Description
Return clusterError=independentError*sqrt(designEffect), requiring nonnegative independent error and positive design effect.
A statistical term depends on its data summaries, weighting convention, centering, denominator, and boundary rules. A contribution, derivative, domain check, or converted summary answers a narrower question than a complete estimator. Those conventions must match before terms are combined or compared.
Return clusterError=independentError*sqrt(designEffect), requiring nonnegative independent error and positive design effect.
When to use this tool
Use Standard Error Cluster Weight Reference when you need this specific summary conversion, contribution, domain rule, or sensitivity term with matching statistical conventions.
How to use the tool and how it works
- Enter independentError, designEffect. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read clusterError. Use the output convention below.
Return clusterError=independentError*sqrt(designEffect), requiring nonnegative independent error and positive design effect.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| independentError | Independent Error | Required. Finite number; minimum 0. |
| designEffect | Positive variance multiplier; its square root scales the error. | Required. Finite number; strictly above 0. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| clusterError | Cluster Error | Required. Finite number. |
{
"input": {
"independentError": 2,
"designEffect": 4
},
"output": {
"clusterError": 4
}
}Limitations and assumptions
- DesignEffect multiplies variance, so its square root scales a standard error. The factor is supplied rather than estimated from clusters, and no sample design or confidence interval is validated.
- The field tables state structural bounds; the mechanics also specify cross-field constraints. Malformed inputs and unsupported domains cause errors. A successful false flag, empty branch value, or zero placeholder has the meaning stated above and is distinct from an input error.
- Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Signed zero is exported as zero.
Alternative or Complementary methods
Use a complete estimator or model when a contribution, sensitivity, or domain check is insufficient. Verify the weight semantics, centering, denominator, and sampling assumptions before combining summaries.
References
Similar or alternative tools
- Weight Rescale Standard Error Invariance Check
Calculate effectiveSize=sumWeights²/sumSquaredWeights and return the same number as rescaledEffectiveSize with invariant true.
- Effective Sample Size Weight Split
Split oldWeight into firstPart and its remainder, replace its squared contribution in sumSquaredWeights, and compare effective sizes sumWeights²/squaredSum before and after.