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Kang, Yeon, and Han: A Review on the Use of Effect Size in Nursing Research
Review Article

Journal of Korean Academy of Nursing 2015; 45(5): 641-649.

Published online: 30 October 2015

DOI: https://doi.org/10.4040/jkan.2015.45.5.641

A Review on the Use of Effect Size in Nursing Research

Hyuncheol Kang, Kyupil Yeon, Sang-Tae Han

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 2 September 2015       Revised 3 September 2015       Accepted 7 October 2015

© 2015 Korean Society of Nursing Science

(open-access):

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.

Abstract

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.

Keywords: Effect size, Significance probability, Power of test, Statistical significance, Practical significance

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