By name: checking-theory-saturation description: 当用户需要检验扎根理论饱和度，包括新概念识别、范畴完善度、关系充分性和理论完整性评估时使用此技能 version: 1.1.0 author: socienceAI.com tags: [grounded-theory, saturation-analysis, qualitative-research, concept-identification, category-development, planning-with-files] compatibility: Claude 3.5 Sonnet and above metadata: domain: qualitative-research methodology: grounded-theory complexity: intermediate integration_type: analysis_tool last_updated: "2026-01-23" dependencies: - planning-with-files allowed-tools: [python, bash, read_file, write_file]
理论饱和度检验技能 (Checking Theory Saturation)
Overview
为扎根理论研究提供科学、系统的理论饱和度检验，确保理论构建的完整性和可靠性。
When to Use This Skill
Use this skill when the user requests:
- Assessment of theoretical saturation in grounded theory
- Determination of whether new concepts are still emerging
- Evaluation of category development completeness
- Checking if sufficient data has been collected
- Validation of theoretical framework completeness
- Decision-making about ending data collection
- Assessment of concept, category, and theory sufficiency
- Evaluation of theoretical explanation adequacy
- Need for systematic planning and progress tracking in saturation analysis
- Integration with planning-with-files for project management
Quick Start
When a user requests saturation assessment:
- Analyze new data for emerging concepts
- Evaluate category development completeness
- Assess relationship network stability
- Validate theoretical explanation adequacy
- Determine if additional data is needed
使用时机
当用户提到以下需求时，使用此技能：
- "理论饱和度" 或 "饱和度检验"
- "理论是否饱和" 或 "检查饱和度"
- "需要更多数据" 或 "补充数据"
- "可以结束研究" 或 "研究完成度"
- "理论完整性" 或 "理论完善度"
- 需要评估理论构建的充分性
脚本调用时机
当需要执行理论饱和度检验时，调用对应的脚本：
- 概念饱和检验：
assess_concept_saturation.py - 范畴饱和检验：
assess_category_saturation.py - 关系饱和检验：
assess_relationship_saturation.py - 理论饱和检验：
assess_theory_saturation.py - 综合饱和度判断：
make_saturation_judgment.py
统一输入格式
{
"saturation_context": {
"research_topic": "研究主题",
"current_coding_stage": "当前编码阶段",
"theoretical_perspective": "理论视角",
"saturation_purpose": "饱和度检验目的"
},
"input_data": {
"existing_theory": {
"concepts": [
{
"id": "概念ID",
"name": "概念名称",
"frequency": "出现频率",
"last_appearance": "最后出现位置"
}
],
"categories": [
{
"id": "范畴ID",
"name": "范畴名称",
"attributes": ["属性列表"],
"dimensions": ["维度列表"],
"relationships": ["关系列表"]
}
],
"relationships": [
{
"id": "关系ID",
"from": "源概念/范畴ID",
"to": "目标概念/范畴ID",
"type": "关系类型",
"strength": "关系强度(0-1)"
}
],
"theoretical_framework": "理论框架描述"
},
"new_data": [
{
"id": "新数据ID",
"content": "新数据内容",
"type": "数据类型",
"source": "数据来源"
}
],
"saturation_criteria": {
"concept_threshold": 0.05,
"category_threshold": 0.90,
"relationship_threshold": 0.10,
"theory_threshold": 0.90
}
},
"analysis_parameters": {
"confidence_level": 0.95,
"statistical_significance": 0.05,
"minimum_sample_size": 10
}
}
统一输出格式
{
"summary": {
"saturation_level": "fully_saturated|partially_saturated|not_saturated",
"overall_saturation_score": "总体饱和度分数(0-1)",
"confidence_level": "置信度(0-1)",
