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Abstract
More survey results are available today than ever before. This increase in survey data has been accompanied by growing concerns about their quality. With the present study, we aim to investigate to what extent the public draws on survey quality information when evaluating the trustworthiness of survey results. We implemented a vignette experiment in an online panel survey (N = 3,313), in which respondents each received four different survey descriptions with varying methodological information. Compared with respondent characteristics, survey quality information had only a minor effect on perceptions of trustworthiness. However, trust in the survey results was significantly influenced by sample size and sample balance. Finally, the relevance of survey quality information increased with the cognitive ability of the respondent.

Background: COVID-19 vaccine hesistancy is a serious policy issue in Germany as vaccinations have stagnated at low levels compared to most other European countries. In this context, we study whether and how health workers can be leveraged to promote the COVID-19 vaccination campaign. Methods: We employed an information experiment with health workers in Germany to quantify how access to information related to (i) AstraZeneca's vaccine safety, (ii) misinformation, (iii) individual health risks, and (iv) public health risks can sway health workers' recommendations for any of the following vaccines: AstraZeneca, Johnson & Johnson, Moderna, Pfizer/BioNTech, Sinopharm, and Sputnik-V. The information experiment was conducted as a randomized controlled trial with four treatment arms and was embedded in an online survey. Results: Health workers reduce their willingness to recommend four out of six vaccines once they learn about different statements of European and German health authorities with respect to the safety of the AstraZeneca vaccine. Consistent with the discussion on AstraZeneca's safety focusing on possible side effects among younger women, we find that especially female health workers become less likely to recommend the majority of COVID-19 vaccines. Lastly, we show that health workers vaccine recommendations are not affected by misinformation and appeals to individual or public health. Conclusion: In order to mobilize health workers in the fight against vaccine hesitancy, information campaigns need to be tailor-made for the target audience. In particular, health workers react to different types of information than the general public. As with the general public, we provide suggestive evidence that health workers require unambigious messages from drug authorities in order to support vaccination efforts. We believe that a more coordinated and coherent approach of public authorities can reduce the amount of mixed signals that health workers receive and therefore contribute to health workers engagement in the outroll of mass COVID-19 vaccination campaigns.

Transparency and reproducibility are key elements of good science, and this also holds for the process
of data collection in scientific surveys. To conduct analyses based on survey data collected by others,
researchers heavily depend on accurate documentation of all stages in the data collection process, either
for generating new scientific evidence or for reviewing previous research findings (e.g., in replication
studies). In this contribution, we propose documentation guidelines for mail surveys. In doing this,
we not only focus on mail-only surveys but also cover documentation guidelines for self-administered
mixed-mode surveys, thus taking into account their growing importance in the survey landscape.

Survey data are still the most commonly used type of data in the quantitative social sciences. However, as not everything that is of interest to social scientists can be measured via surveys, and the self-report data they provide have certain limitations, such as recollection or social desirability bias, researchers have increasingly used other types of data that are not specifically created for research. These data are often called "found data" or "non-designed data" and encompass a variety of different data types. Naturally, these data have their own sets of limitations. One way of combining the unique strengths of survey data and these other data types and dealing with some of their respective limitations is to link them. This guideline first describes why linking survey data with other types of data can be useful for researchers. After that, it focuses on the linking of survey data with three types of data that are becoming increasingly popular in the social sciences: geospatial data, social media data, and sensor data. Following a discussion of the advantages and challenges associated with linking survey data with these types of data, the guideline concludes by comparing their similarities, presenting some general recommendations regarding linking surveys with other types of (found/non-designed) data, and providing an outlook on current developments in survey research with regard to data linking.

Mixing survey modes for data collection can have positive effects on response rates, sample balance, and survey costs. However, data collected in multiple modes may also suffer from mode measurement effects. In this Survey Guideline, we give an overview of empirical evidence related to the benefits and drawbacks of using multiple modes for data collection and outline some recommendations for the im­plementation of mixed-mode surveys. Finally, we provide a brief outlook on the perspectives of mixed­mode surveys in the survey landscape.

For many years, web surveys have already been the most frequently used survey mode in Germany and elsewhere (ADM, 2018; ESOMAR, 2018). Moreover, respondents increasingly use mobile devices, especially smartphones (or less often tablets), to access the Internet and participate in surveys. Because of those new developments within the Internet usage landscape, this contribution expands an earlier Survey Guideline on web surveys (Bandilla, 2015) by addressing methodological advantages and disadvantages of mixed-device as well as mobile web surveys. Moreover, it provides best practice advice on the implementation of such surveys in the areas of sampling, questionnaire design, paradata collection, and software solutions.

