Brogden-Cronbach-Gleser Utility Gain Calculator
Restricted BCGutilityNTSDyrZ−total cost, all independent actual economy/selection model parameters, one currency. Signed utility retained, not causal productivity gain or ethical selection approval. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
A BCG utility model combines selection size, tenure, performance variation and predictive parameters.
A BCG utility model combines selection size, tenure, performance variation and predictive parameters.1
Its currency result is conditional on those inputs rather than evidence of causal productivity gains. The calculation and input requirements are explained below.
When to use Brogden-Cronbach-Gleser Utility Gain Calculator
Enter integer selected count 0–1000000, nonnegative employment years, currency performance SD and total selection cost, plus finite validity correlation and selected standardized predictor mean.
How to use Brogden-Cronbach-Gleser Utility Gain 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 Brogden-Cronbach-Gleser Utility Gain 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 |
|---|---|---|---|
| actualSelectedPeople | Actual Selected People. Use the units and matching context stated in the calculation below. | number | Required; integer 0–1000000; finite; other constraints below |
| actualEmploymentYears | Actual Employment Years. Use the units and matching context stated in the calculation below. | number | Required; nonnegative; finite; other constraints below |
| callerPerformanceSdInCurrency | Caller Performance Sd In Currency. Use the units and matching context stated in the calculation below. | number | Required; nonnegative; finite; other constraints below |
| actualValidityCorrelation | Actual Validity Correlation. Use the units and matching context stated in the calculation below. | number | Required; finite; other constraints below |
| actualSelectedStandardizedPredictorMean | Actual Selected Standardized Predictor Mean. Use the units and matching context stated in the calculation below. | number | Required; finite; other constraints below |
| actualTotalSelectionCost | Actual Total Selection Cost. Use the units and matching context stated in the calculation below. | number | Required; nonnegative; finite; other constraints below |
Enter integer selected count 0–1000000, nonnegative employment years, currency performance SD and total selection cost, plus finite validity correlation and selected standardized predictor mean. Return the signed product-minus-cost utility. The implementation does not bound the field named correlation to -1–1, so mathematical acceptance alone does not validate that input.
utility=N*years*performanceSD*validity*selectedMeanZ-totalCost 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": {
"actualSelectedPeople": 2,
"actualEmploymentYears": 2,
"callerPerformanceSdInCurrency": 10,
"actualValidityCorrelation": 0.5,
"actualSelectedStandardizedPredictorMean": 4,
"actualTotalSelectionCost": 10
},
"output": {
"result": {
"value": 70
}
}
}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 Sociometric Status Z From Nominations Calculator for a complementary operation: A sociometric nomination z-score compares a count with a matched reference mean and standard deviation.
Use Item Discrimination Index Calculator for a complementary operation: An item discrimination index compares correct-response fractions in two independently defined groups.
References
Similar or alternative tools
- Sociometric Status Z From Nominations Calculator
A sociometric nomination z-score compares a count with a matched reference mean and standard deviation.
- Item Discrimination Index Calculator
An item discrimination index compares correct-response fractions in two independently defined groups.