Adapting CCDF Plots for Visualizing Ordinal Regression Results

2026-07-02

AI Summary

This study addresses the lack of intuitive visualization methods for ordinal regression results, which has hindered their application in fields such as visualization and human-computer interaction. To bridge this gap, the paper proposes, for the first time, the use of modified complementary cumulative distribution function (mCCDF) plots to visualize outputs from cumulative link ordinal regression models. This approach not only fills a critical void in the clear and direct representation of ordinal regression outcomes but also effectively conveys key conclusions consistent with those erroneously derived when treating ordinal variables as continuous. By doing so, the method substantially enhances the interpretability and communicability of model results, offering a principled yet accessible visual framework for practitioners and researchers alike.

Abstract

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.

Problem

• ordinal regression
• visualization
• CCDF plots
• Likert data
• result communication

Innovation

• ordinal regression
• mCCDF plots
• visualization
• cumulative-link models
• Likert data