Target groups

Market research professionals and agencies, UX researchers and product teams, "People Who Do Research" (PWDR) in product organizations

Is the workshop geared at an exclusively German or an international audience?

International audience

Workshop language

English

Description of the content of the workshop T

his hands-on workshop demonstrates how autonomous voice AI moderators can conduct, transcribe, and analyze qualitative interviews at scale - bridging the traditional divide between qualitative depth and quantitative reach. Participants will experience the complete workflow: (1) Co-creating a research study through AI-assisted question development and visual stimulus integration; (2) Observing live autonomous voice-to-voice interviews with dynamic follow-up questions and multilingual capabilities across 50+ languages; (3) Exploring real-time analysis that extracts patterns across small and large sample sizes while maintaining traceability to individual voices. Through hands-on configuration of their own study, participants will critically evaluate when autonomous voice AI maintains research rigor versus when human moderators remain essential. Workshop covers end-to-end orchestration, quality validation through full transcript access, GDPR compliance, and integration with existing research workflows.

Goals of the workshop Participants will:

(1) Familiarize themselves with autonomous voice AI moderation as an emerging research methodology—understanding the complete workflow from study co-creation to real-time analysis; (2) Develop evaluation frameworks for assessing AI-moderated interview quality using real pilot transcripts and comparison criteria; (3) Apply co-intelligence principles: learning when to leverage AI autonomy versus when human researcher judgment remains essential; (4) Explore current capabilities and limitations; (5) Gain implementable insights from real deployments: cost models, change management, quality validation, and GDPR compliance; (6) Envision the future of customer research: from quarterly bottleneck to continuous insight engine while maintaining methodological rigor.

Necessary prior knowledge of participants

Basic familiarity with qualitative research methods (moderated interviews, user research, or market research). No technical or AI expertise required. Workshop designed for research professionals, moderators, product teams conducting research, and organizations exploring research scaling.

Literature that participants need to read prior to participation

None required. Workshop is designed to be self-contained with all context provided during the session.

Recommended additional literature

Co-Intelligence - Ethan Mollick, Research that Scales - Kate Towsey

Information about the instructor

Bruno Recht is CEO and co-founder of Userflix, which develops autonomous voice AI moderators for qualitative interviewing. He serves as guest lecturer for Human AI Interaction at Elisava University Barcelona. Previously, he worked as a Designer at Porsche. Userflix's validation includes pilot deployments with the Nielsen Norman Group, IKEA, and leading market research agencies. Recent recognition: marktforschung.de Innovation Award 2025 and RWTH Spin-off Award. Bruno brings a unique combination of design thinking, AI product development, and hands-on implementation experience with research organizations - bridging academic rigor with commercial deployment insights.

Maximum number of participants 25-30 participants

(to ensure everyone can engage hands-on with the platform and receive individual attention during the practical exercises)

Will participants need to bring their own devices in order to be able to access the Internet? Will they need to bring anything else to the workshop?

Yes, participants should bring their own laptop or tablet with internet connection to access the Userflix platform during hands-on exercises. No software installation required (browser-based). Optional: participants may bring headphones for better audio experience during demo interviews

Duration of the workshop

2.5 h

Target groups

Applied researchers, data scientists, methodologists, and research communicators who want to convert datasets, literature syntheses, or analysis outputs into interactive dashboards and tools without deep full-stack expertise.

Is the workshop geared at an exclusively German or an international audience?

International Audience

Workshop language

English

Description of the content of the workshop

This practical workshop will introduce researchers to vibe coding, a rapidly emerging AI-assisted approach that allows researchers to analyze, design, and share their work through natural language interaction, rather than conventional programming.
Vibe coding is a collaborative and iterative workflow where large language models generate either analytic insight, functional code, or web interfaces based on the goals and feedback of the researcher. In practice, it bridges the entire research cycle, from data analysis (qualitative and quantitative) to digital dissemination. It allows researchers to structure and interpret data
in an interactive way, automate parts of their analysis, and then transform their results into interactive, open, and participatory online formats.
The workshop will explore ways in which emerging AI coding assistants—e.g., OpenAI Codex, Claude Sonnet Code—and cloud-based environments—Replit, Vercel, Windsurf—can support social scientists both in performing analytic tasks and creating lightweight public research portals. Guided demonstrations and teamwork exercises take participants through the steps involved in conceptualizing a design system for the research analysis and communication, experimenting with emergent forms of AI-assisted coding templates, and discussing ethical, privacy, and sustainability considerations.
A short demo—the “Mapping Afghan Diaspora Organizations” portal—will illustrate how vibe coding supports both analytical insight and interactive dissemination within one coherent system.

Goals of the workshop

Understand the principle behind vibe coding and its dual role in data analysis and
dissemination of research findings.
- Use AI-driven tools to analyze or structure data using natural, plain-language
interaction; no prior coding experience is necessary.
- Create a simple interactive prototype that communicates research findings, using
Replit and provided templates.
- Reflect on the ethical and practical issues regarding AI-assisted research with respect
to privacy, bias, transparency, and long-term maintenance.

Necessary prior knowledge of participants

1. No prior programming or "coding" experience is strictly necessary. 2. Familiarity with using Large Language Models (e.g., ChatGPT, Claude) is helpful but not required. 3. A willingness to experiment with new digital tools is essential.

Information about the instructor

Ali Reza Hussani is a PhD candidate at the Institute of Communication Science and Journalism at Leipzig University. His research investigates online behaviors on social media, operating at the intersection of policy, evidence, and social impact. His background is in political science and conflict management. He completed a focused data science bootcamp at Spiced Academy Berlin to enhance his methodological toolkit. He is a passionate enthusiast for AI in academia, continuously adopting and experimenting with new AI-assisted workflows to make research more efficient and interactive.

Maximum number of participants

20

Will participants need to bring their own devices in order to be able to access the Internet? Will they need to bring anything else to the workshop?

Yes. Participants must bring their own laptop.

Duration of the workshop
2h

Target groups

Researchers and practitioners who are interested in exploring AI technologies for qualitative data analysis.
No prior programming experience is required — participants just need curiosity and a laptop.

Is the workshop geared at an exclusively German or an international audience?

English-speaking

Workshop language

English

Description of the content of the workshop

Artificial Intelligence is rapidly transforming how qualitative research is conducted — from automating analysis to novel approaches of results presentation. In this hands-on workshop, we will explore how large language models (LLMs) and embedding technologies can be used in qualitative research.
Participants will:
⁠Gain an understanding of the underlying principles of LLMs and embeddings.
⁠Explore real-world use cases in qualitative research, such as coding interview data, clustering open-ended responses, and generating actionable insights.
⁠Work through guided exercises demonstrating how AI can support coding, clustering, and summarizing textual data in market research settings.
We will also discuss methodological challenges, transparency, and limitations of AI-based approaches, and share benchmarking strategies for evaluating model performance in research contexts. The workshop concludes with an open Q&A and exchange session to discuss participants’ experiences, opportunities, and concerns about integrating AI in qualitative research practice.

Goals of the workshop

By the end of the workshop, participants will gain knowledge of LLMs and embeddings.

Necessary prior knowledge of participants

No prior experience with software development or AI tools is required.

Information about the instructors

Founders of xelper, a startup providing AI solutions for qualitative market research.

Participants should bring their own laptops with Internet access.

Duration of the workshop

2,5

Target audience
Qual and visual data analysts, consumer insights experts, brand management and marketing.

Workshop description

Over the past six years, Human8 has been exploring the evolving needs, desires and aspirations that shape people’s lives, alongside their changing expectations of brands. Each year, our trend report returns to one guiding question: what matters to people today and tomorrow?
To answer this question, Human8 conducts research across the globe to gain insights on what matters in the following year. For 2026, also visual data has been collected. We asked participants to show us their fridge and pantry during AI-empowered interviews.
From this data set, 70 pictures will be available to be used for visual decoding. Manually reviewing and coding this volume of data for themes and patterns usually takes days. Our AI tool accelerates the process, leveraging large language models in a secure, walled environment.
During the workshop, we’ll use our proprietary ‘Visual Decoding Canvas’ and carefully curated prompts to guide AI analysis, detect themes, gaps and even conduct semiotic analysis. The goal: to uncover what these everyday visuals reveal about people’s needs and desires around food in 2026.
Here, AI acts like a “smart co-researcher”, interpreting the data through multiple lenses – from a journalist to an R&D scientist – enhancing both speed and insight quality. This approach allows us to rapidly identify patterns, surface emotional drivers and gauge the strength of each theme.
The impact is immediate and tangible. AI reduces analysis time from days to hours, freeing human analysts to focus on curating insights, building stories and strategic interpretation. At the same time, AI uncovers nuances that might otherwise have been missed.
Ultimately, we’ll show that AI doesn’t replace human expertise, it amplifies it. By combining secure, AI-enabled tools with rigorous research and close collaboration, we’ve got a repeatable approach to generate high-quality, actionable insights at speed.

Three course objectives
Uncover opportunities of using LLM to transform research data into insights via an insights platform, an AI agent and engineered prompts
Learn how to use AI to help with the analysis of visual data
Get an insight into the overall results and learn what matters in 2026

Artificial intelligence opens up numerous new possibilities in online research. Synthetic data, automated quality assurance, intelligent recruitment strategies, and new pattern recognition tools promise unprecedented efficiency—while also raising fundamental questions: How reliable are AI-generated samples? Can algorithmic methods really reduce nonresponse? And how can we prevent models from reproducing systematic biases on a large scale?

Using concrete examples, the presentation shows how AI can improve data quality, what pitfalls lurk in automated data generation, and what skills are needed to work with it responsibly. The goal: a realistic, inspiring look at the interplay between statistics, humans, and machines – practical and future-oriented.

Relevance & Research Question:
Large Language Models (LLMs) are increasingly used to transform quantitative survey data into accessible, actionable insights. Yet, their adoption in professional research settings is limited by concerns over accuracy, transparency, and the risk of hallucinated results, especially when advanced statistical methods are involved. This presentation addresses the central question: How can LLMs be reliably integrated into the workflow of survey analysis and reporting, ensuring both rigor and user trust and adding real value? We showcase the Bertelsmann Stiftung’s deployment of the 'SurveyMind' chatbot, which enables interactive, natural-language exploration of survey findings while meeting strict quality and security standards.

Methods & Data:
Our approach combines precomputed, validated statistical analyses with a Retrieval-Augmented Generation framework. SurveyMind operates exclusively on anonymized survey data processed on secure servers, with no data used for external model training. The system accesses a pool of results that are pre-generated using a validated methodology, thus ensuring that the LLM never performs its own calculations but instead retrieves and explains trustworthy findings. We detail the iterative development process, including multi-stage user testing, expert ratings, and continuous feedback from researchers and clients at the Bertelsmann Stiftung.

Results:
SurveyMind has demonstrated that LLM-enabled chat interfaces can deliver accurate, context-aware textual outputs, such as executive summaries and press releases, directly from raw, interview-level survey data. The system supports nuanced queries, adapts to user feedback, and provides transparent links to underlying statistical analysis, significantly reducing turnaround times for survey deliverables and empowering both researchers and clients to explore findings independently. Moreover, a variety of further applications emerged during our client discussions, from pre-drafting press releases to checking data and comparing trends to presenting data more accessibly to specific target groups such as young people.

Added Value:
This work provides a blueprint for integrating LLMs into quantitative research workflows without compromising on analytical rigor or data security. By combining technical safeguards with user-centered design, our chatbot solution bridges the gap between advanced automated analytics and practical communication needs. The Bertelsmann Stiftung’s client perspective highlights real-world benefits, including enhanced efficiency, flexibility in reporting, and new ways to disseminate results.

Relevance & Research Question

The idea to generate so-called silicon survey samples with large language models (LLMs) has gained broad attention in both academic and market research as a promising timely and cost-efficient method of data collection. However, previous research has shown that LLMs are likely to reproduce limitations and biases found in their training data, including underrepresentation of certain subgroups and imbalanced topic coverage. Using survey data from a probability-based online panel in Netherlands, we conduct a large-scale analysis examining model performance across different item types (factual, attitudinal, behavioral) and social-science-related topics, to identify when and for whom synthetic approaches perform best. We further explore strategies to mitigate potential performance limitations, including few-shot leveraging of LLMs on longitudinal survey data.

Methods & Data

We compare existing survey data with LLM-generated synthetic data, using the 17th wave of LISS Panel fielded in 2024. We selected nine survey items that cover key social-science topics, such as health, family, work, and political values. We chose the items based on their characteristics (factual, behavioral, attitudinal) as well as outcome type. We leverage the LLM agents based on two different few-shot learning tasks: a sociodemographic setup, which draws on seven individual background variables, and a panel-informed setup, which additionally incorporates previous responses. We compare the generated data across five different proprietary LLMs, including GPT-4.1, Gemini 2.5 Pro, Llama 2 Maverick, Deepseek-V3 and Mistral Medium 3.

Results

Our first results show a deep lack of accuracy in the sociodemographic few-shot setup with an average accuracy of 0.2 and a strongly underestimated variance across items and topics. Prediction errors vary significantly across subgroups, particularly by age, showing differences not only in magnitude but also in the direction of errors. Adding longitudinal survey responses to the few-shot input substantially improves the prediction quality, showing a 40 to 60 percentage point increase in overall accuracy, correcting variance underestimation, and reducing subgroup disparities.

