Examining Limits of Small Multiples: Frame Quantity Impacts Judgments With Line Graphs

Area of Science:

• Data Visualization
• Human-Computer Interaction
• Cognitive Psychology

Background:

• Small multiples are widely used for displaying multiple data views.
• Human cognitive capacity limits how much information can be processed simultaneously.
• Understanding these limits is crucial for effective visualization design.

Purpose of the Study:

• To investigate the impact of cognitive capacity limitations on the performance of small multiples visualizations.
• To test theories on how frame count, scale, and time affect user performance.
• To identify optimal design strategies for small multiples in data analysis.

Main Methods:

• Two online studies (N=141, N=360) and an eye-tracking analysis (N=5) were conducted.
• Participants performed tasks using small multiples of line charts in an energy grid scenario.
• Variables included the number of frames, frame scale, and time constraints.

Main Results:

• Accuracy decreased linearly as the number of frames increased across seven tasks.
• Frame size differences did not fully explain the accuracy decline, indicating visual search issues.
• Highlighting frames partially mitigated visual search difficulties but did not eliminate them.

Conclusions:

• The number of frames in small multiples significantly impacts user accuracy due to cognitive load.
• Visual search is a key challenge, even with frame highlighting.
• Visualization design should consider human cognitive limits for enhanced data interpretation.