You can't guess effect size, but you can estimate it using various methods. Estimating effect size helps you understand the practical significance of your research findings.
1. Using Past Research
- Literature Review: Look at previous studies in your field to see what effect sizes have been reported. This gives you a baseline for your own research.
- Meta-Analysis: If there are multiple studies on a similar topic, a meta-analysis can combine their results to provide a more robust estimate of the effect size.
2. Using Power Analysis
- Before Your Study: Power analysis helps you determine the sample size needed to detect a specific effect size. This can provide an initial estimate of the effect size you might expect.
- After Your Study: Power analysis can also be used to assess the power of your study, which can help you interpret the results more accurately.
3. Using Confidence Intervals
- Confidence Intervals: These provide a range of values within which the true effect size is likely to fall. You can use the width of the confidence interval to get an idea of the precision of your estimate.
4. Using Practical Considerations
- Clinical Significance: Consider the practical implications of the effect size. For example, a small effect size might be meaningful in a clinical setting if it leads to improved patient outcomes.
- Cost-Benefit Analysis: Weigh the costs and benefits of implementing a particular intervention or treatment based on the estimated effect size.
Remember that estimating effect size is not an exact science. It involves making assumptions and using best practices.
