Weighting Techniques

Comprehensive guide to different statistical weighting methods and when to use them.

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Statistical Weighting Techniques

Different weighting techniques serve different research needs. Understanding when and how to apply each method ensures optimal results for your specific survey data and research objectives.

Common Weighting Techniques

1

Post-Stratification Weighting

Most Common

Adjusts sample proportions to match known population characteristics after data collection. Works by creating weight classes based on cross-tabulations of demographic variables.

Best Used When:

  • • Complete cross-tabulation data available
  • • Simple demographic adjustments needed
  • • Clear population benchmarks exist
  • • Moderate number of weighting variables

Limitations:

  • • Requires complete population cross-tabs
  • • Can create empty cells with sparse data
  • • Limited to available benchmark data
  • • May not handle complex interactions
2

Raking (Rim Weighting)

Most Flexible

Iteratively adjusts weights to match multiple marginal distributions simultaneously. Perfect when you have targets for individual variables but not their interactions.

Best Used When:

  • • Only marginal distributions available
  • • Multiple weighting variables needed
  • • Targets come from different sources
  • • Complex demographic adjustments

Limitations:

  • • May not preserve variable interactions
  • • Convergence can be slow or unstable
  • • Increases weight variability
  • • Requires iterative algorithms
3

Propensity Score Weighting

Advanced

Uses statistical models to predict response probability and adjusts for non-response bias. Particularly effective for complex survey designs with multiple bias sources.

Best Used When:

  • • Complex non-response patterns
  • • Many auxiliary variables available
  • • Traditional methods insufficient
  • • Causal inference needed

Limitations:

  • • Requires statistical modeling expertise
  • • Model specification critical
  • • Assumes data missing at random
  • • Computationally intensive
4

Calibration Weighting

Precise Control

Ensures weighted sample totals exactly match known population totals for auxiliary variables. Offers fine-grained control over multiple constraints simultaneously.

Best Used When:

  • • Exact population totals known
  • • Multiple constraints needed
  • • Official statistics available
  • • Precise estimates required

Limitations:

  • • Requires exact population totals
  • • May produce extreme weights
  • • Complex to implement correctly
  • • Sensitive to constraint conflicts

Choosing the Right Technique

Decision Framework

The choice depends on your data characteristics, available auxiliary information, and research objectives.

By Data Availability

Complete Cross-tabs Available

→ Use Post-Stratification

Only Marginal Distributions

→ Use Raking

Exact Population Totals

→ Use Calibration

Rich Auxiliary Data

→ Consider Propensity Scoring

By Complexity Level

Beginner

→ Post-Stratification (simple, reliable)

Intermediate

→ Raking (flexible, widely applicable)

Advanced

→ Calibration (precise control)

Expert

→ Propensity Scoring (complex bias patterns)

Implementation Success Tips

Start Simple

Begin with post-stratification before moving to complex methods.

Validate Results

Always check weight distributions and compare methods when possible.

Document Process

Record all decisions and rationale for transparency and replication.

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