We find rotation group bias in reports of spending in the U.S. Consumer Expenditure Survey. Contrary to our expectations, the more waves respondents complete, the higher the quality of their responses. Respondents become more likely to report the amount of money spent on purchases and less likely to report rounded amounts. There is no change over waves in the number of purchases reported or the average amount of money spent on the purchases.

Several studies have shown that high response rates are not associated with low bias in survey data. This paper shows that, for face-to-face surveys, the relationship between response rates and bias is moderated by the type of sampling method used. Using data from Rounds 1 through 7 of the European Social Survey, we develop two measures of selection bias, then build models to explore how sampling method, response rate, and their interaction affect selection bias. When interviewers are involved in selecting the sample of households or respondents for the survey, high reported response rates can in fact be a sign of poor data quality. We speculate that the positive association detected between response rates and selection bias is because of interviewers’ incentives to select households and respondents who are likely to complete the survey.

Panel survey participation can bring about unintended changes in respondents’ behaviour and/or their reporting of behaviour. Using administrative data linked to a large panel survey, we analyse whether the survey brings about changes in respondents’ labour market behaviour. We estimate the causal effect of panel participation on the take‐up of federal labour market programmes by using instrumental variables. Results show that panel survey participation leads to an increase in respondents’ take‐up of these measures. These results suggest that panel survey participation not only affects the reporting of behaviour, as previous studies have demonstrated, but can also alter respondents’ actual behaviour.

Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect. We introduce the “generalized multitrait-multimethod” (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and administrative data to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-administrative data from the German Federal Employment Agency on income from of employment, and a simulation study evaluates the estimates obtained and their robustness to model misspecification. Supplementary materials for this article are available online.