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Understanding our Automated Qualitative Data Cleaning

Overview

The Automated Qualitative Data Cleaning process is a proprietary system developed by Potloc to streamline the assessment of open-ended responses and ensure the highest data quality. The automation applies a series of sophisticated algorithms to evaluate each respondent’s answers and determine if their responses meet the quality criteria.

How It Works

This automated solution performs several data quality checks on respondents’ open-ended answers, including:

Each check is designed to identify patterns of low-quality responses, such as repeated phrases, nonsensical answers, or responses that do not align with the question context. If a respondent fails one or more of these checks, they lose quality points from their overall score.

Quality Scoring Mechanism

Determining whether an open-ended response is of good quality can be subjective. To address this, Potloc’s automated cleaning assigns a quality score to each respondent. Here’s how it works:

  1. Scoring & Points Deduction: Each quality check is assigned a weight based on the severity of the issue. When a respondent fails a check, points are deducted from their initial score.
  2. Threshold Evaluation: If a respondent’s quality score falls below a predetermined threshold—set as the minimum acceptable quality—the respondent is flagged and excluded from the final dataset. This threshold is determined based on extensive testing and represents the point at which a response is considered undoubtedly poor quality.

Benefits of Automated Cleaning

  • Efficiency: Automation reduces the time spent on manual data reviews, ensuring results are delivered faster.
  • Consistency: The automated nature of the checks means that all responses are treated equally, eliminating human biases.
  • Improved Data Quality: With multiple checks in place, the final dataset is thoroughly vetted to ensure high-quality insights.

Final Review by Experts

It’s important to emphasize that our internal team of experts conducts a thorough review of each project following the implementation of our data cleaning automations. This meticulous process ensures that no detail is overlooked, allowing us to deliver only the cleanest and highest-quality sample to our end clients.