Posted: May 24th, 2022

Different Representations of Graphs Essay

Different Representations of Graphs

Abstract
Data can be displayed in a range of chart kinds and formats, allowing designers to present the same information in various ways. Certain limitations apply to this incredible level of creative freedom. While subtle changes in chart styles might distort interpretations of size, relationships, and the significance of effects (Hegarty, 2011), compelling visualizations should assist cognition (Correll, Bertini, & Franconeri, 2019). Because aesthetic preference and erroneous assumptions frequently complicate design recommendations (Kosara, 2016), cognitive scientists should investigate quantitative differences in data interpretations and understand the cognitive mechanisms underpinning this effect.
Shapes, lines, and points are commonly used in data visualizations to illustrate a link between concepts. (Lauer & O’Brien, 2020) The geographical arrangement of data markers shows trends and highlights disparities. The proportions between markings and space are adjusted by modifying chart axes, resulting in visual metaphors that facilitate data interpretation. When the ranges of chart axes in a scatterplot are enlarged, relationships are thought to be stronger as data markers become compressed (Cleveland, Diaconis, & McGill, 1982). Causation can be distorted in a time series if the x-axis is selectively trimmed to suggest a single event triggered a trend (Lauer & O’Brien, 2020).
The practice of truncating the quantitative axis has been the subject of extensive inquiry. Non-significant differences are accentuated by limiting the y-axis range. A y-axis range that is set too wide, on the other hand, can distort data by failing to capture significant differences (Witt, 2019). It is vital to be aware of the conventions used by chart components and their spatial layout to account for a recipient’s visual experience. This work evaluates bar graphs, which are the most common visualization in news and scientific papers. Start the y-axis/vertical axis with a value other than zero.
Bar graphs are the most vulnerable to truncation deception since this practice can violate their aesthetic standards (e.g., Witt, 2019; Correll, Bertini, & Franconeri, 2019). Categorical data is represented in bar charts by the proportionate height of forms, allowing values to be extracted and compared. The physical representation of numbers on the graphic’s surface should be proportional to the displayed numerical quantities. Tufte. This is broken by truncation.
It continuously misleads, regardless of graph literacy. The suggestive effect is determined by the first perception, which is instinctively understood. As a result, the relative height of bars serves as a magnitude index, which is a well-known standard. When presented with graph-like diagrams, children with no prior graphing training may determine relative value as a function of vertical location (Gattis, 2002). In a hierarchy, the placement of fewer/more/accelerating, etc. The magnitude of things having a more considerable spatial extent is greater. Truncation goes against the grain and leads facts to be interpreted unexpectedly. The anchoring impact of first perceptions on judgment is abnormally strong. First impressions are biased, resulting in poor decision-making.
The idea that graphs are objective has scientific validity and that a difference highlighted in a chart represents a meaningful comparison unit. Although most studies focus on truncation at the axis’s base, ranges that span the entire length can also be misleading. Examine the area mentioned above and the financial performance of a corporation. A lot of things have gone overlooked. Psycholinguistics teaches us that context is critical for value assessments.
Within the participant design of this study, all individuals were exposed to both independent variables (truncated/full and vertical/horizontal) at both levels (truncated/complete and steep/flat).
Participants We used pavlovia.org to perform this research. A total of 67 undergraduate psychology students (x males, x women, x non-binary gender) took part in the study (Mage = x years, SDage = x). Sona-systems, an online recruitment platform that awards students with credits for their involvement, was used to attract students. Normal or corrected-to-normal vision (glasses or contact lenses) and fluency in English were among the eligibility criteria.
Materials. A set of bar charts with accompanying text were shown in each experiment (Figure 1).

Figure 1: Example trial
The more significant number was shown in Graph A, represented by the data above. The graphs were identical, with a zero baseline and a single bar displaying the same magnitude. Each chart in each set featured a full scale showing the entire range of possible outcomes, whereas the other had a limited capacity with truncation at the axis’s top. To reduce order effects, the pair’s relative positions were counterbalanced. Axis scales were not labeled adequately since we wanted to test participants’ first perceptions of charts rather than their ability to extract numbers accurately; hence the axis scale was only marked at the base and tip.
In condition 1, bars were displayed vertically with a y-axis scale, whereas in condition 2, bars were displayed horizontally with an x-axis scale. To minimize transfer and learning effects, these conditions were also counterbalanced. There were three parts to the accompanying text. The first element conveyed details about a hypothetical scenario. The topics covered various issues, from popular culture to product ratings. To strike a compromise between ecological validity and methodological rigor, we chose cases that could be found in newspapers or advertisements yet were purposefully non-controversial. Each scenario said that data from two sample groups had been collected and compared. The second part of the text said that the sample groups were all the same size and that the data was presented in a pair of graphs. Finally, participants were asked to choose the graphic with the most significant magnitude. Consider the following scenario:
A neighborhood art gallery is hosting two exhibitions. For each show, the same amount of visitor surveys was collected. The graphs below illustrate the percentage of visitors who would tell a friend about the exhibition. Which diagram depicts the more prominent figure? A considerable number can be seen in graph A.
Procedure
Pavlovia.org was used to host the experiment. Participants were instructed to complete the investigation on a desktop or laptop computer rather than a tablet or smartphone. After giving informed consent, participants provided their age and gender and five self-report measures to assess graph literacy. Participants were reminded that they might withdraw throughout the study but that their data would remain in the project after anonymization. Participants were given instructions on reading information about a series of paired data visualizations and rating their associated values.
.
A single, forced response was required for each trial. Participants chose the chart that they thought had the highest value. A small phrase above the bar charts guided this response and said that answers should be related to the plotted data (for example, “Which of these graphs indicates the bigger number?”). Throughout the study, X specific questions were employed. This complete assessment took the form of a true or false Boolean question related to information supplied in the prior experiment (for example, “Did the graphs on the previous screen plot data concerning 1* reviews?”). This guaranteed that participants were involved in the study at all times. Filler experiments served as a check. Throughout, it’s also used. Before leaving, participants were told that all of the circumstances in this experiment were made up.
Results

