The DOI matches its Crossref record; 15 of the 39 references it cites were found in a registry or an index.
Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods
Abstract
The subject of graphical methods for data analysis and for data presentation needs a scientific foundation. In this article we take a few steps in the direction of establishing such a foundation. Our approach is based on graphical perception—the visual decoding of information encoded on graphs—and it includes both theory and experimentation to test the theory. The theory deals with a small but important piece of the whole process of graphical perception. The first part is an identification of a set of elementary perceptual tasks that are carried out when people extract quantitative information from graphs. The second part is an ordering of the tasks on the basis of how accurately people perform them. Elements of the theory are tested by experimentation in which subjects record their judgments of the quantitative information on graphs. The experiments validate these elements but also suggest that the set of elementary tasks should be expanded. The theory provides a guideline for graph construction: Graphs should employ elementary tasks as high in the ordering as possible. This principle is applied to a variety of graphs, including bar charts, divided bar charts, pie charts, and statistical maps with shading. The conclusion is that radical surgery on these popular graphs is needed, and as replacements we offer alternative graphical forms—dot charts, dot charts with grouping, and framed-rectangle charts.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Robust Locally Weighted Regression and Smoothing Scatterplots
Cleveland, W. S. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481038 · Cited by 11,594
Self-citationThe Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
Representation and recognition of the spatial organization of three-dimensional shapes
Marr, D., & Nishihara, H. K. (1978) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1978.0020 · Cited by 2,159
Tukey, J. W. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704711 · Cited by 1,934
Graphs in Statistical Analysis
Anscombe, F. J. (1973) · The American Statistician · doi:10.1080/00031305.1973.10478966 · Cited by 976
Information and Persuasion: Rivals or Partners?
McCoy, K. (2000) · Design Issues · doi:10.1162/07479360052053342 · Cited by 345
A theory of preattentive texture discrimination based on first-order statistics of textons
Julesz, B. (1981) · Biological Cybernetics · doi:10.1007/bf00335367 · Cited by 231
The Relative Merits of Circles and Bars for Representing Component Parts
Eells, W. C. (1926) · Journal of the American Statistical Association · doi:10.1080/01621459.1926.10502165 · Cited by 108
Some Remarks on the Role in Statistics of Graphical Methods
Cox, D. R. (1978) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2346220 · Cited by 89
Bar Charts versus Circle Diagrams
Croxton, F. E., & Stryker, R. E. (1927) · Journal of the American Statistical Association · doi:10.1080/01621459.1927.10502976 · Cited by 81
Flagged first, then by citations
24 of the 39 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
UpSetR: an R package for the visualization of intersecting sets and their properties
Conway, J. R., Lex, A., & Gehlenborg, N. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx364 · Cited by 3,586
UpSet: Visualization of Intersecting Sets
Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., & Pfister, H. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346248 · Cited by 2,340
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
(2021) · doi:10.1145/3411764 · Cited by 1,240
Vega-Lite: A Grammar of Interactive Graphics
Satyanarayan, A., Moritz, D., Wongsuphasawat, K., & Heer, J. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2599030 · Cited by 883
What Makes a Visualization Memorable?
Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.234 · Cited by 667
Metadata
- Authors
- Cleveland, W. S., & McGill, R.
- Affiliations
- AT&T (United States) (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1984-09
- Volume
- 79
- Issue
- 387
- Pages
- 531-554
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1971781829
- Cited by
- 1,796
- References
- 39
- Citations by year
- 2026: 86 · 2025: 141 · 2024: 84 · 2023: 75 · 2022: 69 · 2021: 119 · 2020: 94 · 2019: 81 · 2018: 116 · 2017: 73 · 2016: 71 · 2015: 82 · 2014: 73 · 2013: 60 · 2012: 70
- FWCI
- 12.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- graphical perception, task sequencing, graph construction, bar charts, pie charts, statistical maps, shading, data visualization, data presentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
