Spatial legend compatibility within versus between graphs in multiple graph comprehension

Riechelmann, Eva; Huestegge, Lynn

doi:10.3758/s13414-018-1484-0

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Future studies should use more data points with different numbers of trend reversals (i.e., slopes of adjacent lines from positive to negative or vice-versa), as it was shown that they have an impact on comprehension time (Carswell et al, 1993 ). Future studies should also examine how schema switches might affect graph processing when a single task involves comparisons between multiple (similar or different) graphs, that is, in complex graph display (e.g., see Riechelmann and Huestegge, 2018 ; Poetzsch et al, 2020 ). Other types of tasks should be used in future studies, such as a more basic “which is larger” comparison, A + B vs. C + D, as pie charts are ideal to combine even non-adjacent slices compared to summing up heights in bar graphs (Spence and Lewandowsky, 1991 ).…”

Section: Discussionmentioning

confidence: 99%

Graph schema and best graph type to compare discrete groups: Bar, line, and pie

Zhao

Gaschler

2022

Front. Psychol.

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Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

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Section: Discussionmentioning

confidence: 99%

Graph schema and best graph type to compare discrete groups: Bar, line, and pie

Zhao

Gaschler

2022

Front. Psychol.

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“…Research on data visualization was first concerned with the optimization of single chart visualizations, starting with Eells (1926). Corresponding research on data encoding effectiveness peaked in the 80s and 90s, when landmark studies like those by Cleveland and McGill (1984), who invented dot plots, or by Hollands and Spence (1992), who evaluated line charts vs. bar charts as the most effective means to communicate change in data (see also Huestegge and Philipp, 2011;Riechelmann and Huestegge, 2018), emerged. Scatterplots, on the other hand, were later considered an optimal choice for visualizing correlations (Harrison et al, 2014;Kay and Heer, 2016).…”

Section: Background and Previous Workmentioning

confidence: 99%

Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications

Poetzsch

Germanakos

Huestegge

2020

Front. Artif. Intell.

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Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The results implied that for adapting to user traits, statistical expertise is a relevant dimension that should be considered. Additionally, for adapting to user states different user intentions such as monitoring and analysis should be accounted for. These results were used to develop a taxonomy which adapts visualization recommendations to these (and other) factors. A preliminary attempt to validate the taxonomy in Study 4 tested its visualization recommendations with a group of experts. While the corresponding results were somewhat ambiguous overall, some aspects nevertheless supported the claim that a user-adaptive data visualization approach based on the principles outlined in the taxonomy can indeed be useful. While the present approach to user adaptivity is still in its infancy and should be extended (e.g., by testing more participants), the general approach appears to be very promising.

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Integration processes during frequency graph comprehension: Performance and eye movements while processing tree maps versus pie charts

Huestegge

Pötzsch

2018

Applied Cognitive Psychology

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SummaryFrequency graph types differ in the way how data are translated into visual representations. We compared 2 visualization methods, a traditional circular representation (pie chart) and a rectangular representation (constant column width tree map), which were hypothesized to differ regarding the cognitive ease of visual comparison processes. Performance was evaluated in tasks involving proportion and comparison judgments under both highly controlled and more realistic circumstances. The results showed performance benefits (in terms of reduced response times or error rates) for rectangular representations. Additional eye movement analyses revealed that this benefit was mainly due to a facilitation of scanning the graph for relevant information. The results suggest that facilitating comparison processes by representing the critical variable in less complex visual dimensions (i.e., straight length with constant orientation instead of surface area or curved length) eventually enhances the efficiency of integration processes during graph comprehension.

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Spatial legend compatibility within versus between graphs in multiple graph comprehension

Cited by 3 publications

References 35 publications

Graph schema and best graph type to compare discrete groups: Bar, line, and pie

Graph schema and best graph type to compare discrete groups: Bar, line, and pie

Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications

Integration processes during frequency graph comprehension: Performance and eye movements while processing tree maps versus pie charts

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