No matter how you mark the points on the fever curve – threatening shapes do not add to threat of climate change

Graphs have become an increasingly important means of representing data, for instance, when communicating data on climate change. However, graph characteristics might significantly affect graph comprehension. The goal of the present work was to test whether the marking forms usually depicted on line-graphs, can have an impact on graph evaluation. As past work suggests that triangular forms might be related to threat, we compared the effect of triangular marking forms with other symbols (triangles, circles, squares, rhombi, and asterisks) on subjective assessments. Participants in Study 1 (N = 314) received 5 different line-graphs about climate change, each of them using one out of 5 marking forms. In Study 1, the threat and arousal ratings of the graphs with triangular marking shapes were not higher than those with the other marking symbols. Participants in Study 2 (N = 279) received the same graphs, yet without labels and indeed rated the graphs with triangle point markers as more threatening. Testing whether local rather than global spatial attention would lead to an impact of marker shape in climate graphs, Study 3 (N = 307) documented that a task demanding to process a specific data-point on the graph (rather than just the line graph as a whole) did not lead to an effect either. These results suggest that marking symbols can principally affect threat and arousal ratings but not in the context of climate change. Hence, in graphs on climate change, choice of point markers does not have to take potential side-effects on threat and arousal into account. These seem to be restricted to the processing of graphs where form aspects face less competition from the content domain on judgments.

Public Health Infographic Design for the public – the evidence base

In order to make effective infographics, one needs to understand the science behind public health infographic design. This presentation introduces guidelines for public health infographic design based on gathered academic evidence of effectiveness as well as information design principles. We tackle the topic from a variety of angles exploring issues of attention, comprehension, recall and behavioral change and focuses on infographics designed for a lay audience. Despite the exhaustive research conducted on say, graph comprehension, there remains a gap in how we account for the effectiveness of public health infographic design more broadly. The presentation also covers a brief examination of ‘hidden' historical precedents for the design of engaging health infographics, beyond the oft-cited visual work of John Snow or Florence Nightingale. We argue that notions of data spectacle and the need to grab attention remain vital today. The presentation concludes by reflecting on the future of infographics for displaying public health data, particularly with reference to the use of COVID-19 graphics in 2020/21.

A Common, Categorical Misinterpretation of Bar Graphs: Readout-based Measurement Reveals the Bar-tip Limit Error

Here, we make three contributions to the study of graph cognition. First, we introduce a framework for measuring graph comprehension via the elicitation of a readout: a relatively concrete and detailed record of thought. Second, we create a flexible new readout-based measure, called Draw Datapoints on Graphs (DDoG), to assess the comprehension of graphs that abstract away from their raw, underlying data. Third, using this new measure, we identify a common error in the interpretation of bar graphs of means. The error we identify is an apparent conflation of bar graphs of means with bar graphs of counts. It occurs when the raw underlying data is assumed to be limited by, rather than spread across, the bar-tip. We therefore call it the Bar-Tip Limit Error (BTLE). In a large, demographically diverse sample, we observe BTLE in about one in five persons, across educational levels, ages, and genders, and despite thoughtful responding and relevant foundational knowledge. The identification of BTLE provides a case-in-point that simplification via abstraction can risk severe, high-prevalence misinterpretation. DDoG reveals the nature and likely cognitive mechanisms of BTLE with an ease that speaks both to DDoG’s value as a measure of graph comprehension and, more broadly, to the efficacy of our readout-focused framework. We conclude that bar graphs of means may be misinterpreted by a large proportion of the population, and that readout-focused measurement holds promise for accelerating the study of graph cognition.

Improving Teachers’ Comprehension of Curriculum-Based Measurement Progress-Monitoring Graphs

The authors examined three instructional approaches for improving teachers’ curriculum-based measurement (CBM) graph comprehension, each differing in the extent to which reading the data, interpreting the data, and linking the data to instruction were emphasized. Participants were 164 elementary school teachers who were randomly assigned to one of three CBM instructional approaches or a control condition. Instruction was delivered via videos. Prior to and after receiving instruction, teachers completed a CBM graph-comprehension task. They also evaluated the instructional videos. Teachers in the three instructional groups improved more in CBM graph comprehension than control teachers. Improvements were seen primarily in interpreting and linking the data to instruction, two important but difficult aspects of CBM graph comprehension. Differences between the instructional groups were found for interpreting the data. Teachers evaluated the videos positively. Results indicate that teachers’ CBM graph comprehension can be improved via video instruction. Implications for teaching teachers to implement CBM are discussed.

Preprint of Graph Construction: Setting the Range of the Y‐Axis by Jessica Witt (Under review at Meta-Psychology)

Graphs are an effective and compelling way to present scientific results. With few rigid guidelines, researchers have many degrees‐of‐freedom regarding graph construction. One such choice is the range of the y‐axis. A range set just beyond the data will bias readers to see all effects as big. Conversely, a range set to the full range of options will bias readers to see all effects as small.Researchers should maximize congruence between visual size of an effect and the actual size of the effect. To achieve congruency in scientific fields for which effects are standardized, the y‐axis range should be a function of the standard deviation. This improved graph comprehension by increasing sensitivity and reducing bias relative to the other options for the y‐axis range.