Multivariate visualization: A Taxonomy and novel visualization methods

While intuitive visualizations for bivariate analyses are numerous and able to be constructed with relative ease, the same is not true for multivariate analyses. Commonly utilized multivariate visualization strategies are often cognitively taxing for readers and there is little guidance for researchers seeking to decide upon the proper visualization for their analysis. In this paper we seek to rectify these limitations by developing a data analysis taxonomy that allows one to easily identify appropriate visualizations. This taxonomy aims to provide guidance to researchers in their decision-making regarding which multivariate visualization strategy best fits their research question. Our taxonomy classifies research questions into five different categories (zero-order effects, conditioning, moderation, mediation, and clustering), providing example research questions and analyses for each. Throughout, we identify tools appropriate for multivariate visualizations, including ghost lines, added variable plots, and paneling. All these tools are freely available in R through the Flexplot package, as well as in the Visual Modeling module in JASP.