In Germany, the population registers with addresses of individuals can be used for address-based sampling. However, unlike countries with a centralized register, municipalities in Germany administer their registers themselves. This not only makes sampling for a nation­wide survey more costly and cumbersome but may also result in gaps in the gross sample, as selected municipalities may refuse to allow their registers to be used for sampling pur­poses. If substitute municipalities are not available, other sampling methods are required. The present study tested the feasibility of using the address database from Deutsche Post Direkt (ADB-DPD) as an alternative frame for address-based sampling in Germany. We simultaneously conducted two almost identical surveys in the German city of Mannheim with gross samples of equal size (N = 3,000). One sample was drawn from the city's popu­lation register, the other from the commercial ADB-DPD. Our findings suggest that the ADB-DPD performs well both in terms of survey response and up-to-dateness. Due to relatively low costs and the fast provision of addresses, the ADB-DPD could be particu­larly attractive for survey projects with limited budgets and tight schedules. However, these benefits come at considerable cost. First, the use of the ADB-DPD is limited to self-admin­istered surveys. More importantly, in the net sample of the DPD survey, women and young persons were considerably underrepresented. This indicates coverage issues about which DPD provided no further information. Based on our analyses, we offer practical insights into the feasibility of using the ADB-DPD for sampling purposes and suggest avenues for future research.

Self-administered mixed-mode surveys are increasingly used as an alternative to face-to-face surveys for collecting data from the general population. However, little is known about how decisions regarding the incentive scheme and the mode-choice design jointly affect key outcomes such as response rates, net sample composition, and survey costs. To study this, we drew a probability sample of the residential population of the city of Mannheim, Germany (N = 2,980) and randomly assigned target persons to one of four incentive schemes (€0, €1, or €2 prepaid incentive on first contact, and €2 delayed prepaid incentive) and one of two mode-choice designs (concurrent or sequential [web-push]). Our results indicate that small prepaid monetary incentives work better in concurrent than in sequential designs. Moreover, a €2 prepaid incentive in a concurrent design proved particularly successful for older target persons, probably reinforcing their sense of trust and reciprocity, while also fitting better with their survey-mode preferences. Finally, a €2 delayed prepaid incentive in a sequential design primarily motivated target persons aged under 50 years. This combination of incentive scheme and mode-choice design also proved to be most cost-effective in that age group. Based on our results, we recommend using sampling frame information on age to address different age groups with different combinations of incentive scheme and mode-choice design. This may help to maximize response rates, achieve a balanced net sample composition, and minimize survey costs.

This survey guideline addresses the practical question of how best to inform survey participants about the collection and use of paradata in web surveys. We provide an overview of different personal and non-personal web paradata and the associated information and consent requirements. Best practices regarding the procedure, wording, and placement of non-personal web paradata information are discussed. In addition, we propose a sample wording for web paradata information in German and English.

Social Networking Sites (SNS) offer survey scientists a relatively new tool to recruit participants, especially among otherwise hard-to-reach populations. Facebook and Instagram, in particular, allow the distribution of advertisements to specific subsets of their users at low cost. Researchers can use such targeted advertisements to guide participants to their online questionnaires. In recent years, an increasing number of studies have shown that this approach can be successfully applied to a range of different target groups. However, a certain familiarity with the tools and mechanisms provided by Meta is necessary to employ this sampling method. Therefore, in this guideline, we will first give a general introduction to sampling via advertisements on Facebook and Instagram before providing detailed instructions on the implementation of such a recruitment campaign. This will be followed by a brief summary of a recent study conducted by GESIS using Meta's platforms to recruit professionals in the German health care sector. Finally, we provide recommendations with respect to the reporting of methodological parameters when using this approach, propose a flowchart to visualize sample sizes at different points during the recruitment process and offer a glossary containing definitions of essential terms researchers are confronted with when using Meta's advertisement interface.

Combining surveys and digital trace data can enhance the analytic potential of both data types. We present two studies that examine factors influencing data sharing behaviour of survey respondents for different types of digital trace data: Facebook, Twitter, Spotify and health app data. Across those data types, we compared the relative impact of four factors on data sharing: data sharing method, respondent characteristics, sample composition and incentives. The results show that data sharing rates differ substantially across data types. Two particularly important factors predicting data sharing behaviour are the incentive size and data sharing method, which are both directly related to task difficulty and respondent burden. In sum, the paper reveals systematic variation in the willingness to share additional data which need to be considered in research designs linking surveys and digital traces.