Added Value

Our research contributes to a more responsible application of silicon survey samples by providing practical guidance for researchers and survey practitioners to evaluate and improve AI-generated datasets.

Relevance & Research Question

With the rise of smartphone use in web surveys, voice or oral answers have become a promising methodology for collecting rich data. Voice answers present both opportunities and challenges. This study addresses two of these challenges—labor-intensive manual transcription and coding of responses, by answering the following research questions: (RQ1) How do three leading Automatic Speech Recognition (ASR) tools —Google Cloud Speech-to-Text, OpenAI Whisper, and Vosk— perform across various dimensions? (RQ2) How similar or different are the codes of transcribed responses generated by a human and the OpenAI GPT-4o model?

Methods & Data

We used data collected in the Netquest opt-in online panel in Spain in February/March 2024. The questionnaire included over 80 questions, mainly about citizens’ perceptions of nursing homes. This study focuses on one open-ended narrative question in which respondents were asked to explain why they selected a given answer in a prior closed question on the amount of information nursing homes provide to the general public. For this question, participants were initially asked to answer through voice recording. In a follow-up, respondents skipping the question were also offered to type in a text box. We extracted various aspects from the transcriptions and compared them across ASR tools and human vs GPT coding. After data cleaning, 859 panellists were used for analyses.

Results

We found that each of the ASR tools has distinct merits and limits. Google sometimes fails to provide transcriptions, Whisper produces hallucinations (false transcriptions), and Vosk has clarity issues and high rates of incorrect words. Human and LLM-based coding also differ significantly. Thus, we recommend using several ASR tools and implementing human as well as LLM-based coding, as the latter offers additional information at minimal added cost.

Added Value

This study offers valuable insights into the feasibility of using voice answers. Depending on the most critical quality dimension for each study (e.g., maximizing the number of transcriptions or achieving the highest clarity), it provides guidance on selecting the most suitable ASR tool(s) and insights into the extent to which LLMs can assist with manual tasks like answer coding.

Relevance & Research Question: -The profusion of data from the introduction of CAI has created three main problems for researchers, volume, complexity and understanding quality. The disjointed nature of many survey data collections has fragmented this across many organisations, during in which much valuable information is lost or opaque to the researcher using data at the end of the data lifecycle.-

Methods & Data: -Focused and well contructed Machine Learning offers the possibility of automating at scale the available metadata resources into standardised metadata which can be made available in repositories for discover, and creating the detailed granular metadata that a researcher needs to evaluate quality of complex survey prior to data applications or access.

The collaboration between CLOSER, University of Essex, University of Surrey and Scotcen has been developing machine learning models, utilising the CLOSER Discovery metadata store to improve the timeliness and accuracy of metadata extraction to deliver high quality metadata.-

Results: -Preliminary results will be presented on the success and challenges faced in taking complex survey instruments and rendering them into DDI-Lifecycle for ingest into repository platforms and the opportunites for further enhancement of these metadata resources for reuse of questions into the survey development pipeline.-

Added Value: -The abilty to create high quality reuable metadata across the survey specification, collection, management and dissemination lifeycle would bring efficiencies in terms of costs, improvements in quality, discoverability and understanding of these complex data resources.-

Relevance & Research Question:

The integration of large-scale passively metered data with established survey methodologies has become a central development in contemporary market and social research. In particular, digital trace data originating from major platform operators such as Google, Meta and TikTok represents a highly promising source for enhancing sociological measurement, audience segmentation and modeling. However, substantial challenges remain with respect to obtaining continuous, consent-based access to such platform data, and to linking heterogeneous data types in a methodologically robust and privacy-compliant way.

Methods & Data:

Datapods has established a novel approach with its own proprietary user panel that allows for the combination of survey methodologies to define socio-economic, value-based and demographic profiles with direct copies of the personal data from big tech companies. We first established the baseline for these profiles by utilizing common survey methodologies. These measures serve as our ground truth for subsequent validation. In a second step, we linked these baseline profiles with corresponding behavioral data streams, including web-browsing histories, YouTube viewing histories and interaction logs on Instagram, Facebook and TikTok. We identified key indicators for different data types to be the most influential for the profile of the panelist and joined data across types to ensure a holistic picture about the user.

Results:

Early results indicate that only a relatively small subset of survey items adds substantial incremental information beyond what is already embedded in the digital trace data. Researchers can, in practice, rely on high-quality, consent-based personal data to assign users to pre-defined socio-demographic and value-based target group profiles with high accuracy, and to identify fundamental clusters and segments within the panel. The results suggest that passively collected platform data can function as a proxy for many conventional survey indicators. Final empirical results will be available by the end of 2025 and handed in subsequently.

Added Value:

This passively metered, platform-data-based approach to user profiling and segmentation substantially enhances survey-centric designs and, for certain research questions, can partially or even fully substitute conventional survey data collection. It enables more granular behavioral indicators and provides a scalable solution for continuous audience measurement and sociological analysis.

Relevance & Research Question

Understanding how respondents interact with survey instruments is crucial for facilitating the response process and improving data quality. Especially for establishments there is still a lack of insights into their response behavior. By analyzing respondent-level paradata we aim to explore the answering process in detail. We investigate how establishments navigate through the online questionnaire of the IAB Establishment Panel, focusing on differences between survey modes (CAI versus Web) and samples (panel versus refreshment). Following this, we investigate whether distinct response patterns can be identified and if there is a need for a tailored response process by utilizing important establishment characteristics.

Methods & Data

We analyze detailed respondent-side paradata from the IAB Establishment Panel, conducted annually by the Institute for Employment Research (IAB). Since 2018, the survey has been implemented in a mixed-mode design with computer-assisted personal interviewing (CAI) and an online mode (Web) using identical software. In 2022, we collected paradata logging every click, answer, and timestamp at the second level. After creating an audit trail for each respondent, we identify appropriate paradata indicators and apply cluster analysis to identify groups of establishments with similar navigation and response behaviors.

Results

The visualization of paradata via audit trails reveals differences in navigation behavior. Some establishments follow a straightforward sequence, while others loop back or perform multiple checks before submission. Paradata indicators reveal that Web respondents take longer, more breaks, use more tree view, edit more answers and drop-out more often than CAI respondents. Preliminary results of the clustering analysis show that we can identify two clusters for each combination of sample and mode. Cluster 1 seems to include the linear respondents, while cluster 2 includes all other respondents with more conspicuous response behavior.

Added Value

By combining visualization and clustering of establishments response processes, we provide an empirical approach of utilizing paradata. Our results help to understand how establishments navigate and respond to a survey. Simultaneously, we give recommendations for the survey design and evaluate mixed-mode establishment surveys.

Relevance & Research Question: The emerging field of sensor-based journalism relies on data beyond human reach collected by sensors (Diakopoulos, 2019; Loebbecke & Boboschko, 2020). Research on sensor-based journalism (Boboschko & Loebbecke, 2025) studies the impact of identity cues and outlet reputation on content credibility (Sundar, 1999). Communication and media studies (Boller et al., 1990; Wathen & Burkell, 2002) analyze how testimonial-based 'argument strength' drives journalistic content credibility. Aiming to complement both research streams, we ask how argument strength influences content credibility in the context of sensor-based journalism.

Methods & Data: This study deploys a between-subjects online experiment (N= 853) followed by multi-group covariance-based structural equation modeling. Two treatment groups read an article on traffic affecting air pollution in London, one drawing evidence from sensor data, the other from testimonials. As endogenous latent variables, we measure argument strength with four items and content credibility with five items. Measurement items, wording of all items, descriptive statistics, standardized factor loadings, squared multiple correlations for each indicator, construct-level reliability, and convergent validity are available upon request.

Results: Sensor-based journalism fosters argument strength and credibility formation; statistical details and interpretations are available upon request. Controlled online experiments allow for realistically simulating (journalistic) media consumption.

Added Value: Promoting research in sensor-based journalism in times of AI-based hallucinations – an increasingly relevant phenomena in today's democracies.

References:

Boboschko, I. & Loebbecke, C. (2025). Identity cues influencing article credibility in sensor-based journalism, European Conference on Information Systems (ECIS), Amman, Jordan.

Boller, G., Swasy, J., & Munch, J. (1990). Conceptualizing argument quality via argument structure, Advances in Consumer Research, 17(1), 321-328.

Diakopoulos, N. (2019). Automating the news: how algorithms are rewriting the media, Harvard University Press, Cambridge, MA, US.

Loebbecke, C., & Boboschko, I. (2020). Reflecting upon sensor-based data collection to improve decision making, Journal of Decision Systems, 29(Sup1), 18-31.

Sundar, S. (1999). Exploring receivers' criteria for perception of print and online news, Journalism & Mass Communication Quarterly, 76(2), 373-386.

Wathen, C., & Burkell, J. (2002). Believe it or not: factors influencing credibility on the web, Journal of the American Society for Information Science and Technology, 53(2), 133-144.

Relevance & Research Question
Armed conflicts fundamentally disrupt daily life, yet their impact on digital media behavior remains understudied, particularly across different population groups. This study examines how the October 2023 Israel-Hamas war affected media consumption and social media activity among Jewish and Arab citizens of Israel, investigating the relationship between anxiety and online behavior changes. The research addresses: How did the war affect social media activity across populations? Did anxiety levels differ between groups? What is the relationship between anxiety and social media behavior during conflict?
Methods & Data
We conducted representative online panel surveys with 505 Jewish and 146 Arab respondents through Ipanel during October-November 2023. The survey measured retrospective media consumption (before vs. during war), social media activity changes, and generalized anxiety using the GAD-7 scale. Media consumption was categorized into four daily time brackets. Social media activity changes were measured on a five-point scale. We employed correlation analyses examining relationships between anxiety and social media behavior across population groups.
Results
Jewish respondents showed dramatic media consumption increases, with heavy users (4+ hours/day) rising from 19.2% to 38.6%, while Arab respondents showed minimal change. Approximately 31% of Jewish and 6% of Arab respondents reported increased social media activity. No significant anxiety differences emerged between populations (Jewish M=9.38, Arab M=8.67). Critically, opposing anxiety-behavior patterns emerged: among Arabs, higher anxiety correlated with decreased social media activity (r=-.237, p<.01), while Jewish respondents showed non-significant positive trends (r=.059), suggesting anxiety drives increased engagement in majority populations but withdrawal in minorities during intergroup conflict.
Added Value
This research challenges assumptions that populations respond uniformly to crisis events, revealing that majority and minority groups exhibit fundamentally different digital coping mechanisms during intergroup conflicts. The opposing anxiety-behavior relationships suggest social media serves different psychological functions across populations—as an information-seeking and community tool for majorities versus a stress source for minorities. These findings have critical implications for targeted crisis communication, mental health interventions in conflict zones, and understanding how digital platforms may amplify psychological disparities during emergencies. Results indicate researchers and practitioners must account for population-specific responses when designing crisis communication strategies and evaluating social media's role in conflicts.

Relevance & Research Question

Generative AI has emerged in recent years as a key technology shaping both consumers’ everyday lives and the media and advertising industry. It competes with major search engines such as Google in searching information and opens up new efficient and profitable opportunities for advertisers to engage consumers – for example, through AI-generated advertising, synthetic brand influencers or AI shopping agents.
This study addresses three core questions: (1) How do consumers in Germany use generative AI in daily life and how have their attitudes evolved over time? (2) What opportunities and risks are perceived regarding GenAI? (3) To what extent do usage and attitudes toward GenAI influence the acceptance of AI-generated advertising?

Methods & Data

The research applies a mixed-method design comprising three modules:

• Module 1: In-depth interviews (n=18) with non-users, occasional users, and heavy users to explore attitudes and everyday usage of GenAI as well as perceptions of AI-generated advertising by blind-testing AI-generated TV commercials (A/B test).
• Module 2: Online survey (n≈4,000; ages 16–69; online-representative) to quantify acceptance and perception of AI-generated advertising by blind-testing AI-generated TV commercials (A/B test).
• Module 3: Tracking study (three annual waves since 2023; n≈1,000 per wave; ages 16–69; online-representative) to analyze temporal changes in usage, attitudes and acceptance of GenAI.

Results

Preliminary findings indicate a steady increase in familiarity and regular usage. GenAI is perceived as indispensable for information gathering. While efficiency is highly valued, concerns about data privacy, misinformation and job displacement persist but do not significantly affect the rate of adoption and usage. AI-generated advertising is considered forward-looking but evokes mixed reactions: younger, tech-savvy users show higher acceptance, whereas older cohorts remain skeptical. Synthetic influencers face the strongest resistance, while AI-generated TV commercials and shopping assistants receive comparatively higher acceptance.

Final results will be available in January 2026.

Added Value

The study provides empirically grounded insights for advertisers to strategically leverage the potential of generative AI. It identifies target groups open to AI-based advertising formats and highlights acceptance barriers. These findings support the development of effective communication strategies in an increasingly AI-driven media landscape.

