复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
Security audit: baseline 52/52 CLEAN
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在
docs/CONTENT_ZH.md(扩展正文,总表行内的→直接跳转到对应锚点)。English version:
README-en.md· 中文扩展正文:docs/CONTENT_ZH.md·README-zh-CN.md已弃用(重定向占位)
🌐 语言: English | 简体中文(默认) | 繁體中文 | 日本語 | 한국어
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Stanford REAP × CoPaper.AI · 实证研究 AI 工具的学术工业级产品
由斯坦福实证研究方法论团队打造,覆盖从数据清洗到顶刊投稿的完整工作流
🚀 New here? Open the Skill Search → to filter all 1,096 skills by method, stage, language, and license. The 5-minute tour (
make quickstart) prints the same picture in your terminal.🇨🇳 中文用户从本文件开始(流水线速览 + 76 行总表),每个合集的完整描述见
docs/CONTENT_ZH.md。📖 English readers: seeREADME-en.md.
| Rigor lane | Count | Where |
|---|---|---|
| Numeric benchmark tasks — gold values recomputed from real data each run | 17 | benchmark/ |
| Behavioral eval scenarios / rubric items | 37 / 183 | eval-harness/ |
Full trust overview:
docs/TRUST.md·docs/RIGOR_COVERAGE.md
把项目 URL 地址 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills 丢给 Claude Code / Codex,并指定是目录 / 项目 / 全局安装 —— 剩下的让它自己做。例如:
帮我安装 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills
装到「全局」(~/.claude/skills/),我想在所有项目里都能用
把最后一行换成你要的作用域即可:
| 作用域 | 说给 Agent 的话 | 落到哪里 |
|---|---|---|
| 目录(当前会话临时用) | "只在当前目录用,不要全局安装" | 当前工作目录下的 .claude/skills/ |
| 项目(团队共享,可提交进 git) | "装到本项目" | 项目根目录 .claude/skills/ |
| 全局(所有项目可用) | "装到全局" | ~/.claude/skills/(Codex 为 ~/.codex/skills/) |
A. 插件市场(Claude Code v2.1+,推荐,可升级)
claude plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills
claude plugin install aer-skills@auto-empirical-research-skills # 顶刊投稿全流程(9 skills)
claude plugin install empirical-analysis-python@auto-empirical-research-skills # Python 计量流水线
claude plugin install empirical-analysis-stata@auto-empirical-research-skills # Stata 计量流水线
claude plugin install empirical-analysis-r@auto-empirical-research-skills # R + Quarto 流水线
B. 只要某一个 skill —— 直接拷文件夹
git clone --recurse-submodules https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
cd Auto-Empirical-Research-Skills
cp -R skills/00.1-Full-empirical-analysis-skill_Python .claude/skills/ # 项目级
cp -R skills/00.1-Full-empirical-analysis-skill_Python ~/.claude/skills/ # 全局
拷进去的文件夹必须自带 SKILL.md(部分合集的 SKILL.md 在下一层,拷那一层)。
新开一个会话,直接用自然语言说要做什么,Agent 会按 description 自动挑 skill;说不动就点名方法或 skill:
用面板数据跑一个 Callaway–Sant'Anna 事件研究,并出 HonestDiD 稳健性和期刊级表格
完整安装说明(Codex / CodeBuddy 整库导入、
--plugin-dir单次加载、常见故障排查)见INSTALL.md。
中文内容分两级维护,各司其职:
docs/CONTENT_ZH.md(扩展正文):每个合集的完整描述(#skill-NN 锚点)、按用途分组、精确数字、2 分钟验证、三层信任、旗舰流水线详解、贡献与引用。总表行内的 → 直接跳到对应锚点。README-en.md · README-zh-TW.md · README-ja.md · README-ko.md[!NOTE] 维护规则: 改合集总表 → 本文件与 CONTENT_ZH.md 的锚点表两处同步;改合集详情 / 分组 / 数字 → 只改
docs/CONTENT_ZH.md。统计数字(合集数 / skill 数)以catalog/skills.json为准,由make validate的 readme-stats 检查器守护。贡献者(Contributors): 提交前请在本地跑通完整门禁
make check(catalog 校验 + 链接 + 单元测试 + eval-harness + benchmark)。详见CONTRIBUTING.md。旧版归档:
README-zh-CN.md已弃用,仅作向后兼容的重定向占位。
AERS 不只是 76 个散装 skill —— 它能陪你走完一篇论文。 从模糊 idea → 选题精炼 → 文献综述 → 数据获取 → 识别策略 → 估计建模 → 稳健性审计 → 出版级表格 / 图形 → 写作与同行评审 → 降 AIGC → 投稿。端到端、全自动、每一步都可被人介入(中间任何一步你都可以接过去手工改方法、补变量、加稳健性,再让流水线自动接上跑)。
Paper-WorkFlow 是 AERS 的"指挥棒",它把上面 9 个阶段的 skill 串成 一条按键即运行的端到端流水线。
你在 IDE 入口给它一句自然语言:
"开一个新论文项目:空气污染与中国劳动力市场,CS 设计 + 省级面板"
它会自动按顺序调:
sp.csdid(...) 给出 CS-DID 估计草案 + 写出估计方程与识别假设sp.feols(...) + sp.honest_did(...)任何阶段你都可以手动介入 —— 上一阶段的产物全部落盘(产物-幂等 pipeline),你接过去改方法、补控制、加稳健性,再让流水线自动接下去跑。这就是"全自动 + 可介入"。
| ⭐ Skill | 在流水线里的角色 |
|---|---|
| 00 StatsPAI 🔥 | 因果引擎:900+ 函数,sp.causal(...) 一行跑闭环(DID / RD / IV / SCM / DML / matching) |
