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Argus: Interactive a priori Power Analysis

Xiaoyi Wang, Alexander Eiselmayer, Wendy E. Mackay, Kasper Hornbaek, Chat Wacharamanotham Orcid Logo

IEEE Transactions on Visualization and Computer Graphics, Volume: 27, Issue: 2, Pages: 432 - 442

Swansea University Author: Chat Wacharamanotham Orcid Logo

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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...

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Published in: IEEE Transactions on Visualization and Computer Graphics
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
first_indexed 2022-08-15T13:49:17Z
last_indexed 2023-01-13T19:20:51Z
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spelling 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 MACS 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 Mathematics and Computer Science School COLLEGE CODE MACS 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
author_id_str_mv 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
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
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 2021-02-01T09:18:54Z
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score 11.06032

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