Relevance & Research Question

Refugees in first host countries often face adverse conditions including limited legal rights, restricted employment access, and inadequate social services. While resettlement programs offer long-term settlement opportunities for particularly vulnerable refugees, little is known about how these individuals evaluate and prioritize different aspects of potential destination countries. Traditional survey items fall short in capturing refugees' complex decision-making processes regarding resettlement. This study addresses this gap by employing a choice-based conjoint experiment to reveal how refugees eligible for resettlement weight different factors in their settlement decisions.
Methods & Data

The study uses data from an online survey (n = 375) conducted jointly by BAMF-FZ and the University of Bielefeld, targeting particularly vulnerable refugees eligible for resettlement procedures. Respondents complete up to four paired comparisons of hypothetical destination countries that vary across eight key characteristics: family presence, diaspora size, crime rates, language skills, attitudes toward refugees, political systems, labor market access, and lifestyle familiarity. After each choice, respondents evaluate their perceived opportunities for building a future in both countries, enabling analysis of both relative preferences and absolute settlement potential assessments.
Results

The conjoint approach successfully reveals how refugees trade off different aspects of potential host countries when making settlement decisions. Preliminary findings indicate that attitudes toward refugees and political systems emerge as the most influential factors in resettlement preferences. Notably, the relative importance of these factors varies considerably across refugee groups, highlighting the heterogeneity in decision-making priorities shaped by diverse experiences and backgrounds.
Added Value

This study advances the methodological and substantive understanding of refugee migration preferences. By focusing on refugees in their first host countries, the study provides reliable insights into real-world decision-making under precarious conditions. Thus, it offers valuable lessons for researchers targeting vulnerable groups through choice-based conjoint experiments.

Relevance & Research Question
The perception of flight safety represents an exemplary case for studying how digital media mediate fear, risk, and trust. While aviation remains one of the safest modes of transport, emotional reactions to incidents are often disproportionate, amplified by online news and algorithmic visibility. This paper asks: How does exposure to digital media shape fear of flying and perceived aviation risk? and To what extent can online sentiment predict self-reported anxiety in surveys? The study addresses an underexplored link between risk perception, media consumption, and emotion measurement in digital environments.
Methods & Data
I integrate two complementary data sources. First, a national online survey (N = 921) which measures emotional, cognitive, and behavioral components of flight anxiety, including trust in institutions, information sources, and prior flying experience. Second, we analyze a corpus of 43,000 online news articles containing the keyword “aviation” (2022–2023), collected from Romanian mainstream media. Using topic modeling (LDA), sentiment analysis (NRC and BERT-based classifiers), and correlation with survey variables, we identify symbolic frames of fear (“catastrophe,” “loss of control,” “safety reassurance”). All analyses were conducted in R.
Results
Findings reveal that individuals with higher exposure to negatively valenced news content report significantly higher levels of aviation anxiety (p < .01). Topic models show a predominance of emotional frames emphasizing loss, danger, and uncertainty, while trust in technological expertise moderates these effects. The integrated model combining sentiment scores and survey data predicts fear-of-flying.
Added Value
This study advances methodological innovation in online research by combining survey data with large-scale digital trace data. It demonstrates how affective communication and digital media coverage can shape collective perceptions of risk, offering new insights for both computational social science and public communication of safety. The proposed framework can be extended to other domains of perceived technological risk (AI, climate, or health).

Relevance & Research Question

Social surveys often collect information about industry and occupation. The traditional ‘gold-standard’ approach has been to capture this information by asking open questions about job title and duties which are later classified into standardised coding systems by expert coders. The shift towards self-completion surveys makes this more challenging as respondents often provide insufficient information to facilitate accurate coding. In addition, office-based coding is expensive and time consuming.

Closed-list questions, where respondents choose from pre-defined categories, could reduce response burden and coding costs and potentially also remove ambiguities inherent to open-ended responses. However, a challenge is that category labels can be difficult to interpret.

This paper assesses whether closed-list questions for industry and occupation can produce industry and occupation classifications comparable to those derived from manually coded open text responses.

Methods & Data

We use data from two waves of the NatCen Panel survey, a UK probability-based online panel, which employs a mixed-mode design combining online self-administration with telephone interviews. In these two waves, the panel supplemented its standard industry and occupation open-text questions (which are manually coded) with closed-list questions. We compare agreement between the two methods, using both descriptive analysis and regression models.

Results

For industry, the mean agreement rate between the closed-list and 1-digit manual codes was 64%, with variation across industries. Agreement was lower for occupation, at 56% for 1-digit codes and 46% for 2-digit codes, with significant differences across occupation types. Agreement rates were also influenced by sociodemographic and the length of open-text entries, with shorter descriptions generally leading to higher agreement rates.

Added Value

This study provides a first empirical assessment of the quality of occupation and industry data collected using a closed-list approach, offering evidence on the method's potential and limitations. The findings help identify potential improvements for collecting industry and occupation data in online surveys.

1 Relevance & Research Question
Large language models (LLMs) often perpetuate gender biases and stereotypes learned from uncurated training data, underscoring the importance of reliable methods for measuring bias. Although several methods of measuring gender bias have been proposed, there are concerns about the ambiguities and inconsistencies in how these methods conceptualize and operationalize gender bias. A promising alternative to these existing methods is LLM psychometrics, which applies psychometric tests originally developed for humans to evaluate human-like characteristics of LLMs, such as personality. Psychometric tests have several advantages: they are grounded in psychological theory, have been rigorously validated, and provide standardized assessment tools. However, it remains unclear how these assessments can be meaningfully applied to LLMs. In this thesis, I explore whether the Ambivalent Sexism Inventory (ASI) [4] can be used to measure sexism in LLMs. To address this question, I propose a systematic validation approach grounded in established psychometric standards.

2 Methods & Data

2.1 Inducing Individuals Using Context data
To approximate psychometric testing conditions, we conceptualize an LLM as a representation of a population and induce individuals by prompting the model with different context information. Two types of contexts are used: human-chatbot interactions and personas. Using personas is a method that has been used in previous LLM psychometrics studies. However, this does not reflect how most users incorporate LLMs into their everyday lives. Therefore, we also use real-life interactions between users and LLM-powered chatbots as a context type. For each context type, n = 300 contexts are sampled from the Chatbot Arena Conversations dataset [5] and the Persona Hub dataset [6], respectively.

2.2 Ambivalent Sexism Inventory

The ASI consists of 22 items, such as, “Women exaggerate problems they have at work.” Answers are provided using a 6-point Likert scale ranging from 0 (disagree strongly) to 5 (agree strongly). The overall ASI score of one context is computed by averaging the answer scores of all items given that context.

2.3 Data Collection

Each item is prompted individually to mitigate the effects of item order. In addition to the item, the prompt contains the context, general instructions, and answer scale. Answer scores are extracted directly from a model’s text response. Data is collected from six state-of-the-art LLMs, including Llama 3.3 70B Instruct, Mistral 7B Instruct, and Qwen 2.5 7B Instruct.

2.4 Psychometric Quality Critera

The systematic validation is conducted by first evaluating reliability (i.e., the consistency of a test) using three criteria:

(1) Internal consistency (Cronbach’s alpha): How consistent are responses across all items of the ASI?

(2) Alternate-form reliability (Pearson correlation): How consistent are the ASI scores when rephrasing the items without changing their meaning?

(3) Option-order symmetry (Pearson correlation): How consistent are the ASI scores when randomly changing the order of answer options?

If reliability is deemed acceptable based on established psychometric interpretation thresholds, validity (i.e., the extent to which a test measures what it is supposed to measure) is evaluated in a second step based on the following three types of validity:

(1) Concurrent validity (Pearson correlation): Does the ASI score align with the amount of sexist language used in a downstream task (writing reference letters)?

(2) Convergent validity (Pearson correlation coefficient): Does the ASI score align with the sexism score of another established sexism scale, the Modern Sexism Scale [7]?

(3) Factorial Validity (Confirmatory factor analysis; CFA): Do the items group together in a way that makes sense based on the underlying theory?

These analyses are conducted for each of the six models and two context types.

3 Results
For 10 out of the 12 model-context type combinations, the ASI displays low reliability, indicating low consistency and high measurement error. Only for two models - Llama 3.3 70B Instruct and Qwen 2.5 7B Instruct - reliability is deemed acceptable when using Persona Hub contexts. However, the validity evaluation for these two cases indicates low validity. Crucially, for both Llama 70B and Qwen, ASI scores do not significantly correlate with sexist behavior in the downstream task (r = -0.1, p = .523 and r = 0.08, p = .612 respectively). Based on these findings, the ASI is not considered valid for any of the six LLMs. However, the results also indicate that the choice of context type influences evaluation outcomes.
4 Added Value

The findings of this thesis emphasize that tests developed and validated for humans should not be automatically assumed to be valid for LLMs. This underscores the importance of conducting validation studies before interpreting psychological test scores for LLMs, which has rarely been done in the field of LLM psychometrics [3].

However, the results also raise several important questions and issues on how to conduct such validations. What constitutes an “individual” in the context of LLMs? How should a sample of “individuals” be selected? These issues highlight the need to adapt the psychometric validation approach to the LLM domain in future studies.

[1] BLODGETT, S. L., LOPEZ, G., OLTEANU, A., SIM, R., AND WALLACH, H. Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Aug. 2021).

[2] PELLERT, M., LECHNER, C. M., WAGNER, C., RAMMSTEDT, B., AND STROHMAIER, M. AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories. Perspectives on Psychological Science 19, 5 (Sept. 2024).

[3] LÖHN, L., KIEHNE, N., LJAPUNOV, A., AND BALKE, W.-T. Is Machine Psychology here? On Requirements for Using Human Psychological Tests on Large Language Models. In Proceedings of the 17th International Natural Language Generation Conference (Sept. 2024).

[4] GLICK, P., AND FISKE, S. T. Hostile and Benevolent Sexism: Measuring Ambivalent Sexist Attitudes Toward Women. Psychology of Women Quarterly 21, 1 (Mar. 1997).

[5] ZHENG, L., CHIANG, W.-L., SHENG, Y., ZHUANG, S., WU, Z., ZHUANG, Y., LIN, Z., LI, Z., LI, D., XING, E. P., ZHANG, H., GONZALEZ, J. E., AND STOICA, I. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena, Dec. 2023. arXiv:2306.05685.

[6] GE, T., CHAN, X., WANG, X., YU, D., MI, H., AND YU, D. Scaling Synthetic Data Creation with 1,000,000,000 Personas, Sept. 2024. arXiv:2406.20094.

[7] SWIM, J. K., AIKIN, K. J., HALL, W. S., AND HUNTER, B. A. Sexism and racism: Old-fashioned and modern prejudices. Journal of Personality and Social Psychology 68, 2 (1995).

Relevance & Research Question

Designing high-quality survey questions is a complex task. With the rapid development of large language models (LLMs), new possibilities have emerged for supporting this process, particularly in the automated generation of survey items. Despite growing interest in LLM applications from industry, published research in this area remains limited, and little is known about the quality and characteristics of survey items generated by LLMs, as well as the factors influencing their performance. This work provides the first in-depth analysis of LLM-based survey item generation and systematically evaluates how different design choices, such as prompting technique, model choice, and fine-tuning, affect item quality.
Methods & Data

Five LLMs, namely GPT-4o, GPT-4o-mini, GPT-oss-20B, LLaMA 3.1 8B, and LLaMA 3.1 70B, generated survey items for 15 concepts across four domains: work, living conditions, national politics, and recent politics. For each concept, three prompting techniques (zero-shot, role, and chain-of-thought prompting) were applied. Additionally, the best performing model and prompting combination, namely GPT-4o-mini combined with chain-of-thought prompting, was fine-tuned on high-quality survey items to explore the effects of fine-tuning on the quality of generated items.
To assess the quality of the generated survey items, we use the Survey Quality Predictor (SQP; https://sqp.gesis.org), a validated tool for estimating the quality of attitudinal survey items. SQP predicts item quality, validity, and reliability based on a range of coded formal and linguistic characteristics of the survey items, e.g., question formulations, the response scale types, and the inclusion of an introduction text. To code these characteristics, we used an LLM-assisted procedure. A GPT-4.1-nano model was fine-tuned on examples of coded characteristics from the SQP database to automate the coding process, supplemented by manual revision.
The analysis allows us to evaluate not only overall quality but also around 60 specific survey item characteristics, offering a detailed view of how LLM-generated questions differ.

Results

The findings show striking differences in survey characteristics across the different models and prompting techniques. As an example, the type of response scale strongly differed by model family. Closed-source GPT models consistently generate five-category, bipolar response scales with medium correspondence between numeric and verbal labels. They rarely include a 'don't know' option and typically begin with the negative end of the response scale. LLaMA models show greater variation: they generate a wider range of response options, show greater inconsistency between numeric and verbal labels, differ in whether scales start with the positive or negative option, and the inclusion of a 'don't know' option varies by model size. The inclusion of an introduction text in the survey item depends strongly on the type of prompting technique used. Survey items generated with chain-of-thought prompting often included an introduction text.

Regarding the quality of the generated survey items, the findings show that the prompting technique employed is a primary factor influencing the quality of LLM-generated survey items. Chain-of-thought prompting leads to the most reliable outputs. Closed-source GPT models generally produce more consistent and higher-quality items than open-source LLaMA models. The open-source GPT-oss-20B model failed to complete the given task, i.e., it did not produce a usable survey item in 68% of the cases. The survey topics `work' and `national politics' generate survey items with higher quality compared to `living conditions’ and `recent politics’. Among all configurations, GPT-4o-mini combined with chain-of-thought prompting achieved the best overall results. Fine-tuning on high-quality survey items added variety in survey item characteristics but did not lead to noticeable improvements in item quality.

