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While the individual components of total survey error have been well documented in the literature, relatively little is known about the intersection of these error sources. In particular, there is scant empirical work on the interplay between nonresponse error and measurement error – despite the potentially significant implications for data quality as well as techniques used to recruit respondents. In this paper we investigate the connection between these two error sources using data from a survey of University of Maryland alumni. The availability of administrative records for seven items on the survey instrument (donations, membership in the alumni association, and multiple measures of academic performance) make this dataset particularly well-suited for this type of analysis. We evaluate several causal models related to the nonresponse / measurement error nexus. These models predict differential effects for particular subgroups of the population: recent versus older graduates and alumni who demonstrated low versus high academic achievement.