"concepts_emerging_rate": "新概念出现率(0-1)",
"categories_development_score": "范畴发展分数(0-1)",
"processing_time": "处理时间(秒)"
},
"details": {
"concept_saturation": {
"new_concepts_identified": [
{
"id": "新概念ID",
"name": "新概念名称",
"significance": "重要性(0-1)",
"data_source": "数据来源"
}
],
"new_concepts_count": "新概念数量",
"average_per_dataset": "每份数据平均新概念数",
"significance_level": "重要性水平(high/medium/low)",
"trend_analysis": "趋势分析"
},
"category_saturation": {
"attributes_completeness": "属性完整度(0-1)",
"dimensions_coverage": "维度覆盖度(0-1)",
"relations_stability": "关系稳定性(0-1)",
"category_maturity_scores": {
"category_id": "成熟度分数(0-1)"
}
},
"relationship_saturation": {
"new_relationships_count": "新关系数",
"relationships_stability": "关系稳定性(0-1)",
"network_completeness": "网络完整度(0-1)",
"new_relationships": [
{
"id": "新关系ID",
"from": "源概念/范畴ID",
"to": "目标概念/范畴ID",
"type": "关系类型",
"significance": "重要性(0-1)"
}
]
},
"theory_saturation": {
"explanation_coverage": "解释覆盖度(0-1)",
"internal_consistency": "内部一致性(0-1)",
"phenomena_explained_count": "解释现象数",
"theory_maturity": "理论成熟度(0-1)"
},
"statistical_analysis": {
"confidence_interval": "置信区间",
"statistical_significance": "统计显著性",
"sample_size": "样本量",
"effect_size": "效应量"
}
},
"recommendations": {
"continue_data_collection": "是否继续收集数据(true/false)",
"focus_areas": ["需要关注的领域列表"],
"next_steps": ["下一步建议列表"],
"data_collection_strategy": "数据收集策略建议"
},
"metadata": {
"timestamp": "时间戳",
"version": "版本号",
"skill": "checking-theory-saturation",
"analysis_method": "分析方法"
}
}
核心流程
第一步：概念饱和评估
- 新概念识别：分析新数据中是否出现新概念
- 概念重要性评估：评估新概念对理论的贡献
- 概念抽象层次检查：验证概念抽象层次适当性
- 概念频率统计：计算新概念出现频率
第二步：范畴饱和评估
- 属性完整性检查：评估范畴属性发展充分性
- 维度完整性检查：评估范畴维度覆盖全面性
- 范畴间关系稳定性：检查范畴关系是否稳定
- 范畴定义清晰度：验证范畴边界清晰性
第三步：关系饱和评估
- 新关系识别：检查是否出现新概念关系
- 关系稳定性：验证现有关系是否稳定
- 关系强度评估：评估关系强度合理性
- 关系网络完整性：检查关系网络覆盖完整性
第四步：理论饱和评估
- 解释覆盖度：验证理论解释现象的全面性
- 理论一致性：检查理论内部逻辑一致性
- 理论贡献度：评估理论的学术贡献
- 理论适用性：验证理论的实践适用性
第五步：综合判断
- 多维度证据整合：整合各层面饱和度证据
- 饱和度信心评估：评估饱和度判断的信心水平
- 后续步骤建议：提供是否继续收集数据的建议
- 质量保证措施：实施饱和度验证措施
第六步：规划与进度管理
- 使用planning-with-files初始化项目规划
- 创建理论饱和度检验任务计划文档
- 跟踪各评估阶段的进度和完成情况
- 记录饱和度检验过程中的关键发现和洞察
- 监控项目整体进度和里程碑达成情况
输出格式
{
"summary": {
"saturation_level": "fully_saturated|partially_saturated|not_saturated",
"confidence_level": 0.85,
"concepts_emerging_rate": 0.05,
"categories_development_score": 0.92
},
"details": {
"concept_saturation": {
"new_concepts_recent": 2,
"average_per_data_set": 0.3,
"significance_level": "low"
},
"category_saturation": {
"attributes_completeness": 0.88,
"dimensions_coverage": 0.91,
"relations_stability": 0.94
},
"theory_saturation": {
"explanation_coverage": 0.95,
"internal_consistency": 0.89,
"phenomena_explained": 23
}
},
"recommendations": {
"continue_data_collection": false,
"focus_areas": ["minor_refinements"],
"next_steps": ["proceed_to_selective_coding"]
}
}
质量标准
- 采用多维度饱和度评估方法
- 基于充分证据进行饱和度判断
- 考虑中国研究语境的特殊性
- 提供明确的后续步骤建议
深入学习
- 扎根理论方法论文献
- 理论饱和度评估指南
- 中国语境下的饱和度评估案例
- 质性研究质量评估资源
完成标志
完成理论饱和度检验后应产出：
- 明确的饱和度判断结果
- 详细的多维度评估报告
- 基于证据的判断理由
- 清晰的后续步骤建议
此技能为扎根理论研究提供系统的理论饱和度检验方法，确保理论构建的科学性、完整性和可靠性。