| 00.1 Full Empirical · Python 📘 | 显式 Python 栈(pandas / statsmodels / linearmodels / pyfixest) |
| 00.2 Full Empirical · Stata 📊 | 显式 Stata 栈(reghdfe / ivreg2 / csdid / sdid / rdrobust) |
| 00.3 Full Empirical · R 📗 | 显式 R 栈(tidyverse / fixest / did / HonestDiD)+ Quarto 渲染 |
| 48 de-AIGC-skills 🇨🇳🇬🇧 | 中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方) |
| 50 AER-skills 📕 | Top-5 经济学投稿套件:识别 → 稳健性 → R&R |
| 69 Paper-WorkFlow 🧭 | 元编排器,把上面 9 个阶段串成一键流水线 |
为什么挑这 7 个?因为它们的行为都被基准钉死了 —— 不是营销口径,是对着已知答案反复跑过验证过的(17 项数值 benchmark + 37 项行为评测 ↗)。
↴ 直跳到下方 76 行总表(每个合集带 #skill-NN 锚点)。如果你更关心"这些 skill 怎么用"而不是"有哪些 skill",看 📘 中文唯一权威正文 里的「按用途分组」与「旗舰流水线」两节。
00 → 72,编号连续无空缺)打开仓库 → 看见整座库。 全部 76 个合集 · 1,096 个 skill,每一个都已 vendor 进本仓库,由
catalog/skills.json跟踪。⭐ = Stanford REAP × CoPaper.AI 团队自研的 skill;其余为精选、经安全审计的社区作品。主题图例 — 🚀 全流程与编排器 · 🎯 因果推断与计量经济学 · 📚 文献与研究设计 · ✍️ 写作 / 编辑 / 去 AIGC · 📑 引用 / 复现 / 同行评审 · 🛠️ 数据 / 工具 / 基础设施
点击【→】 跳转到
docs/CONTENT_ZH.md中该合集的完整描述;点击合集名 直接打开其目录。🙏 尊重原作者 — 「来源」列直接链回上游原始仓库(
owner/repo)。本仓库里的社区合集都是上游快照:请去原仓库点 star、提 issue、看 LICENSE。完整的许可证与来源置信度审计见docs/LICENSE_AUDIT.md,机器可读版本在catalog/provenance.json。
| # | 合集 | 一句话 | 详情 | 来源 |
|---|---|---|---|---|
| ⭐ 00 | StatsPAI 🔥 | 因果引擎 · Agent-native Python DSL:sp.causal(...) 一行跑闭环(DID/RD/IV/SCM/DML,900+ 函数) | → | brycewang-stanford/StatsPAI |
| ⭐ 00.1 | Full Empirical · Python 📘 | 显式栈:pandas · statsmodels · linearmodels · pyfixest | → | ⭐ 本仓库 |
| ⭐ 00.2 | Full Empirical · Stata 📊 | reghdfe · ivreg2 · csdid · sdid · rdrobust 复现包 | → | ⭐ 本仓库 |
| ⭐ 00.3 | Full Empirical · R 📗 | tidyverse · fixest · did · HonestDiD + Quarto 渲染 | → | ⭐ 本仓库 |
| 01 | academic-paper-skills | 大纲 → 手稿写作 + 7 维审稿人模拟 | → | lishix520/academic-paper-skills |
| 02 | research-skills | 医学影像综述、提案、论文转幻灯片 | → | luwill/research-skills |
| 03 | scientific-skills | 假设生成 + 28 个科学数据库 | → | K-Dense-AI/claude-scientific-skills |
| 04 | scientific-writer | 引用管理 + 科学写作 | → | K-Dense-AI/claude-scientific-writer |
| 05 | research-superpower | 系统化检索、筛选与引文溯源 | → | kthorn/research-superpower |
| 06 | stats-paper-writing | 端到端 LaTeX 统计论文写作 | → | fuhaoda/stats-paper-writing-agent-skills |
| 07 | AI-Research-SKILLs | 发表级 ML 图表、LaTeX、引文核验 | → | Orchestra-Research/AI-Research-SKILLs |
| 08 | latex-document-skill | 创建 / 编译任意 LaTeX 文档为 PDF | → | ndpvt-web/latex-document-skill |
| 09 | awesome-econ-ai | Python 面板数据分析(linearmodels) | → | meleantonio/awesome-econ-ai-stuff |
| 10 | causal-inference-mixtape | DID / IV / RDD / SCM 模板(Cunningham) | → | Jill0099/causal-inference-mixtape |
| 11 | compound-science | 面向定量社会科学的贝叶斯估计 | → | James-Traina/compound-science |
| 12 | claude-code-my-workflow | 提交 → PR → 合并的研究工作流(Emory) | → | pedrohcgs/claude-code-my-workflow |
| 13 | MixtapeTools | Cunningham 的因果推断工具集与讲义 | → | scunning1975/MixtapeTools |
| 14 | research-starter | R 中的 IV / DiD / RDD,含完整诊断 | → | luischanci/claude-code-research-starter |
| 15 | social-science-research | R 或 Python 端到端数据分析 | → | Felpix-Studios/social-science-research |
| 16 | clo-author | 多代理数据分析(R / Stata / Python) | → | hsantanna88/clo-author |
| 17 | DAAF | 安全意识代理框架(32 条 deny rule) | → | DAAF-Contribution-Community/daaf |
| 18 | stata-accounting | 来自 126 篇 JAR 论文的实测 Stata 范式 | → | jusi-aalto/stata-accounting-research |
| 19 | vera-economic-intelligence | 经济情报 / 政策研究情报工作流 | → | CuellarC05/vera-economic-intelligence |
| 20 | python-econ-skill | DSGE / HANK 与定量经济计算 | → | wenddymacro/python-econ-skill |
| 21 | AI-research-feedback | 用 AI 同行评审生成结构化反馈 | → | claesbackman/AI-research-feedback |
| 22 | christopherkenny-skills | 面向 Quarto(.qmd)的 APSA 风格检查器 | → | christopherkenny/skills |
