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Hypothesis Operationalization

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Strategy: 将 working hypothesis 精确化为可测试形式

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  • Added May 27, 2026
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npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill hypothesis-operationalization --agent claude-code

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SKILL.md
---
name: hypothesis-operationalization
description: "Strategy: 将 working hypothesis 精确化为可测试形式"
version: 1.0.0
category: hypothesis-formation
type: strategy
campaign: hypothesis-formulation
tactics:
  - falsifiability-audit
sops:
  - operationalization
  - falsifiability-check
  - boundary-condition-specification
  - variable-identification
dependencies:
  skills:
    - context-management
    - subagent-spawning
    - literature-engine
---

# Hypothesis Operationalization

将 working hypothesis 精确化为可测试形式:将模糊的方向性想法或概念性假设转化为每个术语都有操作定义、每个变量都有测量方法的精确可测试命题。

## 适用场景

- 已有方向性假设("我认为 X 可能影响 Y"),但尚未精确化
- 假设含有抽象构念(construct),需要具体化为可观测指标
- 准备进入研究设计阶段,需要给出可直接操作的假设版本
- 审稿人或合作者反馈"假设太模糊"

不适用:尚未有任何假设方向 → 先用其他三个 strategy 生成假设,再回到本 strategy 精化。

## 思维框架

**Abstract → Concrete**

每个术语获得操作定义,每个变量获得测量方法。

操作化的五个层次:

1. **构念澄清**(Construct clarification):假设中的每个术语意味着什么?(概念层面)
2. **变量识别**(Variable identification):哪些是可操作的变量?(分析层面)
3. **操作定义**(Operational definition):如何测量/操作每个变量?(方法层面)
4. **边界条件**(Boundary conditions):假设在什么范围内成立?(适用层面)
5. **可证伪标准**(Falsifiability criteria):什么观察结果会推翻此假设?(判断层面)

**常见的操作化失败模式**:
- 循环定义(用 X 定义 X)→ 操作定义必须引用可观测行为或测量
- 测量与构念不匹配(operationalism gap)→ 需要论证测量工具确实捕捉到构念
- 边界条件过于宽泛("在所有情境下")→ 必须具体到样本、情境、时间范围

## Budget Gate

| Tier | 操作化完整性 | 变量测量 | 边界条件 | 可证伪性 |
|------|---------|---------|---------|---------|
| S | 所有抽象术语有操作定义 | 所有变量有测量方法草案 | 主要边界条件明确 | 1 个 falsification scenario |
| M | 同上 + 操作化合理性论证 | 变量测量含信效度考量 | 完整边界条件 | ≥2 个 falsification scenarios |
| L | 同上 + 竞争操作化方案比较 | 主要变量含多操作化方案 | 边界条件 + 外部效度声明 | 完整 falsifiability audit |

## 默认参考流

1. 调用 `variable-identification` SOP:识别假设中所有构念,分类为 IV/DV/调节变量/中介变量
2. 调用 `operationalization` SOP:为每个构念提供操作定义(含测量方法/工具/指标)
3. 调用 `boundary-condition-specification` SOP:明确假设的适用范围(群体、情境、时间、文化)
4. 调用 `falsifiability-check` SOP(via `falsifiability-audit` tactic):生成 falsification scenarios,确认假设可证伪性

## context-checkpoint

每轮结束后记录:
- 操作化前的假设原始版本
- 每个构念的操作定义(含测量方法)
- 操作化后的精确假设(If [operationalized X], then [operationalized Y])
- 边界条件清单
- Falsification scenarios
- 操作化质量自评(是否有循环定义、测量-构念匹配度)

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