operationalization

name	operationalization
description	SOP: operationalize abstract concepts into measurable indicators and methods
version	1.0.0
category	hypothesis-formation
type	sop
campaign	hypothesis-formulation
input	Abstract variable description (from variable-identification output)
output	Operational definition + measurement method + validity argument (content/construct/criterion)
dependencies	{"skills":["subagent-spawning"]}

Operationalization

Convert the abstract concepts in a hypothesis into concrete, measurable indicators, and argue for measurement validity.

HARD-GATE

Preconditions (all must hold before starting): 1. At least 1 variable needs operationalizing (especially when operationalizable ≠ "high") 2. The variable's theoretical definition is provided (from the description field of variable-identification)

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"