Overview
The Data Cleaning Report is designed to give you a complete overview of the data cleaning process applied to your survey, ensuring the quality of your final dataset. With this report, you can explore the cleaning methodology in detail, track key metrics, and understand how Potloc maintains data integrity at every step.
The report is divided into three key sections:
- Key Metrics
- Data Cleaning Performance
-
Data Quality Checks
Key Metrics: Survey Overview
This section provides a snapshot of the core survey metrics, helping you understand respondent behaviour and how respondents were cleaned throughout the survey to reach your final sample. Key figures include:
- Survey Attempts: Total number of people who tried to take the survey.
- Partial Respondents: Participants who did not complete the survey.
- Qualification Terminations: Respondents who did not meet the qualifying criteria.
- Quality Terminations: Respondents removed due to low-quality responses or behavior.
- Overquota Respondents: Extra respondents or those collected beyond the contractual quota.
- Sample Respondents: The respondents that make up your final, cleaned sample.
Data Cleaning Performance: Stage Breakdown
This part of the report outlines data cleaning performance across the three stages, showcasing how quality is upheld at every level:
- Pre-Survey Validation: Prevents bots, fraudsters, and duplicate respondents from entering your survey.
- In-Survey Validation: Removes ineligible and dishonest participants, while flagging potential low quality respondents.
- Post-Survey Validation: Checks the accuracy of open-ends and confirms bad quality respondents.
Data Quality Checks: Detailed Breakdown
This section details all of the quality checks applied at each stage of the cleaning process. It’s the best place to see the impact of each check and how it contributes to refining your final dataset. Each check is listed alongside its purpose, ensuring complete transparency.
At Potloc we employ 15 data quality checks across our data cleaning process to detect, filter and terminate bad quality respondents.