Rep Data’s In-Survey Data Cleaning, powered by ReDem, evaluates survey responses during fieldwork to identify potentially fraudulent, inattentive or low-quality data. Responses are assessed across multiple quality dimensions and combined into a Quality Score for each respondent.

Key Capabilities

• Open-Ended Analysis: Evaluate open-ended responses for relevance, effort, duplication, nonsensical text and potential AI-generated content.
• Coherence Checks: Identify potential contradictions across a respondent’s answers and highlight specific inconsistencies.
• Behavioural Analysis: Assess typing behaviour, response patterns and other indicators of potentially artificial or non-engaged activity.
• Timing Analysis: Compare response times at question level to identify respondents who consistently complete questions unusually quickly.
• Grid Pattern Detection: Identify straightlining, repetitive and other mechanical response patterns in grid and matrix questions.
• Quality Scoring: Combine different quality measures into a single score for each respondent, with detailed information available to support review.

How It Works

1. Integrate through an API with a compatible survey platform, or upload completed survey data for analysis.
2. Responses are assessed across multiple quality dimensions as data is collected.
3. Use Quality Scores and detailed indicators to flag or remove potentially low-quality responses during or after fieldwork.

Use Cases

• Online Surveys — monitor response quality throughout fieldwork.
• Open-Ended Research — assess the quality and authenticity of written responses.
• Large-Scale Studies — automate quality checks across high volumes of responses.
• Data Quality Management — identify potentially inattentive, inconsistent or low-quality respondents.
• Multi-Source Research — apply consistent quality checks across different sample sources.
• Post-Survey Review — assess completed data and identify responses requiring further review.

In-Survey Data Cleaning can be combined with Rep Data’s Pre-Survey Fraud Prevention to apply quality controls before and during the survey.