Graphical methods for multivariate linear models in psychological research: An R tutorial


This paper is designed as a tutorial to highlight some recent developments for visualizing the relationships among response and predictor variables in multivariate linear models (MLMs), and implemented in convenient packages for R. These models include multivariate multiple regression analysis (MMRA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA). The methods we describe go well beyond what can be understood and explained from simple univariate graphical methods for the separate response variables. We describe extensions of these methods for the case of more than just a few response variables, where the important relationships can be readily seen in the low-dimensional (2D) space that accounts for most of the relevant information. As befits the tutorial nature of this paper, we analyze some sample psychological research studies utilizing these multivariate designs, showing examples in R. In the process, we also take up several practical problems related to the assumptions of MLMs, and how these can be dealt with using graphical methods. Finally, we provide guidelines to aid researchers in conducting multivariate research, pertaining to the analysis, visualization, and reporting of such designs. The graphical and statistical methods described here are all freely available and implemented in the R packages candisc, car, heplots, and mvinfluence.

The Quantitative Methods for Psychology, 13(1), 20–45. doi: 10.20982/tqmp.13.1.p020