yogsoth-ai Operationalization
Convert the abstract concepts in a hypothesis into concrete, measurable indicators, and argue for measurement validity.
HARD-GATE
Not satisfied → stop, return error: variable-identification must be completed first.
Pipeline
- Precondition check: verify completeness of the variable description
- Concept analysis: decompose the variable's core attributes (conceptual dimensions)
- Indicator selection: select 1-2 measurable indicators for each dimension
- Measurement method determination: specify the data collection method (survey/experiment/observation/archival/computational)
- Validity argument:
- Content validity: do the indicators cover all key dimensions of the concept?
- Construct validity: do the indicators converge with related constructs and diverge from unrelated ones?
- Criterion validity: are the indicators correlated with a validated standard measure?
- Output the operational definition
Output Format
[
{
"variable": "Variable name",
"theoretical_definition": "Abstract definition",
"dimensions": ["Dimension 1", "Dimension 2"],
"indicators": [
{
"indicator": "Indicator name",
"measurement_method": "How to collect/measure",
"scale": "nominal | ordinal | interval | ratio",
"validity": {
"content": "Justification",
"construct": "Justification",
"criterion": "Justification or null if not applicable"
}
}
],
"operationalization_notes": "Any remaining challenges or alternatives"
}
]