Beyond Weber's Law: A Second Look at Ranking Visualizations of Correlation

IEEE Transactions on Visualization and Computer Graphics

Volumn 22, Issue 1, 2016, Pages 469-478

  Kay, Matthew ^a   Heer, Jeffrey ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Analytical models; Correlation; Data models; Data visualization; Gaussian distribution; Predictive models; Visualization

Indexed keywords

ANALYTICAL MODELS; CORRELATION METHODS; DATA HANDLING; DATA STRUCTURES; FLOW VISUALIZATION; GAUSSIAN DISTRIBUTION; VISUALIZATION;

BAYESIAN STATISTICS; CENSORED REGRESSION; DATA COLLECTION PROCESS; INDIVIDUAL DIFFERENCES; PREDICTIVE ACCURACY; PREDICTIVE MODELS; VISUALIZATION DESIGNS; WEBER-FECHNER LAWS;

DATA VISUALIZATION;

BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER GRAPHICS; HUMAN; PHYSIOLOGY; REGRESSION ANALYSIS; VISION;

BAYES THEOREM; COMPUTER GRAPHICS; HUMANS; MODELS, NEUROLOGICAL; REGRESSION ANALYSIS; VISUAL PERCEPTION;

EID: 84946607177     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2015.2467671     Document Type: Article

References (21)

1. L. Harrison, F. Yang, S. Franconeri, and R. Chang, "Ranking Visualizations of Correlation Using Weber's Law," IEEE Trans. Vis. Comput. Graph., vol. 20, no. 12, pp. 1943-1952, 2014.
2. W. S. Cleveland, C. S. Harris, and R. McGill, "Judgments of circle sizes on statistical maps," J. Am. Stat. Assoc., vol. 77, no. 379, pp. 541-547, 1982.
3. R. A. Rensink and G. Baldridge, "The perception of correlation in scatterplots," Comput. Graph. Forum, vol. 29, no. 3, pp. 1203-1210, 2010.
4. S. S. Stevens, "On the psychophysical law," Psychol. Rev., vol. 64, no. 3, pp. 153-181, 1957.
5. D. M. Greem and R. Duncan Luce, "Variability of magnitude estimates: A timing theory analysis," Percept. Psychophys., vol. 15, no. 2, pp. 291-300, 1974.
6. T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning: data mining, inference and prediction, Second Edi. Springer, 2009.
7. E. Limpert, W. a. Stahel, and M. Abbt, "Log-normal Distributions across the Sciences: Keys and Clues," Bioscience, vol. 51, no. 5, p. 341, 2001.
8. G. E. P. Box and D. R. Cox, "An analysis of transformations," J. R. Stat. Soc. Ser. B, vol. 26, no. 2, pp. 211-252, 1964.
9. T. James, "Estimation of Relationships for Limited Dependent Variables," Econometrica, vol. 26, no. 1, pp. 24-36, 1958.
10. T. Amemiya, "Tobit models: A survey," J. Econom., vol. 24, pp. 3-61, 1984.
11. J. K. Kruschke, "Bayesian data analysis," Wiley Interdiscip. Rev. Cogn. Sci., vol. 1, no. 5, pp. 658-676, Apr. 2010.
12. J. K. Kruschke, Doing Bayesian Data Analysis. Elsevier Inc., 2011.
13. D. Bates, M. Maechler, B. Bolker, and S. Walker, "lme4: Linear mixed-effects models using Eigen and S4, R package version 1.1-7." 2014.
14. D. J. Barr, R. Levy, C. Scheepers, and H. J. Tily, "Random effects structure for confirmatory hypothesis testing: Keep it maximal," J. Mem. Lang., vol. 68, no. 3, pp. 255-278, 2013.
15. S. H. . Hurlbert, "Pseudoreplication and the Design of Ecological Field Experiments," Ecol. Monogr., vol. 54, no. 2, pp. 187-211, 1984.
16. M. Plummer, "JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling," Proc. 3rd Int. Work. Distrib. Stat. Comput. (DSC 2003), 2003.
17. J. W. Tukey, Exploratory Data Analysis. Addison-Wesley, 1977.
18. R. A. Rigby and D. M. Stasinopoulos, "Generalized additive models for location, scale, and shape," Appl. Stat., vol. 54, no. 3, pp. 507-554, 2005.
19. M. Stone, "An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike's Criterion," J. R. Stat. Soc. Ser. B, vol. 39, no. 1, pp. 44-47, 1977.
20. A. E. Raftery and S. M. Lewis, "Comment: One Long Run with Diagnostics: Implementation Strategies for Markov Chain Monte Carlo," Stat. Sci., vol. 7, no. 4, pp. 493-497, 1992.
21. A. Gelman and D. B. Rubin, "lnference from Iterative Simulation Using Multiple Sequences," Stat. Sci., vol. 7, no. 4, pp. 457-472, 1992.