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Review Article
A Review on the Use of Effect Size in Nursing Research
Hyuncheol Kang, Kyupil Yeon, Sang-Tae Han
Journal of Korean Academy of Nursing 2015;45(5):641-649.
DOI: https://doi.org/10.4040/jkan.2015.45.5.641
Published online: October 30, 2015

Department of Applied Statistics, Hoseo University, Asan, Korea.

Address reprint requests to: Han, Sang-Tae. Department of Applied Statistics, Hoseo University, 20 Hoseo-ro 79 beon-gil, Asan 31499, Korea. Tel: +82-41-540-5901, Fax: +82-41-540-5908, sthan@hoseo.edu
• Received: September 2, 2015   • Revised: September 3, 2015   • Accepted: October 7, 2015

© 2015 Korean Society of Nursing Science

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (http://creativecommons.org/licenses/by-nd/4.0/) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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  • Purpose
    The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing.
  • Methods
    For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained.
  • Results
    Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size.
  • Conclusion
    It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.
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