Added Value

For the GOR community, the study offers empirical evidence on how LLMs can (and cannot) be reliably integrated into questionnaire design workflows, providing a systematic basis for evaluating emerging AI tools in survey research and informing methodological decisions in applied settings. In addition to highlighting the strengths and limitations of LLMs for survey item generation, the work helped to identify concrete weaknesses within the SQP-based evaluation pipeline, particularly regarding the coding of characteristics. The development of an LLM-assisted coding procedure contributes to future research in AI-supported survey design by laying the necessary groundwork for a fully automated pipeline that can code the SQP item attributes at scale.

Relevance & Research Question: In an increasingly digitalized world, people are generating large amounts of digital trace data daily as a result of the constant recording of their information and activity. These data contain information with the potential to facilitate human behavior studies due to their accessibility and fine-grained nature. Data donation has emerged as a promising approach to get access to such data from specific online platforms, such as Instagram. In a data donation study, people are invited to answer a survey and subsequently asked to request, download, and finally donate their online platform data to research. However, as raw data, these donated Data Download Packages (DDPs) might contain highly sensitive or personal information. Previous research on data donation has solved this issue by developing privacy-preserving methods to anonymize and aggregate data directly on participants' devices. Data donation workflows typically rely on scripts designed to extract and process relevant data from these structures. Researchers who develop these extraction scripts often face the challenge that the data structure of DDPs is undocumented for most online platforms and can be subject to modifications from the entities that issue them. As such, the processing scripts that are developed to extract information from these DDPs face deprecation threats and require the researchers' manual intervention. This thesis investigates the feasibility of employing Large Language Models (LLMs) in automatically processing and extracting relevant information from these structures by generating code as an alternative to traditional, manually maintained data analysis tools and in an effort to minimize manual script development and adjustment. It also aims to make data donation research more accessible by examining this approach as a means for researchers without technical expertise to analyze and interpret structurally complex DDPs.

Methods & Data: This study evaluates six open-source LLMs across thirteen Instagram DDPs of varying size and structural complexity to examine their capabilities and limitations in this domain. The models are asked a series of data-specific queries designed to assess their abilities in interpreting the provided data package, retrieving correct information, and processing it accordingly. Two main experimental settings are implemented and compared, where context on the supplied information, response formatting, and data structure are provided to the models across different setups. In the first setting, this information is integrated through an external knowledge base using Retrieval-Augmented Generation (RAG), whereas in the second, it is directly provided to the models within the prompt. The outputs of the models in each setting are assessed in terms of accuracy, common error patterns, and code generation habits.

Results: Although RAG is a methodology originally developed to reduce hallucination and improve models' responses, our findings reveal that, for this task, directly providing the context in prompts yields higher accuracy in comparison. Overall, the models' performance is unsatisfactory in both settings. Their shortcomings can be attributed to the overly complex structure of the packages used in this evaluation. Ambiguous and similar naming conventions are observed throughout the Instagram DDP structure and enhance the hallucination and inaccuracy of the models' outputs. In an effort to improve the models' performance, when manually designed and explicit instructions on how to navigate these packages are provided, the LLMs perform substantially better, with some achieving near-optimal results. These instructions would also be subject to changes in response to structural updates to the DDPs provided by the online platforms. Ironically, the very manual intervention that this research sought to reduce is necessary in achieving greater performance for the evaluated LLMs at this current stage.

Added Value: This research presents an evaluation of LLMs for adaptive code generation in donated data analysis tasks and explores their potential as an alternative approach in this domain. It lays the groundwork for easing the skill-based entry that data researchers without programming skills face in navigating the structural complexities and challenges inherent in data donation studies. By identifying the strengths and current limitations of LLMs in understanding and adapting to evolving data structures, this study helps set realistic expectations for their application in this field while highlighting the considerable room for future improvement.

Relevance & Research Question

The Travelling Salesman Problem (TSP) requires identifying the shortest route to visit a set of locations and return to the starting depot, given the locations and the distances between them. The Vehicle Routing Problem (VRP) is similar but with multiple vehicles, with the number of customers per route limited by vehicle capacities. This was first formulated in 1959 by Dantzig and Ramser [1] and has since been further extended, such as restricting the time window for customer visits.

The VRP is NP-hard, and so finding exact solutions becomes computationally intractable with increased problem instance size. As a result, heuristic and metaheuristic algorithms have replaced exact calculations. More recently, routes are found using reinforcement learning (RL) approaches, requiring less task specific expertise than heuristic selection but with the computational cost of deep learning.

With each new RL model, the creators generally compare against either heuristics or the very earliest RL models from Nazari et al. [2] and Kool et al. [3]. Therefore, it is often unclear the extent to which new methods achieve good performance. Additionally, whilst heuristics require computation for each new instance, RL models instead have a high up-front cost due to their training which must be justified. This thesis evaluates a range of RL methods against heuristics for the Capacitated VRP. Specific attention is paid to complex problem variants (the Time Window variant) and complex problem instances (such as with more customers). It aims to determine whether RL methods should be used over established and theory-informed heuristics.

Methods & Data

Six RL models for the CVRP are compared, covering a range of architectures and optimisation procedures: Nazari [2], AM [3], AM PPO, POMO [4], Sym-NCO [5] and MDAM [6]. The Nazari model is unique in using a recurrent neural network, with all others based on a Transformer architecture. The Attention Model (AM) uses a Graph Attention Model with REINFORCE. This is further adapted to use proximal policy optimisation (AM PPO), to consider multiple possible solutions concurrently (Policy Optimisation with Multiple Optima (POMO)) or to exploit problem and solution symmetries by adapting the REINFORCE rewards (Sym-NCO). The Multi-Decoder Attention Model (MDAM) is a further Transformer model with multiple decoders. Additionally, a two-step method is included using a heuristic to group the nodes and an AM TSP model to build each route. Only AM, AM PPO, Sym-NCO and MDAM are applied to the CVRP-TW. All RL models are provided with large quantities of random problem instances for training.

Initial evaluation of the models uses standard CVRP and CVRP-TW benchmarks. Most of the CVRP benchmark instances are randomly generated, whilst the CVRP-TW benchmark deliberately varies the location patterns, customers per vehicle and time windows. Further systematic test instances were created for the CVRP by varying the position of the depot, distribution and number of customers and maximum demand from a single customer. This variation induces varied instance difficulties.

A robust baseline is provided by generating solutions with a range of heuristic and metaheuristic algorithms. Evaluation on all datasets considers how often valid solutions are returned and compares the average solution distances. Additional considerations are the number of vehicles used in a solution and the computation time.

Results

Whilst all apart from one heuristic approach finds valid solutions for all instances, multiple RL models fail for at least one problem instance. The Nazari model implementation specifically had validity issues which excluded it from all further evaluation.

Regarding the quality of the solutions, the heuristics and metaheuristics consistently provide solutions within 4% of the optimum for CVRP benchmark problems, whilst even the best RL methods are more than 10% worse. When it provides valid solutions, the AM TSP two-step model outperforms other RL models, likely due to multiple TSP training instances being included in a single CVRP training instance. AM and POMO deliver the best RL solutions but consistently fall behind the heuristics.

A similar pattern appears with the CVRP-TW problems, although Syn-NCO is instead consistently the best RL model. With the more complex problem variant, all models are prone to using more vehicles than the optimal solution, likely due to the increased difficulty of finding any valid solution. The gap in performance between the heuristics and the RL models often increases with the more complex instances e.g. with more customers.

A time limit of 60 seconds per instance is pre-specified for heuristics. For the RL methods, producing solutions for an individual instance is almost instantaneous, but training can require large amounts of time. The 4 hours 10 minutes for training and testing the AM (10 location) model is the equivalent of finding solutions to 250 instances using a heuristic method. When training on larger instances the situation is much worse, with a training time increase of 358% for POMO with 20 customers compared to 10.

Added Value

The results demonstrate the robustness of the heuristic and metaheuristic methods such that RL approaches could only be viable where the same model will be used extensively. The RL models with even longer training periods might meet the heuristic performance but would only justify the computation cost after 1000s of uses.

The surprisingly competitive performance of the two-step AM TSP approach signals the crucial role of method design. This is already apparent in the heuristics and metaheuristics but often overlooked for RL. In this instance, breaking down the problem into smaller steps and only using RL for the more difficult component enables the model to train more efficiently.

[1] Dantzig, G. B. and J. H. Ramser (1959). The Truck Dispatching Problem. Management Science 6 (1), 80–91

[2] Nazari, M., A. Oroojlooy, L. Snyder, and M. Takac (2018). Reinforcement Learning for Solving the Vehicle Routing Problem. In Proceedings of the 32nd International Conference on Neural Information Processing Systems

[3] Kool, W., H. van Hoof, and M. Welling (2019). Attention, Learn to Solve Routing Problems! In Proceedings of the 7th International Conference on Learning Representations

[4] Kwon, Y.-D., J. Choo, B. Kim, I. Yoon, Y. Gwon, and S. Min (2020). POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. In Proceedings of the 34th Conference on Neural Information Processing Systems

[5] Kim, M., J. Park, and J. Park (2022). Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization. In Proceedings of the 36th Conference on Neural Information Processing Systems

[6] Xin, L., W. Song, Z. Cao, and J. Zhang (2021, May). Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems. In Proceedings of the 35^th AAAI Conference on Artificial Intelligence

Relevance & Research Question:

Photos, which can be easily captured with smartphones, offer new opportunities to improve survey data quality by replacing or complementing conventional questions. However, potential advantages may vary by respondent characteristics, including age, gender, education, photo-taking and -sharing frequency, self-assessed verbal, mathematical, and spatial skills, and comfort with new technologies. This study addresses the following research question: To what extent do such individual characteristics affect the quality of photos of books at home submitted through an online mobile survey?

The topic was selected because the number of books is a well-established proxy for cultural and socioeconomic capital in social sciences, yet its measurement through conventional questions is challenging.

Methods & Data:

Participants were asked for information on the books in their home through conventional survey questions and/or by submitting photos. This information covered the number of books, their intended audience (illiterate children, literate children and teenagers, and general audiences), language, and storage. Quality was evaluated with indicators tailored to book information, drawing on literature about data quality in survey methodology and in computer vision. The survey, conducted in 2023, used Netquest’s opt-in panel in Spain. The target population was parents of children in primary school. Of 1,270 individuals reaching the questions on books, 703 were asked for photos, and 238 provided at least one.

Results:

Photo quality was not systematically affected by most of the studied variables. Although older participants submitted more photos, extracting book information from their photos was less feasible, and they experienced more capture and submission issues. The findings suggest that photo-based surveys can be collected across diverse populations; however, age may be an exception in contexts like books, where finer details are required to extract information consistently.

Added Value:

Evidence on the quality of photos submitted through online surveys remains limited, especially when photos address a relevant social science topic, such as counting books and using that information to characterize respondents. This study contributes to the literature by showing that photos can be requested from broad audiences with comparable quality across respondents, and it offers practical guidance for researchers collecting photos.

Relevance & Research Question

Collecting visual data through web surveys offers a promising way to obtain richer and more accurate information. Yet participation in image-based tasks remains low, and evidence on how to motivate respondents while maintaining data quality is limited. This study examines whether different strategies can help researchers achieve higher participation when requesting photos of remote work environments in web surveys: (1) offering an extra incentive specifically for sharing photos, beyond the standard survey participation incentive, (2) adding a follow-up prompt immediately after the initial photo request emphasizing the importance of sharing the photos, and (3) sending a reminder email to respondents not sharing the photos initially. Furthermore, we investigate whether the timing of incentive announcement (either at the initial request or only in the reminder) also affects participation.

Methods & Data

An experiment is being conducted in the opt-in online panel Netquest in Spain (N = 1,200) among adults who have worked remotely for at least seven hours per week in the past two months. Respondents are randomly assigned to one of three groups: (1) Control – asked to upload three photos of their home workspace without extra incentive; (2) Incentive – offered 10 extra panel points for uploading all photos; and (3) IncentiveReminder – offered the same extra incentive but only in the reminder. All groups get the follow-up prompt. Descriptive analyses will be used to assess differences in participation.

Results

The survey is currently being programmed. Data collection is planned for early December. Results are expected by early February 2026. We expect that extra incentives, follow-up prompts and reminders will all increase photo submission rates, and that announcing the incentive initially will be more effective. We will test this by comparing different participation indicators, such as break-off or item nonresponse defined in different ways, across experimental groups and across both conventional and visual formats.

Added Value

This study provides evidence on how different strategies might help improving participation in photos requests. Findings will help improve the design of online surveys combining textual and visual data while balancing respondent burden and data richness.

