There are limits to what can be measured with survey questions: we can only collect information about things our respondents know, can recall, are willing to tell us – and that fit within a time-constrained questionnaire. Increases in smartphone ownership and use, along with technological changes are creating new possibilities to collect data for surveys of the general population, for example, through linkage or donation of existing digital data, collection of bio-samples or -measures, or use of sensors and trackers. Surveys are therefore developing into systems of data collection: depending on the concept of interest, different methods are used to generate data of the required level of accuracy, granularity, and periodicity.

For example, Understanding Society: the UK Household Longitudinal Study supplements the annual questionnaire-based data with linked data and data derived from bio measures and bio samples. In addition, we are developing and testing protocols to collect data using mobile applications, activity and GPS trackers and air quality sensors. We have conducted a series of mobile app studies, collecting detailed information about household expenditure, daily data about relationships, stressors and wellbeing, detailed body measurements, and spatial cognition. However, in each case, only a sub-set of respondents invited to the mobile app study participated and provided data.

In this talk I will present research from a series of experimental studies carried out on the Understanding Society Innovation Panel, that aim to identify the barriers faced by respondents in participating in mobile app studies, provide evidence on how best to design data collection protocols to maximise participation and reduce selectiveness of participants, and examine the quality of data collected with mobile apps.

Relevance & Research Question: This presentation focuses on the difficulties and solutions related to recruiting web panels through probability-based face-to-face and push-to-web surveys. It also compares the panel composition when using two different survey modes for recruitment.

Methods & Data: As part of the ESS SUSTAIN-2 project, a webpanel was recruited in 2021/22 through a face-to-face survey of ESS R10 in 12 countries. Unfortunately, the recruitment rate was low and the sample size achieved in Hungary was inadequate for further analysis. To increase the size of the webpanel (CRONOS-2), the Hungarian team initiated a probability-based mixed-mode self-completion survey (push-to-web design). Respondents were sent a post inviting them to go online or complete a questionnaire, which was identical to the interviewer-assisted ESS R10 survey.

Results: We will present our findings on how the type of survey affects recruitment to a web panel through probability sampling. We will begin by introducing the design of the two surveys, then discuss the challenges encountered in setting up the panel, and finally compare the composition of the panel recruited through the two surveys (interviewer-assisted ESS R10 and push-to-web survey with self-completion). Our research provides valuable insight into how the type of survey and social and political environment affect recruitment to a web panel.

Added Value: This analysis focuses on the mode effect on the recruitment of participants for a scientific research panel. Our findings highlight the effect of the social and political environment, which could be used as a source of inspiration for other local studies.

Relevance & Research Question

RDS is a network sampling technique for surveying complex populations in the absence of sampling frames. The idea is simple: identify some people (“seeds”) who belong or have access to the target population, encourage them to start a survey invitation chain-referral process in their community, ensure that every respondent can be traced back along the referral chain. But who will recruit? And whom? And which strategies help initiate the referral process?

Methods & Data

We conducted a pilot study in 2023 where we invited 5,000 panel study members to a multi-topic online survey. During the survey, we asked respondents whether they would be willing to recruit up to three of their network members. If they agreed, we asked them about their relationship with those network members as well as these people’s ages, gender, and education and provided unique survey invitation links to be shared virtually. As part of the study, we experimentally varied the RDS consent wording, information layout, and survey link sharing options. We also applied a dual incentive scheme, rewarding seeds as well as recruits.

Results

Overall, 624 initial respondents (27%) were willing to invite network members. They recruited 782 people (i.e., on average 1.25 people per seed). Recruits were mostly invited via email (46%) or WhatsApp (43%) and belonged to the seeds’ family (53%) and friends (38%). Only 20% of recruits are in contact with the seed less than once a week, suggesting recruitment mostly among close ties. We find an adequate gender balance (52% female) and representation of people with migration background (22%) in our data, but a high share of people with college or university degrees (52%) and high median age (52 years). The impact of the experimental design on recruitment success is negligible.

Added Value

While in theory, RDS is a promising procedure, it often fails in practice. Among other challenges, this is commonly due to the fact that seeds will not or only insufficiently start the chain-referral process. Our project shows in which target groups initiating RDS may work and to what extent UX enhancements may increase RDS success.

Relevance & Research Question:

Survey navigation paradata presents a unique opportunity to delve into the web survey completion behavior of respondents, particularly actions like revisiting questions and potentially altering answers. Such behavior could be indicative of motivated misreporting, especially when respondents revisit filter or looping questions to modify answers and circumvent subsequent inquiries — a manifestation of satisficing behavior. Conversely, altering answers upon revisiting may also signify optimizing behavior, where respondents strive for utmost accuracy.

This study focuses on the revisiting behavior of web survey respondents, aiming to quantify its frequency, identify associated respondent characteristics, and ascertain who shortens their questionnaire through revisiting.

Methods & Data:

Using paradata from the probability-based online-administered Generations and Gender Programme (GGP) survey in Estonia (N=8916), we analyze the frequency of revisiting questions, characteristics of these questions, and the ensuing actions. We investigate the connection between revisiting behavior and respondent characteristics using a zero-inflated Poisson regression model and check which respondents’ characteristics were connected with a higher proportion of shortening the questionnaire as a result of revisiting questions.

Results:

We find a discernible pattern of revisiting questions during the survey, notably prevalent in immediate filter questions, where almost half of respondents go back after a filter question (that can change the routing of the questionnaire).
Contrary to our expectations, the regression analysis did not conclusively support revisiting as a sole indicator of satisficing behavior. The questionnaire size emerged as the most influential factor in revisiting behavior, suggesting that larger questionnaires may burden respondents and potentially lead to motivated misreporting—a form of strong satisficing behavior.
The revisiting observed may reflect respondents' strategies to optimize responses or alleviate survey burden. The complexity of the questionnaire, coupled with respondent motivation and cognitive ability, plays pivotal roles in shaping revisiting behavior, particularly in the case of immediate filters where revisiting may lead to questionnaire shortening.

Added Value:

This study contributes a nuanced understanding of respondents' behavior during web survey self-completion. Utilizing paradata enhances insights into respondents' survey completion patterns and various behavioral types, providing valuable insights for survey design and data quality management.

Relevance and Research Question:

Social desirability (SD-) bias (i.e., the tendency to report socially desirable opinions and behaviors instead of revealing true ones) is a widely known threat to response quality and the validity of self-reports. Previous studies investigating socially desirable responding in a longitudinal context provide mixed evidence on whether SD-bias increases or decreases with repeated interviewing and how these changes affect response quality in later waves. However, most studies were non-experimental and only suggestive of the underlying mechanisms of observed changes in SD-bias over time.

