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operationalization

Research
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SOP: operationalize abstract concepts into measurable indicators and methods

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How to use this skill

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  3. Review the proposed files and risks before you approve installation.
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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/operationalization/SKILL.md

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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

  1. Precondition check: verify completeness of the variable description
  2. Concept analysis: decompose the variable's core attributes (conceptual dimensions)
  3. Indicator selection: select 1-2 measurable indicators for each dimension
  4. Measurement method determination: specify the data collection method (survey/experiment/observation/archival/computational)
  5. 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?
  6. 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"
  }
]