| name | alterlab-survey-analysis |
| description | Analyzes complex-sample survey data with design-based inference — declares a survey design (weights, strata, PSUs/clusters, FPC) before estimating means, totals, proportions, ratios, and quantiles, computes design-adjusted standard errors via Taylor linearization or replicate weights (BRR, Jackknife, Bootstrap), calibrates with post-stratification / raking / GREG, and fits design-adjusted GLMs (linear, logistic, Poisson). Uses samplics (stable Python), the emerging svy successor, or the field-standard R survey + srvyr via Rscript. Use when analyzing GSS/ANES/ESS/DHS/Eurobarometer or any weighted/stratified/clustered survey, when a dataset ships survey weights, or when someone quotes unweighted percentages from a complex survey. For questionnaire and sampling-plan DESIGN prefer alterlab-survey-design; for the sampling-adequacy gate prefer alterlab-ssci-sampling-gate; for causal identification prefer alterlab-causal-inference. Part of the AlterLab Academic Skills suite. |
| license | MIT |
| allowed-tools | Read Bash(python:*) |
| compatibility | Requires (declare in-session, no runtime install on Anthropic API): Python samplics>=0.6 (stable; TaylorEstimator) — or svy>=0.18 (samplics' successor; API still maturing, pin + re-verify) — OR the field-standard R survey>=4.5 + srvyr>=1.3 via Rscript (csSampling+brms for Bayesian design-based models). Runs locally via `uv run python` / `Rscript`; no API key. |
| metadata | {"skill-author":"AlterLab","version":"1.0.0","depends_on":"alterlab-survey-design (item/sample design), alterlab-ssci-sampling-gate (frame/size gate), alterlab-statistical-analysis; audited by alterlab-ssci-inference-gate"} |
Survey Analysis — Declare the Design Before You Estimate Anything
Skill type: ANALYSIS MODULE. Complex-sample surveys (GSS, ANES, ESS, DHS, Eurobarometer) are
drawn with stratification, clustering, and unequal probabilities. Analyzing them as if they were a
simple random sample underestimates standard errors and yields falsely narrow CIs and wrong
tests. The discipline is design-based inference: a declared design object comes first, every
estimate flows through it.
Core Mission
YOU MUST WEIGHT (AND DECLARE STRATA + PSUs) BEFORE QUOTING ANY NUMBER FROM A COMPLEX SURVEY.
When to Use This Skill
- "Give me the weighted % who [X] from ANES/GSS/DHS, with correct standard errors."
- "Why are my survey confidence intervals so narrow?" (← design ignored)
- "Post-stratify / rake my sample to census margins."
- "Fit a logistic regression on this weighted, clustered survey."
Does NOT Trigger
| The request is really about… | Route to | Why not this skill |
|---|
| Writing questionnaire items / choosing a sampling frame | alterlab-survey-design | Instrument & sampling design, not weighted analysis. |
| Whether the sample size / frame is adequate | alterlab-ssci-sampling-gate | Sampling-adequacy gate, upstream. |
| Causal identification (DiD/IV/RDD) | alterlab-causal-inference | Design-based survey SEs ≠ causal identification. |
| Plain unweighted descriptive/inferential stats | alterlab-statistical-analysis | No survey design to honor. |
The design-object-first rule
Declare weights + strata + PSU/cluster + FPC before any estimate:
- weights — the inverse-inclusion-probability weight; scales the sample to the population.
- strata — variances are computed within each stratum and pooled. Dropping strata leaves
point estimates unchanged but inflates SEs (you lose the variance reduction).
- PSU / cluster — the unit of randomization. If whole districts were sampled, the district
is the PSU; lower units are not independent. Declaring the PSU is what corrects the SE upward
for the clustering.
