The estimation of nonresponse bias and measurement error shares the problem of usually not having a criterion to assess the quality of the estimate. Nonresponse bias analysis could use responders within the survey sample who are in some way similar to nonresponders to estimate the potential bias. Measurement error studies have similar problems with estimating systematic error (e.g. re-interviews) to provide a measure of the quality of response. Statistical matching uses responders or aggregate data from other sources to model the difference in estimates between sources. The matching is done on respondent characteristics common to both sources. In this study I will use several different sources to attempt to triangulate the differences.