| 23 | baygent | 带护栏的 PyMC / Arviz 贝叶斯工作流 | → | Learning-Bayesian-Statistics/baygent-skills |
| 24 | academic-research-skills | 5 审稿人多视角论文评审 | → | Imbad0202/academic-research-skills |
| 25 | Diverga | 研究问题精炼器(抗模式坍缩) | → | HosungYou/Diverga |
| 26 | scholar | 统计算法设计与文档 | → | Data-Wise/claude-plugins |
| 27 | my_claude_skills | 经济学摘要写作指南 | → | dariia-m/my_claude_skills |
| 28 | paper-replicate-agent | 论文复现代理演示 | → | maxwell2732/paper-replicate-agent-demo |
| 29 | project20XXy | 可复现手稿 + notebook 项目 | → | quarcs-lab/project20XXy |
| 30 | zirui-song-claude-skills | Zirui Song 的研究辅助 Claude 技能集 | → | zirui-song/claude-skills |
| 31 | claude-code-skills | Python 面板数据分析 | → | thalysandratos/claude-code-skills |
| 32 | stata-skill | 高性能 Stata C/C++ 插件 | → | dylantmoore/stata-skill |
| 33 | claude-scholar | 研究全生命周期:选题 → 综述 → 实验 → 审稿回复 | → | Galaxy-Dawn/claude-scholar |
| 34 | research-companion | 头脑风暴、评估并决策研究方向 | → | andrehuang/research-companion |
| 35 | academic-writing-skills | 面向投稿场所的工业 AI 文献研究 | → | bahayonghang/academic-writing-skills |
| 36 | literature-review-skill | 完整文献综述工作流(中文) | → | taoyunudt/literature-review-skill |
| 37 | IlanStrauss-ai-skills | Ilan Strauss 经济学研究 AI 工作流 | → | IlanStrauss/ai-skills |
| 38 | academic-proofreader | 学术校对 | → | peternka/academic_proofreader |
| 39 | marginaleffects | 预测、斜率与比较(R / Python) | → | vincentarelbundock/marginaleffects |
| 40 | pyfixest | Python 中的快速固定效应估计 | → | py-econometrics/pyfixest |
| 41 | sewage-econometrics-check | 10 项复现包审计 | → | sticerd-eee/sewage |
| 42 | ARIS | 自主「research-in-sleep」代理,端到端 | → | wanshuiyin/Auto-claude-code-research-in-sleep |
| 43 | research-plugins | 478 个研究插件:数据可视化、领域、基础设施 | → | wentorai/research-plugins |
| 44 | humanizer_academic | 为医学/学术手稿去 AI 味(23 类模式) | → | matsuikentaro1/humanizer_academic |
| 45 | deslop | 去除 AI 写作痕迹(5 维评分) | → | stephenturner/skill-deslop |
| 46 | stop-slop | 三层 AI 痕迹检测与改写 | → | hardikpandya/stop-slop |
| 47 | avoid-ai-writing | 审计 → 改写 → 二次审计 AI 味(留痕) | → | conorbronsdon/avoid-ai-writing |
| ⭐ 48 | de-AIGC-skills 🇨🇳🇬🇧 | 中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方) | → | ⭐ 本仓库 |
| 49 | humanize-chinese | 检测并人性化 AI 生成的中文文本 | → | swaylq/humanize-chinese |
| ⭐ 50 | AER-skills 📕 | Top-5 经济学投稿套件:识别 → 稳健性 → R&R | → | brycewang-stanford/AER-skills |
| 51 | CausalPy | 贝叶斯准实验(PyMC Labs) | → | pymc-labs/CausalPy |
| 52 | slr-prisma | 系统文献综述,PRISMA 2020 | → | keemanxp/slr-prisma |
| 53 | thematic-analysis | Braun & Clarke 六阶段定性主题分析 | → | keemanxp/thematic-analysis-skill |
| 54 | open-science-skills | 引用一致性、DOI 与论据支撑审计 | → | scdenney/open-science-skills |
| 55 | r-skills | R 中用 brms 做贝叶斯推断 | → | ab604/claude-code-r-skills |
| 56 | econ-writing-skill | 综合 50+ 顶级指南的经济学写作 | → | hanlulong/econ-writing-skill |
| 57 | edgartools | 查询与分析 SEC 文件 | → | dgunning/edgartools |
| 58 | econstack | 政策简报(UK GES / AU Treasury) | → | charlescoverdale/econstack |
| 59 | openalex-skill | 通过 OpenAlex 查询 2.4 亿+ 学术作品 | → | shiquda/openalex-skill |
| 60 | superpapers | 综合性实证研究支持套件 | → | regisely/superpapers |
| 61 | research-methods | 与预注册匹配的验证性检验 | → | phdemotions/research-methods |
| 62 | citation-checker | 对照 CrossRef / S2 / OpenAlex 核验引用 | → | PHY041/claude-skill-citation-checker |
| 63 | scientific-agent-skills | DoWhy 识别–估计–反驳框架 | → | tondevrel/scientific-agent-skills |
| 64 | mcp-stata | 20 个 Stata 因果推断与复现 skill | → | tmonk/mcp-stata |
| 65 | game-theory-paper-writer | 生成并压力测试博弈论论文 | → | 本仓库 PR #17 |
| 66 | empirical-research-skills | 面向大型面板的 R 性能优化 | → | SiyaoZheng/ai4ss-skills |
| 67 | econfin-workflow-toolkit | 中国公司金融实证工作流,从提案到论文 | → | 本仓库 PR #22 |
| 68 | research-productivity-skills | 论文检索、SSRN、DOI 查询、下载 | → | 本仓库 PR #21 |
