Conferences

Survey methodologists are increasingly concerned with the interaction of multiple error sources. Particularly prominent are discussions about nonresponse and measurement error. One hypothesis that is often found among practitioners is that sample cases that are brought into the survey only after repeated attempts and alternated recruitment strategies, are more likely to provide low quality data (e.g. Groves and Couper 1998). Data quality is often internally assessed through the proportion of missing items, proportion of don’t knows and the like (e.g. Fricker 2007). Rarely, in these studies, are external data available to evaluate the quality of respondents’ answers (e.g. Cannell & Fowler 1963, Olsen 2006).

The panel study PASS (Trappmann et al. 2009) is a novel dataset in the field of labor market, welfare state and poverty research in Germany. With almost 19,000 interviewed persons (in CATI or CAPI-mode) in more than 12,500 households, PASS is currently one of the most comprising panel surveys in Germany. In PASS, survey data on the employment and unemployment history, income and education of participants can be linked to corresponding data from respondents’ administrative records.

Based on this study, we give an assessment of data quality as a function of contactability and response propensity as estimated from the survey’s paradata. Only for some variables, the measurement error (variance or bias) assessed through the administrative records is increased with decreasing contactability and response propensity of the target persons. In particular, this is found in case of retrospective questions. Here, the differing length of time between date of interview and event explains a large part of the difference in measurement error between respondents with high vs. low response propensity.