HTMT Discriminant Validity Ratio

Observed HTMT mean absoluteheterotrait correlations/geometric mean within trait absolute correlations, actual matched distinct pairs/exclude diagonals; no bootstrap interval or automatic discriminant validity. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.

Description

HTMT compares cross-construct correlations with within-construct correlations.

HTMT compares cross-construct correlations with within-construct correlations.^[1]

The inputs require distinct matched pairs and exclusion of diagonal entries. The calculation and input requirements are explained below.

When to use HTMT Discriminant Validity Ratio

Enter three nonempty lists of absolute correlations from 0 to 1.

How to use HTMT Discriminant Validity Ratio 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 HTMT Discriminant Validity Ratio.
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
actualCrossConstructAbsoluteCorrelations	Actual Cross Construct Absolute Correlations. Use the units and matching context stated in the calculation below.	array of number	Required; other constraints below
actualFirstWithinConstructAbsoluteCorrelations	Actual First Within Construct Absolute Correlations. Use the units and matching context stated in the calculation below.	array of number	Required; other constraints below
actualSecondWithinConstructAbsoluteCorrelations	Actual Second Within Construct Absolute Correlations. Use the units and matching context stated in the calculation below.	array of number	Required; other constraints below

Enter three nonempty lists of absolute correlations from 0 to 1. Require positive means within both constructs. Divide cross-construct mean by the geometric mean of the two within-construct means. The lists need not share lengths; results are not clamped at one.

HTMT=mean(cross)/sqrt(mean(withinFirst)*mean(withinSecond))

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

json

{
  "input": {
    "actualCrossConstructAbsoluteCorrelations": [
      0.4,
      0.6
    ],
    "actualFirstWithinConstructAbsoluteCorrelations": [
      0.25,
      0.25
    ],
    "actualSecondWithinConstructAbsoluteCorrelations": [
      0.25,
      0.25
    ]
  },
  "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 SRMR Fit Band Lookup for a complementary operation: SRMR summarizes standardized residual discrepancies in a fitted model.

Use RMSEA Cutoff Band Lookup for a complementary operation: RMSEA interpretation depends on fitted-model and sampling context.

References

HTMT Discriminant Validity Ratio — tool.io

Similar or alternative tools

SRMR Fit Band Lookup
SRMR summarizes standardized residual discrepancies in a fitted model.
RMSEA Cutoff Band Lookup
RMSEA interpretation depends on fitted-model and sampling context.