What is Rim Weighting?

Rim weighting, also known as raking, is an iterative process that adjusts sample weights to match multiple marginal distributions simultaneously. It's called "rim" weighting because it works with the margins (rims) of cross-tabulation tables.

When to Use Rim Weighting

Ideal Scenarios

• Marginal distributions available for multiple variables
• Cross-tabulation data is incomplete or unavailable
• Need to weight on 3+ variables simultaneously
• Population benchmarks come from different sources

Considerations

• May not preserve known interactions between variables
• Convergence issues with conflicting targets
• Requires iterative algorithm implementation
• Weight variability increases with more variables

The Raking Algorithm

How It Works

The algorithm cycles through each weighting variable, adjusting weights to match the target distribution, then moves to the next variable. This process repeats until all targets are met within acceptable tolerance.

Initialize Base Weights

Start with equal weights for all respondents (usually 1.0) or existing design weights if available.

Initial State

Adjust First Variable

Calculate adjustment factors to match the target distribution for the first weighting variable (e.g., age groups).

Age Adjustment

Cycle Through Variables

Apply the same process to each subsequent variable (gender, region, etc.), maintaining previous adjustments.

Iterative Process

Check Convergence

Repeat the cycle until all variables meet their targets within the specified tolerance level (typically 0.1% or 0.01%).

Convergence Check

Technical Implementation

Convergence Criteria

• Maximum iterations (typically 50-100)
• Tolerance threshold (0.1% difference)
• All targets within acceptable range

Quality Metrics

• Weight efficiency (ESS/n)
• Maximum weight ratio
• Coefficient of variation

Rim Weighting (Raking) Guide