Suchergebnisse

Filter questions are used to administer follow-up questions to eligible respondents while allowing respondents who are not eligible to skip those questions. Filter questions can be asked in either the interleafed or the grouped formats. In the interleafed format, the follow-ups are asked immediately after the filter question; in the grouped format, follow-ups are asked after the filter question block. Underreporting can occur in the interleafed format due to respondents' desire to reduce the burden of the survey. This phenomenon is called motivated misreporting. Because smartphone surveys are more burdensome than web surveys completed on a computer or laptop, due to the smaller screen size, longer page loading times, and more distraction, we expect that motivated misreporting is more pronounced on smartphones. Furthermore, we expect that misreporting occurs not only in the filter questions themselves but also extends to data quality in the follow-up questions. We randomly assigned 3,517 respondents of a German online access panel to either the PC or the smartphone. Our results show that while both PC and smartphone respondents trigger fewer filter questions in the interleafed format than the grouped format, we did not find differences between PC and smartphone respondents regarding the number of triggered filter questions. However, smartphone respondents provide lower data quality in the follow-up questions, especially in the grouped format. We conclude with recommendations for web survey designers who intend to incorporate smartphone respondents in their surveys.

Zentraler Vorteil von Onlinebefragungen auf mobilen Endgeräten (Tablet, Smartphone) ist ihre Allgegenwart und ihr technologisches Potenzial. Umfragemethodisch sind solche Befragungen jedoch eine Herausforderung und die Konsequenzen für die Datenqualität nicht ignorierbar.

In two studies, we examined whether open science practices, such as making materials, data, and code of a study openly accessible, positively affect public trust in science. Furthermore, we investigated whether the potential trust-damaging effects of research being funded privately (e.g. by a commercial enterprise) may be buffered by such practices. After preregistering six hypotheses, we conducted a survey study (Study 1; N = 504) and an experimental study (Study 2; N = 588) in two German general population samples. In both studies, we found evidence for the positive effects of open science practices on trust, though it should be noted that in Study 2, results were more inconsistent. We did not however find evidence for the aforementioned buffering effect. We conclude that while open science practices may contribute to increasing trust in science, the importance of making use of open science practices visible should not be underestimated.

The ease with which large amounts of data can be collected via the Internet has led to a renewed interest in the use of non-probability samples. To that end, this paper performs a case study, comparing two non-probability datasets - one based on a river-sampling ap­proach, one drawn from an online-access panel - to a reference probability sample. Of particular interest is the single-question river-sampling approach, as the data collected for this study presents an attempt to field a multi-item scale with such a sampling method. Each dataset consists of the same psychometric measures for two of the Big-5 personality traits, which are expected to perform independently of sample composition. To assess the similarity of the three datasets we compare their correlation matrices, apply linear and non-linear dimension reduction techniques, and analyze the distance between the datasets. Our results show that there are important limitations when implementing a multi-item scale via a single-question river sample. We find that, while the correlation between our data sets is similar, the samples are composed of persons with different personality traits.

In this article, we examine two types of item nonresponse in a face-to-face population survey: 'don't know' (DK) and 'item refusal' (REF). Based on the cognitive model of survey response, the theory of survey satisficing and previous research, we derive explanatory variables on three levels: question, respondent and interviewer characteristics. The results of our cross-classified model show that while the two levels question and respondents' characteristics affected both types of item nonresponse, interviewer characteristics affected only DK answers. Our results also confirm that DK and REF are substantially different item nonresponse types resulting from distinguishable disruptions of the cognitive response process. Since most results are in line with prior theoretical predictions, they suggest that survey practitioners are well-advised by continuing to follow the large body of practical guidance derived from the theories tested here.

Typically, interviewer training is implemented in order to minimize interviewer effects and ensure that interviewers are well prepared to administer the survey. Leading professional associations in the survey research landscape recommend the standardized implementation of interviewer training. Some large-scale multinational survey programs have produced their own training guidelines to ensure a comparable level of quality in the implementation of training across participating countries. However, the length, content, and methodology of interviewer training guidelines are very heterogeneous. In this paper, we provide a comparative overview of general and study-specific interviewer training guidelines of three multinational survey programs (ESS, PIAAC, SHARE). Using total survey error (TSE) as a conceptual framework, we map the general and study-specific training guidelines of the three multinational survey programs to components of the TSE to determine how they target the reduction of interviewer effects. Our results reveal that unit nonresponse error is covered by all guidelines; measurement error is covered by most guidelines; and coverage error, sampling error, and processing error are addressed either not at all or sparsely. We conclude, for example, that these guidelines could be an excellent starting point for new - small as well as large-scale - surveys to design their interviewer training, and that interviewer training guidelines should be made publicly available in order to provide a high level of transparency, thus enabling survey programs to learn from each other.