Methods and Data:

This study investigates socially desirable responding in panel studies using a longitudinal survey experiment comprising six panel waves. The experiment manipulated the frequency of receiving identical sensitive questions (target questions) and assigned respondents to one of three groups: One group received the target questions in each wave (fully conditioned), the second group received the target questions in the last three waves (medium conditioned), and the control group received the target questions only in the last wave of the study (unconditioned). The experiment was conducted within a German non-probability (n = 1,946) and a probability-based panel study (n = 4,660), resulting in 2x3 experimental groups in total. The analysis focusses on between-group and within-group comparisons of different sensitive behavioral measures. It further includes measures on the questions’ degree of sensitivity as a moderating variable. These measures result from an additional survey (n = 237) in which respondents were asked to rate the sensitivity of multiple attitudinal and behavioral questions. To further examine the underlying mechanisms of change, I use a measure on respondents’ trust towards the survey (sponsor) and the scores of an established SD-scale.

Results:

Results will be presented at the conference in February.

Added Value:

Altogether, this study provides experimental evidence on the impact of repeated interviewing on changes in social desirability bias. It further contributes to the understanding of what causes these changes by examining different levels of exposure to identical sensitive questions and including measures on respondents’ trust towards the survey (sponsor) and their scores on a SD-scale.

Relevance & Research Question
Web surveys encounter a critical challenge related to measurement error and diminishing data quality, primarily stemming from respondents' engagement in satisficing behavior. Satisficing reflects suboptimal execution of cognitive steps in the answering process. Paradata, encompassing completion time, mouse movements, and revisiting survey sections, among other metrics, serve to assess respondents' cognitive effort, multitasking tendencies, and motivated misreporting. Despite their individual usage, a comprehensive examination combining various paradata types to discern patterns of satisficing and optimizing behavior has been lacking.

This study seeks to investigate the interplay between different paradata types and data quality indicators derived from survey data, aiming to identify distinct patterns characterizing respondents' satisficing and optimizing behaviors.

Methods & Data

Employing a laboratory two-wave experiment with a crossover design involving 93 students, participants were randomly assigned to either satisficing or optimizing conditions in the first wave, with groups reversed in the second. Participants were asked to complete a web survey in either satidficing or in optimising manner. Manipulation checks were used to ensure participants' compliance with a condition. The survey encompassed open-ended, factual, and matrix questions, coupled with reliable scales gauging trust, values, and other sociological and psychological measures. Paradata, such as completion time, mouse movements, browser focus, reaction to warnings, scrolling, and resizing, were collected using the One Click Survey (1ka.si) online software.
Results
The results revealed that respondents in the optimizing condition exhibited higher data quality compared to those in the satisficing condition, as evidenced by test-retest reliability, completion time, straightlining, and subjective cognitive load. Exploratory factor analysis was employed to scrutinize patterns of advanced paradata values in tandem, shedding light on disparities in survey completion strategies between optimizing and satisficing conditions. The study elucidates the connections between satisficing or optimizing behavior and data quality indicators derived from paradata and survey responses.

Added Value
This research advances the understanding of satisficing behavior in web surveys by analysing diverse paradata types and uncovering distinctive patterns in respondents' behavior. The findings emphasize the potential of utilizing combined paradata to gain nuanced insights into the survey completion process, thereby enhancing overall data quality.

Relevance & Research Question

Data donations are a new tool for collecting research data. They can ensure informed consent, highly granular, retrospective, and potentially less biased behavioral traces, and are independent from APIs or webscraping pipelines. We thus seek to explore the viability of data donations for a type of highly personal data: WhatsApp chat logs. Specifically, we are exploring a wide range of demographic, psychological, and relational charactersitics and how they relate to peoples donation willingness, censoring, and actual data donation behavior.
Methods & Data

We used an opt-in survey assessing demographics, personality, relationship characteristics of a self-selected social relationship, and concerns for privacy. Participants were also asked whether they are willing to donate a WhatsApp chat from a 1:1 chat from the respective relationship. If they agreed, participants were forwarded to an online platform where they could securely upload, review, self-censor, and donate the chat log. Donated chats were anonimized automatically by first extracting variables of interest (e.g. number of words per message, emoji, smilies, sent domains, response time) and then deleting the raw message content. In a second step, participants selected which parts of the anonymized data should be included in the donations. The study was reviewed and accepted by the ethics committee of Ulm University. So far, 244 people participated in the survey and 140 chat log files with over 1 million messages in total were donated.

Preliminary Results

Preliminary results (based on 198 ppts.) show that participants were mostly university students. Self-indicated willingness to donate a chat was surprisingly high (73%), with a sizable gap to actual donations (39.4%). Interestingly participants rarely excluded any data manually after the automatic anonimization step. Furthermore, we did not find any meaningful differences in data donation willingness and behavior with respect to demographics, personality, privacy concerns, or relationship characteristics.

Added Value
Our preliminary results highlight, that opt-in data donations can be a viable method to collect even highly sensitive digital trace data if sufficient measures are taken to ensure anonimization, transparancey, and ease-of-use. We will discuss further implications for study design and participant incentivation based on the larger dataset.

Relevance & Research Question

For decades, social sciences have focused on broad one-time assessments and neglected the role of momentary experiences and behaviors. Now, novel digital tools facilitate the ambulatory collection of data on a moment-to-moment basis via experience sampling methods. But the compliance to answer short questionnaires in daily life varies considerably between and within participants. Compliance and consequently mechanisms leading to missing data in experience sampling studies, however, still remain in the dark today. In our study we therefore explored person-, context- and behavior-related patterns associated with participants’ compliance in experience sampling studies.

Methods & Data

We used a data set part (N = 592) of the Smartphone Sensing Panel Study recruited according to quotas representing the German population. We extracted over 400 different person-, context-, and behavior-related variables by combining assessments from traditional surveys (e.g., personality traits), experience sampling (e.g., mood), and passively collected mobile sensing data (e.g., smartphone usage, GPS). Based on more than 25,000 observations, we predicted participants' compliance to answer experience sampling questionnaires. For this purpose, we used a machine learning based modeling approach and benchmarked different classification algorithms using 10-fold cross-validation. In addition, we applied methods from interpretable machine learning to better understand the importance of single variables and constellations of variable groups.

