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
(2000) · doi:10.1016/s0261-3794(99)00028-1 · Cited by 620
Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations
Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J. D., Howe, B., & Heer, J. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467191 · Cited by 504
Visualization Rhetoric: Framing Effects in Narrative Visualization
Hullman, J. R., & Diakopoulos, N. A. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.255 · Cited by 563
Review of Graph Comprehension Research: Implications for Instruction
Shah, P., & Hoeffner, J. (2002) · Educational Psychology Review · doi:10.1023/a:1013180410169 · Cited by 431
Wickham, H. (2009) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2009.07098 · Cited by 400
Beyond Memorability: Visualization Recognition and Recall
Borkin, M. A., Bylinskii, Z., Kim, N. W., Bainbridge, C. M., Yeh, C. S., Borkin, D., Pfister, H., & Oliva, A. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467732 · Cited by 385
Project portfolio selection through decision support
Ghasemzadeh, F., & Archer, N. (2000) · Decision Support Systems · doi:10.1016/s0167-9236(00)00065-8 · Cited by 380
Statistical methods in water resources
Helsel, D. R., & Hirsch, R. M. (2002) · doi:10.3133/twri04a3 · Cited by 367
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey
Kehrer, J., & Hauser, H. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.110 · Cited by 367
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco
Moritz, D., Wang, C., Nelson, G. L., Lin, H., Smith, A. M., Howe, B., & Heer, J. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865240 · Cited by 317
LineUp: Visual Analysis of Multi-Attribute Rankings
Gratzl, S., Lex, A., Gehlenborg, N., Pfister, H., & Streit, M. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.173 · Cited by 308
Top 10 unsolved information visualization problems
Chen, C. (2005) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2005.91 · Cited by 302
An Information-Processing Analysis of Graph Perception
Simkin, D., & Hastie, R. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478448 · Cited by 301
Graphical Perception of Multiple Time Series
Javed, W., McDonnel, B., & Elmqvist, N. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.162 · Cited by 273
Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error
Correll, M., & Gleicher, M. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346298 · Cited by 258
Scatterplot Matrix Techniques for Large N
Carr, D. B., Littlefield, R. J., Nicholson, W. L., et al. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478445 · Cited by 242
DeepEye: Towards Automatic Data Visualization
Luo, Y., Qin, X., Tang, N., & Li, G. (2018) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2018.00019 · Cited by 239
Ranking Visualizations of Correlation Using Weber's Law
Harrison, L., Yang, F., Franconeri, S., & Chang, R. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346979 · Cited by 212
Exploratory Data Analysis for Complex Models
Gelman, A. (2004) · Journal of Computational and Graphical Statistics · doi:10.1198/106186004x11435 · Cited by 215
Modeling Color Difference for Visualization Design
Szafir, D. A. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744359 · Cited by 213
The Many Faces of a Scatterplot
Cleveland, W. S., & McGill, R. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477098 · Cited by 213
Self-citationVisual task characterization for automated visual discourse synthesis
Zhou, M. X., & Feiner, S. K. (1998) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '98 · doi:10.1145/274644.274698 · Cited by 207
Timelines Revisited: A Design Space and Considerations for Expressive Storytelling
Brehmer, M., Lee, B., Bach, B., Riche, N. H., & Munzner, T. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2614803 · Cited by 202
Considerations for Visualizing Comparison
Gleicher, M. L. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744199 · Cited by 199
The effect of information presentation on decision making: A cost-benefit analysis
Vessey, I. (1994) · Information & Management · doi:10.1016/0378-7206(94)90010-8 · Cited by 199
Ten guidelines for effective data visualization in scientific publications
Kelleher, C. A., & Wagener, T. (2011) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2010.12.006 · Cited by 186
Task-based effectiveness of basic visualizations
Saket, B., Endert, A., & Demiralp, C. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2829750 · Cited by 178
AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization
Wu, A., Wang, Y., Shu, X., Moritz, D., Cui, W., Zhang, H., Zhang, D., & Qu, H. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3099002 · Cited by 182
Software Design Patterns for Information Visualization
Heer, J., & Agrawala, M. (2006) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2006.178 · Cited by 181
Benefitting InfoVis with Visual Difficulties
Hullman, J., Adar, E., & Shah, P. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.175 · Cited by 159
Lee, S., Kim, S.-H., Hung, Y., Lam, H., Kang, Y., & Yi, J. S. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467195 · Cited by 168
An experiment in graphical perception
Cleveland, W. S., & McGill, R. (1986) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(86)80019-0 · Cited by 165
Self-citationGraphical inference for infovis
