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.

一旦被招募，基于概率的在线小组已被证明可以实现高质量和高频率的数据收集。在日新月异的社会中，以及最近在大流行时期，这种在线调查基础设施对于社会研究而言是无价的。在没有电子邮件采样框架的情况下，招募这样的专家组的一种方法是通过邮政。但是，很少有研究研究如何最好地采用方法，然后将样本成员从最初的邮件联系方式转换为在线专家组注册。为了弥补这一空白，我们在2018年德国互联网小组（GIP）样本的招募中实施了一项大规模实验，该小组在以下四个实验条件下采用了不同的小组招募设计：仅在线，并发模式，在线优先和纸质第一的。我们的结果表明，仅在线设计比其他招聘设计提供了更高的在线面板注册率。此外，所有实验条件都导致在关键的社会人口统计学特征上具有相似的代表性样本。