DIET – Data, Interpretation, Estimation, Trends: How bar and dot plots shape perception of (nutritional) averages [BSc. thesis Shoma Berkemeyer]
Bibliographic record
Abstract
This study aims to investigate whether perception of bar graphs and dot plots differ in university students of psychology. Graphical perception refers to the visual decoding of information encoded on graphs, which includes both theory and experimentation to test the theory (Cleveland & McGill, 1984). Summary statistics, such as, means with standard deviations, allow the processing, abstracting and compressing of large data-sets within a single group of numbers (Franconeri et al., 2021). Data visualizations represent simple summaries that can reveal data features, which statistics modelling (Unwin, 2020) or summary statistics (Franconeri et al., 2021) could miss. For example, many sets of data can generate the same summary statistic, even so the data could have different patterns (Franconeri et al., 2021). Thus, data visualization gives clues about unusual data distribution, local patterns, clusters, gaps, missing values, evidence of rounding or heaping, implicit boundaries and outliers (Unwin, 2020), allowing viewers to see beyond summary statistics (Franconeri et al., 2021). Variations, though, exist in how viewers interpret graphs based on summary statistics (Kerns & Wilmer, 2021). Perception of bar graphs as visualization for summary statistics appears to be more biased in comparison to perception of dot plots (Godau et al., 2016; Okan et al., 2018). When asked to estimate the mean from a data graph with bar graphs, observers underestimated the mean – which was not the case when the same data were plotted in a dot plot. An investigation on the reaction-time task of viewers in processing bar graphs and dot plots, though, indicated that both graphs elicited similar reaction-time with similar graph processing (Zhao & Gaschler, 2021), suggesting probably similar x-y-coordinate system graph perception schema. In estimation-of-means task, literature has indicated differences in perception of bar graphs compared to other graphs (Okan et al., 2018). Perception mechanism using pure visualization-based estimation of means from bar graph used lower working memory, perception mechanism using calculation of means from the bar graph used higher working memory (Padilla et al., 2018). Thus, controlling for perception mechanism within bar graphs appears pertinent. Graphs are a communication tool not only for the scientific community with its various disciplines (Okan et al., 2018; Riedel et al., 2022) but also for public communication, such as, news and policies (Franconeri et al., 2021; Otten & Cheng, 2015). Perception errors by the viewers of the visualizations, though, can occur with potential of decision-making errors in real world (Okan et al., 2018). Bar graphs are often used for nutritional summary statistics, which harbor real world policy ramifications and recommendations (Ruxton et al., 2021; Schienkiewitz et al., 2020), however, to date have not been tested for summary statistics visualization tasks. A systematic bias in perceiving means from bar graph by underestimating has been reported (Godau et al., 2016). Given above findings, we hypothesize that using pure visualization-based estimation of means, the perception of (nutritional) means of bar graphs by viewers will be lower than that of dot plots by the same set of viewers of university students of psychology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".