KnowledgePanel® Calibration is a statistical technique to improve accuracy in non-probability survey research results. GfK developed this solution in recognition of the market need for addressing bias in non-probability sample data leveraging KnowledgePanel.
What is it? Blend KnowledgePanel sample with non-probability panel samples with calibration weights using early adopter and other questions.
What research need does it address? Confidence and re-assurance that major errors in opt-in panel samples are corrected
What type of studies? Typically, sample studies for which we do not have enough KnowledgePanel sample yet the need to most accurately measure change, brand health, incidence/target size and innovation opportunity is key.
Value? Higher accuracy than opt-in alone.
With KnowledgePanel Calibration, we field the same screening and main questionnaire among KnowledgePanel respondents as well as a companion sample of respondents from an opt-in web panels.
KnowledgePanel interviews provide the statistical information needed to calibrate the interviews from the non-probability sample source, correcting for sampling error in the non-probability web panels – such as exclusion of non-internet households and over-representation of hyper internet users and of early adopters of new products and services, to name just a few.
While the calibration approach cannot correct for all the error present in the opt-in panel interviews, the calibration will improve accuracy of study findings and insights, giving researchers more confidence in the data investments they have made.