Results

We found that compliance to experience sampling questionnaires could be successfully predicted above chance and that among the compared algorithms the linear elastic net model performed best (M_AUC = 0.723). Our follow-up analysis showed that study-related past behaviors such as the average response rate to previous experience sampling questionnaires were the most informative, followed by location information such “at home” or “at work”.

Added Value

Our study shows that compliance in experience sampling studies is related to participants' behavioral and situational context. Accordingly, we illustrate systematic patterns associated with missing data. Our study is an empirical starting point for discussing the design of experience sampling studies in social sciences and for pointing out future directions in research addressing experience sampling methodology and missing data.

Relevance & Research Question

The emergence of social media has transformed the way people consume and share information. As such platforms widely lack mechanisms to ensure content quality, their increasing popularity has raised concerns about the spread of fake news and conspiracy beliefs – with potentially harmful effects on public opinion and social cohesion.

Our research aims to understand the underlying mechanisms of media perception and sharing behaviour when people are confronted with factual vs conspiracy-based media contents. Under which circumstances do people believe in a media content? Do traditional indicators of quality matter? Are pre-existing views more important than quality (confirmation bias)? How is perceived credibility linked to sharing behaviour?

Methods & Data

To empirically assess these questions, we administered a survey experiment to a general population sample in Germany via Bilendi in August 2023. As respondents with a general susceptibility to conspiracy beliefs are of major substantive interest, we made use of responses from a previous survey to oversample “conspiracy thinkers”.

Respondents were asked to evaluate the credibility of different media contents related to three vividly debated topics: vaccines against Covid-19, the climate crisis, and the Ukraine war. We analyze these evaluations regarding the objective quality of the content (measured by author identity and data source), its visual layout (newspaper vs tweet), and previous respondent beliefs on the respective topic to measure confirmation bias.

Results

Our findings suggest that the inclination to confirm pre-existing beliefs is the most important predictor for believing a media content, irrespective of its objective quality. This general tendency applies to both, the mainstream society and “conspiracy thinkers”. However, according to self-reports, the latter group is much more likely to share media contents they believe in.

Added Value

Methodologically, we use an interesting survey experiment that allows us to vary opinion (in)consistency and objective quality of media contents simultaneously, meaning that we can estimate the relative effect of these features on the credibility of media contents. We provide insights into the underlying mechanisms of the often debated spread of conspiracy beliefs through online platforms, with their practical implications for public opinion formation.

Relevance & Research Question
This study addresses a pressing concern in the digital age: the evolution of (online) political participation among young adults. As digital platforms reshape how society engages with politics, traditional definitions and measurements of political involvement require reassessment. The research seeks to unravel the perceived dichotomy between declining conventional political activities and the burgeoning new forms of engagement in digital spaces. Specifically, our research questions aim to identify and comprehend the spectrum of political participation online and offline among young adults, understand topic-centric engagements, and analyze how participation behaviors differ based on factors like education and digital service utilization. Ultimately, by gauging the behaviors of young adults in the realm of political engagement, this research contributes to both the refinement of existing definitions of political participation and the debate on youth's political engagement trajectory in contemporary settings.
Methods & Data
We will conduct an online survey of 16–29-year-olds in December 2023. The respondents for this survey will be recruited via Meta advertisements.
Results
Since the survey will take place in December 2023, nothing can be said about the results at this point. The results will be available in early February 2024.
Added Value
This study delves into the evolving definitions of political participation and offers methodological insights. It, therefore, explores what can be seen as political participation from the new possibilities digital space offers. Using both closed and open-ended survey questions, we aim to capture a broader spectrum of (online) political participation, potentially filling some gaps in conventional survey techniques. This approach allows us a more comprehensive understanding of the subject. Additionally, we are working to adjust and propose survey items that reflect current (online) political participation patterns. Through this, our research provides a clearer picture of young adults' political engagement and suggests ways to improve data collection for future research. Finally, our study also provides insight into the extent to which Meta advertisements are suitable for recruiting young people into surveys.

Relevance & Research Question:

Within the backdrop of Israel's turbulent 2022 elections, the fifth round of elections within three years, This study delves into the complex interplay between political trust, efficacy, and engagement. It seeks to unravel how individuals' trust in politicians and the political system, coupled with their sense of political efficacy, influences their online and offline engagement in the political process. The research question focuses on identifying the specific predictors of political engagement in a context characterized by political unpredictability and frequent elections.

Methods & Data:

The study analyzes a representative survey of 530 Israeli respondents during the 2022 Israeli election period. The research evaluates the influence of various variables. These include trust in politicians, the political system, and political efficacy in online and offline political engagement. The analysis focuses on the differentiation between online engagement, such as social media activity, and offline engagement, like attending rallies or voting.

Results:

Statistical analysis reveals a robust correlation between political efficacy and both forms of political engagement (r = .62 for online, r = .57 for offline, p < .01). Trust in the political system emerges as a significant predictor of offline engagement (β = .36, p < .01), while trust in politicians is more strongly associated with online engagement (β = .41, p < .01). Notably, a mediation analysis indicates that political efficacy serves as a mediator in the relationship between trust in politicians and online engagement (indirect effect = 0.15, 95% CI [0.07, 0.24], p < .01). In contrast, such mediating effects between system trust and offline engagement are not observed.

Added Value:

By examining the nuanced factors influencing political engagement during political uncertainty, this study offers new insights into the differentiated impact of trust in politicians and the political system. It underscores the distinct psychological pathways that drive online and offline political engagement, enhancing our understanding of citizen behavior in democracies facing political instability. These findings have critical implications for political strategists, policymakers, and scholars seeking to foster civic engagement in similar contexts.

Der Artificial Intelligence Act (AI Act) der EU ist das erste Regelwerk, das sich mit der Regulierung von Künstlicher Intelligenz (KI) befasst. Mit dem AI Act will die EU einen weltweiten Goldstandard und eine Blaupause für die Regulierung von KI schaffen. Doch kann der AI Act tatsächlich zum Innovationsmotor für vertrauenswürdige KI werden oder wird er zum wirtschaftlichen Hemmschuh?

