1. What is the SurveyMonkey Formula?

The SurveyMonkey formula calculates the required sample size for a survey based on desired confidence level, proportion estimate, and margin of error. It provides a statistical foundation for determining how many responses are needed for reliable results.

2. How Does the Calculator Work?

The calculator uses the SurveyMonkey formula:

Where:

• \( Z \) — Z-score (dimensionless)
• \( p \) — Proportion estimate (dimensionless)
• \( e \) — Margin of error (dimensionless)

Explanation: The formula calculates the minimum sample size needed to achieve a desired level of precision in survey results, accounting for confidence level and expected variability in responses.

3. Importance of Sample Size Calculation

Details: Proper sample size calculation ensures survey results are statistically significant, reliable, and representative of the target population, while avoiding unnecessary data collection costs.

4. Using the Calculator

Tips: Enter Z-score (typically 1.96 for 95% confidence), proportion estimate (use 0.5 for maximum variability), and margin of error (typically 0.05 for ±5%). All values must be valid (Z > 0, 0 ≤ p ≤ 1, 0 < e ≤ 1).

5. Frequently Asked Questions (FAQ)

Q1: What Z-score should I use?
A: Common values are 1.645 (90% confidence), 1.96 (95% confidence), and 2.576 (99% confidence).

Q2: What if I don't know the proportion estimate?
A: Use p = 0.5 for the most conservative estimate, as this maximizes the required sample size.

Q3: How does margin of error affect sample size?
A: Smaller margins of error require larger sample sizes. Halving the margin of error quadruples the required sample size.

Q4: Does this formula work for small populations?
A: For populations under 20,000, a finite population correction should be applied to the formula.

Q5: When should I use this calculation?
A: Use this for simple random sampling designs. More complex sampling methods may require additional adjustments.