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Probability sample (PS) surveys are considered the gold standard for population-based inference but face many challenges due to decreasing response rates, relatively small sample sizes, and increasing costs. In contrast, the use of nonprobability sample (NPS) surveys has increased significantly due to their convenience, large sample sizes, and relatively low costs, but they are susceptible to large selection biases and unknown selection mechanisms. Integrating both sample types in a way that exploits their strengths and overcomes their weaknesses is an ongoing area of methodological research. We build on previous work by proposing a method of supplementing PSs with NPSs to improve analytic inference for logistic regression coefficients and potentially reduce survey costs. Specifically, we use a Bayesian framework for inference. Inference relies on a probability survey with a small sample size, and through the prior structure we incorporate supplementary auxiliary information from a less-expensive (but potentially biased) NPS survey fielded in parallel. The performance of several strongly informative priors constructed from the NPS information is evaluated through a simulation study and real-data application. Overall, the proposed priors reduce the mean-squared error (MSE) of regression coefficients or, in the worst case, perform similarly to a weakly informative (baseline) prior that does not utilize any nonprobability information. Potential cost savings (of up to 68 percent) are evident compared to a probability-only sampling design with the same MSE for different informative priors under different sample sizes and cost scenarios. The algorithm, detailed results, and interactive cost analysis are provided through a Shiny web app as guidance for survey practitioners. [ABSTRACT FROM AUTHOR]

Survey researchers are often confronted with the question of how long to set the length of the field period. Longer fielding time might lead to greater participation yet requires survey managers to devote more of their time to data collection efforts. With the aim of facilitating the decision about the length of the field period, we investigated whether a longer fielding time reduces the risk of nonresponse bias to judge whether field periods can be ended earlier without endangering the performance of the survey. By using data from six waves of a probability-based mixed-mode (online and mail) panel of the German population, we analyzed whether the risk of nonresponse bias decreases over the field period by investigating how day-by-day coefficients of variation develop during the field period. We then determined the optimal cut-off points for each mode after which data collection can be terminated without increasing the risk of nonresponse bias and found that the optimal cut-off points differ by mode. Our study complements prior research by shifting the perspective in the investigation of the risk of nonresponse bias to panel data as well as to mixed-mode surveys, in particular. Our proposed method of using coefficients of variation to assess whether the risk of nonresponse bias decreases significantly with each additional day of fieldwork can aid survey practitioners in finding the optimal field period for their mixed-mode surveys. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Various open probability-based panel infrastructures have been established in recent years, allowing researchers to collect high-quality survey data. In this report, we describe the processes and deliverables of setting up the GESIS Panel, the first probability-based mixed-mode panel infrastructure in Germany open for data collection to the academic research community. The reference population for the GESIS Panel is the German-speaking population aged between 18 and 70 years permanently residing in Germany. In 2013, approximately 5,000 panelists had been recruited from a random sample drawn from municipal population registers. We describe the outcomes of the sampling strategy and the multistep recruitment process, involving computer-aided personal interviews conducted at respondents’ homes. Next, we describe the outcomes of the two self-administered survey modes (online and paper-and-pencil) of the GESIS Panel used for the initial profile survey and all subsequent bimonthly data collection waves. Across all stages of setting up the GESIS Panel, we report sample composition discrepancies for key demographic variables between the GESIS Panel and established benchmark surveys. Overall, the findings highlight the usefulness of pursuing a mixed-mode strategy when building a probability-based panel infrastructure in Germany. [ABSTRACT FROM AUTHOR]

In this article, we investigate changes in survey reporting due to prior interviewing. Two field experiments were implemented in a probability-based online panel in which the order of the questionnaires was switched. Although experimental methods for studying panel conditioning are favorable, experiments in longitudinal studies are rare. Studies on conditioning demand additional resources and might influence respondents’ answers. Panel conditioning is mostly associated with measurement errors. However, the discussion that sees it exclusively as a negative phenomenon is not comprehensive. Learning the rules of the interview may lead to increases or decreases in data quality (advantageous vs. disadvantageous conditioning). Overall, little evidence of advantageous conditioning and no disadvantageous conditioning is found. Apart from this reassuring finding, this aricle advances the field by using propensity score weighting to account for attrition and other confounding factors and by using paradata to evaluate the plausibility of alternative explanations of panel conditioning. [ABSTRACT FROM AUTHOR]