Survey Question Bias Detector

This template helps ChatGPT or Claude act as a meticulous survey design reviewer that flags bias, ambiguity, double-barreled wording, and scale problems before you publish. It is especially useful when you already have draft questions and need a fast quality check that protects data integrity. The prompt asks the model to explain why each issue matters, which makes revisions easier for non-researchers.

It is built for researchers, growth teams, and operations leads who need cleaner responses from internal or external audiences. The output separates high-risk issues from minor wording improvements, so you can prioritize edits quickly. It also recommends alternative phrasing and scale adjustments where needed. For teams collecting customer feedback, employee sentiment, or market data, a strong bias check can prevent misleading conclusions and wasted analysis time.

Customize the prompt by pasting your draft survey, target audience, and response scale. You can also specify whether the survey is anonymous, mobile-first, or intended for a technical audience. The model will return a review table with issue type, explanation, and rewrite suggestion. If you want more rigor, ask it to evaluate each item against a specific research objective. This makes the template a practical safeguard for any questionnaire audit workflow.