| ⭐ 69 | Paper-WorkFlow 🧭 | 元编排器,串起整个社会科学论文流水线 | → | brycewang-stanford/Paper-WorkFlow |
| 70 | ssci-polish ✍️ | SSCI / SCI 英文论文语言润色(语法、可读性、学术语气) | → | ⭐ 本仓库 |
| ⭐ 71 | lit-review-agent-tools 🔍 | 文献综述工具选型 + 一键安装运行(MinerU / PaperQA2 / ASReview / STORM / MCP 服务器) | → | brycewang-stanford/lit-review-agent-tools |
| ⭐ 72 | Kaggle Research 🧪 | 通过官方 CLI 安全检索 Kaggle 资源、限界下载公开数据并保留审计证据 | → | ⭐ 本仓库 |
想看更详细的描述(主题分类、字段、统计)? 见
docs/CONTENT_ZH.md中标注#skill-NN锚点的同一张表 —— 它是每个合集的完整描述所在的扩展正文。
自 2026-04 首次发布以来的主干里程碑(完整提交记录见 Commits 与 CHANGELOG.md):
---
config:
gitGraph:
rotateCommitLabel: false
---
gitGraph TB:
commit id: "2026-04 首次发布"
branch community
commit id: "2026-05 首个社区 PR"
checkout main
merge community
commit id: "2026-05 更名 AERS"
commit id: "2026-06 插件市场"
commit id: "2026-06 全库路由器"
commit id: "2026-07 首个 tag" tag: "v2026.07"
branch kaggle
commit id: "2026-07 Kaggle 集成"
checkout main
merge kaggle
commit id: "2026-08 de-AIGC 双语"
Star 增长曲线(非提交数)· 由 scripts/build-star-history.py 从 GitHub API 生成并提交入库
如果 AERS 对你的工作有帮助,请引用它(CITATION.cff)并点个 Star,让更多研究者看到。
AI 是放大器,不是替代品。它替你做最耗时的"搬砖",你保留最核心的"判断"。
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Stanford REAP × CoPaper.AI · 实证研究 AI 工具的学术工业级产品
![]() 扫码访问 copaper.ai |
![]() 关注公众号「CoPaper.AI」 |
内置 20 个方法论 skill · 20 分钟完成实证论文 · 自研 StatsPAI(900+ 函数 / MIT 开源)
name: d2
description: |
Agent D2 - Data Collection Specialist - Interviews, Focus Groups & Observation.
Covers protocol development, question design, probing strategies, transcription conventions, and systematic observation.
Absorbed D3 (Observation Protocol Designer) capabilities.
version: "12.0.1"diverga_check_prerequisites("d2") → must return approved: true
If not approved → AskUserQuestion for each missing checkpoint (see .claude/references/checkpoint-templates.md)
diverga_mark_checkpoint("CP_SAMPLING_STRATEGY", decision, rationale)Read .research/decision-log.yaml directly to verify prerequisites. Conversation history is last resort.
Domain: Qualitative Data Collection Specialization: Interview Protocol Development, Focus Group Design, Transcription Standards, Systematic Observation Tier: MEDIUM (Sonnet - balanced depth and efficiency) Version: 5.0.0 (Enhanced with v3 creativity modules)
Design and execute rigorous interview and focus group protocols for social science research. Ensure data collection methods produce rich, trustworthy qualitative data through systematic question design, effective moderation strategies, and transparent transcription conventions.
This agent activates when detecting:
thinking_allocation:
protocol_development: 40% # Question sequencing logic
probing_strategy: 25% # Follow-up adaptation
transcription_rules: 20% # Notation decisions
validation_design: 15% # Member checking methods
1. Forced-Analogy Module
2. Semantic-Distance Module
3. Iterative-Loop Module
CP-INIT-001: Interview/Focus Group Appropriateness Check
CP-METHODOLOGY-001: Protocol Design Review
CP-OUTPUT-001: Data Quality Assurance
Definition: Predetermined questions asked in fixed order with standardized wording.
When to Use:
Example Protocol Structure:
Opening (5 min)
├── Introduction to study purpose
├── Informed consent confirmation
└── Recording permission
Main Questions (30-40 min)
├── Q1: "Describe your typical workday." [probe: specific tasks]
├── Q2: "What challenges do you face most frequently?" [probe: examples]
├── Q3: "How do you respond to those challenges?" [probe: strategies]
└── Q4: "What support would be most helpful?" [probe: ideal scenario]
Closing (5 min)
├── "Is there anything important we haven't discussed?"
