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Weighting Of Survey Data Calculator

Work out weighting of survey data instantly with clear inputs, formula shown and shareable results.

About Weighting Of Survey Data Calculator

Post-stratification weighting scales each group's responses by population share over sample share. A group forming 22% of the population but only 14% of the sample gets a weight of 1.571, so its 140 responses count as 220. Weights far from one inflate variance, which is why they are usually trimmed at about 3.

Formulas

Design weight

weight = population share / sample share

Weighted contribution

effective = respondents x weight

Worked example

With the default values below, Weighting Of Survey Data Calculator returns Design Weight of 1.5714. Change any input above and every figure here is recalculated in your browser.

Inputs used

Share of the population in this groupaccepts 0–100 %
22 %
Share of the sample in this groupaccepts 0–100 %
14 %
Respondents in this groupminimum 0
140

Results

Design Weight
1.5714
Weighted Contribution
220.0 responses
Representation
Under-represented - responses are weighted up
Adjustment Applied
57.14%

Step by step

  1. 1Values used
  2. 2Design weight
  3. 3Weighted contribution
  4. 4Design Weight= 1.5714
  5. 5Weighted Contribution= 220.0 responses
  6. 6Representation= Under-represented - responses are weighted up
  7. 7Adjustment Applied= 57.14

Assumptions and limits

  • The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Why trim extreme weights?

Because a weight of 8 means a handful of respondents drive a whole subgroup estimate, inflating variance and instability far more than it corrects bias.

Does weighting fix non-response bias?

Only the part correlated with the weighting variables. If non-respondents differ on unmeasured characteristics, weighting cannot recover it.

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