Skip to main content
SpringerLink
Log in

A Survey of Variables Used in Empirical Studies for Visualization

  • Chapter
  • First Online:
Foundations of Data Visualization

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Anderson, E.W., Potter, K.C., Matzen, L.E., Shepherd, J.F., Preston, G.A., Silva, C.T.: A user study of visualization effectiveness using EEG and cognitive load. Comput. Graph. Forum 30(3), 791–800 (2011)

    Google Scholar 

  2. Bae, J., Watson, B.: Reinforcing visual grouping cues to communicate complex informational structure. IEEE Trans. Vis. Comput. Graph. 20(12), 1973–1982 (2014)

    Google Scholar 

  3. Borgo, R., Abdul-Rahman, A., Mohamed, F., Grant, P.W., Reppa, I., Floridi, L., Chen, M.: An empirical study on using visual embellishments in visualization. IEEE Trans. Vis. Comput. Graph. 18(12), 2759–2768 (2012)

    Google Scholar 

  4. Borgo, R., Proctor, K., Chen, M., Jänicke, H., Murray, T., Thornton, I.M.: Evaluating the impact of task demands and block resolution on the effectiveness of pixel-based visualization. IEEE Trans. Visualization & Computer Graphics 16(6), 963–972 (2010)

    Google Scholar 

  5. Card, S.K., Mackinlay, J.: The structure of the information visualization design space. In: Proceedings of IEEE Symposium on Information Visualization, pp. 92–99 (1997)

    Google Scholar 

  6. Chen, M., Edwards, D.J.: Isms in visualization. In: Chen, M., Hauser, H., Rheingans, P., Scheuermann, G. (eds.) Foundations of Data Visualization. Springer (2019)

    Google Scholar 

  7. Cohen, P.R.: Methods for Artificial Intelligence. MIT Press, Cambridge (1995)

    MATH  Google Scholar 

  8. Correll, M., Albers, D., Franconeri, S., Gleicher, M.: Comparing averages in time series data. In: Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1095–1104 (2012)

    Google Scholar 

  9. Correll, M., Heer, J.: Regression by eye: Estimating trends in bivariate visualizations. In: Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1387–1396 (2017)

    Google Scholar 

  10. Correll, M., Li, M., Kindlmann, G., Scheidegger, C.: Looks good to me: visualizations as sanity checks. IEEE Trans. Vis. Comput. Graph. 25(1), 830–839 (2019)

    Google Scholar 

  11. Correll, M.A., Alexander, E.C., Gleicher, M.: Quantity estimation in visualizations of tagged text. In: Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 2697–2706 (2013)

    Google Scholar 

  12. Demiralp, C., Bernstein, M.S., Heer, J.: Learning perceptual kernels for visualization design. IEEE Trans. Vis. Comput. Graph. 20(12), 1933–1942 (2014)

    Google Scholar 

  13. Eysenck, M.W.: Psychology: A Student’s Handbook. Psychology Press (2000)

    Google Scholar 

  14. Gramazio, C.C., Laidlaw, D.H., Schloss, K.B.: Colorgorical: creating discriminable and preferable color palettes for information visualization. IEEE Trans. Vis. Comput. Graph. 23(1), 521–530 (2017)

    Google Scholar 

  15. Gramazio, C.C., Schloss, K.B., Laidlaw, D.H.: The relation between visualization size, grouping, and user performance. IEEE Trans. Vis. Comput. Graph. 20(12), 1953–1962 (2014)

    Google Scholar 

  16. Griffin, A.L., Robinson, A.C.: Comparing color and leader line highlighting strategies in coordinated view geovisualizations. IEEE Trans. Vis. Comput. Graph. 21(3), 339–349 (2015)

    Google Scholar 

  17. Haroz, S., Whitney, D.: How capacity limits of attention influence information visualization effectiveness. IEEE Trans. Vis. Comput. Graph. 18(12), 2402–2410 (2012)

    Google Scholar 

  18. Heer, J., Bostock, M.: Crowdsourcing graphical perception: Using mechanical turk to assess visualization design. In: Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 203–212 (2010)

    Google Scholar 

  19. Heer, J., Kong, N., Agrawala, M.: Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. In: Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1303–1312 (2009)

    Google Scholar 

  20. Javed, W., McDonnel, B., Elmqvist, N.: Graphical perception of multiple time series. IEEE Trans. Vis. Comput. Graph. 16(6), 927–934 (2010)

    Google Scholar 

  21. Kantowitz, B., Roediger III, H., Elmes, D.: Experimental Psychology. Wadsworth Publishing, Belmont (2014)

    Google Scholar 

  22. Kijmongkolchai, N., Abdul-Rahman, A., Chen, M.: Empirically measuring soft knowledge in visualization. Comput. Graph. Forum 36(3), 73–85 (2017). https://doi.org/10.1111/cgf.13169

