Z Composite Average Standardized Tool
Arithmetic mean independently matched standardized components, not a unit SD renormalizedcomposite or inversecovariancefactor score. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
A mean of standardized components combines scores on matched reference scales.
A mean of standardized components combines scores on matched reference scales.1
The resulting average does not automatically have a standard deviation of one. The calculation and input requirements are explained below.
When to use Z Composite Average Standardized Tool
Enter a nonempty list of independently standardized finite z-scores on matched reference scales.
How to use Z Composite Average Standardized Tool 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 Z Composite Average Standardized Tool.
- 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 |
|---|---|---|---|
| actualIndependentlyStandardizedZScores | Actual Independently Standardized Z Scores. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
Enter a nonempty list of independently standardized finite z-scores on matched reference scales. Return the arithmetic mean, retaining sign. No additional standardization to unit variance is performed.
meanZ=sum(zScores)/count(zScores) 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.value | Numeric result under the formula and output units explained above. | number |
{
"input": {
"actualIndependentlyStandardizedZScores": [
0,
1,
2
]
},
"output": {
"result": {
"value": 1
}
}
}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 T-Score Conversion Mean 50 SD 10 for a complementary operation: A T-score convention maps a matched z-score to a scale centered at 50 with a standard deviation of 10.
Use Sten Score From Z Converter for a complementary operation: Sten scores group standardized observations into ten categories under a stated rounding convention.
References
-
Z Composite Average Standardized Tool — tool.io
Similar or alternative tools
- T-Score Conversion Mean 50 SD 10
A T-score convention maps a matched z-score to a scale centered at 50 with a standard deviation of 10.
- Sten Score From Z Converter
Sten scores group standardized observations into ten categories under a stated rounding convention.