Assortative Mating Correlation Calculator
Audit actual known couple-pair score correlation by composing existing Pearson calculation while retaining included/unknown IDs. Pairing, score meaning and cohort sampling independently verified; not causal partner selection, identity inference or relationship recommendation.
Description
Assortative-mating correlation describes a linear association between matched partner scores.
Assortative-mating correlation describes a linear association between matched partner scores.1
Known-pair selection and score definitions determine the observed sample correlation.
When to use Assortative Mating Correlation Calculator
Use this tool to obtain observedKnownCorrelation, includedCallerIds, unknownCallerIds from the supplied records using the stated calculation. Establish the population, period, variable definitions, sample coverage and independently applicable instrument or calibration.
How to use Assortative Mating Correlation Calculator and understand its results
- Establish the population, period, variable definitions, sample coverage and independently applicable instrument or calibration.
- Enter callerCoupleIds, independentlyMatchedPartnerOneScores, independentlyMatchedPartnerTwoScores, pairedEvidenceKnownFlags, using the field meanings and rules below, then run the tool.
- Read result.observedKnownCorrelation, result.includedCallerIds, result.unknownCallerIds. Use the units and sign conventions below, and review any unknown records separately.
- Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| callerCoupleIds | Caller couple ids | list of string | Required |
| independentlyMatchedPartnerOneScores | Independently matched partner one scores | list of number | Required |
| independentlyMatchedPartnerTwoScores | Independently matched partner two scores | list of number | Required |
| pairedEvidenceKnownFlags | Paired evidence known flags; binary 1 means the matched observation is known, 0 means unknown | list of number | Required |
Supply nonempty unique nonblank couple IDs and aligned finite partner score lists with binary known flags. Select known pairs and calculate Pearson correlation; at least two known pairs and nonzero finite marginal variance are needed. Return included and unknown IDs separately. Scores need not be positive; no causal conclusion or missing-score imputation is added.2
r=Σ((x_i−meanX)×(y_i−meanY))/sqrt(Σ(x_i−meanX)²×Σ(y_i−meanY)²), over known pairs Use the matching field units and the exact boundary, ordering and reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Calculated observation or qualified reference | object |
| result.observedKnownCorrelation | Pearson linear correlation among known matched partner-score pairs. | number |
| result.includedCallerIds | Known matched-pair identifiers included in the correlation. | list |
| result.unknownCallerIds | Identifiers whose paired observations are marked unknown. | list |
{
"input": {
"callerCoupleIds": [
"a",
"b",
"c"
],
"independentlyMatchedPartnerOneScores": [
1,
2,
3
],
"independentlyMatchedPartnerTwoScores": [
2,
4,
6
],
"pairedEvidenceKnownFlags": [
1,
1,
1
]
},
"output": {
"result": {
"observedKnownCorrelation": 1,
"includedCallerIds": [
"a",
"b",
"c"
],
"unknownCallerIds": []
}
}
}Limitations and assumptions
Population definitions, survey design, calibration and observation coverage are independently established. A result describes the supplied record or conditional model without demonstrating causation, validating an instrument or supplying representative population evidence.
Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries, subject to the narrower lengths and code rules above. String lists allow at most 128 entries of at most 128 UTF-16 units each; blank and duplicate rules are stated above. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related step, use Intergenerational Elasticity Calculator: Intergenerational income elasticity relates log child income to log parent income in matched families. For a complementary comparison, use Local Income Rank Status Anxiety Calculator: A local income midrank locates one observation within a declared reference sample.
References
Similar or alternative tools
- Intergenerational Elasticity Calculator
Intergenerational income elasticity relates log child income to log parent income in matched families.
- Local Income Rank Status Anxiety Calculator
A local income midrank locates one observation within a declared reference sample.