With the growing amount of data and its consequences for scientific paradigms quantitative research and statistical methodology will become more and more important even for scientific discoveries in social sciences and other disciplines. As can be seen in many high profile academic journals, however, quantitative studies lack statistical knowledge and are prone to mistakes, many of which are based on false beliefs and misleading peer consensus.
In the following analysis I will address some of these common mistakes and false beliefs in basic statistical research. One of the issues discussed in this analysis will be the selection process of covariates within the context of different regression models. It has become quite common in economic and social science that researchers include a bunch of covariates into their regression models in order to “control for” other variables, a practice that often leads to multicollinearity. The following analysis will discuss the issue of “bad control” or multicollinearity.