Ejecuta cualquier Skill en Manus
con un clic

Ejecuta cualquier Skill en Manus con un clic

alterlab-survey-analysis

Estrellas46

Forks6

Actualizado2 de julio de 2026 a las 15:44

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.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Ejecutar en Manus

Fuente

AlterLab-IEU

AlterLab-IEU/AlterLab-Academic-Skills

Abrir repositorio de GitHub Ver repositorios del creador

Descarga

Ejecutar en Manus

Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Asistentes de investigación en ciencias socialesCiencias de la vida, físicas y sociales·SOC 19-4061

Explorador de archivos

4 archivos

SKILL.md

readonly

Más de este repositorio

mismo repositorio

alterlab-skill-finder

AlterLab-IEU/AlterLab-Academic-Skills

The AlterLab front door and multi-agent launcher — routes a task to the right AlterLab skill(s) when the user invokes the suite without naming one, and for a multi-stage goal (or on the keyword 'alterflow', aliases 'alterresearch' / 'ultralab') it CLARIFIES the goal with a few questions, SELECTS the skills the task needs, and runs a dynamic multi-agent workflow composing them (via alterlab-workflow-orchestration, alterlab-research-pipeline, or alterlab-ssci-orchestrator). Triggers on 'use AlterLab skills', 'which AlterLab skill for X', 'is there an AlterLab skill for…', a multi-stage research goal, 'alterflow …', or any generic AlterLab request where the user does not know skill names. It always asks clarifying questions before executing a multi-step run. Use when someone references AlterLab generically, describes a multi-stage goal, or fires the alterflow keyword; when the user already names a specific skill, defer to that skill directly. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-meta-analysis

AlterLab-IEU/AlterLab-Academic-Skills

Runs quantitative meta-analysis — computes effect sizes (Hedges' g / standardized mean difference, log odds/risk ratios) with their variances, pools them under fixed-effect and random-effects models, quantifies heterogeneity (I-squared, tau-squared, Cochran's Q), draws forest and funnel plots, and tests publication bias (Egger's regression, trim-and-fill) — using statsmodels.stats.meta_analysis in Python or the field-standard R metafor via Rscript. It enforces PRISMA reporting and the random- vs fixed-effect decision. Use when pooling effect sizes across studies, running a systematic review's quantitative synthesis, or assessing heterogeneity and publication bias. For finding and screening the literature prefer alterlab-deep-research; for a single study's statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-missing-data

AlterLab-IEU/AlterLab-Academic-Skills

Handles missing data with principled methods — forces an explicit MCAR / MAR / MNAR mechanism statement, then applies multiple imputation by chained equations (MICE) with Rubin's-rules pooling of estimates and standard errors, or full-information maximum likelihood (FIML) where a likelihood/SEM model applies. Uses statsmodels MICE / MICEData in Python or the field-standard R mice via Rscript, and warns that single (mean/regression) imputation and scikit-learn's IterativeImputer return one completed dataset without Rubin's-rules pooling, so they understate standard errors if used as multiple imputation. Use when a dataset has missing values, when choosing an imputation strategy, or when reporting how missingness was handled. For general modeling on complete data prefer alterlab-statistical-analysis; for latent-variable models with FIML prefer alterlab-sem-psychometrics. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-multilevel-models

AlterLab-IEU/AlterLab-Academic-Skills

Fits and reports mixed-effects / multilevel / hierarchical models for clustered, nested, longitudinal, and repeated-measures data — random intercepts and slopes, variance components and the ICC, cross-level interactions, and GLMMs (logistic/Poisson) — using statsmodels MixedLM and bambi (Bayesian on PyMC) in Python, or the field-standard R lme4 / glmmTMB / brms via Rscript. It enforces the reporting items reviews find under-reported: full fixed + random specification, centering, variance components + ICC, estimation method, assumption checks, model comparisons, and effect sizes. Use when data are grouped/nested (students in schools, repeated measures, panel/longitudinal) and the question concerns within- vs between-cluster variation. For general single-level regression prefer alterlab-statsmodels; for panel fixed effects used for causal identification prefer alterlab-causal-inference. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-qualitative-analysis