Foundation Models stehen im Zentrum des gegenwärtigen Hypes um (Generative) Künstliche Intelligenz. Sie besitzen das Potential, die Art und Weise, wie wir arbeiten, branchen- und aufgabenübergreifend zu revolutionieren.. Wir präsentieren ein aktuelles Projekt, in dem LLMs für personalisiertes Marketing genutzt werden und wagen einen Blick in die Zukunft von KI. Ein besonderer Fokus liegt auf der Rolle von Open Source in der Demokratisierung der KI-Technologie, dem Potenzial autonomer Agenten, die menschliche Arbeit unterstützen und ergänzen, sowie den Möglichkeiten, die Small Language Models für spezialisierte Anwendungen bieten.

Relevance & Research Question
Nowadays, designing online surveys means designing for mixed device surveys. One of the challenges in designing mixed device surveys is the presentation of grid questions. In this experiment we compare various design options for grid questions. Our main research questions are: 1) To what extent do these different grid designs differ with respect to response quality and respondent satisfaction? 2) Does this differ for respondents on PCs and respondents on smartphones?
Methods & Data In 2023 an experiment was conducted with a sample of 12060 persons of the general Dutch population aged 16 and older. Sample units were randomly assigned to an online survey in either the standard stylesheet as currently used by Statistics Netherlands (n=2824, 40% of the sample) or an experimental stylesheet (n=7236, 60% of the sample).

Within the current stylesheet, half of the sample units were randomly assigned to the standard grid design as currently used (a table format for large screens and a stem-fixed vertical scrollable format for small screens) and the other half to a general stem-fixed grid design (stem-fixed design for both the large and the small screen). Within the experimental stylesheet, one third of the sample was randomly assigned to either the general stem-fix grid design, a carrousel grid design (in which only one item is displayed at the time and after answering one item, the next item automatically ‘flies in‘) or an accordion grid design (all items are presented vertically on one page, and answer options are automatically closed and unfolded after an item is answered).

Various indicators are used to assess response quality, e.g. break-off, item non response, straightlining, mid-point reporting. Respondent satisfaction is assessed with a set of evaluation questions at the end of the questionnaire.

Results Data are currently being analyzed.

Added Value This experiment with a general population sample adds to the knowledge of previous studies on grids. which have mainly been conducted with (access) panels.

Towards a mobile web questionnaire for the Vacation Survey: UX design challenges

Vivian Meertens & Maaike Kompier

Key words: Mobile Web Questionnaire Design, Smartphone First Design, Vacation Survey, Statistics Netherlands, UX testing, Qualitative Approach, Mixed Device Surveys

Relevance & Research Question: —your text here—

Despite the fact that online surveys are not always fit for small screens and mobile device navigation, the number of respondents that start online surveys on mobile devices instead of PC or laptop device, is still growing. Statistics Netherlands (CBS) has responded to this trend by developing and designing mixed device surveys. This study focuses on the redesign of the Vacation Survey, applying a smartphone first approach.

The Vacation Survey is a web only panel survey, that could only be completed on a PC or laptop. The layered design with a master detail approach was formatted in such a way that a large screen was needed to be able to complete the questionnaire. Despite a warning in the invitation letter that a PC or laptop should be used to complete the questionnaire, 14.5% of first-time logins in 2023 were via smartphones, resulting in a redesign with a smartphone first approach. The study examines the applicability and understandability of the Vacation Survey’s layered design, specifically its master-detail approach, from a user experience (UX) design perspective.

Results: —your text here—
This study shares key findings of the qualitative UX test conducted at the CBS Userlab. It will explore how visual design aspects influence respondent behaviour on mobile devices, stressing the importance of observing human interaction when filling in a questionnaire on a mobile phone. The results emphasize the need for thoughtful UX design in mobile web questionnaires to enhanced user engagement and response accuracy.

Added Value: —your text here
The study provides valuable insights into challenges and implications of transitioning social surveys to mobile devices. By discussing the necessary adaptations for a functional, user-friendly mobile questionnaire, this research contributes to the broader field of survey methodology, offering guidance for future survey designs that accommodate the growing trend of mobile device usage.

Relevance & Research Question: Recall-based travel diaries require respondents to report their travel behaviour over a period ranging from one to seven days. During this period, they are asked to indicate the start and end times and locations, modes of transport, distances, and the number of people on each trip. Depending on the mode, additional questions are asked to gather information on ticket types and costs or fuel types. Due to the specificity of the requested information and its non-centrality for most respondents, travel diaries pose a substantial burden, increasing the risk of satisficing behaviours and trip underreporting. Methods & Data: In this presentation, we describe key decisions made during the design of the Wales National Travel Survey. This push-to-web project includes a questionnaire and a 2-day travel diary programmed into the survey. Results: Critical aspects of these decisions include the focus of the recall (trip, activity, or location based) and the sequence of follow-up questions (interleaved vs. roster approach). Recent literature suggests that location-based diaries align better with respondents’ cognitive processes than trip-based diaries and help reduce underreporting. Therefore, a location-based travel diary was proposed with an auto-complete field to match inputs with known addresses or postcodes. Interactive maps were also proposed for user testing. While they can be particularly useful when respondents have difficulty describing locations or when places lack formal addresses, previous research warns that advanced diary features can increase drop-off rates. Regarding the follow-up sequence, due to mixed findings in the literature and limited information on the performance of these approaches in web-based travel diaries, experimentation is planned to understand how each approach performs in terms of the accuracy of the filter questions and the follow-up questions. Additionally, this presentation discusses the challenges and options for gathering distance data in recall-based travel diaries, along with learnings from the early phases of diary testing based on the application of a Questionnaire Appraisal System and cognitive/usability interviews. Added Value: These findings offer valuable insights into the design of complex web-based surveys with multiple loops and non-standard features, extending beyond travel diaries.

Relevance & Research Question:

Survey measures of political knowledge, widely used in political science research, face challenges in online administration due to potential cheating. Previous research reveals a significant proportion of participants resort to online searches when answering political knowledge questions, casting doubt on measurement quality. Existing studies testing potential interventions to curb cheating have relied on indirect measures of cheating, such as catch questions. This study introduces a novel approach, employing direct observations of participants' Internet browsing via web trackers, combined with an experimental design testing two strategies to prevent cheating (instructions and time limit). The paper explores three research questions: what proportion of participants looks up information when posed political knowledge questions (RQ.1)? What is the impact of the interventions on the likelihood of individuals looking up information (RQ.2)? How do estimates from direct observations differ from indirect proxies (e.g., self-reports, paradata) (RQ.3)?
Methods & Data:

A web survey experiment (N = 1,200) in Spain was deployed within an opt-in access online panel. Cross quotas for age and gender, and quotas for educational level, and region were used to ensure a sample matching on these variables to the Internet adult population. Participants answered six knowledge questions on political facts and current events. Cheating was identified by analysing URLs from web tracking data, and alternative indirect measures were applied, including catch questions, self-reports, and paradata.
Results:

Two noteworthy patterns emerge. Firstly, cheating prevalence from web tracking data is below 5%, markedly smaller than levels estimated by indirect measures (2 to 7 times larger). Secondly, based on web tracking data the anti-cheating interventions have no effect. Nonetheless, using indirect measures of cheating we find that both interventions significantly reduce the likelihood of cheating.
Added Value:

This study pioneers the integration of web tracking data and experimental design to examine cheating in online political knowledge assessments. Despite requiring further validation, the substantial differences between web tracking data and indirect approaches suggest two competing conclusion: either cheating in online surveys is substantially lower than first thought, or web tracking data may not be suitable for identifying cheating in online surveys.

