If you take theory and models seriously, then (a) you need to elaborate clearly for yourself ‘what counts’ and how things supposedly fit together, and (b) you must hold yourself accountable to data.… From my perspective, theory is – or should be – the lifeblood of the empirical scientist. (Schoenfeld, 2010, p. 105)
The process of theorizing, collecting evidence, testing theory, revising theory, and then working through the cycle again is the basis of science. Theory is no less important when conducting a data analysis using quantitative methods. All statistical textbooks spend significant space on discussing the assumptions of statistical tests, the data requirements for a given estimation procedure, and the boundaries around the conclusions that can be drawn from results. Theory about the phenomenon that one is examining through quantitative data analysis is not only the driver for the methods used to collect evidence, but most importantly, the decisions made about how to model and test that theory
