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In this case study, we examine a novel aspect of data collected in a typical probability and a typical nonprobability panel: mobile app data. The data were collected in Great Britain in 2018, using the Innovation Panel of the UK Household Longitudinal Study and the Lightspeed online access panel. Respondents in each panel were invited to participate in a month-long study, reporting all their daily expenditures in the app. In line with most of the research on nonprobability and probability-based panel data, our results indicate differences in the data gathered from these data sources. For example, more female, middle-aged, and highly educated people with higher digital skills and a greater interest in their finances participated in the nonprobability app study. Our findings also show that resulting differences in the app spending data are difficult to eliminate by weighting. The only data quality aspect for which we do not find evidence of differences between the nonprobability and probability-based panel is behavior in using the spending app. This finding is contrary to the argument that nonprobability online panel participants try to maximize their monetary incentive at the expense of data quality. However, this finding is in line with some of the scarce existing literature on response behavior in surveys, which is inconclusive regarding the question of whether nonprobability online panel participants answer questions less conscientiously than probability-based panel respondents. Since the two panels in our case study differ in more aspects than the sample selection procedure, more research in different contexts is necessary to establish generalizability and causality. [ABSTRACT FROM AUTHOR]

Despite its importance in terms of survey participation, the literature is sparse on how face-to-face interviewers differentially affect specific groups of sample units. This paper demonstrates how an alternative parametrization of the random components in multilevel models, so-called separate coding, delivers valuable insights into differential interviewer effects for specific groups of sample members. In the example of a face-to-face recruitment interview for a probability-based online panel, we detect small interviewer effects regarding survey participation for non-Internet households, whereas we find sizable interviewer effects for Internet households. We derive practical guidance for survey practitioners to address differential interviewer effects based on the proposed variance decomposition. [ABSTRACT FROM AUTHOR]

As ever more surveys are conducted, recruited respondents are more likely to already have previous survey experience. Furthermore, it has become more difficult to convince individuals to participate in surveys, and thus, incentives are increasingly used. Both previous survey experience and participation in surveys due to incentives have been discussed in terms of their links with response quality. This study aims to assess whether previous web survey experience and survey participation due to incentives are linked with three indicators of response quality: item non‐response, primacy effect and non‐differentiation. Analysing data of the probability‐based CROss‐National Online Survey panel covering Estonia, Slovenia and Great Britain, we found that previous web survey experience is not associated with item non‐response and the occurrence of a primacy effect but is associated with non‐differentiation. Participating due to the incentive is not associated with any of the three response quality indicators assessed. Hence, overall, we find little evidence that response quality is linked with either previous web survey experience or participating due to the incentive. [ABSTRACT FROM AUTHOR]

Driven by innovations in the digital space, surveys have started to move towards online data collection across the world. However, evidence is needed to demonstrate that online data collection strategy will produce reliable data which could be confidently used to inform policy decisions. This issue is even more pertinent in cross‐national surveys, where the comparability of data is of the utmost importance. Due to differences in internet coverage and willingness to participate in online surveys across Europe, there is a risk that any strategy to move existing surveys online will introduce differential coverage and nonresponse bias. This paper explores representativeness across waves in the first cross‐national online probability‐based panel (CRONOS) by employing R‐indicators that summarize the representativeness of the data across a range of variables. The analysis allows comparison of the results over time and across three countries (Estonia, Great Britain and Slovenia). The results suggest that there are differences in representativeness over time in each country and across countries. Those with lower levels of education and those who are in the oldest age category contribute more to the lack of representativeness in the three countries. However, the representativeness of CRONOS panel does not become worse when compared to the regular face‐to‐face interviewing conducted in the European Social Survey (ESS). [ABSTRACT FROM AUTHOR]

The outbreak of COVID‐19 has sparked a sudden demand for fast, frequent and accurate data on the societal impact of the pandemic. This demand has highlighted a divide in survey data collection: Most probability‐based social surveys, which can deliver the necessary data quality to allow valid inference to the general population, are slow, infrequent and ill‐equipped to survey people during a lockdown. Most non‐probability online surveys, which can deliver large amounts of data fast, frequently and without interviewer contact, however, cannot provide the data quality needed for population inference. Well aware of this chasm in the data landscape, at the onset of the pandemic, we set up the Mannheim Corona Study (MCS), a rotating panel survey with daily data collection on the basis of the long‐standing probability‐based online panel infrastructure of the German Internet Panel (GIP). The MCS has provided academics and political decision makers with key information to understand the social and economic developments during the early phase of the pandemic. This paper describes the panel adaptation process, demonstrates the power of the MCS data on its own and when linked to other data sources, and evaluates the data quality achieved by the MCS fast‐response methodology. [ABSTRACT FROM AUTHOR]