup analysis involves splitting the sample into subgroups based on a hypothesized moderator variable and examining the relationship between the predictor and criterion variable within each subgroup. MRA, on the other hand, involves testing whether the relationship between the predictor and criterion variable changes as a function of the moderator variable. The paper also discusses the implications of identifying different types of moderator variables for marketing research, including the need to consider measurement error and the heterogeneity of relationships across subgroups. The paper concludes that the proposed framework for identifying moderator variables is effective in distinguishing between different types of moderator variables and provides a basis for further research in marketing. The framework is validated using simulated data, which demonstrates its ability to detect the presence and type of moderator variables. The paper emphasizes the importance of understanding the different types of moderator variables and their implications for marketing research.This paper addresses the confusion surrounding the definition and identification of moderator variables in marketing research. It presents a typology of moderator variables and a framework for identifying their presence and type. The framework includes two approaches: subgroup analysis and moderated regression analysis (MRA). The paper also uses simulated data to illustrate and validate the framework. Moderator variables are defined as variables that systematically modify the form and/or strength of the relationship between a predictor and criterion variable. There are two main types of moderator variables: homologizers, which influence the strength of the relationship through the error term, and pure or quasi moderators, which modify the form of the relationship. Homologizers are variables that do not interact with the predictor variable and are not significantly related to either the predictor or criterion variable. Pure moderators interact with the predictor variable and are not related to the criterion variable, while quasi moderators are related to the criterion variable and interact with the predictor variable. The paper discusses the differences between subgroup analysis and MRA in identifying moderator variables. Subgroup analysis involves splitting the sample into subgroups based on a hypothesized moderator variable and examining the relationship between the predictor and criterion variable within each subgroup. MRA, on the other hand, involves testing whether the relationship between the predictor and criterion variable changes as a function of the moderator variable. The paper also discusses the implications of identifying different types of moderator variables for marketing research, including the need to consider measurement error and the heterogeneity of relationships across subgroups. The paper concludes that the proposed framework for identifying moderator variables is effective in distinguishing between different types of moderator variables and provides a basis for further research in marketing. The framework is validated using simulated data, which demonstrates its ability to detect the presence and type of moderator variables. The paper emphasizes the importance of understanding the different types of moderator variables and their implications for marketing research.