Consortium

Before any industry-led initiative on online data quality...
On January 11, 2007, in collaboration with Kraft Foods (now Kraft/Mondelez), Unilever, Estee Lauder and Revlon, Sigma Validation held a Consortium of thought-leaders in online research to develop an industry-wide approach for elevating respondent data quality.  These end clients looked to Sigma and their key partners to generate a system that would create a new level of transparency in online research data collection and panel management practices.

Through the Consortium, which included executive-level decision-makers from the following elite research companies, Sigma Validation’s Repeat Respondent® Search was borne:

ACNielsen BASES (Nielsen)
Added Value
ComScore
DMS Research (AOL)
e-Rewards (ResearchNow)
GfK NOP (GfK)

GMl
Greenfield Online (Toluna)
lpsos Interactive Services
Millward Brown
Lightspeed Research
Marketing Analysts, Inc.

New Directions, Inc.
Newman-Stein Inc.
PERT Survey Research
Survey Sampling, Inc.
Synovate (lpsos)
Your2Cents (Toluna)


A simple, no cost, beta test of the Repeat Respondent® Search conducted in July of 2007, provided an audit of previously-conducted online studies.  This audit revealed to end clients that their research standards were not being implemented as expected.  The findings helped to awaken these end clients, their partners and the industry, to a widespread need for ongoing monitoring and auditing of online research data collection and respondent quality.

Sigma Validation’s Repeat Respondent® Search is the first-ever technology-driven service that allows end clients and research companies to “peek under the hood” of online sampling and create transparency regarding sample management practices.  The process is simple and economical, yet powerful and effective.

Sigma Validation’s foundational program, the Duplicate Number® Search, was developed in 1978 in collaboration with General Foods (now Kraft/Mondelez). The Duplicate Number® Search was the first-ever computerized service for ensuring respondent-level data quality across in-person quantitative and qualitative research.

  


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