Technology incubators have emerged in many places as a tool in facilitating the establishment and survival of high-technology firms. Some incubators develop quickly and produce a fast-increasing number of new ventures, while others remain stable in size. Despite a growing public investment in technology incubators, systematic studies of the factors determining their growth are scarce, meaning that policy decisions are taken without sufficient practical insights into critical conditions for growth. In response to that situation, we explore several factors in determining differences in growth patterns. We use a quantitative approach derived from the field of artificial intelligence that matches with meta-analysis and qualitative (and sometimes fuzzy) data—that is, rough set analysis. Benefits and challenges of rough set analysis are discussed, including experience with a stepwise procedure with various accuracy checks. The findings suggest that a strong performance of incubators mainly rests on diversity in stakeholder involvement and a location in nonmetropolitan areas. Rough set analysis turns out to be a helpful tool in comparative project analysis, but there is still a need for standardization of measures used in the interpretation of the results.