There is an ongoing debate in the survey research literature about whether and when probability and nonprobability sample surveys produce accurate estimates of a larger population. Statistical theory provides a justification for confidence in probability sampling as a function of the survey design, whereas inferences based on nonprobability sampling are entirely dependent on models for validity. This article reviews the current debate about probability and nonprobability sample surveys. We describe the conditions under which nonprobability sample surveys may provide accurate results in theory and discuss empirical evidence on which types of samples produce the highest accuracy in practice. From these theoretical and empirical considerations, we derive best-practice recommendations and outline paths for future research.

Survey records are increasingly being linked to administrative databases to enhance the survey data and increase research opportunities for data users. A necessary prerequisite to linking survey and administrative records is obtaining informed consent from respondents. Obtaining consent from all respondents is a difficult challenge and one that faces significant resistance. Consequently, data linkage consent rates vary widely from study-to-study. Several studies have found significant differences between consenters and non-consenters on socio-demographic variables, but no study has investigated the underlying mechanisms of consent from a theory-driven perspective. In this study, we describe and test several hypotheses related to respondents’ willingness to consent to an earnings and benefit data linkage request based on mechanisms related to financial uncertainty, privacy concerns, resistance towards the survey interview, level of attentiveness during the interview, the respondents’ preexisting relationship with the administrative data agency, and matching respondents and interviewers on observable characteristics. The results point to several implications for survey practice and suggestions for future research.

The past decade has seen a rise in the use of online panels for conducting survey research. However, the popularity of online panels, largely driven by relatively low implementation costs and high rates of Internet penetration, has been met with criticisms regarding their ability to accurately represent their intended target populations. This criticism largely stems from the fact that (1) non-Internet (or offline) households, despite their relatively small size, constitute a highly selective group unaccounted for in Internet panels, and (2) the preeminent use of nonprobability-based recruitment methods likely contributes a self-selection bias that further compromises the representativeness of online panels. In response to these criticisms, some online panel studies have taken steps to recruit probability-based samples of individuals and providing them with the means to participate online. Using data from one such study, the German Internet Panel, this article investigates the impact of including offline households in the sample on the representativeness of the panel. Consistent with studies in other countries, we find that the exclusion of offline households produces significant coverage biases in online panel surveys, and the inclusion of these households in the sample improves the representativeness of the survey despite their lower propensity to respond.