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Overview

DQC collects and maps metadata from survey transactions to ensure both internal and external consistency. By aligning your data signals with the DQC format, you can evaluate data quality not only between your teams and projects, but also across the broader sample industry allowing DQC's platform to provide crucial insights.

The survey transaction metadata that DQC uses to map dispositions can be broken into 4 types:

1. Survey and Audience Data​

This is data about the survey itself and about the survey's respondents. For example:

  • The supplier the respondent came from
  • The geographical location of the respondent
  • Length of interview
  • Incidence
  • Audience type

2. Technical Data​

This is data about the respondent's device and is essential for fraud detection and participant tracking. For example:

  • UUID -- a Unique Universal ID specific to the transaction
  • Machine ID (From DQC quality tool or from an outside quality tool) that identifies the respondent's survey taking equipment
  • Device Fraud scores
  • Entry & Exit timestamp for the respondent in survey

3. Quality Check Data​

This is data about an individual survey's quality checks. These checks are designed to test for attentiveness, duplication, and fraud. These quality signals can come from an outside provider, they can be automated checks within the survey, or they could be manual checks completed post-survey. For example:

  • Speeding checks to evaluate whether the respondent has spend the appropriate amount of time on the survey questions
  • Logical inconsistencies between survey questions for a respondent
  • Respondent duplication within the survey
  • Open-end question responses that are gibberish, manufactured by a machine, or not answering the question asked

More about mapping quality failures can be found on the Quality Check Mapping page here.

4. Participant Disposition Data​

This is the result of each participant engagement, the status or outcome of the survey.

  • Terminated, Overquota, Complete, Partial, etc...

Using this metadata, DQC maps your quality signals to a shared disposition map. It is with this map of consistent data, you can achieve industry-wide insights.

A more detailed view of the exact data that DQC accepts can be found here