Relevance & Research Question
Designing high-quality survey questions is crucial for reliable and valid research outcomes. However, this process often relies on subjective expertise. In response to this challenge, Saris and colleagues developed the Survey Quality Predictor (SQP), a web-based tool to predict the quality of survey items for continuous latent variables. The research questions driving this presentation are: How can the quality of survey items be predicted? How can SQP 3.0 be effectively integrated into the questionnaire development process?
Methods & Data
The quality prediction algorithm (i.e., random forest) of the latest SQP version (3.0) is grounded in a comprehensive analysis involving more than 6,000 survey questions that were part of multitrait-multimethod (MTMM) experiments in 28 languages and 33 countries. The quality prediction of new survey items is based on their linguistic and formal characteristics (e.g., layout and polarity of the answer scale). It is important to note that SQP is not designed to replace traditional methods like cognitive pretesting but serves as a complementary tool in the development phase of questionnaires.
Results
This presentation showcases practical applications of SQP 3.0 in the questionnaire development process. The audience will gain insights into how SQP predicts the quality of survey items. Also, researchers will get to know how they can leverage SQP to identify survey items, enhance item quality before data collection, and detect discrepancies between source and translated versions of survey items.
Added Value
By incorporating SQP into the questionnaire development toolkit, researchers can enhance the efficiency and objectivity of their survey design processes, ultimately contributing to the advancement of survey research methodologies. In addition, I will highlight the collaborative nature of SQP as an ongoing and evolving research project on survey data quality, emphasizing avenues for potential collaboration among researchers.

Relevance & Research Question: Probability-based online and mixed-method panels are widely used in scientific research, but not as much for market research or political opinion polling. This presentation will explore the case of "Panelstory", the first Hungarian probability-based mixed-method panel, which was established in 2022 with the purpose of utilizing scientific methods to address market research and political opinion polling issues.
Methods & Data: We will provide a thorough assessment of panel data based on the total survey error framework to evaluate the quality of indicators such as financial situation, alcohol consumption, interest in politics, health, marital status and media use. Additionally, we will examine the panel composition, response rates, dropout, and recruitment statistics. Non-probability online data collections, face-to-face surveys, and administrative data will be used as reference points. We also relate this to the characteristics of Internet penetration.
Results: The research conducted thus far has revealed that Hungary's Internet penetration rate (82 percent) necessitates a mixed-method design. This is due to the fact that a clear pattern of Internet penetration has been identified in correlation with the indicators being studied. Based on the characteristics of internet penetration in Hungary, in 67 percent of the estimates were biased. For relevant research dimensions such as interest in politics, religiosity, health and marital status, the online data collection significantly under- or overestimates the likely real population proportions.The results of single-mode and mixed-method are notably different in terms of all of the indicators tested.
Added Value: It is especially important to assess how surveys from probability-based online and mixed-method panels compare to traditional methods such as face-to-face and single-mode designs. This presentation will provide a discussion of a new panel, highlighting both the advantages and potential issues of using scientific results in terms of data quality.

Relevance & Research Question

Open-ended survey questions are crucial e.g., for capturing unpredictable trends, but the resulting unstructured text data poses challenges. Quantitative usability requires categorization, a labor-intensive process in terms of costs and time, especially with large datasets. In the case of the German Longitudinal Election Study (GLES) spanning from 2018 to 2022, with nearly 400,000 uncoded mentions, it prompted us to explore new ways of coding. Our objective was to test various machine learning approaches to determine the most efficient and cost-effective method for creating a long-term solution for coding responses, ensuring high quality simultaneously. Which approach is best suited for the long-term coding of open-ended mentions regarding the "most important political problem" in the GLES?

Methods & Data

Pre-2018, GLES data was manually coded. Shifting to a (partially) automated process involved revising the codebook. Subsequently, the extensive dataset comprising nearly 400,000 open responses to the question regarding the "most important political problem" in the GLES surveys conducted between 2018 and 2022 was employed. The coding process was facilitated using the Large Language Model BERT (Bidirectional Encoder Representations from Transformers). During the entire process, we tested a whole host of important aspects (hyperparameter finetuning, downsizing of the “other” category, simulations of different amounts of training data, quality control of different survey modes, using training data from 2017) before arriving at the final implementation.
Results

The "new" codebook already demonstrates high quality and consistency, evident from its Fleiss Kappa value of 0.90 for the matching of individual codes. Utilizing this refined codebook as a foundation, 43,000 mentions were manually coded, serving as the training dataset for BERT. The final implementation of coding for the extensive dataset of almost 400,000 mentions using BERT yields excellent results, with a 0/1 loss of 0.069, a Micro F1 score of 0.946 and a Macro F1 score of 0.878.
Added Value

The outcomes highlight the efficacy of the (partially) automated coding approach, emphasizing accuracy with the refined codebook and BERT's robust performance. This strategic shift towards advanced language models signifies an innovative departure from traditional manual methods, emphasizing efficiency in the coding process.

Relevance & Research Question

The analysis of open-ended questions in large-scale surveys can provide detailed insights into respondents' views that often can't be assessed with closed-ended questions. However, due to the large number of respondents, it takes a lot of resources to review the answers within open-ended questions and thus provide them as research results. This contribution aims to show the potential benefits and limitations of using AI-based tools (e.g. ChatGPT), for analyzing open-ended questions in large-scaled surveys. It therefore also aims to highlight the challenge of conducting systematic analysis methods with AI.

