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The literature on the effects of incentives in survey research is vast and covers a diversity of survey modes. The mode of probability-based online panels, however, is still young and so is research into how to best recruit sample units into the panel. This paper sheds light on the effectiveness of a specific type of incentive in this context: a monetary incentive that is paid conditionally upon panel registration within two weeks of receiving the initial postal mail invitation. We tested early bird cash incentives in a large-scale recruitment experiment for the German Internet Panel (GIP) in 2018. We find that panel response rates are significantly higher when offering early bird cash incentives and that fieldwork progresses considerably faster, leading to fewer reminders and greater cost-effectiveness. Furthermore, sample representativeness is similarly high with or without early bird incentives. [ABSTRACT FROM AUTHOR]

Once recruited, probability-based online panels have proven to enable high-quality and high-frequency data collection. In ever faster-paced societies and, recently, in times of pandemic lockdowns, such online survey infrastructures are invaluable to social research. In absence of email sampling frames, one way of recruiting such a panel is via postal mail. However, few studies have examined how to best approach and then transition sample members from the initial postal mail contact to the online panel registration. To fill this gap, we implemented a large-scale experiment in the recruitment of the 2018 sample of the German Internet Panel (GIP) varying panel recruitment designs in four experimental conditions: online-only, concurrent mode, online-first, and paper-first. Our results show that the online-only design delivers higher online panel registration rates than the other recruitment designs. In addition, all experimental conditions led to similarly representative samples on key socio-demographic characteristics. [ABSTRACT FROM AUTHOR]

Research has shown that the non-Internet population is hesitant to respond to online survey requests. However, also subgroups in the Internet population with low digital affinity may hesitate to respond to online surveys. This latter issue has not yet received much attention by scholars despite its potentially detrimental effects on the external validity of online survey data. In this article, we explore the extent to which a person's digital affinity contributes to nonresponse bias in the German Internet Panel, a probability-based online panel of the general population. With a multidimensional classification of digital affinity, we predict response to the first online panel wave and participation across panel waves. We find that persons who belong to different classes of digital affinity have systematically different sociodemographic characteristics and show different voting behavior. In addition, we find that initial response propensities vary by classes of digital affinity, as do attrition patterns over time. Our results demonstrate the importance of digital affinity for the reduction in nonresponse bias during fieldwork and for postsurvey adjustments. [ABSTRACT FROM AUTHOR]

Because research on the impact of web survey incentives has exclusively focused on Western settings, it is unclear to what extent current insights translate and generalize to non-Western societies, which are usually characterized by very different economic conditions, cultural traditions, and survey climates. The current article presents the results of a web survey incentives experiment among almost 4,440 Ghanaian university students who were offered conditional and unconditional incentives of different values (in the form of telephone credit). Our analyses partly replicate Western findings: Higher value incentives produce higher participation rates and unconditional incentives outperform conditional ones in the lower value conditions. In the case of relatively high incentives, however, conditional outperforms unconditional incentives. No differential effects of incentives on response quality were found. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Inferential statistics teach us that we need a random probability sample to infer from a sample to the general population. In online survey research, however, volunteer access panels, in which respondents self-select themselves into the sample, dominate the landscape. Such panels are attractive due to their low costs. Nevertheless, recent years have seen increasing numbers of debates about the quality, in particular about errors in the representativeness and measurement, of such panels. In this article, we describe four probability-based online and mixed-mode panels for the general population, namely, the Longitudinal Internet Studies for the Social Sciences (LISS) Panel in the Netherlands, the German Internet Panel (GIP) and the GESIS Panel in Germany, and the Longitudinal Study by Internet for the Social Sciences (ELIPSS) Panel in France. We compare them in terms of sampling strategies, offline recruitment procedures, and panel characteristics. Our aim is to provide an overview to the scientific community of the availability of such data sources to demonstrate the potential strategies for recruiting and maintaining probability-based online panels to practitioners and to direct analysts of the comparative data collected across these panels to methodological differences that may affect comparative estimates. [ABSTRACT FROM AUTHOR]