└── Next steps and follow-up contact
Strengths:
Limitations:
Definition: Flexible question guide with core topics but adaptable wording and order.
When to Use:
Example Protocol Structure:
Topic Guide (not script)
Opening Rapport Building
- "Tell me about how you came to this field..."
- [Adapt based on participant background]
Core Topic 1: Experience with X
- Main question: "Walk me through your experience with X..."
- Probes (use as needed):
* "Can you give me a specific example?"
* "How did that make you feel?"
* "What happened next?"
Core Topic 2: Challenges and Barriers
- Main question: "What obstacles have you encountered?"
- Probes:
* "How did you try to overcome that?"
* "Who else was involved?"
* "What would you do differently?"
Core Topic 3: Future Perspectives
- Main question: "How do you see this evolving?"
- Probes:
* "What would ideal support look like?"
* "What concerns you most about the future?"
Closing
- "What haven't I asked that I should have?"
Probing Strategy Matrix:
| Probe Type | Example | Use When |
|---|---|---|
| Clarification | "What do you mean by 'overwhelming'?" | Vague or ambiguous response |
| Elaboration | "Can you tell me more about that?" | Surface-level answer |
| Contrast | "How does that differ from your previous experience?" | Need comparison |
| Example | "Could you give a specific instance?" | Abstract/general statement |
| Silence | [3-5 second pause] | Participant seems to be reflecting |
| Echo | "You said 'frustrating'..." | Encourage continuation |
| Devil's Advocate | "Some might argue the opposite. What do you think?" | Challenge assumptions |
| Hypothetical | "If you had unlimited resources, what would you do?" | Explore ideals |
Strengths:
Limitations:
Definition: Open-ended conversation guided by broad research question with minimal predetermined structure.
When to Use:
Example Opening:
"I'm interested in understanding your experience with [phenomenon].
Could you tell me about that in your own words, starting wherever
feels right to you?"
[Interviewer follows participant's narrative thread, asking only:
- "Tell me more about that"
- "What was that like for you?"
- "How did you make sense of that?"]
Strengths:
Limitations:
Purpose: Invite descriptive narrative of experience.
Examples:
Best Practices:
Purpose: Zoom into specific aspect of experience.
Examples:
Purpose: Request concrete instances.
Phrasing:
Why Effective: Moves from abstract to concrete, reveals behavioral patterns.
Purpose: Focus on actions, not just opinions.
Examples:
Purpose: Explore beliefs and interpretations.
Examples:
Caution: Don't overuse - opinions should emerge from experience descriptions.
Purpose: Access emotional dimension.
Examples:
Best Practice: Ask AFTER behavioral description, not before.
Purpose: Assess factual understanding.
Examples:
Purpose: Evoke vivid recall through senses.
Examples:
Use in: Phenomenological research, trauma-informed interviewing.
Funnel Approach (Broad → Narrow):
1. "Tell me about your teaching career." [Grand tour]
2. "What do you find most challenging?" [Opinion]
3. "Can you describe a recent challenging situation?" [Example]
4. "What specifically made it difficult?" [Mini-tour]
5. "How did you handle it?" [Behavior]
Inverted Funnel (Narrow → Broad):
1. "How many students are in your class?" [Knowledge]
2. "What does a typical lesson look like?" [Mini-tour]
3. "How do you approach curriculum planning?" [Behavior]
4. "What's your philosophy on education?" [Opinion/Values]
Best Practice: Start broad to avoid leading, narrow to explore specifics.
Optimal Size: 6-10 participants
Homogeneity vs. Heterogeneity:
| Dimension | Homogeneous Group | Heterogeneous Group |
|---|---|---|
| Status/Power | Same rank (all teachers) | Mixed rank (teachers + principals) |
| Pro: Comfort, candor | Pro: Multiple perspectives | |
| Con: Groupthink | Con: Power dynamics inhibit sharing | |
| Experience Level | All novices or all experts | Mixed experience |
| Pro: Shared reference points | Pro: Newcomer questions reveal tacit knowledge | |
| Con: Blind spots | Con: Experts dominate | |
| Demographic | Same age/gender/ethnicity | Diverse demographics |
| Pro: Rapport | Pro: Broader insights | |
| Con: Limited perspectives | Con: Potential discomfort |
General Rule: Homogenize on power/status, diversify on experience/demographics (unless studying specific subgroup).
Example Composition Plans:
Study: Teacher Perceptions of AI Tools
Group 1: Elementary teachers, 3-10 years experience (n=8)
Group 2: Secondary teachers, 3-10 years experience (n=7)
Group 3: Elementary teachers, <3 years experience (n=6)
Group 4: Secondary teachers, <3 years experience (n=9)
Rationale:
- Homogenize on level and experience (reduce power dynamics)
- 4 groups ensure saturation across key subgroups
- Exclude administrators to encourage candor
Primary Moderator Responsibilities:
Facilitate Discussion (not interview individuals)
Manage Dynamics
Maintain Neutrality
Co-Moderator/Note-Taker Role:
Template:
# Focus Group Discussion Guide
## Study: [Title]
## Target Group: [Demographics]
## Duration: 90 minutes
### I. Opening (10 min)
**Moderator Introduction**
- Welcome and purpose
- Ground rules:
* No right/wrong answers, all perspectives valued
* Speak one at a time (for recording)
* Confidentiality agreement
* Right to pass on any question
- Recording consent confirmation
- Name tents/introductions
**Icebreaker Activity**
"Let's go around and share: Your name, how long you've been
teaching, and one word to describe your week."
