Abstract
Models are an important component of research design that serve as intermediaries between theories and data, often directing decisions about methods and statistics. This article discusses the basic differences and assumptions associated with process and variance models as a way of introducing the four articles contained within this special issue of Family Business Review on “Process and Variance Methods.” Specifically, we highlight three key issues regarding modeling—time and causality, measurement and operationalization, and model specification—making specific ties to the challenges often associated with family business research.
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