Sort line-chart x order when the goal is correlation judgment

Sort the x order to create an ordered line chart when readers need to judge correlation strength from a line-based display. For example, take a plain line chart of two quantitative variables and sort the x positions by value so the relationship is shown as an ordered line rather than the unsorted line form.

This is a change inside the line-chart design, not a different dataset. The improvement came from altering the explicit variable order on the x axis.

Mechanism: Sorting the x order changed the line display into a form with smaller JNDs for positive correlations, making nearby association strengths easier to distinguish.

Evidence: Ordered line charts depicting positive correlations significantly outperformed plain positive line charts, and ordered line performance was also much more stable across positive and negative directions; the 2023 review collated this as empirical guidance for correlation-related chart design (Harrison et al., 2014; Zeng & Battle, 2023).

• User Goal: Improve correlation judgment while staying in a line-based display.
• Task: Compare nearby association strengths.
• Data: Two quantitative variables shown with a line chart.
• Chart Setting: A static line-based view where x order can be sorted instead of left unsorted.
• Success Criterion: More reliable discrimination of nearby correlation values.

Break it when: The line chart must preserve its existing x ordering and cannot be sorted. Why: The tested improvement came specifically from changing the x order to form the ordered line chart.

Sacrifice: You give up the original unsorted x ordering. Risk: If the unsorted order is the main message, reordering changes what the line view shows. Mitigation: Use the ordered version only when judging correlation strength is the main job of the line chart.

Mistake: Keeping an unsorted line chart for correlation judgment when x order is free to change. Why it fails: The positive ordered-line version was more precise than the plain positive line version.

Failure Sign: The plain line chart makes nearby correlations look too similar to judge reliably. Quick Check: Render the same data as a plain line chart and as an ordered line chart, then compare them on close correlation values. Stronger Test: Ask reviewers to choose which of two close-correlation displays is more correlated in each version and keep the version with more consistent answers.

• Sort the x order to create the ordered-line version of the chart.
• Compare the ordered and plain line versions with the same data and display size.
• Keep the ordered version when it yields more reliable correlation judgments.