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Survey Methodologist — Questionnaire Design, Review & Sampling
Turns your agent into a rigorous survey methodologist that designs, reviews, and fixes questionnaires — question wording, response scales, sampling, and data quality.
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See it in action
You say
Review this survey question: "How satisfied are you with our service and pricing on a scale of 1-5 (1 = Extremely Dissatisfied, 5 = Extremely Satisfied)?"
Your agent does
Three fixes. (1) Double-barreled — "service" and "pricing" are two things; split them into two questions, since a respondent who feels differently about each can't answer cleanly and you won't know which drove the score. (2) Replace the "Extremely" anchors with "Very" — people avoid labeling themselves at an extreme, which compresses your variance. (3) Start the scale at the absence point for this unipolar construct: Very dissatisfied (0) → Dissatisfied (1) → Neither (2) → Satisfied (3) → Very satisfied (4), with "Prefer not to say" (-99) offset below. Result: two clean, single-idea items on a consistent scale.
What you get
About this skill
Most survey problems are baked in before the first response arrives: a double-barreled question, a scale that leads, a check-all list that quietly under-counts, a sample that can't support the claim you want to make. This skill gives your agent a working methodologist's judgment for catching those problems and fixing them.
It's organized around Total Survey Error, so every recommendation traces back to whether it changes what you measured or who you reached. The core covers question wording, response-option design, scale selection, survey flow, and a pre-delivery QA checklist. Six reference files go deeper — question and scale design, the psychometrics of response options, reliability and validity, sampling and weighting, survey experiments, and a source-verified reading list — and the agent loads only what a task needs. Use it to design a new instrument, review a draft someone hands you, tighten weak items, choose an established scale, or size and plan a sample. It stays honest about what the evidence supports: conventions are labeled as conventions, cutoffs as guidance, and correlational data never gets causal language.
Written for researchers, UX and market researchers, product and policy teams, nonprofits, and graduate students — anyone who fields a survey and needs the results to hold up.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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