Practical Statistics for HCI Sample JMP data table and output

Jacob O. Wobbrock [contact]
The Information School
University of Washington

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Current Version: 23-Dec-2011 (v2.2)
Download ZIP file: ps4hci.zip

About

Practical Statistics for HCI is a set of independent study modules that aim for the "practical middle" between theoretical statistics treatments on the one hand, and mere help manuals and tutorials on the other. Students coming into the field of Human-Computer Interaction from computer science, information science, engineering, and design will benefit by taking this self-paced 10-week independent study, which covers most of the major topics necessary to become proficient in both understanding and producing statistical results, striking a balance between theory and practice.

Coursera MOOC

This independent study has been equivalently reproduced as a Coursera MOOC. It is taught in the R statistical programming language using the RStudio environment. The course is Designing, Running & Analyzing Experiments.

Statistics Tools

The tools currently used in the independent study are SAS JMP and IBM SPSS.

There have been many requests for an R version. As noted above, the equivalent R version is a MOOC course offered through Coursera. Also, a related project by the author, Statistical Analysis and Reporting in R, provides numerous R code snippets for performing statistical analyses in R.

Author's Statistics Papers

  1. Wobbrock, J.O. (2017). The relevance of nonparametric and semi-parametric statistics to HCI. Workshop on "Moving Transparent Statistics Forward." ACM Conference on Human Factors in Computing Systems (CHI '17). Denver, Colorado (May 6-11, 2017). Paper No. 2.
  2. Wobbrock, J.O. and Kay, M. (2016). Nonparametric statistics in human-computer interaction. Chapter 7 in J. Robertson & M.C. Kaptein (eds.), Modern Statistical Methods for HCI. Switzerland: Springer, pp. 135-170.
  3. Wobbrock, J.O. (2011). Practical statistics for human-computer interaction: An independent study combining statistics theory and tool know-how. Annual Workshop of the Human-Computer Interaction Consortium (HCIC '11). Pacific Grove, California (June 14-18, 2011).
  4. Wobbrock, J.O., Findlater, L., Gergle, D. and Higgins, J.J. (2011). The Aligned Rank Transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '11). Vancouver, British Columbia (May 7-12, 2011). New York: ACM Press, pp. 143-146.

Acknowledgement

This material is based upon work supported by the National Science Foundation under Grant No. IIS-0952786. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.


Copyright © 2011-2013 Jacob O. Wobbrock. All rights reserved.
Last updated January 6, 2019.