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Detection of Sample Differences from Dot Plot Displays

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Diagrammatic Representation and Inference (Diagrams 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5223))

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Abstract

Cleveland and McGill [10] concluded that dot plots are effective when one judges position along a common scale. We assessed the ability of graph readers to detect sample mean differences in multipanel dot plots. In Experiment 1, plots containing vertically arranged panels with different sample sizes and levels of variability were presented. Sensitivity was greater with large samples and low variability. In Experiment 2, sensitivity depended on the location of the comparison sample, with vertical and superimposed arrays yielding greater sensitivity than horizontal or diagonal arrays. Horizontal arrays also produced a bias to judge data in right-most panels as having higher means. Experiment 3 showed that ordering of data had little effect on sensitivity or bias. The results suggest that good graph design requires attention to how the specific features of a graphical format influence perceptual judgments of data

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Author information

Authors and Affiliations

  1. Department of Psychology, University of New Brunswick, P.O. Box 5050, Saint John, NB E2L 4L5, Canada

    Lisa A. Best

  2. Department of Psychology, University of Maine, 301 Little Hall, Orono, ME 04469-5782, USA

    Laurence D. Smith & D. Alan Stubbs

Authors
  1. Lisa A. Best
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  2. Laurence D. Smith
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  3. D. Alan Stubbs
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Editor information

Gem Stapleton John Howse John Lee

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© 2008 Springer-Verlag Berlin Heidelberg

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Best, L.A., Smith, L.D., Stubbs, D.A. (2008). Detection of Sample Differences from Dot Plot Displays. In: Stapleton, G., Howse, J., Lee, J. (eds) Diagrammatic Representation and Inference. Diagrams 2008. Lecture Notes in Computer Science(), vol 5223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87730-1_27

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  • DOI: https://doi.org/10.1007/978-3-540-87730-1_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87729-5

  • Online ISBN: 978-3-540-87730-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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