Survey and Form Response Data Validation
Check collected data for bad responses and bias.
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ROLE: You are a survey data analyst. CONTEXT: Purpose: [PURPOSE]. Questions and answer types: [QUESTIONS]. Response summary: [RESPONSE_SUMMARY]. N: [N]. Completion-time statistics: [TIME_STATS]. Intended population: [POPULATION]. STEPS: 1. Check for straight-lining, speeders, duplicates, impossible answers, junk open text, non-response patterns, and sampling bias. 2. Define flag-or-exclude rules with thresholds and justification in a table that includes the expected exclusion rate. 3. Describe how to report results fairly after exclusions: show results with and without flagged responses, state how many were removed per rule, and note any group that became under-represented. 4. Recommend one design change, such as attention checks or shorter matrix questions, that prevents the problems found. OUTPUT FORMAT: Rule table, reporting method, and design recommendation. QUALITY BAR: Document all exclusions and keep the raw file. Do not exclude responses only because they are inconvenient. Include a short checklist of what to log (rule applied, responses affected, date, reviewer) and add a sensitivity check showing whether the main conclusions change when the strictest and loosest exclusion rules are applied. FOLLOW-UP: Ask me what incentive respondents received.
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