Steve Haroz

ISOTYPE Visualization: Working Memory, Performance, and Engagement with Pictographs

Although the infographic and design communities have used simple pictographic representations for decades, it is still unclear whether they can make visualizations more effective. Using simple charts, we tested how pictographic representations impact (1) memory for information just viewed, as well as under the load of additional information, (2) speed of finding information, and (3) engagement and preference in seeking out these visualizations. We find that superfluous images can distract. But we find no user costs -- and some intriguing benefits -- when pictographs are used to represent the data.

The Connected Scatterplot for Presenting Paired Time Series

The connected scatterplot visualizes two related time series in a scatterplot and connects the points with a line in temporal sequence. News media are increasingly using this technique to present data under the intuition that it is understandable and engaging. To explore these intuitions, we (1) describe how paired time series relationships appear in a connected scatterplot, (2) qualitatively evaluate how well people understand trends depicted in this format, (3) quantitatively measure the types and frequency of misinter pretations, and (4) empirically evaluate whether viewers will preferentially view graphs in this format over the more traditional format. The results suggest that low-complexity connected scatterplots can be understood with little explanation, and that viewers are biased towards inspecting connected scatterplots over the more traditional format. We also describe misinterpretations of connected scatterplots and propose further research into mitigating these mistakes for viewers unfamiliar with the technique.

Special Interest Group on Transparent Statistics in HCI

Transparent statistics is a philosophy of statistical reporting whose purpose is scientific advancement rather than persuasion. We propose a SIG to discuss problems and limitations in statistical practices in HCI and options for moving the field towards clearer and more reliable ways of writing about experiments.

Moving Transparent Statistics Forward at CHI

Transparent statistics is a philosophy of statistical reporting whose purpose is scientific advancement rather than persuasion. We ran a SIG at CHI 2016 to discuss problems and limitations in statistical practices in HCI and options for moving the field towards clearer and more reliable ways of writing about experiments, and received an overwhelming response. This SIG resulted in rough drafts of reviewer guidelines, resources for authors, and other suggestions for advancing a vision of transparent statistics within the field; this year, we propose a concentrated one-day writing workshop to develop those documents into a polished state with input from a diverse cross-section of the CHI community.

Perceptual proxies for extracting averages in data visualizations

Across science, education, and business, we process and communicate data visually. One bedrock finding in data visualization research is a hierarchy of precision for perceptual encodings of data (e.g., that encoding data with Cartesian positions allows more precise comparisons than encoding with sizes). But this hierarchy has only been tested for single-value comparisons, under the assumption that those lessons would extrapolate to multivalue comparisons. We show that when comparing averages across multiple data points, even for pairs of data points, these differences vanish. Viewers instead compare values using surprisingly primitive perceptual cues (e.g., the summed area of bars in a bar graph). These results highlight a critical need to study a broader constellation of visual cues that mediate the patterns that we can see in data, across visualization types and tasks.

Serial Search Can Occur in Multiple Feature Dimensions at the Same Time

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