Wickham, H., Cook, D., Hofmann, H., & Buja, A. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.161 · Cited by 156
Criteria for Rigor in Visualization Design Study
Meyer, M., & Dykes, J. A. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934539 · Cited by 167
Evaluating the usability of visualization methods in an exploratory geovisualization environment
Koua, E. L., MacEachren, A. M., & Kraak, M. J. (2006) · International Journal of Geographical Information Systems · doi:10.1080/13658810600607550 · Cited by 146
Constructing Visual Representations: Investigating the Use of Tangible Tokens
Huron, S., Jansen, Y., & Carpendale, S. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346292 · Cited by 141
Composition and Configuration Patterns in Multiple-View Visualizations
Chen, X., Zeng, W., Lin, Y., Al-Maneea, H. M. A., Roberts, J. C., & Chang, R. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030338 · Cited by 136
The Shaping of Information by Visual Metaphors
Ziemkiewicz, C., & Kosara, R. (2008) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2008.171 · Cited by 132
Towards a Model of Information Aesthetics in Information Visualization
Lau, A., & Moere, A. V. (2007) · doi:10.1109/iv.2007.114 · Cited by 134
Data-Driven Guides: Supporting Expressive Design for Information Graphics
Kim, N. W., Schweickart, E., Liu, Z.-C., Dontcheva, M., Li, W., Popović, J., & Pfister, H. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598620 · Cited by 139
Four Experiments on the Perception of Bar Charts
Talbot, J., Setlur, V., & Anand, A. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346320 · Cited by 127
A statistical procedure for determining the best performing air quality simulation model
Cox, W. M., & Tikvart, J. A. (1990) · Atmospheric Environment Part A General Topics · doi:10.1016/0960-1686(90)90331-g · Cited by 132
An Algebraic Process for Visualization Design
Kindlmann, G., & Scheidegger, C. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346325 · Cited by 119
Multivariate Data Glyphs: Principles and Practice
Ward, M. O. (2007) · Springer eBooks · doi:10.1007/978-3-540-33037-0_8 · Cited by 125
Communicating centrality in policy network drawings
Brandes, U., Kenis, P., & Wagner, D. (2003) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2003.1196010 · Cited by 124
Learning Perceptual Kernels for Visualization Design
Demiralp, Ç., Bernstein, M. S., & Heer, J. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346978 · Cited by 128
A Systematic Review of Experimental Studies on Data Glyphs
Fuchs, J., Isenberg, P., Bezerianos, A., & Keim, D. A. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2549018 · Cited by 117
Perceptual Guidelines for Creating Rectangular Treemaps
Kong, N., Heer, J., & Agrawala, M. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.186 · Cited by 115
Visual Reasoning Strategies for Effect Size Judgments and Decisions
Kale, A., Kay, M., & Hullman, J. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030335 · Cited by 109
KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation
Li, H., Wang, Y., Zhang, S., Song, Y., & Qu, H. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114863 · Cited by 127
Graphical Overlays: Using Layered Elements to Aid Chart Reading
Kong, N., & Agrawala, M. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.229 · Cited by 99
Bezerianos, A., & Isenberg, P. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.251 · Cited by 100
Price, M. A., Cameron, R., & Butow, P. N. (2007) · Patient Education and Counseling · doi:10.1016/j.pec.2007.08.006 · Cited by 98
Glanceable Visualization: Studies of Data Comparison Performance on Smartwatches
Blascheck, T., Besançon, L., Bezerianos, A., et al. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865142 · Cited by 96
Sketchy Rendering for Information Visualization
Wood, J., Isenberg, P., Tobias Isenberg, A., Dykes, J. A., Boukhelifa, N., & Slingsby, A. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.262 · Cited by 95
Statistical Graphics: Mapping the Pathways of Science
Wainer, H., & Velleman, P. F. (2001) · Annual Review of Psychology · doi:10.1146/annurev.psych.52.1.305 · Cited by 94
Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline
Chen, Z., Wang, Y., Wang, Q., Wang, Y., & Qu, H. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934810 · Cited by 90
The Perceptual Scalability of Visualization
Yost, B., & North, C. L. (2006) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2006.184 · Cited by 93
A Brief History of the Mosaic Display
Friendly, M. L. (2002) · Journal of Computational and Graphical Statistics · doi:10.1198/106186002317375631 · Cited by 92
Visualization by Demonstration: An Interaction Paradigm for Visual Data Exploration
Saket, B., Kim, H., Brown, E. T., & Endert, A. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598839 · Cited by 80
Eick, S. G., & Wills, G. J. (1995) · European Journal of Operational Research · doi:10.1016/0377-2217(94)00188-i · Cited by 84
Line Graph or Scatter Plot? Automatic Selection of Methods for Visualizing Trends in Time Series
Wang, Y., Han, F., Zhu, L., et al. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2653106 · Cited by 78
Vega-Lite: A Grammar of Interactive GraphicsPreprint
Satyanarayan, A., Moritz, D., Wongsuphasawat, K., et al. (2018) · doi:10.31219/osf.io/mqzyx · Cited by 78
Guidelines for Effective Usage of Text Highlighting Techniques
