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Topic Set Size Design and Power Analysis in Practice

Published: 12 September 2016 Publication History

Abstract

Topic set size design methods provide principles and procedures for test collection builders to decide on the number of topics to create. These methods can then help us keep improving the test collection design based on accumulated data. Simple Excel tools are available for such purposes. Post-hoc power analysis tools, available as simple R scripts, can help IR researchers examine the achieved power of a reported experiment and determine future sample sizes for ensuring high power. Thus, for example, underpowered user experiments can be detected, and a larger sample size can be proposed. If used appropriately, these Excel and R tools should be able to provide the IR community with better experimentation practices. The main objective of this tutorial is to let IR researchers familiarise themselves with these tools and understand the basic ideas behind them.

References

[1]
Y. Nagata. How to Design the Sample Size (in Japanese). Asakura Shoten, 2003.
[2]
T. Sakai. Information Access Evaluation Methodology: For the Progress of Search Engines (in Japanese). Coronasha, 2015.
[3]
T. Sakai. Topic set size design. Information Retrieval Journal, 2015.
[4]
T. Sakai. Statistical significance, power, and sample sizes: A systematic review of SIGIR and TOIS, 2006--2015. In Proceedings of SIGIR 2016, 2016.
[5]
T. Sakai. Two sample t-tests for IR evaluation: Student or welch? In Proceedings of SIGIR 2016, 2016.
[6]
T. Sakai and L. Shang. On estimating variances for topic set size design. In Proceedings of EVIA 2016, 2016.
[7]
T. Sakai, L. Shang, Z. Lu, and H. Li. Topic set size design with the evaluation measures for short text conversation. In Proceedings of AIRS 2015 (LNCS 9460), pages 319--331, 2015.
[8]
H. Toyoda. Introduction to Statistical Power Analysis: A Tutorial with R (in Japanese). Tokyo Tosyo, 2009.

Cited By

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  • (2023)How Many Crowd Workers Do I Need? On Statistical Power when Crowdsourcing Relevance JudgmentsACM Transactions on Information Systems10.1145/359720142:1(1-26)Online publication date: 22-May-2023
  • (2023)On the Reliability of User Feedback for Evaluating the Quality of Conversational AgentsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615286(4185-4189)Online publication date: 21-Oct-2023
  • (2019)Statistical Significance Testing in Theory and in PracticeProceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3341981.3358959(257-259)Online publication date: 26-Sep-2019
  • Show More Cited By

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    cover image ACM Conferences
    ICTIR '16: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval
    September 2016
    318 pages
    ISBN:9781450344975
    DOI:10.1145/2970398
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 12 September 2016

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    Author Tags

    1. effect sizes
    2. experimental design
    3. statistical power
    4. statistical significance
    5. test collections
    6. variances

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    ICTIR '16 Paper Acceptance Rate 41 of 79 submissions, 52%;
    Overall Acceptance Rate 235 of 527 submissions, 45%

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    Cited By

    View all
    • (2023)How Many Crowd Workers Do I Need? On Statistical Power when Crowdsourcing Relevance JudgmentsACM Transactions on Information Systems10.1145/359720142:1(1-26)Online publication date: 22-May-2023
    • (2023)On the Reliability of User Feedback for Evaluating the Quality of Conversational AgentsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615286(4185-4189)Online publication date: 21-Oct-2023
    • (2019)Statistical Significance Testing in Theory and in PracticeProceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3341981.3358959(257-259)Online publication date: 26-Sep-2019
    • (2019)Conducting Laboratory Experiments Properly with Statistical ToolsProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291378(830-831)Online publication date: 30-Jan-2019
    • (2018)Conducting Laboratory Experiments Properly with Statistical ToolsThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210182(1369-1370)Online publication date: 27-Jun-2018
    • (2017)Statistical Significance Testing in Information RetrievalProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3082065(1387-1389)Online publication date: 7-Aug-2017

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