Publications (13)
The Arrangement of Marks Impacts Afforded Messages: Ordering, Partitioning, Spacing, and Coloring in Bar Charts
Racquel Fygenson, Steven Franconeri, Enrico Bertini
Data visualizations present a massive number of potential messages to an observer. One might notice that one group's average is larger than another's, or that a difference in value…
Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull
Cindy Xiong, Cristina R. Ceja, Casimir J. H. Ludwig +1
In visual depictions of data, position (i.e., the vertical height of a line or a bar) is believed to be the most precise way to encode information compared to other encodings (e.g.…
Illusion of Causality in Visualized Data
Cindy Xiong, Joel Shapiro, Jessica Hullman +1
Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program wou…
Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation
Cindy Xiong, Chase Stokes, Yea-Seul Kim +1
When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. Therefore, one bedrock principle of science is to minimiz…
Gridlines Mitigate Sine Illusion in Line Charts
Clayton Knittel, Jane Awuah, Steven Franconeri +1
Sine illusion happens when the more quickly changing pairs of lines lead to bigger underestimates of the delta between them. We evaluate three visual manipulations on mitigating si…
Truncating the Y-Axis: Threat or Menace?
Michael Correll, Enrico Bertini, Steven Franconeri
Bar charts with y-axes that don't begin at zero can visually exaggerate effect sizes. However, advice for whether or not to truncate the y-axis can be equivocal for other visualiza…
How to evaluate data visualizations across different levels of understanding
Alyxander Burns, Cindy Xiong, Steven Franconeri +2
Understanding a visualization is a multi-level process. A reader must extract and extrapolate from numeric facts, understand how those facts apply to both the context of the data a…
What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts
Cindy Xiong Bearfield, Chase Stokes, Andrew Lovett +1
Reading a visualization is like reading a paragraph. Each sentence is a comparison: the mean of these is higher than those; this difference is smaller than that. What determines wh…
Data-Induced Groupings and How To Find Them
Yilan Jiang, Cindy Xiong Bearfield, Steven Franconeri +1
Making sense of a visualization requires the reader to consider both the visualization design and the underlying data values. Existing work in the visualization community has large…
Why Shouldn't All Charts Be Scatter Plots? Beyond Precision-Driven Visualizations
Enrico Bertini, Michael Correll, Steven Franconeri
A central concept in information visualization research and practice is the notion of visual variable effectiveness, or the perceptual precision at which values are decoded given v…
Rethinking the Ranks of Visual Channels
Caitlyn M. McColeman, Fumeng Yang, Steven Franconeri +1
Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants coul…
Same Data, Diverging Perspectives: The Power of Visualizations to Elicit Competing Interpretations
Cindy Xiong Bearfield, Lisanne van Weelden, Adam Waytz +1
People routinely rely on data to make decisions, but the process can be riddled with biases. We show that patterns in data might be noticed first or more strongly, depending on how…
Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways
Cindy Xiong, Vidya Setlur, Benjamin Bach +3
Well-designed data visualizations can lead to more powerful and intuitive processing by a viewer. To help a viewer intuitively compare values to quickly generate key takeaways, vis…