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"The half-open interval procedure seems to offer an inexpensive method of reducing undercoverage in housing unit frames during data collection. Interviewers check the areas near their selected cases, and, if they find any units missing from the frame, give them a chance of selection. However, the effectiveness of the method in the field has not been tested. This paper reviews how the procedure should work and presents evidence from two surveys about its performance in practice. We show experimentally that the procedure often fails to reduce undercoverage and can introduce overcoverage. The authors conclude with thoughts about the appropriate role for the half-open interval procedure in household surveys in the future." (author's abstract)

"Education researchers who study the effect of family social background on student achievement often use students' survey reports of parental education to investigate these effects. However, past research has demonstrated that students misreport their parents' education levels. The authors expand upon this research in two ways. First they use cognitive theories about the response process to develop and test hypotheses about reporting inconsistencies across these variables. Second they evaluate the impact of student misreporting on estimates of the relationship between parental education levels and student math achievement. Using data from the German administration of PISA 2000 (OECD Programme for International Student Assessment) in which both students and parents were asked to report parental variables, the authors show that reporting inconsistencies are a function of student achievement: students with higher math scores tend to provide reports that are more consistent with their parents' reports. This interesting case of differential measurement error has consequences for comparisons of the effects of parental background on student achievement across different subgroups of the population and across countries (a common use of PISA data and other international studies similar to PISA)." (author's abstract)

Wissenschaftliche Umfragen können keine aussagekräftigen Ergebnisse liefern, wenn ihre Datenqualität durch fehlende oder verfälschte Antworten beeinträchtigt wird. Eine Herausforderung der Sozialforschung besteht darin, solche Fehlerquellen zu erkennen und zu kontrollieren. Der Band präsentiert Erkenntnisse und Methoden zur Behandlung von Unit Nonresponse, Missing Data und verschiedene Arten von Messfehlern im Kontext von Web und Mixed-Mode Panel, Mobile Web und Faceto-Face-Befragungen.