Journal article 419 views
Argus: Interactive a priori Power Analysis
IEEE Transactions on Visualization and Computer Graphics, Volume: 27, Issue: 2, Pages: 432 - 442
Swansea University Author:
Chat Wacharamanotham
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1109/tvcg.2020.3028894
Abstract
A key challenge HCI researchers face when designing a controlled experiment is choosing the appropriate number of participants, or sample size. A priori power analysis examines the relationships among multiple parameters, including the complexity associated with human participants, e.g., order and f...
| Published in: | IEEE Transactions on Visualization and Computer Graphics |
|---|---|
| ISSN: | 1077-2626 1941-0506 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa60608 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
A key challenge HCI researchers face when designing a controlled experiment is choosing the appropriate number of participants, or sample size. A priori power analysis examines the relationships among multiple parameters, including the complexity associated with human participants, e.g., order and fatigue effects, to calculate the statistical power of a given experiment design. We created Argus, a tool that supports interactive exploration of statistical power: Researchers specify experiment design scenarios with varying confounds and effect sizes. Argus then simulates data and visualizes statistical power across these scenarios, which lets researchers interactively weigh various trade-offs and make informed decisions about sample size. We describe the design and implementation of Argus, a usage scenario designing a visualization experiment, and a think-aloud study. |
|---|---|
| Keywords: |
Experiment design, power analysis, simulation |
| College: |
Faculty of Science and Engineering |
| Issue: |
2 |
| Start Page: |
432 |
| End Page: |
442 |