Abstract
Entrepreneurship research has grown exponentially over the last decades, in part thanks to the development of quantitative datasets and new methods of data collection and analysis. Indeed, entrepreneurship scholars are now better equipped than ever to do quantitative research. However, such development does not solve a few fundamental issues with conducting entrepreneurship research, and it even brings a couple of additional challenges to the field. In this chapter, I discuss the main challenges associated with the fact that entrepreneurship fits poorly with the assumption of normality, that it is a broad concept and a non-linear, path-dependent process. I also discuss two sets of relatively newer challenges related to: (1) our increased statistical power and analytic sophistication, and (2) the asymmetry between gains and losses.