checking-theory-saturation

# 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: 1. **Analyze** new data for emerging concepts 2. **Evaluate** category development completeness 3. **Assess** relationship network stability 4. **Validate** theoretical explanation adequacy 5. **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 ```json { "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 ```json { "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 1. **新概念识别**：分析新数据中是否出现新概念 2. **概念重要性评估**：评估新概念对理论的贡献 3. **概念抽象层次检查**：验证概念抽象层次适当性 4. **概念频率统计**：计算新概念出现频率 ### 第二步：范畴饱和评估 1. **属性完整性检查**：评估范畴属性发展充分性 2. **维度完整性检查**：评估范畴维度覆盖全面性 3. **范畴间关系稳定性**：检查范畴关系是否稳定 4. **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 1. **新关系识别**：检查是否出现新概念关系 2. **关系稳定性**：验证现有关系是否稳定 3. **关系强度评估**：评估关系强度合理性 4. **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 1. **解释覆盖度**：验证理论解释现象的全面性 2. **理论一致性**：检查理论内部逻辑一致性 3. **理论贡献度**：评估理论的学术贡献 4. **理论适用性**：验证理论的实践适用性 ### 第五步：综合判断 1. **多维度证据整合**：整合各层面饱和度证据 2. **饱和度信心评估**：评估饱和度判断的信心水平 3. **后续步骤建议**：提供是否继续收集数据的建议 4. **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 1. **使用planning-with-files初始化项目规划** 2. **创建理论饱和度检验任务计划文档** 3. **跟踪各评估阶段的进度和完成情况** 4. **记录饱和度检验过程中的关键发现和洞察** 5. **监控项目整体进度和里程碑达成情况** ## 输出格式 ```json { "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： 1. 明确的饱和度判断结果 2. 详细的多维度评估报告 3. 基于证据的判断理由 4. 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。*