Bootstrap Percentile CI Indirect Effect Calculator
Actual independently resampled bootstrap indirect effect percentile CI with linear(order position) quantiles, not generate bootstrap replicates or infer BCa/causal significance.
Description
A percentile bootstrap interval summarizes already generated effect replicates.
A percentile bootstrap interval summarizes already generated effect replicates.1
Its quantile convention differs from a bias-corrected or BCa interval. The calculation and input requirements are explained below.
When to use Bootstrap Percentile CI Indirect Effect Calculator
Enter nonempty independently generated bootstrap effect replicates and central coverage strictly between 0 and 1.
How to use Bootstrap Percentile CI Indirect Effect Calculator and how it works
- Prepare the measurements or independently established model inputs described below, with matching units and observation scope.
- Enter the required fields and run Bootstrap Percentile CI Indirect Effect Calculator.
- Read the result fields using the formula, output units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| actualBootstrapIndirectEffectReplicates | Actual Bootstrap Indirect Effect Replicates. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
| callerCentralCoverageFraction | Caller Central Coverage Fraction. Use the units and matching context stated in the calculation below. | number | Required; fraction from 0 to 1; finite; other constraints below |
Enter nonempty independently generated bootstrap effect replicates and central coverage strictly between 0 and 1. Sort a copy and use quantiles at (1-coverage)/2 and (1+coverage)/2. Quantiles linearly interpolate sorted positions p*(n-1); one replicate returns the same endpoint twice. No resampling or BCa adjustment occurs.
interval=[quantile(replicates,(1-c)/2),quantile(replicates,(1+c)/2)] Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.observedPercentileLower | Observed Percentile Lower. Interpret its unit, sign, order or availability flag as explained above. | number |
| result.observedPercentileUpper | Observed Percentile Upper. Interpret its unit, sign, order or availability flag as explained above. | number |
{
"input": {
"actualBootstrapIndirectEffectReplicates": [
0,
1,
2,
3,
4
],
"callerCentralCoverageFraction": 0.5
},
"output": {
"result": {
"observedPercentileLower": 1,
"observedPercentileUpper": 3
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Sobel Test Z Mediation Calculator for a complementary operation: The Sobel statistic uses a first-order variance approximation for a product of paths.
Use Simple Slope at +/-1 SD Calculator for a complementary operation: A linear interaction model has a predictor slope that depends on moderator value.
References
Similar or alternative tools
- Sobel Test Z Mediation Calculator
The Sobel statistic uses a first-order variance approximation for a product of paths.
- Simple Slope at +/-1 SD Calculator
A linear interaction model has a predictor slope that depends on moderator value.