What this calculator does
Survey Weighting and Raking is Module 11 of Analysis Studio, and it is a free module. It builds analysis weights in the order survey practice builds them, with each step producing a new dataset version and leaving the step before it intact:
- Base weights from selection probabilities or from stratum frame counts.
- Nonresponse adjustment, by weighting classes or by response-propensity quintiles from a logistic model.
- Calibration to known population margins, by post-stratification on a single variable, by raking (iterative proportional fitting) across several margins, or by linear GREG calibration.
- Trimming of extreme weights, followed by re-raking so the trimmed weights still meet the margins.
The diagnostics are the point. A weighting run reports the Kish design effect, the effective sample size after weighting, the weight distribution, a margin check table comparing weighted counts with targets, the count of trimmed weights, and weighted against unweighted estimates for key variables you choose. A methods paragraph is generated automatically, ready to paste into a report. The module also carries a second procedure, propensity adjustment for nonprobability samples, described below; that procedure sits in the subscription set.
The method, in plain terms
Raking adjusts weights one margin at a time, in rotation. Start from the base weights. Scale every respondent in category one of the first margin so the weighted count meets the population target for that category. Move to the second margin and do the same, which disturbs the first margin slightly. Rotate through all margins until every margin matches within tolerance. The module rakes at a convergence tolerance of one in a million over at most one hundred cycles, and reports the margin check at the end so the convergence is verified rather than assumed.
The cost of weighting is precision. Unequal weights carry a design effect:
Kish design effect = n × (sum of squared weights) ÷ (sum of weights)², and the effective sample size is n divided by that effect.
Worked example
A sample of forty respondents holds twenty-two men and eighteen women. The population is 48 percent men and 52 percent women, out of a population of 1,000.
Post-stratification gives each respondent the population share divided by the sample count for their group, scaled to the population:
- Weight for each man: 480 ÷ 22 = 21.818
- Weight for each woman: 520 ÷ 18 = 28.889
The outcome variable averages 50 among men and 60 among women:
- Unweighted mean: (22 × 50 + 18 × 60) ÷ 40 = 54.5
- Weighted mean: (480 × 50 + 520 × 60) ÷ 1,000 = 55.2
The weighting moves the estimate by seven-tenths of a point because women, who score higher on this outcome, were underrepresented in the sample. That small, explained move is what weighting is for. When a weighting run moves an estimate a long way, the margin check and the weighted-against-unweighted comparison are where the explanation has to be found before the number is reported.
Propensity adjustment for nonprobability samples
Opt-in panels and other nonprobability samples have no selection probabilities, so base weights do not exist. The second procedure in Module 11 builds pseudo-weights instead: it models the probability of appearing in the nonprobability sample against a reference probability sample, inverts that probability into a weight, and then calibrates the result with the same raking machinery. The module is plain about the limit: the adjustment corrects for the variables in the model, and variables nobody measured stay uncorrected.
Promotion, stated with its date: through March 31, 2027, every procedure in the Studio runs free on screen, including propensity adjustment. Downloads and Copy Results stay with the subscription throughout. After the promotion, propensity adjustment returns to the subscription set at $299 per year, while weighting and raking remain free.
Propensity AdjustmentOpen in Analysis Studio →When the calculator fits, and when it does not
It fits when:
- Population margins are known from a census, a frame, or a high-quality reference source, and the sample was drawn to represent that population.
- The weighting story must be documented: the margin check, the design effect, and the generated methods paragraph make the run auditable.
- A nonprobability sample has a probability reference sample to anchor a propensity adjustment.
It does not fit when:
- The population margins are guesses. Calibration to wrong targets manufactures error with confidence.
- The sample missed whole groups entirely. Weighting cannot repair a group with nobody in it; the margin has no respondents to scale.
- Weights would need to vary enormously to hit the targets. The design effect will say so in numbers, and the right response is usually to report the limitation rather than trim the weights into silence.
Run it
Module 11: Survey Weighting and RakingOpen in Analysis Studio →Load your sample file, work the four steps in order, and read the margin check and the effective sample size before using the new weight in any other module.
Questions
What is the difference between raking and post-stratification? Post-stratification calibrates to one variable, or to a full cross of variables with a known joint distribution. Raking calibrates to several margins one at a time when only the separate distributions are known, which is the usual situation with census margins.
How much precision does weighting cost? The Kish design effect answers in one number. Weights that average out to a design effect of 1.5 turn a nominal sample of 900 into an effective sample of 600. The module reports the effect and the effective size on every run.
Should I trim extreme weights? Trimming trades a small bias for a real variance gain, and it is legitimate when a few respondents carry outsized weights. The module trims and then re-rakes, so the margins still hold, and it reports how many weights were trimmed. Trimming to make results look calm, rather than to control variance, is not a use the diagnostics will hide.
Can I weight a volunteer online panel? Yes, through the propensity adjustment procedure, provided a reference probability sample exists to model against. Without a reference sample, calibration to census margins is still available, and the report should say plainly that selection on unmeasured variables remains uncorrected.
Do the weights carry into other modules? Yes. The new weight is written to a new dataset version, your original data untouched, and every design-aware module in the Studio can use it once you declare it in the survey design.