Hyuncheol Kang1, Kyupil Yeon1, Sang Tae Han2. 1. Department of Applied Statistics, Hoseo University, Asan, Korea. 2. Department of Applied Statistics, Hoseo University, Asan, Korea. sthan@hoseo.edu.
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.
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; Power of test; Practical significance; Significance probability; Statistical significance
Authors: Kyung-Sook Bang; Insook Lee; Sungjae Kim; Yunjeong Yi; Iksoo Huh; Sang-Youn Jang; Dasom Kim; Sujin Lee Journal: Int J Environ Res Public Health Date: 2020-10-21 Impact factor: 3.390