However, this opinion is not shared by all. Standard hypothesis testing tools need all distribution assumptions to be met with well-measured quantitative data. Highly significant results are more easily achieved with many data points and few variables. This is the opposite of what occurs in industry studies. Organizational data sets are certainly not lacking in information.
Most researchers gain a great deal of knowledge from their long hours spent on a project.
To guide readers through the industry and have them see with the researcher's eye what lies there is a great accomplishment for an industry study. This chapter discusses the multivariate analysis techniques used in the Competitive Semiconductor Manufacturing Study to study the data. These tools not only give a reader a fair idea of the industry, but also help the researcher pinpoint the promising areas for future research. As interesting questions are extracted, other tools are used to delve into the data and help answer or refine these questions. Problems with the data are discussed and methods used to overcome the difficulties are explained.