A Survey of Variables Used in Empirical Studies for Visualization

This chapter provides an overview of the variables that have been considered in the controlled and semi-controlled experiments for studying phenomena in visualization. As all controlled and semi-controlled experiments have explicitly defined independent variables, dependent variables, extraneous variables, and operational variables, a survey of these variables allows us to gain a broad prospect of a major aspect of the design space for empirical studies in visualization.

[1]  Cláudio T. Silva,et al.  A User Study of Visualization Effectiveness Using EEG and Cognitive Load , 2011, Comput. Graph. Forum.

[2]  David H. Laidlaw,et al.  Colorgorical: Creating discriminable and preferable color palettes for information visualization , 2017, IEEE Transactions on Visualization and Computer Graphics.

[3]  David H. Laidlaw,et al.  The relation between visualization size, grouping, and user performance , 2014, IEEE Transactions on Visualization and Computer Graphics.

[4]  Michael Gleicher,et al.  Quantity estimation in visualizations of tagged text , 2013, CHI.

[5]  Benjamin Watson,et al.  Reinforcing Visual Grouping Cues to Communicate Complex Informational Structure , 2014, IEEE Transactions on Visualization and Computer Graphics.

[6]  Jeffrey Heer,et al.  Regression by Eye: Estimating Trends in Bivariate Visualizations , 2017, CHI.

[7]  Michael S. Bernstein,et al.  Learning Perceptual Kernels for Visualization Design , 2014, IEEE Transactions on Visualization and Computer Graphics.

[8]  Jian Chen,et al.  Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields , 2017, IEEE Transactions on Visualization and Computer Graphics.

[9]  Irene Reppa,et al.  An Empirical Study on Using Visual Embellishments in Visualization , 2012, IEEE Transactions on Visualization and Computer Graphics.

[10]  Michael W. Eysenck Psychology: A Student's Handbook , 2000 .

[11]  Anshul Vikram Pandey,et al.  Towards Understanding Human Similarity Perception in the Analysis of Large Sets of Scatter Plots , 2016, CHI.

[12]  Daniel A. Keim,et al.  Efficient Contrast Effect Compensation with Personalized Perception Models , 2015, Comput. Graph. Forum.

[13]  Min Chen,et al.  Evaluating the impact of task demands and block resolution on the effectiveness of pixel-based visualization , 2010, IEEE Transactions on Visualization and Computer Graphics.

[14]  Yalong Yang,et al.  Many-to-Many Geographically-Embedded Flow Visualisation: An Evaluation , 2019, IEEE Transactions on Visualization and Computer Graphics.

[15]  Mark Gahegan,et al.  Visual Semiotics & Uncertainty Visualization: An Empirical Study , 2012, IEEE Transactions on Visualization and Computer Graphics.

[16]  David Whitney,et al.  How Capacity Limits of Attention Influence Information Visualization Effectiveness , 2012, IEEE Transactions on Visualization and Computer Graphics.

[17]  Min Chen,et al.  Empirically Measuring Soft Knowledge in Visualization , 2017, Comput. Graph. Forum.

[18]  R. Thouless Experimental Psychology , 1939, Nature.

[19]  MIN CHEN,et al.  "Isms" in Visualization , 2020, Foundations of Data Visualization.

[20]  Jeffrey Heer,et al.  Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations , 2009, CHI.

[21]  Jock D. Mackinlay,et al.  The structure of the information visualization design space , 1997, Proceedings of VIZ '97: Visualization Conference, Information Visualization Symposium and Parallel Rendering Symposium.

[22]  Sabarish V. Babu,et al.  Effects of Virtual Human Appearance Fidelity on Emotion Contagion in Affective Inter-Personal Simulations , 2016, IEEE Transactions on Visualization and Computer Graphics.

[23]  Mark A. Livingston,et al.  Evaluation of Trend Localization with Multi-Variate Visualizations , 2011, IEEE Transactions on Visualization and Computer Graphics.

[24]  Carlos Eduardo Scheidegger,et al.  Looks Good To Me: Visualizations As Sanity Checks , 2019, IEEE Transactions on Visualization and Computer Graphics.

[25]  Niklas Elmqvist,et al.  Graphical Perception of Multiple Time Series , 2010, IEEE Transactions on Visualization and Computer Graphics.

[26]  Eugene Wu,et al.  At a Glance: Pixel Approximate Entropy as a Measure of Line Chart Complexity , 2018, IEEE Transactions on Visualization and Computer Graphics.

[27]  Kwan-Liu Ma,et al.  Perceptually-Based Depth-Ordering Enhancement for Direct Volume Rendering , 2013, IEEE Transactions on Visualization and Computer Graphics.

[28]  Karen B. Schloss,et al.  Mapping Color to Meaning in Colormap Data Visualizations , 2019, IEEE Transactions on Visualization and Computer Graphics.

[29]  Danielle Albers Szafir,et al.  Modeling Color Difference for Visualization Design , 2018, IEEE Transactions on Visualization and Computer Graphics.

[30]  Hayeong Song,et al.  Where's My Data? Evaluating Visualizations with Missing Data , 2019, IEEE Transactions on Visualization and Computer Graphics.

[31]  Steven Franconeri,et al.  Comparing averages in time series data , 2012, CHI.

[32]  Kwan-Liu Ma,et al.  A Study On Designing Effective Introductory Materials for Information Visualization , 2016, Comput. Graph. Forum.

[33]  Bongshin Lee,et al.  What's the Difference?: Evaluating Variations of Multi-Series Bar Charts for Visual Comparison Tasks , 2018, CHI.

[34]  Robert Michael Kirby,et al.  Quantitative comparative evaluation of 2D vector field visualization methods , 2001, Proceedings Visualization, 2001. VIS '01..

[35]  Jeffrey Heer,et al.  Crowdsourcing graphical perception: using mechanical turk to assess visualization design , 2010, CHI.

[36]  Anthony C. Robinson,et al.  Comparing Color and Leader Line Highlighting Strategies in Coordinated View Geovisualizations , 2015, IEEE Transactions on Visualization and Computer Graphics.