DIET – Data, Interpretation, Estimation, Trends: How bar and dot plots shape perception of (nutritional) averages [BSc. thesis Shoma Berkemeyer]
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,006 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».