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.

AbstractDeclining response rates worldwide have stimulated interest in understanding what may be influencing this decline and how it varies across countries and survey populations. In this paper, we describe the development and validation of a short 9-item survey attitude scale that measures three important constructs, thought by many scholars to be related to decisions to participate in surveys, that is, survey enjoyment, survey value, and survey burden. The survey attitude scale is based on a literature review of earlier work by multiple authors. Our overarching goal with this study is to develop and validate a concise and effective measure of how individuals feel about responding to surveys that can be implemented in surveys and panels to understand the willingness to participate in surveys and improve survey effectiveness. The research questions relate to factor structure, measurement equivalence, reliability, and predictive validity of the survey attitude scale.The data came from three probability-based panels: the German GESIS and PPSM panels and the Dutch LISS panel. The survey attitude scale proved to have a replicable three-dimensional factor structure (survey enjoyment, survey value, and survey burden). Partial scalar measurement equivalence was established across three panels that employed two languages (German and Dutch) and three measurement modes (web, telephone, and paper mail). For all three dimensions of the survey attitude scale, the reliability of the corresponding subscales (enjoyment, value, and burden) was satisfactory. Furthermore, the scales correlated with survey response in the expected directions, indicating predictive validity.

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.

Declining response rates worldwide have stimulated interest in understanding what may be influencing this decline and how it varies across countries and survey populations. In this paper, we describe the development and validation of a short 9-item survey attitude scale that measures three important constructs, thought by many scholars to be related to decisions to participate in surveys, that is, survey enjoyment, survey value, and survey burden. The survey attitude scale is based on a literature review of earlier work by multiple authors. Our overarching goal with this study is to develop and validate a concise and effective measure of how individuals feel about responding to surveys that can be implemented in surveys and panels to understand the willingness to participate in surveys and improve survey effectiveness. The research questions relate to factor structure, measurement equivalence, reliability, and predictive validity of the survey attitude scale. The data came from three probability-based panels: the German GESIS and PPSM panels and the Dutch LISS panel. The survey attitude scale proved to have a replicable three-dimensional factor structure (survey enjoyment, survey value, and survey burden). Partial scalar measurement equivalence was established across three panels that employed two languages (German and Dutch) and three measurement modes (web, telephone, and paper mail). For all three dimensions of the survey attitude scale, the reliability of the corresponding subscales (enjoyment, value, and burden) was satisfactory. Furthermore, the scales correlated with survey response in the expected directions, indicating predictive validity.

AbstractThe COVID-19 pandemic has spotlighted the importance of high-quality data for empirical health research and evidence-based political decision-making. To leverage the full potential of these data, a better understanding of the determinants and conditions under which people are willing to share their health data is critical. Building on the privacy theory of contextual integrity, the privacy calculus, and previous findings regarding different data types and recipients, we argue that established social norms shape the acceptance of novel practices of data collection and use. To investigate the willingness to share health data, we conducted a preregistered vignette experiment. The scenarios experimentally varied the vignette dimensions by data type, recipient, and research purpose. While some findings contradict our hypotheses, the results indicate that all three dimensions affected respondents' data sharing decisions. Additional analyses suggest that institutional and social trust, privacy concerns, technical affinity, altruism, age, and device ownership influence the willingness to share health data.

AbstractObjectiveWith response rates of large‐scale surveys having decreased significantly over the years and rebounds seeming unlikely, many studies now examine how response rates vary with methodological design and incentives. This investigation delves into how individual‐level factors shape survey participation. Specifically, we examine the influence of individuals' democratic engagement and their trust in institutions on intent to participate in surveys, both directly and indirectly through their perceptions of surveys.MethodsWe collected survey data from a probability sample of adults (N = 1343) in Mannheim, Germany, from November 2019 to March 2020. Structural equation models were estimated to test the hypothesized relationships.ResultsThe analyses support most, but not all, hypothesized relationships. Democratic engagement bolstered intent to participate, directly as well as indirectly through perceptions of surveys. Institutional trust, on the other hand, only influenced the outcome measure indirectly. Perceptions of surveys had a strong bearing overall effect on intent to participate.ConclusionThe study's results suggest that the response rates and larger issues related to the perceived legitimacy of public opinion and survey research might be intertwined with orientations related to people's civic and political life. The article discusses potential ways survey researchers can counteract distrust in surveys.