Methods & Data
As part of a large-scale survey on the use of AI in higher education at a major German university, open-ended questions were included to provide insight into the perceived benefits and challenges for students and lecturers of using AI in higher education. The open-ended responses were then analyzed using a qualitative content analysis. In order to verify whether ChatGPT could be used to analyze the open-ended questions in a faster manner, while maintaining the same quality of results, we asked ChatGPT to analyze the responses in a way similar to our analytical process.

Results
The results show a roadmap of letting ChatGPT analyze our open-ended data. In our case study it obtained categories and descriptions similar to those we obtained by qualitatively analyzing the data ourselves. However, 9 out of 10 times we had to re-prompt ChatGPT to specify the context for the analysis to get the appropriate results. In addition, there were some minor differences in how items were sorted into their respective categories. Yet, despite these limitations, it became clear that 80% of cases, Chat GPT assigned the responses to the derived categories more accurately than our research team did in the qualitative analysis.

Added Value
This paper provides insight into how ChatGPT can be used to simplify and accelerate the standard process of qualitative analysis under certain circumstances. We will give insights into our prompts for ChatGPT, detailed findings from comparing its results with our own, and its limitations to contribute to the further development of systematic analysis methods using AI.

Relevance & Research Question:

In the intersection of qualitative and quantitative research, analyzing open-ended questions remains a significant challenge for data analysts. The incorporation of AI language models introduces the complex embedding space: a realm where semantics intertwine with mathematical principles. This paper explores how Embedding Analytics, a subset of explainable AI, can be utilized to decode and analyze open-ended questions effectively.

Methods & Data:

Our approach utilized the ada_V2 encoder to transform market research responses into spatial representations on the surface of a 1,536-dimensional hypersphere. This process enabled us to analyze semantic similarities using traditional statistics as well as advanced machine learning techniques. We employed K-Means Clustering for text grouping and respondent segmentation, and Gaussian Mixture Models for overarching topic analysis across numerous responses. Dimensional reduction through t-SNE facilitated the transformation of these complex data sets into more comprehensible 2D or 3D visual representations.

Results:

Utilizing OpenAI’s ada_V2 encoder, we successfully generated text embeddings that can be plausibly clustered based on semantic content, transcending barriers of language and text length. These clusters, formed via K-Means and Gaussian Mixture Models, effectively yield insightful and automated analyses from qualitative data. The two-dimensional “cognitive constellations” created through t-SNE offer clear and accessible visualizations of intricate knowledge domains, such as brand perception or public opinion.

Added Value:

This methodology allows for a precise numerical analysis of verbatim responses without the need for labor-intensive manual coding. It facilitates automated segmentation, simplification of complex data, and even enables qualitative data to drive prediction tasks. The rich, nuanced datasets derived from semantic complexity are suitable for robust analysis using a wide range of statistical methods, thereby enhancing the efficacy and depth of market research analysis.

Relevance & Research Question:
The recent development of large language models (LLMs) has spurred discussions about whether these models might provide a novel method of collecting public opinion data. As LLMs are trained on large amounts of internet data, potentially reflecting attitudes and behaviors prevalent in the population, LLM-generated “synthetic samples” could complement or replace traditional surveys. Several mostly US-based studies have prompted LLMs to mimic survey respondents, finding that the responses closely match the survey data. However, the prevalence of native-language training data, structural differences between the population reflected therein and the general population, and the relationship between a country’s socio-political structure and public opinion, might affect the generalizability of such findings. Therefore, we ask: To what extent can LLMs estimate public opinion in Germany?
Methods & Data:
We use the example of vote choice as an outcome of interest in public opinion. To generate a “synthetic sample” of the voting-eligible population in Germany, we create personas matching the individual characteristics of the 2017 German Longitudinal Election Study respondents. Prompting GPT-3.5 with each persona, we ask the LLM to predict each respondents’ vote choice. We examine how the average party vote shares obtained through GPT-3.5 compare to the survey-based estimates, assess whether GPT-3.5 is able to make accurate estimates for different population subgroups, and compare the determinants of voting behavior between the two data sources.
Results:
Based on our prompt design and model configuration, we find that GPT-3.5 does not accurately predict citizens’ vote choice, exhibiting a bias towards the Left and Green parties on aggregate, and making better predictions for more “typical” voter subgroups, such as political partisans. Regarding the determinants of its predictions, it tends to miss out on the multifaceted factors that sway individual voter choices.
Added Value:
By examining the prediction of voting behavior using LLMS in a new context, our study contributes to the growing body of research about the conditions under which LLMs can be leveraged for studying public opinion. The findings underscore the limitation of applying LLMs for public opinion estimation without accounting for the biases and potential limitations in their training data.

Relevance & Research Question
Since the launch of ChatGPT in November 2022, large language models (LLMs) have been the talk of the town. LLMs are artificial intelligence systems that are trained to understand and generate human language based on huge data sets. In all areas where language data play a central role, they have great potential to become part of a researcher’s methodological toolbox. One of these areas is the cognitive pretesting of questionnaires. We identify three tasks where LLMs can augment current cognitive pretesting procedures and potentially render them more effective and objective: (1) identifying potential problems of draft survey questions prior to cognitive testing, (2) suggesting cognitive probes to test draft survey questions, and (3) simulating or predicting respondents’ answers to these probes (i.e., generating ‘synthetic samples'). In this case study, we examine how well ChatGPT performs these tasks and to what extent it can improve current pretesting procedures.
Methods & Data
We conducted a cognitive interviewing study with 24 respondents, testing four versions of a survey question on children’s activity levels. Half of the respondents were parents of children aged 3 to 15 years, the other half were adolescents aged 11 to 17 years. In parallel to applying our common pretesting procedures, we prompted ChatGPT 3.5 to perform the three tasks above and analyzed similarities and differences in the outcomes of the LLM and humans.
Results
With respect to tasks (1) and (2), ChatGPT identified some question problems and probes that were not anticipated by humans, but it also missed important problems and probes identified by human experts. With respect to task (3), the answers generated by ChatGPT were characterized by a relatively low variation between individuals with very different characteristics (i.e., gender, age, education) and the reproduction of gender stereotypes regarding the activities of boys and girls. All in all, they only marginally matched the answers of the actual respondents.
Added Value
To our knowledge, this is one of the first studies examining how LLMs can be incorporated into the toolkit of survey methodologists, particularly in the area of cognitive pretesting.