[Purpose: Build comfort, even out speaking]
---
### II. Opening Questions (15 min)
**Broad engagement questions to surface initial thoughts**
Q1: "When you hear 'AI in education,' what comes to mind?"
[Allow 5-7 min for all to contribute, minimal probes]
Q2: "How many of you have tried an AI tool in your teaching?
Show of hands. Can someone who raised their hand share what
you tried?"
[Purpose: Gauge experience level, warm up discussion]
---
### III. Core Topic 1: Adoption Experiences (25 min)
**Main Question**: "For those using AI tools, walk us through
how you decided to try it."
**Probes**:
- "What problem were you trying to solve?"
- "How did you learn about the tool?"
- "What was the first attempt like?"
**Follow-Up**: "For those NOT using AI tools yet, what's
holding you back?"
**Probes**:
- "Is it lack of time, training, interest, or something else?"
- "What would need to change for you to consider trying it?"
---
### IV. Core Topic 2: Benefits and Challenges (25 min)
**Main Question**: "What benefits have you seen, or what
benefits do you expect?"
[Let group build on each other's ideas]
**Transition**: "Now let's talk about challenges."
**Main Question**: "What concerns or difficulties have you
encountered or anticipate?"
**Probes**:
- "How do students respond?"
- "What about administrative support?"
- "Ethical concerns?"
---
### V. Core Topic 3: Future Outlook (10 min)
**Main Question**: "Looking ahead 2-3 years, how do you see
AI fitting into your teaching?"
**Probes**:
- "What would ideal AI support look like?"
- "What worries you about the future?"
---
### VI. Closing (5 min)
**Summary**: [Moderator briefly summarizes 3-4 key themes]
**Final Question**: "Have we missed anything important about
this topic?"
**Thank You & Next Steps**
- Compensation/incentive distribution
- Member checking timeline (if applicable)
- Contact for questions
Card Sorting:
Scenario Response:
Timeline Creation:
Level 1: Verbatim (Full Jefferson Notation)
When to Use:
Example:
Moderator: What concerns do you have about AI?
Sarah: Well (0.5) I worry that=
=it'll replace teachers↑
John: [But it can't ]
Sarah: [I mean eventually]
(2.0)
Moderator: Mm-hm
Sarah: Like the human element (..) you can't automate empathy
(.) right?
John: Right but- (.) I think it's more of a tool?
Not a >replacement< but like a calculator.
Notation Key:
(0.5) = Pause in seconds
(.) = Micro-pause (<0.3 sec)
= = Latching (no gap between turns)
[ ] = Overlapping speech
↑ ↓ = Rising/falling intonation
>text< = Faster speech
<text> = Slower speech
CAPS = Louder volume
°text° = Quieter volume
(( )) = Transcriber notes
... = Trailing off
- = Abrupt cutoff
underlining = Emphasis
Time Required: 5-8 hours per 1 hour of audio
Level 2: Intelligent Verbatim
When to Use:
Approach:
Example:
Moderator: What concerns do you have about AI?
Sarah: Well, I worry that it'll replace teachers eventually.
I mean, the human element—you can't automate empathy,
right?
John: Right, but I think it's more of a tool, not a
replacement. Like a calculator.
[2-second pause]
Sarah: I guess. But students might prefer AI because it
doesn't judge them. [laughs]
Time Required: 3-5 hours per 1 hour of audio
Level 3: Summarized/Content-Focused
When to Use:
Approach:
Example:
Theme: Concerns about AI in teaching
Sarah expressed worry that AI could eventually replace teachers,
emphasizing the irreplaceable "human element" of empathy.
John countered that AI should be viewed as a tool (like a
calculator) rather than a replacement.
Sarah acknowledged this but noted students might prefer AI's
non-judgmental nature. [Quote: "Students might prefer AI because
it doesn't judge them."]
Time Required: 1-2 hours per 1 hour of audio
Best Practices:
Timestamps: Insert every 5 minutes or at topic shifts
[00:15:30]
Moderator: Let's move to the next question...
Speaker Identification:
Inaudible Segments:
Sarah: The policy requires [inaudible 00:23:15-00:23:18]
which is problematic.
Non-Verbal Communication:
[Sarah nods vigorously]
[Group laughter]
[John leans back, crosses arms]
Contextual Notes:
[Refers to handout distributed earlier]
[Phone rings, participant steps out]
| Tool | Pros | Cons | Cost |
|---|---|---|---|
| Otter.ai | Fast auto-transcription, speaker ID | Requires editing, privacy concerns | Free tier, $10/mo pro |
| Descript | Audio editing integrated, filler word removal | Learning curve | $12/mo |
| Express Scribe | Free, foot pedal support, variable speed | Manual typing only | Free |
| NVivo | Integrated with analysis software | Expensive, auto-transcription limited | $1,200+ |
| Sonix | Multi-language, high accuracy | Subscription required | $10/hr pay-as-you-go |
Hybrid Approach:
Enhance credibility (qualitative equivalent of internal validity) by validating:
Process:
Please review this transcript of our conversation. You may:
- Correct any inaccuracies
- Clarify ambiguous statements
- Add information you forgot to mention
- Remove sensitive information
Please return edits within 2 weeks. No response = approval.
