Home
Back
Design Effect Formula:
| From: | To: |
Design Effect (DE) is a measure used in survey sampling to quantify the impact of cluster sampling on the variance of estimates. It compares the actual variance under the complex sampling design to the variance that would be expected under simple random sampling.
The calculator uses the Design Effect formula:
Where:
Explanation: The formula shows how clustering increases variance compared to simple random sampling. Higher cluster sizes and higher ICC values lead to larger design effects.
Details: Calculating design effect is crucial for determining appropriate sample sizes in cluster sampling, adjusting standard errors, and ensuring the precision of survey estimates in epidemiological and social science research.
Tips: Enter the average cluster size (must be ≥1) and intraclass correlation coefficient (must be between 0-1). The calculator will compute the design effect for your clustered sampling design.
Q1: What does a design effect of 1 mean?
A: A design effect of 1 means the clustered sampling has the same efficiency as simple random sampling (no clustering effect).
Q2: What is considered a high design effect?
A: Design effects above 2 are generally considered high, indicating substantial efficiency loss due to clustering.
Q3: How does ICC affect the design effect?
A: Higher ICC values indicate greater similarity within clusters, leading to larger design effects and reduced sampling efficiency.
Q4: When should design effect be calculated?
A: Design effect should be calculated during survey planning to determine appropriate sample sizes and after data collection to adjust standard errors.
Q5: Can design effect be less than 1?
A: Typically no, as clustering usually increases variance. However, stratified sampling with optimal allocation can sometimes achieve design effects below 1.