Truncation has no effect as the main effect. There is no effect on the direction. There is no interactivity. For horizontal graphs, a small significant negative outcome was seen, which opposed the hypothesis. It also shows that data from other studies is due to misreading rather than a false impression, implying that people are better at accurately evaluating the graph when the baseline is included. Perhaps an eye-tracking study might be suggested to see how long individuals spend viewing charts. The average time spent reading graphs is 16.2 minutes. Do they approach it with a critical/analytic mindset? Individuals use this time to assess the information offered to them critically.
When the height convention is removed, some bias may see the range as probable possibilities (more likely to have a more significant magnitude). People can read graphs more appropriately if the baseline is included, even if it is truncated.
Potential mechanisms
There’s a solid case that there’s a significant mental link between vertical position and perceived value (Tversky, 1997). The use of Gricean maxims by Yang et al. (2021) may help to explain how this association affects the meaning retrieved from charts. According to this account, users will infer (consciously or unconsciously) that a data point’s position reflects its intended purpose. As a result, if a data point appears to reflect a large size, that is how it will be regarded. This is an example of cognitive bias, in which information is interpreted differently depending on the context in which it is presented. Because the context of a graph often gives valuable information, this style of reasoning isn’t always improper; it’s just remarkable in this case.

Height aspect lost.
Even though the charts just plotted probability, the manipulation impacted risk probability and risk severity ratings. This is consistent with other results in the risk literature that suggest a relationship between probability and severity judgments.

Larger scale, a likelihood of higher magnitude.
Issues with the study The display and interpretation of data are becoming increasingly crucial as the emphasis on developing scientific inquiry skills grows. Competence in graph interpretation is required to comprehend today’s reality and be scientifically literate. On the other hand, graph interpretation is a time-consuming and challenging task. Many factors influence graph interpretation skills, including graph properties, graph content, and the viewers’ prior knowledge. For example, pupils’ initial theories and expectations may lead to biases and misinterpretation of graphs.
Small sample group. The mean variation is too slight.
Graphs too simple don’t represent graphs you’d see in real life.
Directions for future research
The main direction for future research is to Invert the axis (e.g., runs 100 to zero). It’s uncertain whether this effect is caused by a low-level perceptual mechanism (instead of a high-level perceptual tool). A high-level cognitive association between absolute position and perceived magnitude), or a high-level mental association between the final part and perceived extent of the mechanism (an association between relative position and perceived importance) (Cleveland et al. 1982). Suppose it’s fueled by mentally expressing the size of a data point in absolute place. It might be as simple as assigning higher values to data points in higher standings (and lower weights to data points in lower classes). Mentally portraying the amplitude of a data point if it is driven by relative position would necessitate a comparison to plausible choices for which no values are shown but suggested by the usage of white space
A follow-up experiment will discover which mechanism is responsible for position effects. adjust the magnitude associated with data points shown at various angles

References
Cleveland, W. S., Diaconis, P., & McGill, R. (1982). Variables on Scatterplots Look More Highly Correlated When the Scales Are Increased. Science, 216(4550), 11381141. https:// doi.org/10.1126/science.216.4550.1138
Correll, M., Bertini, E., & Franconeri, S. (2019). Truncating the Y-Axis: Threat or Menace?
https://doi.org/10.1145/3313831.3376222
Dragicevic, P., & Jansen, Y. (2018). Blinded with Science or Informed by Charts? A Replication Study. IEEE Transactions on Visualization and Computer Graphics, 24(1), 781790. https://doi.org/10.1109/TVCG.2017.2744298
Guri-Rozenblit, S. (1988). IMPACT OF DIAGRAMS ON RECALLING SEQUENTIAL ELEMENTS IN EXPOSITORY TEXTS. Reading Psychology, 9(2), 121139. https://doi.org/10.1080/0270271880090204
Hegarty, M. (2011). The Cognitive Science of Visual-Spatial Displays: Implications for Design. Topics in Cognitive Science, 3(3), 446474.
https://doi.org/10.1111/j.1756-8765.2011.01150.x
Kosara, R. (2016). An Empire Built On Sand: Reexamining What We Think We Know About Visualization. In Proceedings of the Beyond Time and Errors on Novel Evaluation Methods for Visualization – BELIV 16 (pp. 162168). Baltimore, MD, USA: ACM Press. https://doi.org/10.1145/2993901.2993909
Lauer, C., & O’Brien, S. (2020). How Deceptive Tactics influence people in Everyday Charts and Graphs. IEEE Transactions on Professional Communication, 63(4), 327340. https://doi.org/10.1109/TPC.2020.3032053
Pandey, A. V., Manivannan, A., Nov, O., Satterthwaite, M., & Bertini, E. (2014). The Persuasive Power of Data Visualization. IEEE Transactions on Visualization and Computer Graphics, 20(12), 22112220.
https://doi.org/10.1109/TVCG.2014.2346419
Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representations. Learning and Instruction, 13(2), 141156. https://doi.org/10.1016/S0959-4752(02)00017-8
Tal, A., & Wansink, B. (2016). Blinded with science: Trivial graphs and formulas increase ad persuasiveness and belief in product efficacy. Public Understanding of Science (Bristol, England), 25(1), 117125.
https://doi.org/10.1177/0963662514549688
Tufte, E. R. (2001). The visual display of quantitative information (2nd ed). Cheshire, Conn: Graphics Press.

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