Data Weighting Fundamentals
Core concepts and principles of statistical weighting in research.
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Perfect samples are rare in practice. Data weighting corrects for systematic differences between your sample and target population, ensuring your research conclusions accurately reflect the real world.
Types of Bias That Weighting Addresses
Coverage Bias
Some groups are systematically excluded from your sampling frame.
- • Phone surveys miss cell-phone-only users
- • Online panels under-represent seniors
- • Address lists miss recent movers
Non-Response Bias
Certain groups are less likely to participate when contacted.
- • Busy professionals skip surveys
- • Privacy-conscious individuals opt out
- • Language barriers reduce participation
Sampling Bias
Unequal probability of selection across population groups.
- • Quota shortfalls in hard-to-reach groups
- • Clustering effects in geographic sampling
- • Convenience sampling skews demographics
Choosing Weighting Variables
Key Selection Criteria
Choose variables that are strongly related to both survey response patterns and your key research outcomes.
Effective Variables
Demographics
Reliable predictors of response patterns
Geographic
Address coverage and response differences
Avoid These
Unstable Variables
Change frequently or lack reliable benchmarks
Outcome Variables
Can bias the very outcomes you're studying
Weighting Best Practices
Plan Early
Consider weighting needs during survey design, not after data collection.
Monitor Quality
Track weight distributions and effective sample sizes throughout the process.
Document Everything
Record all weighting decisions and rationale for transparency and replication.
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