Management research routinely investigates nuanced socio-technical phenomena, seeking to explicate the complex cause-effect relationships observed in our social and organizational structures. In order to operationalize such research, investigators must identify the focal variables related to the phenomena of interest and the key relationships between such variables. Often the focal concepts under study represent conceptual variables or “broad ideas or thoughts about abstract concepts that researchers establish and propose to measure in their research" (e.g., customer satisfaction; Sarstedt et al., 2016). These conceptual variables pose an additional challenge to research as they are often not directly measurable and must be operationalized through manifest indicator variables (i.e. a survey instrument which measures customer satisfaction). In this research we critically evaluate the dominant statistical methodologies for estimating such conceptual variables from their manifest indicators–the common factor, partial least squares composite, principle component, and fixed-weights component. We explore the implications of the various statistical approaches in terms of theory development and testing and provide suggestions to improve the rigor and validity of management research.  ​