A Review on the Use of Effect Size in Nursing Research

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.

Effect of an emotion management programme for patients with schizophrenia: A quasi‐experimental design

“…The criteria of effect sizes are as follows: Cohen's d = 0.2 (small), d = 0.5 (medium), and d = 0.8 (large; Cohen ; Kang et al . ). We used a per‐protocol analysis, which analyses the data of only those people who are retained in the allocated group and complete the trial.…”

The Effects of the 5-step Method for Infant Cardiopulmonary Resuscitation Training on Nursing Students' Knowledge, Attitude, and Performance Ability

“…To mini-mize the diffusion effect, taking the participants' learning schedule into account, the first and second groups engaging in clinical practice for pediatric nursing in the former part of the course formed the control group and the third and fourth groups engaging in clinical practice in the latter part of the course formed the experimental group. The G*Power Analysis 3.1 program [19] was used to estimate the sample size, and repeated-measures analysis of variance (ANOVA) (measuring 3 times repetitively) found a total minimum number of 46 persons, with an effect size of .40 [20], a significance level (⍺) of .05, and testability (1-β) of .90. Taking a dropout rate of 10% into account, the required sample size was estimated to be a total of 52, and this sample size was exceeded, with 30 participants in the experimental group and 33 in the control group.…”

Comparison of analgesic efficacy of oxycodone and fentanyl after total hip replacement surgery

“…The sample size was calculated using G∗power 3.1 program (Erdfelder, Faul, & Buchner). [12] The minimum sample size required in each group at a significance level (α) of 0.05 and a statistical power of 0.8 was 20. Forty-six patients were included in the study allowing a 10% dropout due to incomplete data collection and exclusion.…”