Adapting CCDF Plots for Visualizing Ordinal Regression Results
For researchers in visualization, HCI, and psychology who use ordinal data (e.g., Likert items), this work offers a practical visualization tool to increase adoption of ordinal regression models.
The paper addresses the challenge of visualizing ordinal regression results, proposing modified Complementary Cumulative Distribution Function (mCCDF) plots to make outcomes more intuitive. It demonstrates that mCCDFs can effectively communicate key takeaways comparable to traditional metric-based analyses.
Cumulative-link ordinal regression models are an alternative approach for analysing ordinal data such as Likert items, which are widely used in Visualization (and other related fields like HCI, psychology etc.). There are many researchers who are strong proponents of this approach, as it makes less stringent assumptions about the data, compared to the more commonly used linear model or ANOVA. Yet, ordinal regression models have seen limited adoption. I posit that one possible reason for this might be due to the difficulty in visually representing the results from such models, and in communicating the key takeaways in an intuitive manner. I propose the use of (modified) Complementary Cumulative Distribution Function (mCCDF) plots to visualize the results of ordinal regression models, and demonstrate how the same takeaways that researchers present from analyses which treat ordinal data as metric can be easily communicated using mCCDFs.