Impact of Data Collection Techniques on Data Quality: Rep Data, Research Defender and DM2 Release New Study

Rep Data, Research Defender and DM2 have released a white paper covering a new study into the impact of various data collection techniques on market research data quality. The research-on-research assesses the efficacy of applying different screening and data quality techniques in a survey setting, measuring the impact across a number of established quality metrics.

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“We found that combining a service-oriented, consultative approach to data collection, with a focus on consistent execution, and specialized tools to reduce fraud, duplication and other negative respondent attributes, we were able to show a statistically significant increase in data quality,” said Patrick Stokes, founder and CEO of Rep Data. “This white paper covers the research in detail, illustrating the positive impact that layering these approaches has on outcomes.”

The white paper, called “Data collection techniques for quality outcomes,” is based on a survey conducted in early Q2 2021 among n=2,002. Rep Data sourced equal sample from four of the market research industry’s larger online sample providers. Completes were evenly distributed across five cells, with providers delivering n=100 to each cell with consistent age and gender quotas. This provided a basis for data comparison among five overall cells using various quality assurance techniques including Research Defender’s proprietary digital fingerprinting, fraud identification, text analytics and respondent-level tracking.

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The data illustrated that:

• Layering fraud mitigation techniques positively impacts outcomes by creating a clean, healthy and efficient market research ecosystem;
• Unbiased, efficient sourcing from multiple panels and sample suppliers delivers more representative results; and
• Using expert project management for fieldwork eliminates common challenges in the data collection process.

Based on the study findings, the paper includes top considerations for primary researchers who wish to take an intelligent, quality-oriented approach to data collection and fieldwork.