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Related Experiment Videos

The classification of graphical elements.

J G Hollands1

  • 1Defence R&D Canada-Toronto, Ontario. justin.hollands@drdc-rddc.gc.ca

Canadian Journal of Experimental Psychology = Revue Canadienne De Psychologie Experimentale
|April 4, 2003
PubMed
Summary
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This study found that stacked bar graphs, unlike pie charts, make it harder to distinguish proportions when graph size also changes. This suggests an integral relationship between proportion and scaling for stacked bars.

Area of Science:

  • Data Visualization
  • Cognitive Psychology
  • Human-Computer Interaction

Background:

  • Visual perception of graphical data is crucial for accurate information processing.
  • Understanding how different graph types affect cognitive load and performance is essential for effective data communication.

Purpose of the Study:

  • To investigate how graph scaling (overall size) interacts with proportion judgment in pie charts versus stacked bar graphs.
  • To determine if these two graphical dimensions are processed integrally or separably.

Main Methods:

  • Three experiments involved participants classifying stimuli depicting pie charts and stacked bar graphs.
  • Classification was based on proportion and graph scaling, with sorting times and errors recorded.
  • Garner interference paradigms were employed to assess dimensional integrality.

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Main Results:

  • Performance was impaired when classifying proportions of stacked bars if scaling varied, indicating integral processing.
  • No such impairment was observed for pie charts, suggesting separable processing.
  • Specific combinations of proportion and scaling levels showed facilitation for pie charts.

Conclusions:

  • Proportion and scaling are processed as an asymmetric integral unit for stacked bar graphs.
  • Proportion and scaling are separable dimensions for pie charts.
  • The findings have implications for the design of effective data visualizations.