ABSTRACT
Graphing data is an important step in the analysis process. Far too often researchers skip the graphing of their data and move directly into analysis without the insights that can come from a careful visual examination of data. It is certainly tempting for researchers to bypass data exploration through graphical analysis and move directly into formal statistical modeling because models generally serve as the tools used to answer research questions. However, if proper attention is not paid to the graphing of data, the formal statistical analyses may be poorly informed regarding the distribution of variables and their relationships with one another. As an example, a model allowing only a linear relationship between a predictor and a criterion variable would be inappropriate if a nonlinear relationship existed between the two variables. Using graphical tools first, it would be possible to see the nonlinearities and appropriately account for them in the model.