  23. Laidlaw, D.H., Davidson, J.S., Miller, T.S., da Silva, M., Kirby, R.M., Warren, W.H., Tarr, M.: Quantitative comparative evaluation of 2D vector field visualization methods. In: Proceedings of IEEE Visualization, pp. 143–150 (2001)

    Google Scholar 

  24. Livingston, M., Decker, J.: Evaluation of trend localization with multi-variate visualizations. IEEE Trans. Vis. Comput. Graph. 17(12), 2053–2062 (2011)

    Google Scholar 

  25. MacEachren, A.M., Roth, R.E., O’Brien, J., Li, B., Swingley, D., Gahegan, M.: Visual semiotics & uncertainty visualization: an empirical study. IEEE Trans. Vis. Comput. Graph. 18(12), 2496–2505 (2012)

    Google Scholar 

  26. Mittelstädt, S., Keim, D.A.: Efficient contrast effect compensation with personalized perception models. Comput. Graph. Forum 34(3), 211–220 (2015)

    Google Scholar 

  27. Pandey, A.V., Krause, J., Felix, C., Boy, J., Bertini, E.: Towards understanding human similarity perception in the analysis of large sets of scatter plots. In: Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 3659–3669 (2016)

    Google Scholar 

  28. Ryan, G., Mosca, A., Chang, R., Wu, E.: At a glance: pixel approximate entropy as a measure of line chart complexity. IEEE Trans. Vis. Comput. Graph. 25(1), 872–881 (2019)

    Google Scholar 

  29. Schloss, K.B., Gramazio, C.C., Silverman, A.T., Parker, M.L., Wang, A.S.: Mapping color to meaning in colormap data visualizations. IEEE Trans. Vis. Comput. Graph. 25(1), 810–819 (2019)

    Google Scholar 

  30. Song, H., Szafir, D.A.: Where’s my data? evaluating visualizations with missing data. IEEE Trans. Vis. Comput. Graph. 25(1), 914–924 (2019)

    Google Scholar 

  31. Srinivasan, A., Brehmer, M., Lee, B., Drucker, S.M.: What’s the difference?: evaluating variations of multi-series bar charts for visual comparison tasks. In: Proceedings of ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 304:1–304:12 (2018)

    Google Scholar 

  32. Szafir, D.A.: Modeling color difference for visualization design. IEEE Trans. Vis. Comput. Graph. 24(1), 392–401 (2018)

    Google Scholar 

  33. Tanahashi, Y., Leaf, N., Ma, K.L.: A study on designing effective introductory materials for information visualization. Comput. Graph. Forum 35(7), 117–126 (2016)

    Google Scholar 

  34. Volante, M., Babu, S.V., Chaturvedi, H., Newsome, N., Ebrahimi, E., Roy, T., Daily, S.B., Fasolino, T.: Effects of virtual human appearance fidelity on emotion contagion in affective inter-personal simulations. IEEE Trans. Vis. Comput. Graph. 22(4), 1326–1335 (2016)

    Google Scholar 

  35. Yang, Y., Dwyer, T., Goodwin, S., Marriott, K.: Many-to-many geographically-embedded flow visualisation: an evaluation. IEEE Trans. Vis. Comput. Graph. 23(1), 411–420 (2017)

    Google Scholar 

  36. Zhao, H., Bryant, G.W., Griffin, W., Terrill, J.E., Chen, J.: Validation of splitvectors encoding for quantitative visualization of large-magnitude-range vector fields. IEEE Trans. Vis. Comput. Graph. 23(6), 1691–1705 (2017)

    Google Scholar 

  37. Zheng, L., Wu, Y., Ma, K.L.: Perceptually-based depth-ordering enhancement for direct volume rendering. IEEE Trans. Vis. Comput. Graph. 19(3), 446–459 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. King’s College London, London, UK

    Alfie Abdul-Rahman

  2. University of Oxford, Oxford, UK

    Min Chen

  3. Brown University, Providence, UK

    David H. Laidlaw

Authors
  1. Alfie Abdul-Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David H. Laidlaw
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alfie Abdul-Rahman .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Min Chen

  2. Department of Informatics, University of Bergen, Bergen, Norway

    Helwig Hauser

  3. University of Maryland, Baltimore County, Baltimore, MD, USA

    Penny Rheingans

  4. Institut für Informatik, Universität Leipzig, Leipzig, Germany

    Gerik Scheuermann

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Abdul-Rahman, A., Chen, M., Laidlaw, D.H. (2020). A Survey of Variables Used in Empirical Studies for Visualization. In: Chen, M., Hauser, H., Rheingans, P., Scheuermann, G. (eds) Foundations of Data Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-34444-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34444-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34443-6

  • Online ISBN: 978-3-030-34444-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation