Effect size for nursing research expresses the magnitude of a difference, association or model contribution. It helps readers judge practical importance and provides a key input for sample-size planning, whereas a p-value alone mainly describes compatibility with a statistical model.
Match the effect size to the question
| Analysis | Useful effect size | Interpretation focus |
|---|---|---|
| Independent t-test | Cohen’s d or Hedges’ g | Standardised difference between groups |
| Correlation | Pearson’s r or Spearman’s rho | Direction and strength of association |
| Chi-square | Phi or Cramér’s V | Strength of categorical association |
| Logistic regression | Odds ratio with confidence interval | Change in odds for a predictor |
| Multiple regression | R², adjusted R² or f² | Explained variance or incremental contribution |
Standardised versus raw nursing effects
A standardised effect supports comparison across differently scaled measures, but a raw mean difference may be easier to interpret clinically. Reporting that an intervention changed pain by 1.5 points can be more meaningful than d = 0.50 if the scale and clinically important threshold are clear. Strong reporting often provides both.
Choosing an effect for G*Power
Prefer meta-analysis, closely matched evidence, pilot data or a clinically meaningful difference. Check population, setting, outcome scale, follow-up and design. An effect from intensive-care patients may not transfer to community nursing, and a paired effect may not match an independent-groups test.
Do not treat benchmarks as universal truths
Labels such as small, medium and large are context dependent. A small reduction in a frequent adverse event may matter clinically, while a statistically large difference on a poorly validated scale may have limited practice value. Benchmarks are most defensible as sensitivity scenarios when better evidence is unavailable.
Finding effect sizes in SPSS output
Different procedures report different measures. Correlations and odds ratios appear directly. ANOVA and regression provide sums of squares, R² and model statistics from which suitable effects can be reported or derived. Some SPSS versions display standardised estimates or effect-size tables; otherwise, compute them transparently from documented statistics.
What to read in SPSS: Begin with R² and adjusted R² in the Model Summary, confirm whether the overall regression model is supported in the ANOVA table, and then interpret each coefficient with its confidence interval, p-value and collinearity diagnostic. These values should be considered together rather than reporting significance alone.
Confidence intervals matter
A point estimate without uncertainty can mislead. A confidence interval shows which effect magnitudes remain compatible with the data. Wide intervals warn that a dissertation cannot distinguish trivial from important effects even when the point estimate appears promising.
Common errors
- Calling every statistically significant result “large”.
- Using odds ratios as though they were risk ratios.
- Ignoring the direction of a negative association.
- Reporting R² without adjusted R² when many predictors are used.
- Using the observed dissertation effect to justify the original sample retrospectively.
- Copying an effect from a study with a different design.
Frequently asked questions
Which effect size is best?
The one aligned with the research question, model and audience. There is no single measure for all nursing analyses.
Can SPSS calculate Cohen’s d?
Depending on version and procedure, it may be displayed or derived from means and pooled variability. State the formula or procedure used.
Should effect size replace the p-value?
No. Report the estimate, confidence interval and relevant inferential result together.
Can I choose a medium effect in G*Power?
Only with transparent justification and preferably sensitivity analyses showing how smaller effects alter the target.
Related Nursing Guides
- G*Power for Nursing Research: Step-by-Step Guide
- Missing Data in Nursing Research: SPSS Dissertation Guide
- How to Calculate Sample Size for a Nursing Dissertation
Conclusion
Effect size connects statistical output to nursing meaning. Select the measure that matches the analysis, justify planning assumptions, report uncertainty and interpret magnitude in the clinical or educational context.
Continue with our nursing SPSS sample-size and power analysis service, SPSS data-analysis service, statistical-test selection guide, SPSS output interpretation guide, or contact us.
References
- Faul, F., Erdfelder, E., Lang, A.-G. and Buchner, A. (2007) ‘G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences’, Behavior Research Methods, 39, pp. 175–191.
- Faul, F., Erdfelder, E., Buchner, A. and Lang, A.-G. (2009) ‘Statistical power analyses using G*Power 3.1’, Behavior Research Methods, 41, pp. 1149–1160.
- Kang, H. (2021) ‘Sample size determination and power analysis using the G*Power software’, Journal of Educational Evaluation for Health Professions, 18, 17.
- Lakens, D. (2022) ‘Sample size justification’, Collabra: Psychology, 8(1), 33267.