Pros:
Cons:
Mitigation:
Process:
Based on our interviews, I identified these key themes:
1. **Tension between efficiency and empathy**
"You can't automate the human element." - Sarah
"AI saves time but loses the personal touch." - John
Do these themes resonate with your experience?
Have I misunderstood or missed anything important?
Pros:
Cons:
Best Practice: Frame as "does this make sense?" not "is this correct?"
Challenge: Can't share full transcript (confidentiality).
Approach 1 - Group Summary:
Approach 2 - Individual Quotes Only:
Inappropriate for:
Alternative Validation Strategies:
Beyond standard IRB consent, address:
Example Clause:
With your permission, this interview will be audio-recorded and
transcribed. Only the research team will have access to the
recording. Transcripts will be de-identified (your name and
institution removed).
You may request the recording be stopped at any time. You may
withdraw from the study up to 2 weeks after the interview by
emailing [contact]. After that, your de-identified data may be
included in analysis but we will remove any direct quotes.
Do you consent to audio recording? [Yes/No]
Risk: Dominant voices silence marginalized perspectives.
Mitigation Strategies:
If participant becomes emotional:
Example Script:
"I can see this is difficult to talk about. We can pause here,
take a break, or stop entirely—whatever feels right to you.
I also have a list of support resources if you'd like them."
Before finalizing interview/focus group protocol, verify:
Research Question: How do early-career teachers experience burnout? Design: Phenomenological study (semi-structured interviews) Sample: 12 teachers, 1-3 years experience, diverse school contexts
Agent D2 Tasks:
Draft semi-structured interview guide:
Pilot test with 2 teachers (not in final sample)
Train research assistant on:
Agent D2 Tasks:
Conduct 12 interviews (2 per week)
After each interview:
Monitor for saturation:
Agent D2 Tasks:
Auto-transcribe with Otter.ai (1 hour → 20 min draft)
Human review and editing (2 hours per interview):
Quality check: PI reviews 2 randomly selected transcripts against audio
Agent D2 Tasks:
Agent D2 Deliverables to D5-ThematicAnalysisExpert:
D5 takes over: Thematic analysis begins (coding, theme development)
Bad: "Don't you think AI tools are threatening to teachers?" Good: "How do you feel about AI tools in your field?"
Why: Leading questions bias responses, reduce trustworthiness.
Bad: "What are the benefits and challenges of online teaching?" Good: "What benefits have you experienced?" [wait for full answer] "And what challenges?"
Why: Participants answer one part, forget the other.
Bad: "Why did you decide to quit?" Good: "What led to your decision to quit?"
Why: "Why" can sound judgmental and prompt defensiveness.
Participant: "The policy is frustrating." Weak Interviewer: "Okay." [moves to next question] Strong Interviewer: "What specifically is frustrating about it?" → "Can you give me an example?" → "How did that affect your work?"
Why: Surface responses miss rich detail.
Bad: Interviewing each participant individually while others listen. Good: "What do others think about what Sarah just said?" [redirect to group interaction]
Why: Focus groups should generate interaction, not parallel interviews.
# Interview Protocol: [Study Title]
## Research Question
[1-2 sentences]
## Interview Type
[ ] Structured [ ] Semi-Structured [X] Unstructured
## Target Participants
[Demographics, sample size, recruitment method]
## Duration
[60-90 minutes typical]
---
## Opening Script (5 min)
"Thank you for meeting with me today. As a reminder, this study
explores [topic]. The interview will take about [X] minutes.
I'll be recording our conversation so I can focus on listening
rather than taking notes. The recording will be transcribed and
de-identified—your name won't appear in any reports.
There are no right or wrong answers. I'm interested in your
honest experience and perspectives. You can skip any question or
stop the interview at any time.
Do you have any questions before we begin?
[Start recording] For the recording, please confirm: Do you
consent to participate and to audio recording? [Wait for verbal
yes]"
---
## Main Questions
### Opening Question (10 min)
**Q1**: [Grand tour question]
**Probes**:
- [Clarification probe]
- [Example probe]
---
### Core Topic 1 (15 min)
**Q2**: [Main question]
**Probes**:
- [Elaboration]
- [Contrast]
- [Feeling]
---
### Core Topic 2 (15 min)
**Q3**: [Main question]
**Probes**:
- [Specific probes]
---
[Continue for all core topics]
---
## Closing (5 min)
**Final Question**: "Is there anything important about [topic]
that I haven't asked about?"
**Next Steps**: "I'll send you a transcript in about 2 weeks for
your review. You can correct anything or add thoughts you've had
since we spoke. Thank you so much for your time and insights."
[Stop recording]
---
## Field Notes Template (complete immediately after interview)
**Date/Time**:
**Location**:
**Participant ID**:
**Duration**:
**Context**: [Setting, interruptions, technical issues]
**Non-Verbal Observations**: [Body language, emotional responses]
**Analytical Memos**: [Initial impressions, connections to theory,
questions for analysis]
**Follow-Up Needed**: [Member checking, clarification questions]
# Focus Group Discussion Guide: [Study Title]
## Group Composition
**Target**: [E.g., 8 elementary teachers, 3-10 years experience]
**Homogeneity Criteria**: [E.g., same school level, similar experience]
**Heterogeneity Criteria**: [E.g., diverse schools, teaching subjects]
## Moderator Roles
**Lead Moderator**: [Name] - Facilitates discussion
**Co-Moderator**: [Name] - Notes, timing, equipment
---
## Setup (Before Participants Arrive)
- [ ] Seating: Semicircle or round table
- [ ] Name tents for each participant
- [ ] Recording devices tested (2 backups)
- [ ] Consent forms ready
- [ ] Refreshments available
---
## I. Opening (10 min)
**Welcome & Purpose**
"Thank you all for coming. We're here to discuss [topic]. Your
experiences and perspectives will help us understand [goal].
