Journal article 330 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
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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 |
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ISSN: | 1077-2626 1941-0506 |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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URI: | https://cronfa.swan.ac.uk/Record/cronfa60608 |
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2022-10-31T15:03:33.4130995 v2 60608 2022-07-22 Argus: Interactive a priori Power Analysis 5310be7eb485ebc96c9671f5a45d6f62 0000-0003-4831-2516 Chat Wacharamanotham Chat Wacharamanotham true false 2022-07-22 SCS 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. Journal Article IEEE Transactions on Visualization and Computer Graphics 27 2 432 442 Institute of Electrical and Electronics Engineers (IEEE) 1077-2626 1941-0506 Experiment design, power analysis, simulation 1 2 2021 2021-02-01 10.1109/tvcg.2020.3028894 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2022-10-31T15:03:33.4130995 2022-07-22T22:21:36.1062196 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Xiaoyi Wang 1 Alexander Eiselmayer 2 Wendy E. Mackay 3 Kasper Hornbaek 4 Chat Wacharamanotham 0000-0003-4831-2516 5 |
title |
Argus: Interactive a priori Power Analysis |
spellingShingle |
Argus: Interactive a priori Power Analysis Chat Wacharamanotham |
title_short |
Argus: Interactive a priori Power Analysis |
title_full |
Argus: Interactive a priori Power Analysis |
title_fullStr |
Argus: Interactive a priori Power Analysis |
title_full_unstemmed |
Argus: Interactive a priori Power Analysis |
title_sort |
Argus: Interactive a priori Power Analysis |
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5310be7eb485ebc96c9671f5a45d6f62 |
author_id_fullname_str_mv |
5310be7eb485ebc96c9671f5a45d6f62_***_Chat Wacharamanotham |
author |
Chat Wacharamanotham |
author2 |
Xiaoyi Wang Alexander Eiselmayer Wendy E. Mackay Kasper Hornbaek Chat Wacharamanotham |
format |
Journal article |
container_title |
IEEE Transactions on Visualization and Computer Graphics |
container_volume |
27 |
container_issue |
2 |
container_start_page |
432 |
publishDate |
2021 |
institution |
Swansea University |
issn |
1077-2626 1941-0506 |
doi_str_mv |
10.1109/tvcg.2020.3028894 |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
college_str |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
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description |
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. |
published_date |
2021-02-01T04:18:51Z |
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1763754253104447488 |
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11.0241995 |