Best Graph Type to Compare Discrete Groups: Bar, Dot, and Tally

Different graph types might differ in group comparison due to differences in underlying graph schemas. Thus, this study examined whether graph schemas are based on perceptual features (i.e., each graph has a specific schema) or common invariant structures (i.e., graphs share several common schemas), and which graphic type (bar vs. dot vs. tally) is the best to compare discrete groups. Three experiments were conducted using the mixing-costs paradigm. Participants received graphs with quantities for three groups in randomized positions and were given the task of comparing two groups. The results suggested that graph schemas are based on a common invariant structure. Tally charts mixed either with bar graphs or with dot graphs showed mixing costs. Yet, bar and dot graphs showed no mixing costs when paired together. Tally charts were the more efficient format for group comparison compared to bar graphs. Moreover, processing time increased when the position difference of compared groups was increased.

Perceptions of Auditor Negligence: The Effects of Big Data Visualisations on Jurors’ Decisions

<p>This study analyses the effects of Big Data visualisations on jurors’ decisions in audit litigation cases. Specifically, the study investigates the effects of different types of Big Data visualisations (word clouds or bar graphs) and different sources of Big Data (emails or social media posts) on jurors’ perceptions of auditors’ work and the size of the negligence awards that jurors recommend. The study theorises that the emotions elicited and the reliability of the data used to create visualisations such as word clouds will have dramatic effects on jury verdicts in audit negligence trials. There is considerable literature to support this assertion. However, after data collection, it was discovered that jurors are not influenced by the emotions elicited by visualisations. Rather, participants were very sceptical of more novel types of visualisations, such as word clouds, but could be persuaded by the inherent emotions elicited and the reliability of the data if they found the visualisation useful.</p>

Perceptions of Auditor Negligence: The Effects of Big Data Visualisations on Jurors’ Decisions

<p>This study analyses the effects of Big Data visualisations on jurors’ decisions in audit litigation cases. Specifically, the study investigates the effects of different types of Big Data visualisations (word clouds or bar graphs) and different sources of Big Data (emails or social media posts) on jurors’ perceptions of auditors’ work and the size of the negligence awards that jurors recommend. The study theorises that the emotions elicited and the reliability of the data used to create visualisations such as word clouds will have dramatic effects on jury verdicts in audit negligence trials. There is considerable literature to support this assertion. However, after data collection, it was discovered that jurors are not influenced by the emotions elicited by visualisations. Rather, participants were very sceptical of more novel types of visualisations, such as word clouds, but could be persuaded by the inherent emotions elicited and the reliability of the data if they found the visualisation useful.</p>

The Fourth (and Influential) Introduction of Spindle Graphs

Close examination of James A. Ford’s self-reported 1952 history of how he developed the centered and stacked bars style of spindle graph for which he is famous indicates he likely invented this kind of spindle graph with a bit of assistance from his colleagues George Quimby and Gordon Willey. In the 1930s, his diagrams of culture change were spatio-temporal rectangles or bar graphs; his first centered and stacked bars spindle diagram appeared in the 1949 published version of his doctoral dissertation. That graph style was picked up by American Southwest archaeologist Paul S(ydney) Martin that same year; Martin had, like many of his colleagues, initially used line graphs and bar graphs to illustrate culture change. Subsequently, numerous individuals adopted Ford’s centered and stacked bars form of spindle diagram. During the 1950s in Europe, French Paleolithic archaeologist François Bordes adopted ogive or cumulative relative frequency curves as a graphic means to compare assemblages of lithic tools. Quickly adopted by many European archaeologists, this graph type was only occasionally used in North America. After Ford, most graphs diagramed variational evolutionary change.

Two people, one graph: the effect of rotated viewpoints on accessibility of data visualizations

In co-located, multi-user settings such as multi-touch tables, user interfaces need to be accessible from multiple viewpoints. In this project, we investigated how this goal can be achieved for depictions of data in bar graphs. We designed a laboratory task in which participants answered simple questions based on information depicted in bar graphs presented from differently rotated points of view. As the dependent variable, we measured differences in response onsets relative to the standard viewpoint (i.e., upright graphs). In Experiment 1, we manipulated graph and label orientation independently of each other. We observed that rotations of the labels rather than rotations of the graph itself pose a challenge for accessing depicted information from rotated viewpoints. In Experiment 2, we studied whether replacing word labels with pictographs could overcome the detrimental effects of rotated labels. Rotated pictographs were less detrimental than rotated word labels, but performance was still worse than in the unrotated baseline condition. In Experiment 3, we studied whether color coding could overcome the detrimental effects of rotated labels. Indeed, for multicolored labels, the detrimental effect of label rotation was in the negligible range. We discuss the implications of our findings for the underlying psychological theory as well as for the design of depicted statistical information in multi-user settings.

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.