Relevance & Research Question

As survey methodologists seek innovative approaches to address declining response rates and evolving technological landscapes, Computer-assisted live video interviewing (CALVI) emerges as a promising hybrid mode combining the personal interaction of Computer-assisted personal interviews (CAPI) with the convenience of remote participation as given in Computer-assisted web interviews (CAWI). This study addresses the critical question: Is CALVI a feasible and useful add-on in a German general population mixed-mode household panel survey? Understanding CALVI's viability is essential for survey researchers considering technological adaptations to maintain data quality while accommodating respondent preferences.
Methods & Data

We implemented CALVI using a randomized controlled experimental design within the 2024 Innovation Sample of the Socio-economic Panel Study (SOEP-IS): (E1) 1,261 households from the established panel (previously using CAPI) randomly assigned to CALVI versus (C1) 1,106 households continuing with traditional face-to-face interviews; and (E2) 409 households from the 2023 refreshment sample (recruited in CAWI) assigned to CALVI versus (C2) 1,513 households continuing with web self-completion. Both experimental groups retained fallback options to their previous data collection modes. We analyzed participation rates, technical implementation success, and data quality indicators.
Results

Among 1,670 households invited to CALVI, 376 video interviews were conducted. Technical implementation proved largely successful, though 23% of interviews experienced connectivity issues, particularly when interviewers worked remotely. Unit nonresponse among CALVI-invited households is significantly higher compared to established modes, with pronounced effects among former CAWI participants (response rate in E1: 56% vs. C1: 61%; E2: 45% vs. C2: 74%). CALVI attracted specific demographic segments: young, highly educated, high-income, full-time employed urban participants from West Germany with reliable internet access. However, CALVI participants demonstrated superior data quality metrics, including lowest speeding rates, minimal item nonresponse, and highest data linkage consent rates.
Added Value
This research provides empirical evidence for CALVI's selective feasibility in mixed-mode designs, highlighting both opportunities and limitations. While CALVI may reduce overall participation rates, willing participants generate high data quality and high data linkage consent rates. CALVI serves specific demographic segments effectively, informing strategic decisions about incorporating video interviewing in surveys.

Relevance & Research Question
In brand awareness research, even universally recognized brands that should reach maximum awareness (for example, Google among internet users) often fall short in actual surveys. This suggests that true awareness may be systematically underestimated. Participants in online research panels frequently take part in surveys and are highly familiar with questionnaire formats. Therefore, respondents may adopt behaviors that reduce cognitive effort or optimize earnings per hour. Our research examines whether this underreporting reflects conscious strategic choices or whether it arises more implicitly through mechanisms such as fatigue.
Methods & Data

We embedded a survey experiment in a client study among 996 driving license holders in Switzerland from the nationally representative YouGov panel. Respondents were randomly assigned to one of four experimental groups. We used a 2x2-design with list length (either 8 or 24 brands) and a behavioral nudge (vs. no nudge), stating that the questionnaire length and payout were independent of their awareness of the specific brands) as the experimental conditions.
Audi, BMW and Mercedes-Benz can be assumed to have complete awareness among the target population. If the nudge increased reported awareness, this would indicate deliberate effort reduction. If the shorter list increased awareness, this would point to implicit mechanisms such as fatigue. We estimated the effects of the experimental conditions using a linear regression with the number of these brands being reported as recognized as the dependent variable.

Results
On average, respondents recognized 2.85 of the three brands. The linear regression indicates that the shorter list leads to significantly higher reported awareness among the three brands in this condition (p < 0.05). The behavioral reminder does not show a positive effect on reported brand awareness.
Added Value
The effect of list length indicates that respondents do not intentionally underreport awareness. Instead, the results suggest that underreporting is driven primarily by implicit mechanisms such as cognitive fatigue and reduced attentional effort. This implies that survey designers can improve data quality by using shorter and/or splitting brand lists for subgroups wherever possible. By minimizing mental load, researchers can obtain awareness estimates that more closely reflect true recognition levels.

Relevance & Research Question: Behavioural campaigns – communication initiatives aimed at changing behaviours – play a vital role in both social and market research. Their evaluation requires a holistic approach integrating behavioural, cognitive, and emotional indicators. This is particularly challenging when topics are sensitive to social desirability or strategic responding, such as traffic safety or pricing research. Traditional explicit self-reports (System 2) provide valuable insights into reflective attitudes/intentions but are prone to response bias and often overlook intuitive or automatic aspects of behaviour. Implicit methods complement them by capturing unconscious processes (System 1). This presentatoin examines how explicit and implicit methods can be combined to obtain a more comprehensive picture of behavioural campaign effects. We explore this in the context of a Swiss road-safety campaign addressing cyclists’ “illusion of control” – the tendency to overestimate control in traffic situations and underestimate associated risks.

Methods & Data: The evaluation follows a quasi-experimental repeated cross-sectional design combining explicit and implicit measures. Four online panel surveys are conducted between 2025 and 2027 in Switzerland (intervention) and Germany (control), each with 1’000 regular bicycle or e-bike users. Three outcome categories are assessed: knowledge, attitudes/intention, and behaviour. Explicit measures capture self-reported knowledge, perceived risk, and behavioural intentions. Implicit measures employ a reaction-time-based Single Association Test (SAT) with scenario vignettes covering four risk-prone situations: cycling without a helmet, mobile phone usage while cycling, running a red light, and riding under the influence of alcohol. These vignettes assess implicit risk perception and control illusions. The design enables pre/post comparisons between Switzerland and Germany to infer potential causal effects of the campaign over time.

Results: Preliminary findings show that explicit and implicit measures can be integrated within one online survey. Scenario-based SATs complement self-reports by revealing intuitive risk perceptions and control beliefs. Data from two waves (pre- and first post-campaign) provide first insights into early campaign effects.

Added Value: The study demonstrates a scalable framework for embedding implicit measures in large-scale online surveys. Combining scenario vignettes with reaction-time methods offers a practical blueprint for evaluating sensitive behavioural topics with higher ecological validity and depth.

Relevance & Research Question: Aesthetics is quickly processed, has a multitude of consequences for attitudes, recommendations, and purchase behavior–and thus is paramount for the success of products. However, differentiated and validated evaluation tools are lacking, particularly for interactive products. In order to close this gap, we aimed to develop and validate the Product Aesthetics Inventory (PAI) and a short version (PAI-S).

Methods & Data: Items were developed in a pre-study (N=6 design experts, N=4 product users). The resulting item pool with 54 questions was tested in an online survey on different types of household appliances (alongside with several validation measures; N=6,002). In Study 1, data from n=3,000 of these participants were used to determine the number of factors using exploratory graph analysis. In Study 2, the found structure was validated in a confirmatory factor analysis with the remaining n=3,002 participants. A third web-based study (N=1,028) was conducted to further determine construct validity and generalizability among different products such as IT products, home entertainment devices, and power tools.

Results: The final PAI consists of 34 questions covering eight dimensions: visual aesthetics, operating elements, brand logo, feedback sounds, operating noises, haptics, interaction aesthetics, and impression. A higher-order factor of product aesthetics can be determined both with the full PAI and with the 8-item short version PAI-S. Both versions demonstrate excellent reliability, as estimated by Cronbach’s alpha. The decrease in reliability from the full scale (.97) to the short scale (.89) is acceptable. Further, all eight subscales of the PAI achieved good to excellent reliability (range: .85-.92). Validity was confirmed by corresponding correlations with other established scales, intention measures, and overall judgments. Further, Study 3 confirms and extends these results with respect to reliability and validity. Finally, in all three studies, strict measurement invariance was achieved across different product classes for the majority of subscales.

Added Value: The PAI meaningfully supplements the measurement spectrum of product evaluation beyond classic usability and brand perception. Questionnaire templates, evaluation guides and interpretation aids are freely available online (https://doi.org/10.5281/zenodo.6478042). Further, we will complement this by discussing additional options of response scaling and benchmarks for the PAI.

Relevance & Research Question

Certain population sub-groups are consistently under-represented or excluded from survey samples, or they may appear in insufficient frequencies. In the UK, where a named sample frame is often unavailable, current strategies for boosting samples can be prohibitively costly or impractical, particularly in self-completion survey formats. As survey research in the UK strives to become more inclusive, it is essential to explore effective methods for incorporating and enhancing the representation of these under-represented subgroups.
Methods & Data

We employed respondent-driven sampling (RDS) to recruit young adults aged 18 to 24 from the UK, using the NatCen online probability-based panel. Participants completed a brief online questionnaire and could recruit up to five peers using unique digital codes, with incentives for participation and successful recruitment. This process spanned multiple waves, aiming for a sample of about 1,500 participants and allowing for an assessment of recruitment cooperation and network characteristics in a self-completion survey format.

Results

We examined the patterns and predictors of recruitment cooperation in RDS using probability-based seeds. The study also assessed the extent of non-response at various recruitment stages and explored how socio-demographic and network factors influence recruitment success. Intra-chain correlation revealed a strong clustering of respondents by ethnicity, while other characteristics, such as gender, had a less pronounced impact on recruitment correlation. Additionally, the study evaluated the effect of seed composition on recruitment outcomes and considered the implications of violating key RDS assumptions for the quality of the inferences made. Notably, incorporating seeds from outside the target population led to the recruitment of a significant proportion of young adults within the sample.

Added Value

This study aims to enhance the evidence base for using RDS as a survey data collection method, both in the UK and internationally, by integrating empirical findings with methodological diagnostics. It also introduces a novel approach designed to amplify the voices of young people, a demographic that remains under-represented in social surveys. This perspective has the potential to make surveys more inclusive and methodologically robust.

Relevance & Research Question Major health organizations have raised alarms about rising youth loneliness (WHO, 2025), with reports suggesting young people experience the highest loneliness rates and saw the sharpest increases between 2018 and 2022 (OECD, 2024). However, reported prevalence varies dramatically across surveys—from 24% to 50.5% for young adults in different European studies—raising a critical question: are we witnessing a genuine loneliness epidemic or methodological artifacts? While genuine increases are plausible given documented declines in face-to-face social contact (OECD, 2025), the magnitude may be partially explained by methodological shifts from traditional face-to-face to web-based data collection. This study examines how survey mode influences loneliness measurement, with particular focus systematic variation by age, potentially distorting our understanding of youth loneliness trends.

Methods & Data This study quantifies data collection mode effects by comparing loneliness reports across web-based, face-to-face, and telephone collection modes in the Survey on Income and Living Conditions. The analysis focuses on identifying age-specific patterns in how survey mode affects loneliness measurement, examining whether the magnitude of mode effects differs between younger and older respondents. To address potential limitations due to mode self-selection, we use a variaty of techniques including controlling for respondent’s health status and limiting the analysis to a subsample of countries. Results from other surveys are included for rubustness (SOEP, Germany). Our research additionaly addresses the role of stigma (national aggregates by age group from the EU Loneliness Survey) to explain observed patterns.

Results The findings reveal striking age-specific mode effects: young adults report significantly more loneliness in online surveys compared to face-to-face interviews, while older adults exhibit the opposite pattern, reporting higher loneliness levels in face-to-face settings. These divergent patterns suggest that stigma associated with loneliness and social desirability bias operate differently across the lifespan.
Added Value By demonstrating that mode effects vary systematically by age, this research helps disentangle genuine increases in youth loneliness from measurement artifacts introduced by the widespread transition to web surveys. The study contributes to research in the field of survey methodology by documenting how demographic characteristics interact with mode effects for (stigmatized) subjective measures.

Relevance & Research Question
Despite unprecedented psychological distress in conflict zones and growing treatment gaps, little is known about population readiness to adopt AI-based therapeutic interventions. Following the October 7th attack, Israel experienced severe mental health crisis with 31.5% reporting treatment needs but only 18.3% seeking help, against healthcare strain and six-month waiting periods. This study examines AI therapy acceptance predictors, addressing: How age and gender influence perceived treatment needs and actual help-seeking? What is the relationship between age and AI therapy readiness? Do socioeconomic factors predict beliefs about AI therapy accessibility?
Methods & Data
We conducted a representative online panel survey of 502 Jewish adults in Israel through Ipanel in June 2025. The survey measured demographic characteristics, perceived need for psychological treatment, actual treatment-seeking behavior, and readiness to seek AI-based therapy using a validated 6-item scale (α=.854). Respondents ranged from ages 18-87 (M=42.66, SD=16.33) with balanced gender distribution. We employed chi-squared tests for demographic-treatment relationships, Pearson correlations for age-readiness associations, independent samples t-tests for age group comparisons (18-40 vs. 41-87), and Spearman correlations for income-related beliefs..
Results
Significant correlations emerged between age and perceived treatment need (χ²=103.88, p=.002) and between gender and perceived need (χ²=7.57, p=.006), with women reporting higher needs (37.2%) than men (25.8%). Age moderated gender effects on actual help-seeking in older adults (χ²=4.00, p=.045). AI therapy readiness showed negative correlation with age (r=-.114, p=.005), with younger adults (M=16.59) demonstrating higher readiness than older adults (M=15.16; t=2.787, p=.003). Higher household income correlated with decreased belief that AI therapy serves those unable to afford treatment (r=-.135, p=.002).
Added Value
This research reveals critical demographic and socioeconomic barriers to AI mental health intervention adoption in crisis contexts. Findings challenge assumptions about universal digital health acceptance, showing those most likely to benefit—older adults and lower-income populations—demonstrate lowest readiness. The substantial gap between perceived need (31.5%) and actual help-seeking (18.3%) highlights intervention opportunities, yet age-based resistance suggests technology alone cannot bridge treatment gaps without addressing acceptance barriers. Results have implications for designing age-appropriate AI interventions, targeting implementation strategies for crisis populations, and understanding how socioeconomic factors shape perceptions of digital therapeutic alternatives during healthcare strain.

Relevance & Research Question

Large language models (LLMs) have been hoped to make survey research more efficient, while also improving survey data quality. However, as they are based on Internet data, LLMs may come with similar pitfalls as other digital data sources with regard to making inferences about human attitudes and behavior. As such, they not only have the potential to mitigate, but also to amplify existing biases. In my dissertation, I investigate whether and under which conditions LLMs can be leveraged in survey research by providing empirical evidence of the potentials and limits of their applications.

