Likert Sum to Mean Rescaler
Actual scoredLikertsum/completed items mean, no imputation/normor assumption ordinal differences psychologically equal. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
A mean item score divides a scored total by completed items.
A mean item score divides a scored total by completed items.1
It preserves the chosen scoring convention without imputing missing responses. The calculation and input requirements are explained below.
When to use Likert Sum to Mean Rescaler
Enter a finite completed-item score sum and integer completed count 1–1000000.
How to use Likert Sum to Mean Rescaler 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 Likert Sum to Mean Rescaler.
- 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 |
|---|---|---|---|
| actualCompletedItemScoreSum | Actual Completed Item Score Sum. Use the units and matching context stated in the calculation below. | number | Required; finite; other constraints below |
| actualCompletedItemCount | Actual Completed Item Count. Use the units and matching context stated in the calculation below. | number | Required; integer 1–1000000; finite; other constraints below |
Enter a finite completed-item score sum and integer completed count 1–1000000. Divide for mean score. Item scale bounds and missing-item imputation are not inferred.
meanScore=completedScoreSum/completedItemCount 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": {
"actualCompletedItemScoreSum": 10,
"actualCompletedItemCount": 5
},
"output": {
"result": {
"value": 2
}
}
}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 Reverse-Code Item Transform Tool for a complementary operation: Reverse coding reflects an item score between its scale endpoints.
Use DASS-21 to DASS-42 Subscale Converter for a complementary operation: Adult DASS-21 subscale totals can be expressed on the conventional DASS-42 metric.
References
-
Likert Sum to Mean Rescaler — tool.io
Similar or alternative tools
- Reverse-Code Item Transform Tool
Reverse coding reflects an item score between its scale endpoints.
- DASS-21 to DASS-42 Subscale Converter
Adult DASS-21 subscale totals can be expressed on the conventional DASS-42 metric.