Survey methodology
Survey Weighting and Raking for Crosstabs
In a weighted crosstab, each eligible respondent contributes their positive numeric weight to the cell instead of contributing one. Use weighted percentages for the estimate and keep the unweighted respondent base beside them. Crosstabs does not execute ordinary weighted p-values or significance letters from an untyped quick-workspace selection. A saved project may run ordinary inference for an explicit non-negative integer frequency weight. Use a completed supported saved survey design for sampling- weight inference; otherwise clear the weight. Raking creates calibration weights by iteratively matching trusted population margins.
Published by crosstabs.com · Named statistical review pending · Last updated
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Open the workspace →First identify what your weight means
A design weight reflects unequal selection probabilities. A calibration or raking weightadjusts a starting weight until selected sample margins align with external targets. A frequency weightsays that one record represents repeated identical records. These can all produce weighted counts, but they do not justify the same variance or significance calculation.
Crosstabs preserves a prepared positive numeric weight column but does not currently create raked weights or infer its meaning. In a saved tab-book project, a specialist can bind a supported Taylor-linearized design with strata, clusters and finite-population correction, or a validated replicate-weight design, and receive design-aware intervals and Rao–Scott or adjusted-Wald association. The ordinary quick workspace rejects an untyped weight, and unsupported designs fail closed.
A defensible weighting workflow
- Document the sampling design, starting weight, target source, target date, variables, category mappings, and missing-value rules.
- Check that every positive target category has observed cases. Sparse or empty cells cannot be repaired by an extreme weight.
- Rake outside the current Crosstabs workspace, inspect convergence, weight spread and any trimming, then save the validated result as a numeric column.
- Open the Crosstabs workspace and preserve that column as a weight candidate. Bind a supported saved survey design before executing it; otherwise keep the quick-workspace table unweighted.
- Publish the weighted estimate, unweighted base, exclusions, weight method, targets, trim rule, and the correct design-aware uncertainty calculation.
How raking changes the weights
For one target margin at a time, raking multiplies every case in category g by the target total for gdivided by its current weighted total. It cycles through all target variables until the largest margin error is within the chosen tolerance or the iteration limit is reached.
Always inspect convergence and the minimum, maximum and spread of the resulting weights. Trimming extreme weights can reduce variance, but it also moves the achieved margins away from their targets; document both the rule and the post-trim discrepancy.
Formula
Definition
weighted cell = Σ wᵢ I(rowᵢ = r, columnᵢ = c)
weighted column % = weighted cell / Σ wᵢ in column c
weight-spread effective n = (Σ wᵢ)² / Σ wᵢ²
- wᵢ
- = the positive finite weight for respondent i
- I(·)
- = 1 when the record belongs in the cell, otherwise 0
- effective n
- = a weight-dispersion diagnostic—not a replacement for a full design-based effective sample size
Worked example
Worked example
When to use it
Use it when
- Estimating survey percentages after a documented weighting process.
- Comparing weighted and unweighted crosstabs to diagnose material shifts.
- Planning a validated frequency, design, or calibration-weight workflow before choosing an authorized execution path.
Not the right tool when
- Using weights to conceal coverage gaps or empty target categories.
- Calling ordinary weighted-count p-values design-corrected inference.
- Reporting a weighted total as the number of respondents interviewed.
How to interpret it
Rule of thumb
Weighting changes who contributes how much to an estimate; it does not create new respondents or automatically fix bias. A useful release table shows the weighted result, the unweighted base, the weight specification, and design-appropriate uncertainty together.
What Crosstabs does—and does not claim
The quick workspace preserves a numeric weight candidate but rejects an untyped selection before returning a table, tests, or evidence. Clearing the selection restores an unweighted table. Fisher's exact test remains limited to eligible unweighted 2×2 integer-frequency tables.
A saved project can bind a supported Taylor or replicate-weight survey design and execute design-aware intervals plus Rao–Scott or adjusted-Wald association. A saved explicit integer-frequency weight can execute ordinary inference; calibration and analytic- weight inference—and weighted Outcome Studio regression—remain unavailable. See the full statistical methods and limitations before publishing inferential claims.
Frequently asked questions
- Should a weighted crosstab show weighted or unweighted bases?
- Keep both. Report weighted counts or percentages for the estimate, and retain the unweighted respondent base for sample-size and data-quality diagnostics. A weighted total is not the number of people interviewed.
- What is raking in survey weighting?
- Raking, also called iterative proportional fitting, repeatedly adjusts case weights so weighted marginal distributions match known population targets for variables such as age, region, and gender. It matches the specified margins; it does not guarantee every joint subgroup is represented well.
- Can I run chi-square and significance letters on weighted data?
- Not through an untyped quick-workspace selection: it is rejected before any table or tests are returned. A saved project can run ordinary tests for an explicit non-negative integer frequency weight. Supported Taylor or replicate-weight survey designs execute design-aware intervals and Rao–Scott or adjusted-Wald association; calibration and analytic weights remain unsupported for ordinary inference.
- Does Crosstabs create raked weights?
- Not in the current workspace. Create and validate the weight in a survey-weighting package or your research workflow. Crosstabs preserves the numeric column, but the quick workspace rejects it until a typed policy exists; supported saved survey designs can execute design-aware inference.
- What happens to zero, negative, missing, or non-numeric weights?
- A supported saved design validates its required weights and reports exclusions. An untyped quick-workspace selection is rejected before any weighted table is returned.
References & further reading
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