1. What is Design Effect?

Design Effect (DE) is a measure used in survey statistics to quantify the impact of cluster sampling design on the variance of estimates. It compares the variance under the actual sampling design to the variance that would be obtained from a simple random sample of the same size.

2. How Does the Calculator Work?

The calculator uses the Design Effect formula:

Where:

• \( Variance_{cluster} \) — Variance under cluster sampling design
• \( Variance_{simple} \) — Variance under simple random sampling

Explanation: The Design Effect indicates how much the variance is increased due to the cluster sampling design compared to simple random sampling.

3. Importance of Design Effect Calculation

Details: Calculating Design Effect is crucial for proper sample size determination, statistical power analysis, and accurate interpretation of survey results in cluster sampling designs.

4. Using the Calculator

Tips: Enter both variance values (must be positive numbers). The calculator will compute the Design Effect ratio.

5. Frequently Asked Questions (FAQ)

Q1: What does a Design Effect greater than 1 indicate?
A: A DE > 1 indicates that cluster sampling produces larger variance (less precision) than simple random sampling of the same size.

Q2: What is the typical range of Design Effect values?
A: Design Effect typically ranges from 1 to 3 in most survey applications, though it can be higher in some clustered designs.

Q3: How is Design Effect used in sample size calculation?
A: The required sample size is multiplied by the Design Effect to maintain the same precision level in cluster sampling as in simple random sampling.

Q4: What factors influence Design Effect?
A: Intraclass correlation coefficient (ICC) and cluster size are the main factors that influence Design Effect in cluster sampling.

Q5: Can Design Effect be less than 1?
A: While uncommon, DE < 1 is possible if the cluster design is more efficient than simple random sampling, though this is rare in practice.