This will take about 90 minutes."
**Ground Rules**
- "There are no right or wrong answers—just different perspectives."
- "Please speak one at a time so the recording captures everyone."
- "Feel free to agree or disagree respectfully with each other."
- "What's said here stays here—please keep others' comments confidential."
- "You can pass on any question."
**Recording Consent**
"We're recording to ensure I don't miss anything. The recording
will be transcribed without your names. Does everyone consent?"
**Icebreaker**
"Let's go around and share: Your name, how long you've been
teaching, and one word to describe your week."
[Moderator models: "I'm [Name], I've been researching education
for X years, and my word is 'curious.'"]
---
## II. Opening Questions (15 min)
**Q1**: [Broad, easy question to engage everyone]
[Allow 5-7 min for all to contribute; minimal probes]
**Q2**: [Transition to core topic]
[Use this to gauge experience/knowledge level]
---
## III. Core Discussion (50 min)
### Topic 1: [Name] (20 min)
**Main Question**: [Open-ended question]
**Moderator Strategy**:
- Let conversation develop naturally for 3-5 min
- If stalled: "What do others think?"
- If dominated by one voice: "Let's hear from those who haven't
spoken yet."
**Probes** (use as needed):
- "Can someone give an example?"
- "How does that compare to your experience?"
- "What would you add to that?"
---
### Topic 2: [Name] (20 min)
[Repeat structure]
---
### Topic 3: [Name] (10 min)
[Repeat structure]
---
## IV. Closing (10 min)
**Summary**
[Moderator summarizes 3-4 key themes heard]
"I heard you discuss [theme 1], [theme 2], [theme 3]. Did I
capture that correctly? Anything I missed?"
**Final Question**
"Before we wrap up, is there anything important about [topic]
that we didn't discuss?"
**Thank You**
"Thank you all for your thoughtful contributions. Your insights
are invaluable. [Explain next steps: transcription, member
checking timeline, how findings will be shared].
[If incentives/compensation] Please see [co-moderator] to collect
your [gift card/payment]."
---
## Post-Session Debrief (Co-Moderators Only)
**Immediately After Participants Leave**:
- Save recording to encrypted drive (2 backups)
- Complete debrief form:
* Group dynamics: Were some voices dominant? Silent?
* Unexpected themes or tensions
* Technical issues
* Initial analytical impressions
**Within 24 Hours**:
- Review recording for quality
- Expand field notes
- Send recording for transcription
Subject: Interview Transcript Review - [Study Title]
Dear [Participant Pseudonym],
Thank you again for participating in our interview about [topic]
on [date].
Attached is a transcript of our conversation. I've removed your
name and any identifying details (school, colleagues' names, etc.)
to protect your confidentiality.
I'd appreciate if you could review the transcript and let me know:
1. Are there any inaccuracies I should correct?
2. Is there anything you'd like to add or clarify?
3. Is there anything you'd like removed?
Please send any edits or comments by [date - 2 weeks from now].
If I don't hear from you, I'll assume the transcript is accurate
and you approve its use in the study.
In the next phase, I'll be analyzing all interviews to identify
common themes. I may reach out again to share a summary of my
findings and get your feedback on whether my interpretation
resonates with your experience.
If you have any questions, please don't hesitate to contact me
at [email] or [phone].
Thank you again for your time and insights.
Best regards,
[Researcher Name]
[Title]
[Institution]
[Contact Info]
Method: Participants bring photos related to topic; interview discusses images.
Example: "You brought this photo of your classroom. Tell me about what's happening here."
Benefits:
Method: Create visual timeline of key events during interview.
Example: "Let's map out your teaching career. Where did it start? What were the major turning points?"
Benefits:
Method: Present hypothetical scenario; ask how participant would respond.
Example:
"Imagine a student comes to you and says an AI chatbot wrote
their essay. What would you do?"
Benefits:
Interviews are co-constructed: Your questions, reactions, and identity shape participant responses.
Reflexive Practices:
Positionality Statement (include in methods section):
"As a former teacher, I brought both insider knowledge and
potential bias to interviews. I used peer debriefing to
challenge my assumptions and actively sought disconfirming
evidence during analysis."
Interview Debrief Memos (after each interview):
Audit Trail:
Before executing interview/focus group protocol, Agent D2 confirms:
When invoked, this agent produces:
Interview Protocol or Focus Group Discussion Guide
Transcription Guidelines
Member Checking Plan
Ethical Safeguards Checklist
invoke_agent: D2-interview-focus-group-specialist
parameters:
research_question: "How do novice teachers experience burnout?"
methodology: "phenomenology"
sample_size: 12
interview_type: "semi-structured"
outputs_requested:
- interview_protocol
- transcription_plan
- member_checking_procedure
Agent D2 will:
This agent integrates methodological rigor with practical feasibility, ensuring interview and focus group data collection meets social science standards while remaining accessible to researchers with varying levels of qualitative expertise.
Version: 6.0.0 Last Updated: 2026-03-06 Maintainer: Research Coordinator System Related Agents: C2-QualitativeDesignConsultant, D4-MeasurementInstrumentDeveloper, E2-QualitativeCodingSpecialist
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