1. What is Cohen's d?

Cohen's d is a measure of effect size that indicates the standardized difference between two means. It's commonly used in statistics to quantify the difference between two groups over time, such as before and after an intervention at 6 months.

2. How Does the Calculator Work?

The calculator uses the Cohen's d equation:

Where:

• \( M_1 \) — Mean at baseline
• \( M_2 \) — Mean at 6 months
• \( SD_{\text{pooled}} \) — Pooled standard deviation

The pooled standard deviation is calculated as: \[ SD_{\text{pooled}} = \sqrt{\frac{(n_1-1)SD_1^2 + (n_2-1)SD_2^2}{n_1 + n_2 - 2}} \]

3. Interpretation of Effect Size

Effect Size Guidelines:

• Small effect: d ≈ 0.2
• Medium effect: d ≈ 0.5
• Large effect: d ≈ 0.8

4. Using the Calculator

Tips: Enter means, standard deviations, and sample sizes for both time points. All values must be valid (SD > 0, n ≥ 1).

5. Frequently Asked Questions (FAQ)

Q1: Why use Cohen's d instead of just mean difference?
A: Cohen's d standardizes the effect size, making it comparable across different studies and measures.

Q2: What's considered a meaningful effect size at 6 months?
A: This depends on the context, but generally d ≥ 0.5 is considered clinically meaningful.

Q3: Can I use this for pre-post designs without a control group?
A: Yes, but interpretation should be more cautious as it doesn't account for natural changes over time.

Q4: How does sample size affect Cohen's d?
A: Larger samples provide more reliable estimates but don't systematically bias the effect size.

Q5: Should I adjust for baseline differences?
A: For more accurate estimates, consider using ANCOVA or regression approaches that adjust for baseline.