Relevance & Research Question: The recent advances and availability of large language models (LLMs), such as OpenAI’s GPT, have created new opportunities for research in the social and behavioral sciences. Questionnaire development and evaluation is a potential area where researchers can benefit from LLMs: Trained on large amounts of text data, LLMs might serve as an easy-to-implement and inexpensive method for both assessing and improving the design of survey questions, by detecting problems in question wordings and suggesting alternative versions. In this paper, we examine to what extent GPT-4 can be leveraged for questionnaire design and evaluation by addressing the following research questions: (1) How accurately can GPT-4 detect problematic linguistic features in survey questions compared to existing computer-based evaluation methods? (2) To what extent can GPT-4 improve the design of survey questions?

Methods & Data: We prompt GPT-4 with a set of survey questions and ask to identify features in the question stem or the response options that can potentially cause comprehension problems, such as vague terms or a complex syntax. For each survey question, we also ask the LLM to suggest an improved version. To compare the LLM-based results with an existing computer-based survey evaluation method, we use the Question Understanding Aid (QUAID; Graesser et al. 2006) that rates survey questions on different categories of comprehension problems. Based on an expert review among researchers with a PhD in survey methodology, we assess the accuracy of the GPT-4- and QUAID-based evaluation methods in identifying problematic features in the survey questions. We also ask the expert reviewers to evaluate the quality of the new question versions developed by GPT-4 compared to their original versions.

Results: We compare both evaluation methods with regard to the number of problematic question features identified, building upon the five categories used in QUAID: (1) unfamiliar technical terms, (2) vague or imprecise relative terms, (3) vague or ambiguous noun phrases, (4) complex syntax, and (5) working memory overload.

Added Value: The results from this paper provide novel evidence on the usefulness of LLMs for facilitating survey data collection.

Relevance & Research Question
While a sequential, push-to-web mode sequence is very well established in survey research and commonly used in survey practice, many large-scale social surveys still prefer to contact older target persons with a concurrent design, offering a paper questionnaire alongside a web-based questionnaire from the first letter onwards. In this presentation, we compare the performance of a sequential design with a concurrent design for target persons older than 60 years. We analyse response rates and compare the sample compositions and distributions in key items within resulting net samples. Ultimately, we aim to investigate whether we can push older respondents to the web and whether a paper questionnaire is still required for this age group.

Methods & Data
Data stems from the 10^th Round of the European Social Survey (ESS) carried out in self-completion modes (CAWI/PAPI) in 2021. In Germany, a mode choice sequence experiment was implemented for all target persons older than 60 years. 50% of this group was invited with a push-to-web approach, offering a paper questionnaire in the third mailing. The control group was invited with a concurrent mode sequence, offering both modes from the beginning on.

Results
Results shows similar response rates for the concurrent design and the sequential design (AAPOR RR2: 38.4% vs. 37.3%). This difference is not statistically significant. In the concurrent group, 21% of the respondents answered the questionnaire online, while in the sequential group this was the case for 50% of all respondents. The resulting net samples are very comparable. Looking at various demographic, socio-economic, attitudinal, and behavioural items, no significant differences were found. In contrast, elderly respondents answering online are younger, more often male, much better educated, economically better off, more politically interested, or more liberal towards immigrants than their peers answering the paper questionnaire.

Added Value
Online questionnaires are considered as not fully appropriate for surveying the older population. This research shows that a higher share of this group can be pushed to the web without negative effects for response rate or sample composition. However, a paper questionnaire is still required for improving the sample composition.

Relevance & Research Question
Web surveys offer the possibility to include additional clarifications to a survey question via info buttons that can be placed directly beside a word in the question text or next to the question. Previous research on the use of these clarifications and their impact on survey response is scarce.
Methods & Data
Using the non-probability Bilendi panel, we randomly assigned 2,000 respondents to a condition in which they A) were presented clarifications as directly visible instructions under the question texts, B) could click / tip on clarifications via an info button next to the word the respective clarification pertained to, C) could click / tip on clarifications via an info button to the right of the respective question text or D) received no clarifications at all. All questions used an open-ended numeric answer format and respondents were likely to give a smaller number as a response if they read the clarification.
Results
Following the last survey question that contained a clarification, we asked respondents in conditions A) through C) whether they had clicked / tipped on or read the clarification. In addition, we measured the use of the on-demand clarifications using a client-side paradata script. Results showed that while 24% (B) and 15% (C) of respondents claimed to have clicked on the last-shown on-demand clarification, only 14% (B) and 6% (C) actually did so for at least one question with clarification. Moreover, the responses to the survey question did not differ significantly between the conditions with on-demand instructions (B and C) and the condition with no clarifications (D). Thus, the only way to ensure that respondents adhere to a clarification is to present it as an always visible instruction as in condition A.
Added Value
The results demonstrate that presenting complex survey questions remains challenging. Even if additional clarification is needed for some respondents only, this clarification should be presented to all respondents; however, with the potential disadvantage of increasing response burden. To learn more about how respondents process clarification features, we are currently carrying out a qualitative follow-up study applying cognitive interviewing.

Relevance & research question:

Researchers are increasingly using social media platforms for survey recruitment. Typically, advertisements are distributed through these platforms to motivate users to participate in an online survey. To date, there is little empirical evidence on how the content and design characteristics of advertisements can affect response quality in surveys based on social media recruitment. This project is the first comprehensive study of the effects of ad design on response quality in surveys recruited via social media.

Methods and data:

We use data from the SoMeRec survey, which was conducted via Facebook ads in Germany and the United States in June 2023 and focused primarily on climate change and migration. The survey ad campaign featured 15 images with different thematic associations to climate change and migration, including strong and loose associations and neutral images. A commercial access panel company was contracted to include identical survey questions serving as benchmark comparison. The Facebook sample consisted of 7,139 respondents in Germany and 13,022 in the US, while the access panel consisted of 1,555 surveys in Germany and 1,576 surveys in the US. In our analyses, we compare common data quality indicators, including completion time, straightlining, item non-response, and follow-up availability, across different ad features.

Results:

First analysis show that survey completion time is higher for thematic ad designs compared to neutral ads and the reference sample. There are differences in the overall item non-response rate, with higher item non-response for the immigration-themed ad designs. There are no significant differences in straightlining between samples and ad designs. Finally, respondents recruited through neutral ads were more likely to be available for follow up surveys than those recruited through themed ads.

Added value:

Our study advances the literature by studying the general population in Germany and the US, by testing various indicators of survey data quality, and by including a benchmark survey of respondents not recruited through social media. The results clearly indicate an effect of ad design on survey data quality and highlight the importance of sample and recruitment design for estimates based on social media recruitment and online surveys.