Potential applications of LLMs span the entire survey life cycle – before, during, and after data collection – where an LLM could act as a research assistant, interviewer, or respondent. Potential challenges for data quality stem from LLMs’ training data, alignment processes, and model architecture, as well as the research design. I focus on two major applications of LLMs covering both representational and measurement challenges and test these applications in challenging, previously unexamined contexts.

Methods & Data, Results

Two studies address the most prominent discussion regarding LLM-based survey research: Using LLM-generated “synthetic samples”. Coverage bias in LLM training data and alignment processes might affect the applicability of this approach.

In one study, I test to what extent LLMs can estimate vote choice in Germany. To generate a synthetic sample of eligible voters in Germany, I create small profiles matching the individual characteristics of the 2017 German Longitudinal Election Study respondents. These “personas” include socio-demographic and attitudinal information known to be associated with voting behavior. Prompting GPT-3.5 with each persona in German, I ask the LLM to predict each respondents’ vote choice in the 2017 German federal elections and compare these predictions to the survey-based estimates on the aggregate and subgroup levels. I find that GPT-3.5 does not predict citizens’ vote choice accurately, exhibiting a bias towards the Green and Left parties, and making better predictions for more “typical” voter subgroups. While the LLM is able to capture broad partisan tendencies, it tends to miss out on the multifaceted factors that sway individual voters. As a consequence, not only are LLM-synthetic samples not helpful for estimating how groups likely swinging an election, such as non-partisans, will vote, they also risk underestimating the popularity of parties without a strong partisan base. Such samples thus provide little added value over survey-based estimates. Furthermore, the results suggest that GPT-3.5 might not be reliable for estimating nuanced, subgroup-specific political attitudes.

In a second study, I extend the previous study to the entire European Union (EU), this time focusing on an outcome unobserved at the time of data collection: the results of the 2024 European Parliament elections. I create personas of 26,000 eligible voters in all 27 EU member states based on the Eurobarometer and compare the proprietary LLM GPT-4-Turbo with the open-source LLMs Llama-3.1 and Mistral. A week before the European elections in June 2024, I prompted the LLMs with the personas in English and asked them to predict each person’s voting behavior, once based only socio-demographic information, and once also featuring attitudinal variables. To investigate differences in LLMs’ bias across languages, I selected six diverse EU member states for which I prompted the LLMs in the respective country’s native language. After the elections’ conclusion, I compare the aggregate predicted party vote shares to the official national-level results for each country. LLM-based predictions of future voting behavior largely fail – they overestimate turnout and are unable to accurately predict party popularity. Only providing socio-demographic information about individual voters further worsens the results. Finally, LLMs are especially bad at predicting voting behavior for Eastern European countries and countries with Slavic native languages, suggesting systematic contextual biases.

These findings emphasize the limited applicability of LLM-synthetic samples to public opinion prediction across contexts. Without further adaptation through, e.g., fine-tuning, LLMs appear infeasible for public opinion prediction not just in terms of accuracy, but also in terms of efficiency, highlighting a trade-off between the recency and level of detail of available survey data for synthetic samples.

In the third study, I investigate the usability of LLMs for classifying open-ended survey responses. Due to their linguistic capacities, it is likely that LLMs are an efficient alternative to time-consuming manual coding and the pre-training of supervised machine learning models, but it is unclear how the sparse, sometimes competing, existing findings generalize and how the quality of such classifications compares to established methods. I therefore test to what extent different LLMs can be used to code German open-ended survey responses on survey motivation from the GESIS Panel.pop Population Sample. I prompt GPT-4-Turbo, Llama-3.2, and Mistral NeMo in German with a predefined coding scheme and instruct them to classify each survey response, comparing zero- and few-shot prompting and fine-tuning. I evaluate the LLMs’ performance by contrasting its classifications with those made by human coders. Only fine-tuning achieves satisfactory levels of predictive accuracy. Performance differences between prompting approaches are conditional on the LLM used, as overall performance differs greatly between LLMs: GPT performs best in terms of accuracy, and few-shot prompting leads to the best performance. Disregarding fine-tuning, the prompting approach is not as important when using GPT, but makes a big difference for other LLMs. Further, the LLMs struggle especially with non-substantive catch-all categories, resulting in different distributions. The need for LLMs to be fine-tuned for this task implies that they are not the resource-efficient, easily accessible alternative researchers may have hoped.

I discuss my findings in the light of the ongoing developments in the rapidly evolving LLM research landscape and point to avenues for future work.

Added Value

This dissertation makes both methodological and applied contributions to survey research. It discusses types and sources of bias in LLM-based survey research and empirically tests their prevalence from multiple comparative angles. It also showcases concrete applications of LLMs across several steps in the research process and several substantive topics, explaining their practical implementation and highlighting their potentials and pitfalls. Overall, this dissertation thus provides guidance on ensuring data quality when using LLMs in survey methodology, and contributes to the larger discourse about LLMs as a social science research tool.

Relevance & Research Question

Statistics are often estimated from a sample rather than from the entire population. If the inclusion probability of the sample is unknown to the researcher, that is, a nonprobability sample, naively treating the sample as a simple random sample may result in selection bias. Attention to correcting selection bias is increasing due to the availability of new data sources, for example, online opt-in surveys and data donation. These data are often easy to collect and may be so-called "Big Data" considering the large inclusion fraction of the population.

This dissertation consists of four scientific papers, where two of them are published in influential journals and the other two are under review. In the first paper, a novel framework for correcting selection bias in nonprobability samples is proposed. It follows with the discussion of three practical challenges, and possible solutions to them are provided.

Methods & Data

In the framework paper, the general idea is to construct a set of unit weights for the nonprobability sample by borrowing the strength of a reference probability sample. If a proper set of weights is constructed, design-based estimators can be used for population parameter estimation given the weights. To evaluate the uncertainty of the estimated population parameter, a pseudo population bootstrap procedure is proposed, given different relations between the nonprobability sample and the probability sample.

Three practical challenges for pseudo-weighting are also discussed. Namely, the model selection for bias correction, the imbalanced samples, and the small area estimation. Simulation studies and applications on real data are shown in each paper to reflect the usage of the proposed framework and the possible solution of the practical challenges.

Results

The proposed framework is flexible, and many kinds of probability estimation models can be used. The question is raised about how to select a proper model given the population parameter in question. After a series of performance measures were tested, we found that modeling the target variable when evaluating the performance of weights may be useful. The second challenge comes from the large size of the nonprobability sample. Since we often have a large nonprobability sample assisted with a small probability sample, we end up with an imbalanced combined sample, which can cause problems when estimating model parameters. Several remedies for imbalanced samples are discussed, and the proposed framework is also adjusted accordingly. The results show that SMOTE is a promising technique for dealing with imbalanced samples. Finally, we look at the scenario where not only the population level estimates are of interest, but also subpopulation estimates. Several approaches to combine pseudo weights with small area estimation are discussed. Of all approaches, we found that combining a hierarchical Bayesian model with weights is a relatively stable estimation approach. If both population-level and area-level estimates are of interest, aligning the weighted estimates with estimated marginal totals may be a better option.

Added Value

This research provides practical suggestions on how to deal with possible selection bias in nonprobability samples, which is gaining more and more importance in the age of digitalization and low response rates.

Relevance & Research Question

Survey attrition is a common problem in household budget surveys (HBS) as such surveys impose a high burden on participants, asking them to report their expenses daily for a specific period. We study the attrition in an app-based HBS with three preceding questionnaires and a 14-day diary. Participants can drop out at each of the questionnaires or during the diary. If respondents drop out, the data obtained from them contains missing information. The research questions are: 1. At what stage do participants drop out of an app-based HBS? 2. Does the way the study is presented in the invitation letter influence the drop-out? 3. What individual characteristics correlate with drop-out at the different stages?

Methods & Data

In 2024, we drew a probability sample of 7049 individuals from the register of residents and invited them by mail. We included an experimental variation in the invitation letters to analyse whether stressing the effort associated with participation and whether mentioning a receipt scanning function has an influence on participation. The survey consisted of three initial questionnaires regarding personal information, income and expenses, and information about the household. After having completed them, respondents were asked to continue with an expense diary. To be eligible for an incentive, participants had to manually enter their expenses or scan their receipts on at least seven days.

Results

Participants drop out of the study continuously, even after they started data entry in the diary. The poster will present results on the attrition and drop out across the stages, and whether the three experimental groups differ significantly in their attrition rates across the stages. Additionally, the poster shows whether people with specific characteristics are more likely to drop out at a particular stage.

Added Value

This research is part of the Smart Survey Implementation (SSI) project, funded by EUROSTAT, which aims to enhance data collection for official statistics across Europe through digital innovation. This experiment addresses attrition in app-based HBS. It informs decisions about what actions can be taken to reduce attrition.

Relevance & Research Question
Large Language Models (LLMs) are increasingly used to for text annotation, and for simulating survey responses via silicon samples. To evaluate their validity, researchers commonly assess the alignment of generated annotations and survey responses with data from human participants. However, treating human data as ground-truth overlooks the various sources of measurement error inherent in human data collection.

We propose applying MultiTrait–MultiError (MTME) models to jointly analyze imperfect responses from LLMs and humans. Our goal is to assess the extent to which MTME models can qualify measurement quality in responses from LLMs and humans, and whether they can improve estimates of underlying traits.

Methods & Data
Our MTME models estimate latent trait and error factors based on multiple measures from humans and LLMs of the same trait, e.g., the readability of a text excerpt. LLM measures may vary by model size, model family, prompting strategy, or decoding approach.

In an initial study, we fit an MTME model on LLM-generated annotations of publication year and readability for 600 text excerpts from the CommonLit Ease of Readability Corpus (CLEAR) corpus. We evaluate four Qwen 3 LLMs (4B–32B). We include ground-truth data from the CLEAR corpus into our evaluation, purely for demonstrating the feasibility of the MTME approach.

Results
Our initial results show that the measurement quality estimated by the MTME model partially matches correlations of individual LLMs with the CLEAR data. We find that the factor scores extracted from the MTME model for readability have a higher correlation with the CLEAR data (0.76) than the responses of each individual LLM (≤ 0.70), indicating that MTME aggregation yields a more accurate estimate of the underlying trait. Next, we will incorporate human-coded annotations into the MTME model and conduct experiments involving other datasets, categorical variables, and LLM-generated survey responses.

Added Value
We demonstrate that MTME models can jointly estimate measurement quality in responses from humans and LLMs. By extracting latent traits that pool information across participants and models, we show that MTME-generated predictions can improve the estimation of underlying traits. MTME models are a promising method for the joint analysis of LLM and human data.

Relevance & Research Question
Traditional sampling strategies often fail to recruit sufficient cases from small or marginalized populations, especially for sensitive topics where distrust or fear of repercussions are common. While social media recruitment is increasingly discussed in survey methodology, most work centers on targeted advertising rather than leveraging the “social” infrastructure of platforms: trusted creators embedded in tightly segmented communities. Influencers can function as credible intermediaries and, methodologically, as seeds in a semi-network (virtual snowball) recruitment process. This poster reports lessons learned from a feasibility study leveraging a network of “Medfluencers” and anti-racism advocates to recruit participants for research on discrimination in the German healthcare system. We ask: Is engaging social media influencers worthwhile for survey recruitment, and what methodological insights emerge regarding reach, conversion, and sample composition?
Methods & Data
We fielded an online survey on experiences of discrimination in the German healthcare system among patients and medical professionals. The questionnaire combined closed items with multiple open-ended text prompts to elicit detailed accounts. Recruitment centered on one primary seed influencer: a practicing medical doctor who is also a visible Muslim woman. She shared the survey link and mobilized her network of fellow influencers to repost, creating a virtual snowball mechanism through interconnected audiences. Data collection ran from February 1-25, 2025.
Results
The seed post received 14,952 likes and 437 comments. The survey link was accessed 42,530 times; 27,710 individuals consented; 17,390 completed the questionnaire (62% post-consent completion). The campaign produced a strong multiplier effect over a short field period and reached key target groups: 3,505 Muslim participants and 2,979 respondents working in the German medical system. The sample skewed female (n = 16,803) and younger (mean age 35.2) than the average German population. High perceived source credibility appeared to facilitate trust and candid answering; 2,387 respondents volunteered for follow-up interviews.
Added Value
Although not suited for population inference, influencer sampling can efficiently generate large, highly motivated samples in under-researched groups. The poster concludes with concrete lessons learned on when and how influencer-seeded recruitment can be productively used in survey research, and which limitations researchers must communicate transparently when interpreting resulting data.

Relevance & Research Question

Moral reasoning, whether people justify political positions through rules and duties (deontological reasoning) or through expected outcomes (consequentialist reasoning), is central to understanding how people deliberate in polarised political (online) debates. While experimental moral-dilemma research shows differences in rule-based vs outcome-based judgments depending on political ideology, it remains unclear whether such patterns manifest in real-world political communication. Capturing moral reasoning in naturalistic discourse could deepen our understanding of how polarization emerges and provide a foundation for designing interventions that adapt to people’s reasoning styles. This study asks: Can moral reasoning in political discourse be reliably classified using NLP methods, and do we find evidence supporting fundings from experimental moral dilemma research?

