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Modern Statistical Methods for HCIEffect Sizes and Power Analysis in HCI

Modern Statistical Methods for HCI: Effect Sizes and Power Analysis in HCI [Null hypothesis significance testing (NHST) is a common statistical analysis method in HCI. But its usage and interpretation are often misunderstood. In particular, NHST does not offer the magnitude of differences observed, which is more desirable to determine the effect of comparative studies than the p value. Effect sizes and power analysis can mitigate over-reliance on the p value, and offer researchers better informed preparation and interpretation on experiments. Many research fields now require authors to include effect sizes in NHST results, and this trend is expected to be more and more common. In this chapter, I first discuss common misunderstandings of NHST and p value, and how effect sizes can complement them. I then present methods for calculating effect sizes with examples. I also describe another closely related topic, power analysis. Power analysis can be useful for appropriately designing experiments though it is not frequently used in HCI. I present power analysis methods and discuss how they should and should not be used.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Modern Statistical Methods for HCIEffect Sizes and Power Analysis in HCI

Part of the Human–Computer Interaction Series Book Series
Editors: Robertson, Judy; Kaptein, Maurits
Yatani, Koji
Modern Statistical Methods for HCI — Mar 23, 2016
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23 pages

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-26631-2
Pages
87 –110
DOI
10.1007/978-3-319-26633-6_5
Publisher site
See Chapter on Publisher Site

Abstract

[Null hypothesis significance testing (NHST) is a common statistical analysis method in HCI. But its usage and interpretation are often misunderstood. In particular, NHST does not offer the magnitude of differences observed, which is more desirable to determine the effect of comparative studies than the p value. Effect sizes and power analysis can mitigate over-reliance on the p value, and offer researchers better informed preparation and interpretation on experiments. Many research fields now require authors to include effect sizes in NHST results, and this trend is expected to be more and more common. In this chapter, I first discuss common misunderstandings of NHST and p value, and how effect sizes can complement them. I then present methods for calculating effect sizes with examples. I also describe another closely related topic, power analysis. Power analysis can be useful for appropriately designing experiments though it is not frequently used in HCI. I present power analysis methods and discuss how they should and should not be used.]

Published: Mar 23, 2016

Keywords: Retrospective Power Analysis; Null Hypothesis Significance Testing (NHST); Calculate Effect Sizes; Repeated-measure ANOVA Test; Effect Size Estimates

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