Part 4: 4 Components of a Great Project Data Quality Approach

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Please review part 3 here.

In our last post, we shared common myths that we need to bust out of MR around Fraud approaches.  Today, we share OpinionRoute’s framework around data quality.  

We see the process as four defined areas: Pre-survey, in-survey, post-survey and lastly the feedback loop.

1. Pre-Survey Protection: 

We focus heavily on the device, but also seek to understand any context about that participant’s previous behavior.  

2. In Survey Evaluation: 

We recommend evaluating open ends in real-time with software that is looking for fraud and overall quality clues. From a design perspective, having open-ends distributed throughout the survey as well as inserting any type of logic-based questions (addition to 100, chipping exercises, etc.) dramatically reduces bot traffic.  

3. Post-field Cleaning: 

Reviewing the open-end data in batches also allows for a deeper analysis of the total dataset and identification of duplicates. This part can complement manual cleaning while also saving many hours on that process.

4. Feedback loop: 

This refers to analyzing the vendor and project performance as part of all research you conduct.  So often vendor choices are made by project factors or qualitative decision making overall.  This macro perspective allows for strategic decisions to be made regarding the sample. We use it to create an annual Sample “Demand Plan” that we review regularly based on quality.In our next post, we’ll share some ideas on how key industry stakeholders can help with the follow-through in modern fraud identification and mitigation.  

Please click here for Part 5/5: How the Industry can Collaborate

If you would like help with your survey data quality with the OpinionRoute ID Suite initiative, reach out here.

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