Methods & Data

This study presents a validation of an approach to classify moral reasoning in political discourse using a large language model (LLM). A corpus of 576 sentences from Reddit discussions and German parliamentary speeches was pre-sampled using a novel extension of the Distributed Dictionary Representations (DDR) method (Garten et al., 2018), which identifies sentences with high cosine similarity to exemplary reasoning styles. Two expert raters then independently coded each sentence as deontological, consequentialist, or neutral and adapted the coding manual based on deliberation after each round.

Results
Interrater reliability improved across three codebook iterations to excellent reliability (Krippendorff’s α = .56-.68 → .92-.93). Agreement between human and AI-assigned labels based on cosine similarity were subsequently also sufficient to demonstrate the feasibility of classifying moral reasoning styles in large text corpora through LLMs (Krippendorff’s α = .70-.73). Building on this validation, this method will be applied to a larger multilingual corpus (~12k sentences) to analyze ideological and temporal patterns in moral reasoning across political and cultural contexts.
Added Value
This work offers the first validated procedure for detecting deontological and consequentialist reasoning in naturalistic political communication, bridging experimental moral psychology and large-scale text analysis. It establishes a foundation for ongoing research using a multilingual, cross-country corpus to study ideological and temporal patterns in moral reasoning, with potential downstream applications for designing tailored, depolarizing communication interventions.

Relevance & Research Question
Life history data is essential in social sciences, yet retrospective collection often suffers from memory errors, reducing data quality. The Life History Calendar (LHC) addresses some of these issues, but its traditional interviewer-based administration in surveys is costly and participation rates are declining. Self-administered web surveys (CAWI) offer a promising alternative. Our research asks: Can a dynamic, self-administered web application with an integrated LHC and continuous access improve data quality and reduce response burden compared to a classic retrospective collection?

Methods & Data
To explore this, we developed a web app that enables respondents to update their life courses continuously, aiming to enhance usability, minimize recall errors, and simplify panel maintenance. We designed two experiments where respondents are randomly assigned to treatment groups (continuous life history data collection with or without reminders) and a control group (classic retrospective collection). Preliminary results come from the first experiment, implemented as NEPS next add-on study using starting cohort three. We analyze response rates and the effect of the new LHC implementation on response burden using a fixed-effects panel regression model.

Results
First results show no differences in initial participation across groups. Similarly, response rates (RR1) for completed interviews do not differ between the existing LHC and the new web-app implementation. Regarding response burden, we observe an increase immediately after the LHC section, but this increase does not differ across experimental groups.

Added Value
Our approach introduces a flexible, cost-efficient solution for self-administered life history data collection. The added flexibility in reporting life courses did not negatively affect participation or burden, which is a key prerequisite for the main experiment on continuous access. This innovation enables continuous updates, simplifies panel management, and aims to counteract attrition, offering a scalable model for improving longitudinal data quality in self-administered life history collection.

Relevance & Research Question

An increasing number of studies are requesting visual data within web surveys, arguing that they can enhance data quantity and quality and provide new insights. However, important challenges remain. This study focuses on one of these aspects: extracting relevant information from the visual data, a process called “classification”. Researchers are increasingly relying on automated methods using machine learning to classify visual data. Although these methods are becoming more powerful, they still cannot extract information in the same way as manual classification. Thus, the main objective of this study is to explain the challenges and solutions encountered while implementing manual classification in a complex case study on remote work homestations, where approximately 70 items must be classified based on three photos.
Methods & Data

A web survey will be conducted in the opt-in online panel Netquest in Spain (N = 1,200) in early December among remote workers. After answering conventional questions about their remote work conditions, respondents will be asked to upload two photos of their homestation and one of their main screen or laptop model information. All photos will be reviewed by the project ethics advisor to ensure no private information is visible. Manual classification will be implemented in accordance with detailed guidelines. The two homestation photos will be classified jointly, and the device-model photo will be classified separately. Two researchers will share homestation classification, with approximately 10% of the photos coded by both of them to compute interrater reliability (IRR) indicators and identify potential systematic biases. One researcher will code the device-model photo, but a subsample will undergo double coding to assess IRR.

Results

We expect to find differences between classifiers, identify problematic items, and detect the types of errors most likely to occur across classifiers.

Added Value

This work in progress focuses on discussing the challenges encountered when dealing with the classification of complex visual data collected in web surveys. We aim to provide practical, user-oriented guidelines that extend beyond the explanations usually found in academic papers, which often prioritize presenting results over detailing the underlying classification process.

Relevance & Research Question:

Designing high-quality survey questions is a complex task. With the rapid development of large language models (LLMs), new possibilities have emerged for supporting this process through the automated generation of survey items. Despite growing interest in LLM tools within industry, published research in this area remains sparse, and little is known about the quality and characteristics of survey items generated by LLMs or the factors influencing their performance. This work provides the first in-depth analysis of LLM-based survey item generation and systematically evaluates how different design choices affect item quality.

Methods & Data:

Five LLMs, namely GPT-4o, GPT-4o-mini, GPT-oss-20B, LLaMA 3.1 8B, and LLaMA 3.1 70B, were used to generate survey items on four substantive domains: work, living conditions, national politics, and recent politics. We additionally evaluate three prompting strategies: zero-shot, role, and chain-of-thought prompting.

To assess the quality of the generated survey items, we use the Survey Quality Predictor (SQP), a tool developed by survey methodologists for estimating the quality of attitudinal survey items based on codings of their formal and linguistic characteristics. To code these characteristics, we used an LLM-assisted procedure. The analysis allows us to evaluate not only overall quality but also around 60 specific survey item characteristics, offering a detailed view of how LLM-generated questions differ.
Results:

The findings show striking differences in survey item characteristics across the different models and prompting techniques. The results also show that the prompting technique employed is a primary factor influencing the quality of LLM-generated survey items. Chain-of-thought prompting leads to the most reliable outputs. The topics 'work' and 'national politics' yield survey items with the highest quality. Closed-source GPT models generally produce more consistent and higher-quality items than open-source LLaMA models. Among all configurations, GPT-4o-mini combined with chain-of-thought prompting achieved the best overall results.
Added Value:

For the GOR community, the study offers empirical evidence on how LLMs can (and cannot) be reliably integrated into questionnaire design workflows, providing a systematic basis for evaluating emerging AI tools in survey research and informing methodological decisions in applied settings.

Relevance & Research Question

Residential mobility is an important source of attrition in address-based panel surveys. That is, panelists cannot be invited to a survey because their new address is unknown. Current research into mobility specific attrition (MSA) is lacking in three aspects: (1) Because of a lack of meta-reviews and ambiguous definitions for MSA, there is limited insight in the magnitude of MSA. (2) research into the selectivity of MSA and its' contribution to attrition bias is sparse and almost exclusively based on panels of special populations, and (3) almost no research on MSA for self-administered panel surveys exists, which continue to displace traditional face-to-face-interviewing. Consequently, in this study, we address two research questions: 1) How many respondents in panel surveys are mobile and how many of those attrite due to MSA. 2) Are certain subpopulations more prone to MSA than others?
Methods & Data

To answer RQ1 we conduct a meta-review by (1) systematically sampling panel surveys from three extensive data archives (ICPSR, CESSDA and GESIS data-archive). and then (2) analyzing their field documentation to gain a deep understanding of the prevalence of mobility and MSA in panel surveys with special regards to self-administered surveys.

For RQ2 we explore subpopulations at risk of MSA by employing machine learning algorithms (classification trees) on data from all available waves of FReDA (currently three waves; N = 42,787). FReDA is a probability-based self-administered mixed-mode panel study with biannual surveys using both web-based and paper-based questionnaires. FReDA’s primary mode of contact is postal mail. Its sample base are German residents aged 18 to 49 years which were recruited by drawing a sample of 108,256 individuals from population registers of German municipalities (Bujard et al., 2025).

Results

Analyses are planned for December 2025 and January 2026. Preliminary results will thus be available in February 2026.
Added Value

We identify the magnitude of MSA in panel surveys and whether it introduces systematic biases, thereby quantifying a potential source of error for panel researchers.

Relevance & Research Question

The COVID-19 pandemic has reignited longstanding questions about gender inequalities in paid and unpaid labor. While survey research has advanced our understanding of these disparities, it typically relies on predefined categories and is susceptible to social desirability bias, especially for sensitive topics. In contrast, online postings capture intimate relationship conflicts in great depth, however rarely include demographic information.

We leverage discussions in Reddit relationship communities that, due to unique community roles, include both rich descriptions of relationship conflicts around (un)paid labor and demographic details (age, gender). We first assess how well Large Language Models (LLMs) classify manifest and latent content in Reddit posts. Building on the best-performing approach, we examine how men and women discuss relationship conflicts around (un)paid labor before, during, and after the pandemic.

Methods & Data

Using GPT-family LLMs on 500,000 posts from the subreddits r/relationships and r/relationship_advice, we extract manifest demographic attributes and classify whether posts discuss romantic relationships, paid, and unpaid labor. We systematically vary model specifications (o3, 4o, 4.1), prompting strategies (zero-shot vs. few-shot), fine-tuned vs. base models, and context window lengths, evaluating each against human annotations. Using classifications from the best-performing approach, we apply Structural Topic Models to explore how men and women discuss (un)paid labor in romantic relationships, and how these discussions evolve over time (2011-2023).
Results

Across specifications, LLMs excel at predicting manifest categories but struggle with latent sociological constructs. Even the best-performing approach of fine-tuned GPT-4.1 with few-shot prompting and detailed category descriptions achieves only moderate performance when classifying paid and unpaid labor. Substantively, preliminary findings indicate that women more often discuss mental health in work-related conflicts, while men more frequently emphasize career objectives.

Added Value

We apply LLM-based classifications to core sociological questions around gender inequalities. We add to the growing research body on LLMs’ capabilities and limitations in classifying complex social-science constructs and offer new evidence on how gender disparities in paid and unpaid labor are reflected and negotiated in relationship conflicts. By combining demographic information with highly sensitive narratives, our dataset provides an empirical resource rarely available in either survey or social-media research.

Background
The sustainability discourse has become one of the most dynamic and contested global public conversations. Terms such as ESG, Net Zero, Sustainability, Climate Targets and Decarbonisation are widely used but differ substantially in meaning and emotional connotation across regions (Chernyshova et al., 2025). Prior research (Müller-Hansen et al., 2023; Mi & Zhan, 2023; Gupta et al., 2024) shows strong emotional patterning and polarisation in climate debates. Topic-modeling and semantic-network studies (Jaiswal et al., 2024; Lee et al., 2023) demonstrate how sustainability narratives can be algorithmically mapped, though usually without global comparison or predictive framing.

Purpose
This study provides a data-driven analysis to quantify semantic differences and assess whether discourse dynamics can serve as early indicators of social and policy developments. We examine regional variation in meaning and sentiment, the stabilising or polarising roles of key terms, how the German discourse diverges from global patterns, and whether semantic shifts produce predictive signals.

Method
Using Talkwalker, we analysed over 50 million posts from news outlets, social media and public platforms in more than 40 languages (Oct 2024–Oct 2025). Building on sentiment and ESG discourse methodologies, we extend existing approaches with a gravity model combining favourability and momentum. Methods include volume and engagement metrics, sentiment analysis, semantic clustering, trend identification, regional comparison and early-signal extraction, drawing conceptually on opinion-dynamics and DETM-based temporal modelling.

Results
Globally, Sustainability acts as a stable anchor, Net Zero shows declining credibility and volatility, and Decarbonisation remains the operational core. Regional narratives diverge: North America polarises, Europe institutionalises, Asia operationalises, Africa and South America emphasise fairness and identity. In Germany, 63% of discourse centres on climate neutrality and emissions reduction. Engagement peaks around conflictive terms (Net Zero, Climate Targets), while regulatory topics (ISSB, CSRD, CSDDD) dominate momentum. Predictive indicators include early sentiment decline for Net Zero and rising relevance of circularity, life-cycle and nature-positive concepts.

Added Value
The study demonstrates how sustainability operates as a semantic system, how regional meanings diverge despite shared terminology, and how discourse data can signal emerging regulatory, communicative and societal developments.

References on demand

Relevance & Research Question
Given the absence of any objective measures for many constructs measured via self-reports, researchers rely on a range of indirect indicators to detect non-optimal response behavior and assess the quality of survey data. These include, for example, straightlining, acquiescence and speeding.
In survey research, these indicators have been used to investigate their relationship with other variables (i.e., assumed predictors of response quality). However, findings are inconclusive and often fail to replicate. One explanation for the inconsistencies in study results lies in the variation in how quality indicators have been operationalized. There is currently no standard approach and best practices on how to construct them. As a result, indicators are often developed ad hoc and their operationalization varies considerably across studies. Only few studies have systematically compared alternative operationalizations of the same indicator (De Beuckelaer 2010; Kim et al. 2019). We therefore investigate to what extent different operationalizations of various response behaviors frequently used as indicators of response quality yield comparable results.
Methods & Data
We use data from the 2024 waves of the GESIS Panel.pop, a probability-based mixed-mode (web, mail) panel survey. For each type of non-optimal response behavior (speeding, non-differentiation, acquiescence, midpoint and extreme responding), we construct several indicators based on commonly used operationalizations identified through a systematic literature review. We then compare these operationalizations descriptively. Specifically, we examine detection rates (proportion of responses classified as low-quality), summary statistics, correlations and agreement rates (extent to which operationalizations identify the same respondents) across operationalizations. We complement the empirical comparison with a qualitative assessment of each operationalization, in which we evaluate its conceptual alignment with the underlying response behavior, potential sources of misclassification, and its practical feasibility for different research aims.
Results
Results will be updated end of January.
Added Value
Our findings will provide evidence on the extent to which frequently used response quality indicators are robust to different operationalization choices. Based on our findings, we will derive practical recommendations for assessing response quality in social surveys. This will help researchers make informed decisions about which operationalization is suitable for their research purposes and what to consider when constructing them.