AlterLab-IEU/AlterLab-Academic-Skills

Analyzes qualitative data as a dispatched pipeline module — codebook development, thematic / framework / content analysis, and inter-coder reliability computed correctly (Krippendorff's alpha as primary via the krippendorff package or a bundled stdlib nominal calculator with bootstrap CIs; Cohen's / Fleiss' kappa via statsmodels) with 95% CIs and thresholds (alpha >= .80 reliable, .667-.80 tentative). It BRANCHES by design: coefficient-based ICR for codebook / content-analytic coding, versus consensus-and-reflexivity for reflexive thematic analysis where a statistic is not the right criterion. Supports human-vs-LLM double-coding with an alpha check against a human gold standard. Use when coding interviews or open-ended text, building a codebook, or reporting intercoder reliability. For topic modeling / embeddings / supervised text classification prefer alterlab-text-as-data; for the reflexivity gate prefer alterlab-ssci-reflexivity-gate. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-ssci-orchestrator

AlterLab-IEU/AlterLab-Academic-Skills

Coordinates a social-science study through the stage-gated methods pipeline — question then design-gate then measurement-gate then sampling-gate then the right analysis module then inference-gate then reporting — holding a single YAML Design Passport that each gate reads and appends, and enforcing gate order with PASS / WARN / BLOCK semantics (design-gate and inference-gate are fail-closed). It is thin: it routes and holds the artifact, it does not run analysis itself. Use when a researcher describes a whole social-science study end to end, asks to run the methods pipeline or a full methodology review, or wants the design-to-inference workflow coordinated rather than a single step. For a single stage trigger that gate directly (alterlab-ssci-design-gate, -measurement-gate, -sampling-gate, -inference-gate); for multi-agent execution mechanics see alterlab-workflow-orchestration. Part of the AlterLab Academic Skills suite.

2026-07-0246

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.
FPC — finite-population correction when the sampling fraction is non-trivial.

Domain (subpopulation) estimation: subset the design object, never filter the data frame first — filtering discards the strata/PSU structure needed for correct domain SEs.

Weight-type discipline

Distinguish design weights (selection probability only) from post-stratification / calibration weights (also correct for sampling error and non-response). One or the other must always be used; report which. "Weight before quoting any percentage."

Variance estimation — support both families

Taylor linearization — the default analytic method.
Replicate weights — BRR, Jackknife (JKn), Bootstrap. Use these when the data provider ships replicate weights (many public files do); do not re-derive a design they already replicated.

Verified calls (pinned)

Python — samplics (stable):

from samplics.estimation import TaylorEstimator
from samplics.utils.types import PopParam
est = TaylorEstimator(PopParam.mean)
est.estimate(y=df["trust"], samp_weight=df["wt"], stratum=df["strata"], psu=df["psu"],
             fpc=df.get("fpc", 1.0), domain=df.get("region"), deff=True, remove_nan=True)

svy (samplics' successor, import svy) mirrors this with svy.Design(...) / svy.Sample(...); its API is still maturing — pin svy>=0.18 and re-verify against the installed package.

R — survey / srvyr (field standard, fully verified):

library(survey)
des <- svydesign(ids = ~psu, strata = ~strata, weights = ~wt, fpc = ~fpc,
                 data = dat, nest = TRUE)
svymean(~trust, des, deff = TRUE)
svyby(~trust, ~region, des, svymean)                 # domain estimation (keeps structure)
svyglm(trust01 ~ age + educ, design = des, family = quasibinomial())
# replicate weights when provided:
rep <- svrepdesign(weights = ~wt, repweights = "wtrep[0-9]+", type = "JKn", data = dat)
# calibration:
des2 <- rake(des, sample.margins = list(~agecat, ~sex),
             population.margins = list(pop.agecat, pop.sex))

Full Taylor-vs-replicate math, calibration (post-stratification / raking / GREG), and the Python caveats vs the canonical R recipes: references/design_and_variance.md, references/python_vs_r.md.

Reporting checklist (put in every survey result)

DESIGN:     weights (type: design | post-strat/calibrated) + strata + PSU + FPC declared
VARIANCE:   Taylor linearization | replicate weights (BRR/JKn/Bootstrap)
N:          unweighted N  vs  weighted population estimate
DEFF:       design effect per key estimate (how much the design inflates variance)
DOMAINS:    subset of the DESIGN object, not a filtered data frame

References

references/design_and_variance.md — Taylor vs replicate variance, calibration math, DEFF, domain estimation.
references/python_vs_r.md — samplics/svy caveats and the canonical R survey/srvyr recipes.

Part of the AlterLab Academic Skills suite.