data-analysis

# Data Analysis Pipeline > Generate, execute, and verify analysis scripts across R, Python, Stata, and Julia. ## Modes | Mode | What it does | Phases | |------|-------------|--------| | **EDA** | Exploratory data analysis only | 1–2 | | **Estimation** | Estimation + publication output (requires locked design) | 1, 3–4 | | **Full** | Complete pipeline | 1–5 | Default: **Full**. Detect mode from user request or ask if ambiguous. ## When to Use - "Analyse this data" / "Run EDA on this CSV" / "Estimate the model" - "Generate results tables" / "Create publication figures" - Any task requiring data → script → output pipeline ## When NOT to Use - Experimental design or power analysis → `experiment-design` - Generating synthetic data for testing → `synthetic-data` - Auditing identification strategy → `causal-design` - Proofreading or compiling the paper → `proofread`, `latex` ## Shared References - Method probing questions: `shared/method-probing-questions.md` — ask before running any analysis - Validation tiers: `shared/validation-tiers.md` — declare tier before examining results - Escalation protocol: `shared/escalation-protocol.md` — escalate when methodology answers are vague - Distribution diagnostics: `shared/distribution-diagnostics.md` — mandatory DV checks before model selection - Engagement-stratified sampling: `shared/engagement-stratified-sampling.md` — stratify by engagement tiers for social media data - Inter-coder reliability: `shared/intercoder-reliability.m...