Documentation of mail data collection (Version 1.0)

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

Mixed-Device and Mobile Web Surveys (Version 1.0)

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.Seit vielen Jahren sind Online-Umfragen der populärste Umfragemodus im In- und Ausland (ADM, 2018; ESOMAR, 2018). Zunehmend benutzen Befragte mobile Endgeräte, insbesondere Smartphones (seltener Tablets), um auf das Internet zuzugreifen und an Befragungen teilzunehmen. Aufgrund dieser neuen Entwicklungen im Nutzungsverhalten erweitert dieser Beitrag eine frühere Guideline für Web-Umfragen (Bandilla, 2015), indem er sich mit den methodischen Vor- und Nachteilen von Mixed-Device-Befragungen und Umfragen auf mobilen Endgeräten befasst. Darüber hinaus behandelt er bewährte Verfahrensweisen zur Durchführung solcher Umfragen in den Bereichen Stichprobenziehung, Fragebogendesign, Paradatenerfassung und Softwarelösungen

Data Linking - Linking survey data with geospatial, social media, and sensor data (Version 1.0)

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

Mixed-Mode Surveys (Version 1.0)

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

Informing about Web Paradata Collection and Use (Version 1.0)

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

A guideline on how to recruit respondents for online surveys using Facebook and Instagram: Using hard-to-reach health workers as an example (Version 1.0)

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.Soziale Netzwerkseiten (SNS) stellen eine vergleichsweise neue Möglichkeit dar, Teilnehmende für wissenschaftliche Befragungsprojekte zu rekrutieren. Besonderes Potential hat der Ansatz mit Blick auf die Rekrutierung anderweitig schwer erreichbarer Zielgruppen. So ermöglichen Facebook und Instagram die Schaltung kostengünstiger Werbung für genau definierte Teilgruppen der Nutzenden dieser Netzwerke. Entsprechende Werbunganzeigen können von Forschenden genutzt werden, um ausgewählte Personen zu ihren Onlinefragebögen zu leiten. In den letzten Jahren hat eine wachsende Zahl von Studien gezeigt, dass dieser Ansatz erfolgreich auf eine Reihe unterschiedlicher Zielgruppen angewendet werden kann. Allerdings ist eine gewisse Vertrautheit mit den von Meta bereitgestellten Instrumenten und Mechanismen erforderlich, um dieses Verfahren anzuwenden. Daher bieten wir in dieser Guideline zunächst eine allgemeine Einführung in das Sampling über Werbeanzeigen auf Facebook und Instagram, bevor wir eine detaillierte Anleitung für die Durchführung einer solchen Rekrutierungskampagne geben. Danach folgt eine kurze Zusammenfassung einer kürzlich von GESIS durchgeführten Studie, bei der die Meta-Plattformen zur Rekrutierung von Fachkräften aus dem deutschen Gesundheitswesen genutzt wurden. Abschließend geben wir Empfehlungen dazu, welche Parameter in Publikationen berichtet werden sollten, um die Vergleichbarkeit der Ergebnisse zu gewährleisten. Mit diesem Ziel stellen wir auch ein Flussdiagramm zur Visualisierung der Stichprobengrößen zu verschiedenen Zeitpunkten des Rekrutierungsprozesses zur Verfügung. Schließlich fassen wir in einem Glossar Definitionen wesentlicher Parameter zusammen, mit denen Forscher bei der Verwendung von Metas Werbeschnittstelle konfrontiert werden

How German health workers’ views on vaccine safety can be swayed by the AstraZeneca controversy

Several COVID-19 vaccines are now licensed, and the success of a rollout often depends on people’s willingness to accept any of them. Health workers are in a unique position to influence the public. Jan Priebe (German Institute for Global and Area Studies), Henning Silber, Christoph Beuthner, Steffen Pötzschke, Bernd Weiß, and Jessica Daikeler (GESIS – Leibniz Institute for the Social Sciences) show how their recommendations change when they are given different types of information about vaccines

Evaluating an Alternative Frame for Address-Based Sampling in Germany: The Address Database From Deutsche Post Direkt

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

The interplay of incentives and mode-choice design in self-administered mixed-mode surveys

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

Twitter Users’ Privacy Behavior: A Reasoned Action Approach

Social networking sites have become a predominant means of communication across the globe. Activities on these sites generate massive amounts of personal information and raise concerns about its potential abuse. Means designed to protect the user’s privacy and prevent exploitation of confidential data often go unused. In this study, we draw on the theory of planned behavior, a reasoned action approach, to explain intentions to adopt privacy behaviors on social networking sites, with a focus on Twitter users. Consistent with the theory, an online survey of Twitter users ( n = 1,060) found that instrumental and experiential attitudes and descriptive and injunctive subjective norms regarding these behaviors were direct predictors of intentions. Perceived behavioral control had a moderating effect, such that subjective norm was a better predictor of intentions for participants high as opposed to low in perceived control. We briefly discuss the implications of these results for developing theory-driven and evidence-based interventions to promote privacy behavior