references.bib

@article{strain_2023,
  title = {The {{Effects}} of {{Contrast}} on {{Correlation Perception}} in {{Scatterplots}}},
  author = {Strain, Gabriel and Stewart, Andrew J. and Warren, Paul and Jay, Caroline},
  year = {2023},
  journal = {International Journal of Human-Computer Studies},
  volume = {176},
  pages = {103040},
  issn = {1071-5819},
  doi = {10.1016/j.ijhcs.2023.103040},
  urldate = {2023-04-11},
  abstract = {Scatterplots are common data visualizations that can be used to communicate a range of ideas, the most intensively studied being the correlation between two variables. Despite their ubiquity, people typically do not perceive correlations between variables accurately from scatterplots, tending to underestimate the strength of the relationship displayed. Here we describe a two-experiment study in which we adjust the visual contrast of scatterplot points, and demonstrate a systematic approach to altering the bias. We find evidence that lowering the total visual contrast in a plot leads to increased bias in correlation estimates and show that decreasing the salience of points as a function of their distance from the regression line, by lowering their contrast, can facilitate more accurate correlation perception. We discuss the implications of these findings for visualization design, and provide a framework for online, reproducible, and large-sample-size (N = 150 per experiment) testing of the design parameters of data visualizations.},
  langid = {english},
  keywords = {Correlation perception,Crowdsourced,Data visualization,Scatterplot}
}