Guttman Reproducibility Coefficient
Declared Guttman reproducibility1−errors/(persons×items), errors independently assigned under actual ordering. Not construct validity, scalogramoptimization or universal acceptable cutoff. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
Guttman reproducibility compares coded errors with all person–item responses.
Guttman reproducibility compares coded errors with all person–item responses.1
The error ordering must be established independently of this ratio. The calculation and input requirements are explained below.
When to use Guttman Reproducibility Coefficient
Enter integer error count 0–1000000 and positive integer respondent and item counts up to 1000000.
How to use Guttman Reproducibility Coefficient 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 Guttman Reproducibility Coefficient.
- 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 |
|---|---|---|---|
| actualIndependentlyCodedGuttmanErrorCount | Actual Independently Coded Guttman Error Count. Use the units and matching context stated in the calculation below. | number | Required; integer 0–1000000; finite; other constraints below |
| actualRespondentCount | Actual Respondent Count. Use the units and matching context stated in the calculation below. | number | Required; integer 1–1000000; finite; other constraints below |
| actualItemCount | Actual Item Count. Use the units and matching context stated in the calculation below. | number | Required; integer 1–1000000; finite; other constraints below |
Enter integer error count 0–1000000 and positive integer respondent and item counts up to 1000000. Errors must not exceed respondents times items. Return one minus error share; the supplied error coding is not optimized.
reproducibility=1-errors/(respondents*items) 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": {
"actualIndependentlyCodedGuttmanErrorCount": 2,
"actualRespondentCount": 5,
"actualItemCount": 4
},
"output": {
"result": {
"value": 0.9
}
}
}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 Cohen's Kappa Inter-Rater Agreement for a complementary operation: Cohen's kappa compares observed categorical agreement with agreement expected from marginal proportions.
Use Fleiss' Kappa Multi-Rater Agreement for a complementary operation: Fleiss' kappa measures categorical agreement across multiple raters.
References
-
Guttman Reproducibility Coefficient — tool.io
Similar or alternative tools
- Cohen's Kappa Inter-Rater Agreement
Cohen's kappa compares observed categorical agreement with agreement expected from marginal proportions.
- Fleiss' Kappa Multi-Rater Agreement
Fleiss' kappa measures categorical agreement across multiple raters.