Relevance & Research Question: Social media advertisement is becoming an increasingly popular method of recruiting participants for studies in the social sciences. Recently, more and more participants of surveys are recruited via social media. This method of recruitment has been particularly prominent for recruiting special populations for surveys, such as migrants or LGBT persons, but recently meta has significantly reduced these selection criteria. However, meta still allows the selection of common socio-demographic characteristics, such as age and gender, when placing an ad. Meta estimates these socio-demographic characteristics based on the user's data. With this information, we took an non-probabilistic quota-sampling-like approach by specifying to meta the desired peoples' proportions for socio-demographic characteristics which should click on the ad and be directed to the recruitment survey of our nonprobabilistic panel.

However, the volatile and hard to control nature of social media recruitment opens it up to scrutiny and demands evaluation. In this study we assess coverage issues and cost effectiveness of utilizing Meta advertisement in recruiting respondents for a non-probabilistic online panel, we consider three aspects in detail. First, we evaluate the extent to which the targeting criteria, namely age and gender achieve a balanced sample at different stages of the registration process into the panel and give recommendations for adjustments. Furthermore, we validate whether these social media targeting criteria are reliable and agree with the survey answers. Third, we assess the cost structure in the light of the response propensities at the different stages of the recruitment process and investigate whether expensive social media ad groups pay off in the long-term.

Methods & Data: We are using data from the recruiment of the new GESIS Panel Plus. The recuitmenr process includes several steps and we sill consider each step individually using multivariate analysis methods.

Results: First results suggest that expensive recruitment groups do not pay off in the long term.

Added Value: These research will open up the black box of cost structure in relation to socio - demographic attributes when using Meta as recruitment frame for cross-sectional and longitudinal surveys.

Relevance & Research Question: Smartphone app data collection has recently gained increasing attention in the social and behavioral sciences, allowing researchers to integrate surveys with sensor data, such as GPS to measure location and movement. Similar to other forms of surveys, participation rates of such studies in general population samples are generally low. Previous research has identified several study- and participant-level determinants of willingness to participate in smartphone app data collection. However, a comprehensive overview of which factors are predictors of willingness and a theoretical framework are currently lacking and some of the effects are inconsistent. To guide future app-based studies, we address the following research questions:

(1) Which study- and participant-level characteristics affect the willingness to participate in smartphone app data collection?

(2) Which theoretical frameworks can be used to understand participation decisions in smartphone app data collection?

Methods & Data: We conduct a systemic review and a meta-analysis on existing studies with app-based data collection guided by the Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) framework (Moher et al. 2009). We compile a list of keywords to search for relevant literature in bibliographic databases. We focus on peer-reviewed articles published in English. We also perform double coding to ensure a reliable selection of literature for the analysis. Finally, we map the identified determinants of willingness to potential theoretical frameworks that can explain participation behavior.

Results: In the systematic review, we summarize findings about study-level characteristics that are under the researchers' control, such as monetary incentives or invitation mode, and participant-level characteristics, such as privacy concerns and socio-demographics. Meanwhile, the meta-analysis focuses on selected characteristics, which have been most often covered in previous research.

Added Value: This study will provide a holistic understanding of the current state of research on participation decisions in app-based studies. The findings will also help researchers to design effective invitation strategies for future studies.

Relevance & Research Question: Dating apps create a unique digital sphere where people must disclose sensitive personal information about their demographics, location, values and lifestyle. Because of these intimate disclosures, dating apps constitute a strategic research site to explore how privacy concerns influence personal information disclosure. We use construal-level theory to understand how context influences a decision to disclose. Construal-level theory refers to the influence of psychological distance: the more psychologically distant an event the more mental effort required to understand it. When people have no direct experience in a context they rely on conventional stereotypes and quick generalizations. Using this theory we ask the research question: Why do people choose to disclose or not disclose personal Information on their dating app profile?
Methods & Data: We use in-depth, key-informant interviews with 27 active male and female users of the dating site Hinge. Interviews were transcribed and assigned descriptive, process-oriented and interpretative codes using Atlas.ti software.
Results: Dating site users distinguish two kinds of privacy risks. One class of threats is other dating app users who may misuse their information for embarrassment, harassment or stalking, particularly if it could identify the user. These are contexts where users have personal experience. People consider very carefully what information to disclose or hide at the user-level. The second class is the platform-level: app providers who use or sell their information for targeted advertisements. In this context users have no direct experience. Platform-level use is abstract and requires serious mental effort to understand it. Hence it is seen as not threatening and it is ignored. These results confirm construal-level theory.
Added Value: This research uncovers a previously unnoticed mechanism that governs privacy awareness. It provides clear policy guidelines for enhancing privacy awareness on social media and the Internet in general. Specifically, to encourage people to protect their personal information psychological distance has to be reduced. This can be done by explicit warnings about data use, or explicit statements about data sale and what third parties may do with the information. Warnings should be easily visible on the home page or other prominent locations.

Relevance & Research Question

The German Federal Statistical Office (Destatis) is continuing to develop its data collection instruments and is working on smart surveys in this context. By smart surveys we mean the combination of traditional question-based survey data collection and digital trace data collection by accessing device sensor data via an application (GPS, camera, microphone, accelerometer, ...).

Unlike traditional surveys, smart surveys not only ask respondents for information but also require them to download an app and allow access to sensor data. Destatis conducted focus groups to learn more about the attitudes, motives and obstacles regarding the willingness to participate in smart surveys. This was done as part of the European Union's Smart Survey Implementation (SSI) project, in which Destatis is participating alongside other project partners.

Methods & Data

Three focus groups with a total of 16 participants were conducted at the end of October 2023. The group discussions were led by a moderator using a guideline. The discussions lasted around two hours each and were video-recorded.

Results

Overall, it became clear that participants are more willing to take part in a survey, to download an app and to grant access to sensor data if they see a purpose in doing so on the one hand and have trust on the other. In order to motivate people to participate, it seems particularly important against this background to provide transparent information explaining why to conduct the survey, why they should participate, why access to the sensor data is desired as well as what is being done to ensure a high level of data protection and data security.

Added Value

In official statistics, the development of new survey methods is seen as an important step towards modern data collection. However, modern survey methods can only make a positive contribution if they are used by respondents. The results are intended to provide information on how potential respondents can best be addressed to participate. In the further course of the SSI project, a quantitative field test for recruitment is planned. The results of the focus groups will also be used to prepare this test.