Recruitment strategies for a probability-based online panel : effects of interview length, question sensitivity, incentives and interviewers

Probability-based online panels represent a comparably new and emerging form of data collection infrastructures. To date, there is little empirical evidence on online panel recruitment. This dissertation aims to fill the gap and contribute experimental evidence. The overall objective is to identify ways to optimize the telephone recruitment process of a probability-based online panel in Germany and derive practical recommendations. Referring to the Total Survey Error perspective (Groves & Lyberg, 2010) optimal is defined in the sense of maximizing the recruitment probability and online participation probability and minimizing the selection bias under given budget constraints. Based on the framework of survey participation (Groves & Couper, 1998), the four studies of this dissertation focus on several aspects of the recruitment process that researchers can decide upon and have control about. In three survey experiments, the effect of varying survey features on the success of the recruitment process is analyzed. The experimental factors are the length of the recruitment interview, the inclusion of a sensitive question, and incentives. In an additional analysis, the role of interviewers as an additional error source during the recruitment process is examined.