1. What is the Simple Expansion Factor?

The Simple Expansion Factor is defined as the ratio of the total number of household addressed in the population to that of the surveyed. It is used to estimate population parameters from sample data in survey research.

2. How Does the Calculator Work?

The calculator uses the Simple Expansion Factor equation:

Where:

• \( a \) — Total number of household in the original population list
• \( b \) — Total number of addresses of households selected as the original sample
• \( d \) — Number of samples where no responses were obtained

Explanation: The equation calculates the expansion factor by dividing the total population households by the effective sample size (total addresses minus non-responses).

3. Importance of Simple Expansion Factor

Details: The Simple Expansion Factor is crucial in survey research for weighting sample data to represent the entire population accurately. It helps in estimating population characteristics from sample surveys.

4. Using the Calculator

Tips: Enter the total number of household (must be positive), total number of addresses (must be positive and greater than number of samples), and number of samples (must be non-negative and less than total addresses).

5. Frequently Asked Questions (FAQ)

Q1: When should I use the Simple Expansion Factor?
A: Use it when you need to expand sample survey results to estimate population parameters, particularly in household surveys and market research.

Q2: What does a higher expansion factor indicate?
A: A higher expansion factor indicates that each responding household represents a larger portion of the total population in the estimation.

Q3: How does non-response affect the expansion factor?
A: Higher non-response rates (larger d values) increase the expansion factor because fewer responses have to represent the same total population.

Q4: Are there limitations to this method?
A: This method assumes that non-responding households are similar to responding ones, which may not always be true and could introduce bias.

Q5: Can this be used for complex sampling designs?
A: For complex sampling designs with stratification or clustering, more sophisticated weighting methods are typically required.