Abstract: A model-based method for reducing selection bias in Internet samples utilizes a series of sample weighting procedures that adjust the distribution of key drivers of ad noting and recognition in Internet samples to mirror the distribution of the drivers found in a full-probability sample. In the first phase of the method, a relatively large number of Starch studies are utilized to explore and understand key drivers of ad noting and recognition using multivariate regression analysis. The second phase compares the distribution of the key drivers found in Internet samples with the distribution of those drivers obtained in a full-probability sample to develop the weighting adjustment. In the third phase, the impact of the weighting adjustment is evaluated using a mean squared error model.