Abstract
Replication and robustness checks of extant research is vital for theory building, theory testing, and domain legitimacy, especially in a field as young as entrepreneurship (Davidsson, 2016). While both methodologies demonstrate the integrity and consistency of statistical models, as well as the validity of findings, these approaches are often overlooked aspects of the scientific method. Moreover, knowledge accumulation in a research domain becomes obfuscated when variables used in highly cited theoretical constructs come from representative samples that contain a large percentage of outliers (Herndon et al., 2014). If that is, indeed, the case, then conducting replications (same data, same analysis) and robustness checks (same data, different analysis), while properly dealing with outliers, may facilitate knowledge accumulation in the domain. This, in effect, is the purpose of our paper.