Strobelt, H., Oelke, D., Kwon, B. C., Schreck, T., & Pfister, H. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467759 · Cited by 76
Bäuerle, A., van Onzenoodt, C., & Ropinski, T. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3057483 · Cited by 75
Yang, Y., Dwyer, T., Marriott, K., et al. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3004137 · Cited by 75
The Visual Rhetoric of Data Displays: The Conundrum of Clarity*
Kostelnick, C. (2008) · IEEE Transactions on Professional Communication · doi:10.1109/tpc.2007.914869 · Cited by 75
Face to Face: Evaluating Visual Comparison
Ondov, B., Jardine, N. L., Elmqvist, N., & Franconeri, S. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864884 · Cited by 69
Stacking Graphic Elements to Avoid Over-Plotting
Dang, T. N., Wilkinson, L., & Anand, A. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.197 · Cited by 72
Illusion of Causality in Visualized Data
Xiong, C., Shapiro, J., Hullman, J., & Franconeri, S. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934399 · Cited by 71
A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones
Brehmer, M., Lee, B., Isenberg, P., et al. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934397 · Cited by 71
Taking Word Clouds Apart: An Empirical Investigation of the Design Space for Keyword Summaries
Felix, C., Franconeri, S., & Bertini, E. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2746018 · Cited by 70
Keshif: Rapid and Expressive Tabular Data Exploration for Novices
Yalçın, M. A., Elmqvist, N., & Bederson, B. B. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2723393 · Cited by 69
Evaluating ‘Graphical Perception’ with CNNs
Haehn, D., Tompkin, J., & Pfister, H. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865138 · Cited by 68
The Impact of Graph Slope on Rate of Change Judgments in Corporate Reports
BEATTIE, V. A., & Jones, M. J. (2002) · Abacus · doi:10.1111/1467-6281.00104 · Cited by 68
The Long and Short of It: Why Are Stocks with Shorter Runs Preferred?
Raghubir, P., & Das, S. R. (2009) · Journal of Consumer Research · doi:10.1086/644762 · Cited by 67
Looks Good To Me: Visualizations As Sanity Checks
Correll, M., Li, M., Kindlmann, G., & Scheidegger, C. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864907 · Cited by 72
Dimara, E., Bezerianos, A., & Dragicevic, P. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2745138 · Cited by 66
Dynamic Composite Data Physicalization Using Wheeled Micro-Robots
Goc, M. L., Périn, C., Follmer, S., Fekete, J., & Dragicevic, P. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865159 · Cited by 61
A Survey of Perception-Based Visualization Studies by Task
Quadri, G. J., & Rosen, P. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3098240 · Cited by 71
Measuring Effective Data Visualization
Zhu, Y. (2007) · Springer eBooks · doi:10.1007/978-3-540-76856-2_64 · Cited by 66
MulteeSum: A Tool for Comparative Spatial and Temporal Gene Expression Data
Meyer, M., Munzner, T., DePace, A. H., & Pfister, H. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.137 · Cited by 63
Color Crafting: Automating the Construction of Designer Quality Color Ramps
Smart, S., Wu, K., & Szafir, D. A. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934284 · Cited by 63
An Exploratory Study of Data Sketching for Visual Representation
Walny, J., Huron, S., & Carpendale, S. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12635 · Cited by 61
Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance
Yoghourdjian, V., Dwyer, T., Klein, K., Marriott, K., & Wybrow, M. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2790961 · Cited by 59
Correlation Judgment and Visualization Features: A Comparative Study
Yang, F., Harrison, L., Rensink, R. A., Franconeri, S., & Chang, R. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2810918 · Cited by 62
Evaluating Cartogram Effectiveness
Nusrat, S., Alam, M. J., & Kobourov, S. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2642109 · Cited by 61
The shifting boxplot. A boxplot based on essential summary statistics around the mean.
Marmolejo‐Ramos, F., & Tian, T. S. (2010) · International journal of psychological research · doi:10.21500/20112084.823 · Cited by 60
Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time
Isaacs, K. E., Bremer, P., Jusufi, I., et al. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346456 · Cited by 61
Carswell, C. M., FRANKENBERGER, S., & BERNHARD, D. (1991) · Behaviour and Information Technology · doi:10.1080/01449299108924304 · Cited by 61
Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation
Brehmer, M., Lee, B., Isenberg, P., & Choe, E. K. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865234 · Cited by 57
VizLinter: A Linter and Fixer Framework for Data Visualization
Chen, Q., Sun, F., Xu, X., Chen, Z., Wang, J., & Cao, N. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114804 · Cited by 73
Challenges for designing and implementing decision aids
Feldman‐Stewart, D., & Brundage, M. (2004) · Patient Education and Counseling · doi:10.1016/j.pec.2003.09.007 · Cited by 60
Representing Uncertainty in Graph Edges: An Evaluation of Paired Visual Variables
Guo, H., Huang, J., & Laidlaw, D. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2424872 · Cited by 60
ggplot2 Compatible Quantile-Quantile Plots in R
Almeida, A., Loy, A., & Hofmann, H. (2019) · The R Journal · doi:10.32614/rj-2018-051 · Cited by 58
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
