Correlation matrix displays are valuable tools for investigating bivariate associations, typically showcasing Pearson’s correlation—a linear association measure—for pairs of numerical variables. However, these displays have limitations in capturing complex non-linear associations and associations involving categorical variables. This thesis addresses these limitations by introducing alternative association measures that accommodate pairs of numerical, ordinal, and categorical variables, as well as mixed pairs where one variable is categorical and the other is numerical. For numerical variables, we incorporate modern non-linear association measures like distance correlation and the maximal information coefficient (MIC). Notably, our displays present multiple association measures for each variable pair, revealing patterns beyond linear associations or associations dependent on levels of a grouping variable. To address space issues with high-dimensional datasets, we also offer a linear layout display, showing one or more association measures for each variable pair. Furthermore, we employ seriation for matrix displays and importance sorting for linear displays to emphasize highly-associated variables or pairs with significant differences, making them easier to discern and interpret. These improvements enhance the effectiveness and efficiency of data analysis, allowing for a more comprehensive understanding of associations in various datasets.