How does this skill help with survey bias?

It offers specific wording strategies to reduce acquiescence bias, identifies double-barreled questions, and provides frameworks to mitigate social desirability bias.

Can I use this for experimental surveys?

Yes, it includes detailed instructions for treatment placement, outcome separation, and randomization to ensure internal and external validity.

Does it support scale construction?

Yes, it covers best practices for 5- to 7-point scales, labeled vs. endpoint-only options, and internal consistency assessment for multi-item indices.

What is the Survey Instrument Designer skill?

It is an agentic skill for Claude Code that provides methodological guidance for designing survey instruments based on rigorous social science standards.

Survey Instrument Designer

Name: Survey Instrument Designer
Author: scdenney

byscdenney

•

データサイエンスとML

Guides the creation of high-quality survey instruments for experimental social science research using evidence-based methodological standards.

The Survey Instrument Designer skill provides comprehensive expertise for researchers and data scientists designing survey instruments for social science experiments. It codifies best practices from methodological texts to help users craft unbiased question wording, optimize response scales, manage survey flow, and implement rigorous treatment-outcome separation. By following these evidence-based standards, the skill helps minimize measurement error, mitigate social desirability bias, and ensure that survey data is robust enough for high-stakes academic and professional research applications.

主な機能

01Frameworks for pretesting, cognitive interviewing, and multi-round pilot studies

0215 GitHub stars

03Optimized survey flow management including treatment placement and block randomization

04Advanced question construction strategies to reduce bias and improve respondent processing

05Evidence-based scale design protocols for optimal measurement precision and reliability

06Respondent burden management through attention checks and speeding detection rules

ユースケース

01Planning a pre-analysis plan for a population-based survey experiment

02Refining existing questionnaires to eliminate double-barreled or leading questions

03Designing a new survey experiment to measure complex political or social attitudes

主な機能

01Frameworks for pretesting, cognitive interviewing, and multi-round pilot studies

0215 GitHub stars

03Optimized survey flow management including treatment placement and block randomization

04Advanced question construction strategies to reduce bias and improve respondent processing

05Evidence-based scale design protocols for optimal measurement precision and reliability

06Respondent burden management through attention checks and speeding detection rules

ユースケース

01Planning a pre-analysis plan for a population-based survey experiment

02Refining existing questionnaires to eliminate double-barreled or leading questions

03Designing a new survey experiment to measure complex political or social attitudes