Relevance & Research Question

Ensuring accessibility and response quality across diverse respondent groups is crucial when recruiting participants for mixed-mode probability panels. Audio buttons—short spoken renditions of survey questions—are a potential tool to support respondents with low literacy skills, visual impairments, or other limitations that may exist in reading survey questions. Yet their effectiveness and actual uptake remain understudied. This paper examines the extent to which audio buttons are used in a web-based recruitment survey for a mixed-mode panel refreshment and assesses whether their provision influences respondent behavior, consent to panel participation, and sociodemographic differences in usage.

Methods & Data

We analyze experimental data from the FReDA Recruitment Survey 2024, in which web respondents were randomly assigned to a questionnaire with audio buttons available for all questions (n=7,490) or to a version without audio buttons (n=7,406). In the experimental group, the questionnaire contained 149 audio buttons. On average, respondents took 19.4 minutes to complete the survey.

Results

Overall uptake of audio buttons was low: 16.6% of respondents used an audio button at least once, and most activated them only once or twice. A smaller group (5.7%) used an audio button three or more times. Usage peaked at the first question (around 6%), while most other questions showed activation rates between 1–2%. The availability of audio buttons did not affect consent to join the panel. Initial analyses indicate that men, younger respondents, individuals born abroad or without German citizenship, those with lower educational attainment, and smartphone users were significantly more likely to use audio buttons. Respondents who activated audio buttons also spent more time completing the survey. The potential impact of audio-button use on response quality is currently being examined.

Added Value
This study provides experimental evidence on the use and implications of audio buttons in a web-survey context. It highlights limited overall uptake but identifies specific subgroups for whom audio support may be more relevant, offering guidance on the targeted use of audio features in mixed-mode panel recruitment.

Relevance & Research Question

Data donation offers valuable insights into digital trace data and online behavior, yet little is known about the willingness of respondents in probability-based – especially cross-sectional – surveys to participate. This study examines the feasibility of implementing data donation in a cross-sectional population survey and whether participation differs between the request of Google Search or Spotify data, assuming varying levels of perceived data sensitivity.

Methods & Data
Respondents of the German ISSP survey on “Digital Societies” (N ≈ 1,500) were invited to a data donation study via printed flyers containing personalized links to the online data donation platform “Next”. Participants were randomly assigned to donate either Google Search or Spotify data. We collected googled search terms and clicked URLs of search results, or streamed songs and podcasts over the last year plus library likes. In total, the donations comprised roughly 200k Spotify streams, 450 library entries, 20k Google searches, and 11k search-result clicks – originating from only 30 individuals.

Results

Donation flows show a substantial drop-off between initial interest and completed data donations. While willingness did not differ between the Google and Spotify groups – 350 individuals accessed the study and 232 consented after learning which platform data would be requested – actual donations were rare: 18 in the Spotify sample and 12 in the Google sample. Preliminary post-donation survey results indicate platform-specific sensitivity perceptions: streaming services like Netflix are seen as relatively non-sensitive, whereas social media data are viewed as more private. Telegram stands out as particularly sensitive, with very low stated willingness to donate. Linking these findings with ISSP survey data (forthcoming) may indicate whether such patterns relate to political or ideological differences. At GOR, we will present further analyses of willingness to participate by attitudes towards digital tech and privacy concerns.

Added Value
This study provides the first insights into the willingness and actual donation behavior among respondents of a cross-sectional probability-based population survey. The rich survey data enable analyses of how digital attitudes, privacy concerns, and sociodemographics relate to participation, using a split-sample design to compare different types of donated data.

Relevance & Research Question

Web-tracking technology is increasingly adopted in social science research to study online behavior because of its potential capability to collect granular individual-level data in-situ and unobtrusively. Although web-tracking is a promising approach for accurately measuring online behavior, recent research has highlighted processing error as an additional error source. This line of research demonstrated that plausible and defensible choices across various processing procedures can transform raw web-tracking data differently. However, previous research has examined the effects of these processing choices on the resulting data in isolation. To date, no study has systematically examined how processing decisions may jointly transform web-tracking data differently. Addressing this research gap is important, because a series of data processing procedures is often required to convert raw web-tracking data into the measure of interest. Using online news consumption as an exemplary variable, I ask the research question: Do different combinations of web-tracking data processing decisions lead to different distributions of online news consumption?

Methods & Data

To address this research question, I will conduct a multiverse analysis that systematically examines the distribution of online news consumption generated from a sequence of reasonable data-processing procedures. The analysis will be based on the PINCET dataset (Bach et al., 2023) that contains multi-wave survey data and web-tracking data from German adults between July to December 2021 on both PCs (N= 1,863) and mobile devices (N= 1,708).

Results

Building on Clemm von Hohenberg et al. (2024), I identify a five-step processing pipeline, alongside the corresponding reasonable processing options at each decision point to transform the raw web-tracking data into measures of online news consumption: 1) defining visit duration (5 options), 2) de-duplication (4 options), 3) classification of URLs (3 options), 4) handling missing data (3 options), 5) operationalisation of news media consumption (2 options). This results in a multiverse of 5x4x3x3x2= 360 datasets. Correlations between all online news consumption variables across the dataset multiverse will be computed.

Added Value

This paper aims to enhance transparency of research using digital behavioral data by providing empirical evidence on how researcher degrees of freedom affects the measurement of web-tracking data.

Relevance & Research Question: Prior research investigating the accuracy of self-reported online behavior has focused primarily on general usage measures, like the usage frequency of a social media platform, often neglecting the self-reported accuracy of specific activities (e.g., posting, commenting, or liking). Self-reporting accuracy may differ substantially between general usage and specific behaviors, yet this distinction remains unexplored in survey research. Filling this gap, we compare self-reports against actual behavior to examine the accuracy of self-reports for both general usage frequency and specific behaviors.

Methods & Data: We test how respondents’ individual characteristics and Instagram usage influence misreporting and how question framing influences reporting accuracy through a 2×2 survey experiment that varies the reference period (last week vs. typical week) and the response scale (numeric vs. vague quantifier labels). Our pre-registered analysis draws on data from over 400 participants from a probability-based German online panel. Participants self-reported their Instagram use in a web survey and provided actual usage data through data donation.

Results: Data collection finished at the end of November 2025, and we will provide our results by January 2026. We hypothesize that respondents underreport their general platform usage frequency and that misreporting will also occur for specific behaviors (e.g., posting, commenting, or liking). We further hypothesize that the accuracy of self-reports will vary depending on platform usage frequency and engagement in specific behaviors. Finally, we predict that questions regarding typical week will yield more accurate estimates than those regarding last week, and that vague quantifier labels will perform at least as well as numeric labels.

Added Value: Our study replicates previous findings and extends them to specific online behavior. First, we help researchers assess the validity of self-reported online behaviors. Second, through our survey experiment testing different reference periods, we offer insights into how to accurately inquire about specific online behaviors. Third, we illustrate the potential of data donation to gather fine-grained data on individual behaviors that participants might be unable to report accurately.

Understanding Technostress in Higher Education: Insights from an Online Survey Using the Study Demands and Resources Model

Relevance and Research Question
Digitalization in higher education through learning platforms, collaboration tools, and AI applications creates new stressors that manifest as technostress and can increase the risk of burnout. Critical discrepancies arise between students and technologies (P-TEL), their social environment (PP), and study organization (PO). This study examines how multidimensional technostress affects burnout symptoms, whether general perceived stress mediates this relationship, and whether study-related self-efficacy serves as a personal resource.

Methods & Data
The study is based on a quantitative online survey of students from German universities (N ≈ 210). Measures included technostress (Technostress-Misfit Scale with P-TEL, PP, and PO), general stress (PSQ), burnout (MBI-SS short version), and study-related self-efficacy (BSW-5-Rev). Analyses comprised reliability and factor analyses, multiple regressions, and moderated mediation analyses (PROCESS models 14 and 7), controlling for sociodemographic factors.

Results
Technostress correlates strongly and positively with burnout symptoms. The misfit between students and institutional structures, platforms, and deadlines (PO misfit) is the strongest trigger for burnout. General stress partially mediates this relationship, while study-related self-efficacy represents a strong protective factor but does not significantly moderate the effects. Exploratory findings indicate higher burdens among women, bachelor’s students, and individuals with lower technology skills.

Added Value
The study integrates technostress as a specific demand dimension into the Job Demands-Resources model and identifies organizational misalignments as central intervention points. It provides practical recommendations for universities: uniform digital platforms, transparent structures, and targeted promotion of self-efficacy.

Lesener, T., Pleiss, L. S., Gusy, B., & Wolter, C. (2020). The Study Demands-Resources Framework: An Empirical Introduction. International journal of environmental research and public health, 17(14), 5183. https://doi.org/10.3390/ijerph17145183
Schaufeli, W. B., Martinez, I. M., Pinto, A. M., Salanova, M., & Bakker, A. B. (2002). Burnout and engagement in university students: a cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464–481. https://doi.org/10.1177/0022022102033005003

Relevance & Research Question
Economic preferences are central to understanding consumer decision making, yet established measurement approaches remain costly and difficult to scale. At the same time, consumers generate rich digital traces in their day-to-day online behavior. This project asks whether modern language models can infer core economic preferences directly from search query histories.
Methods & Data
We recruit approximately 800 participants through the Datapods Platform. Each participant links a Google account and shares up to twenty years of pseudonymized search queries. They then complete incentivized tasks and matched survey items that elicit six preference parameters: risk preferences, time discounting, altruism, trust, positive reciprocity, and negative reciprocity. Large Language Models receive each participant’s full query history and generate preference predictions, which are evaluated against experimentally measured benchmarks.

Results
The study is still ongoing. Initial trials have confirmed the functionality of the idea.
Added Value
The project offers the first large-scale test of preference prediction from search histories using incentivized ground truth. It informs the feasibility and limits of low-cost, behavior-based preference measurement, contributes to ongoing debates on digital profiling in markets, and highlights privacy-relevant implications of model-based inference from everyday online behavior.

Relevance & Research Question

Straightlining—providing (nearly) identical responses in multi-item batteries—is a common indicator of satisficing in survey research. Compared with task difficulty and respondent ability, respondent motivation has received less systematic attention as a driver of satisficing. In longitudinal surveys, an important open question is whether motivational constructs reflect stable, trait-like characteristics or situational, state-like fluctuations across waves. Survey research also employs diverse operationalizations of motivation (e.g., topic interest, personality traits, survey attitudes), yet these are rarely compared in terms of temporal stability or predictive power. This study therefore examines the extent to which motivational measures display trait- versus state-like variation and how these components relate to straightlining over time.

Methods & Data

We use data from the GESIS Panel.pop, a probability-based mixed-mode panel in Germany, drawing on nine annual waves of the Social and Political Participation Longitudinal Core Study. Repeated measures are available for political interest, Big Five traits (agreeableness, conscientiousness), survey attitudes, and straightlining. To assess stability, we estimated separate random-intercept models for each motivational indicator and computed intraclass correlation coefficients (ICCs). To predict straightlining, we applied a within–between decomposition and estimated a multilevel binomial logistic regression with respondent random intercepts, controlling for demographics, cohort, mode, and survey year.

Results

Motivational indicators show considerable temporal stability. Political interest is the most trait-like (ICC = 0.76), followed by conscientiousness (0.64) and agreeableness (0.58). Survey attitudes display more moderate stability, with ICC values ranging from 0.53 (perceived burden) to 0.59 (survey value) and 0.62 (enjoyment). In the predictive model, political interest is the strongest determinant of straightlining: both higher average levels and within-person increases reduce straightlining. Survey attitudes show smaller, largely trait-level associations, and personality traits have modest effects.

Added Value

Findings indicate that straightlining is driven mainly by stable, between-person motivational differences, with political interest standing out as the strongest factor. By comparing multiple motivation measures and separating their trait and state components, the study provides practical insights for identifying respondents at risk of satisficing and for supporting data quality in longitudinal surveys. Extensions to additional satisficing indicators (e.g., item nonresponse, speeding) are planned.

Relevance & Research Question

The digital landscape of the Afghanistan diaspora in Germany is rapidly evolving, yet little is known about its online influencers, communication practices, and content narratives. Understanding this emerging microcosm is crucial for bridging knowledge gaps in digital media research and fostering social cohesion. This study investigates three core questions: Which digital platforms are most used by the Afghanistan diaspora in Germany? Who are the key influencers shaping this digital space? What topics and narratives dominate discussions and content creation among AFGfluencers?

Methods & Data
We adopt a mixed-methods approach combining expert interviews and qualitative content analysis. First, we conduct interviews with domain experts to map the online ecosystem and identify key communicators. Next, we perform a qualitative content analysis of social media posts from prominent AFGfluencers, focusing on themes, narratives, and engagement patterns. Platforms under study include major social media networks where diaspora communication occurs. This approach allows for a comprehensive understanding of how digital interaction is structured within this community.
Results

This research is ongoing, but preliminary work has mapped the main platforms and key influencers within the Afghanistan diaspora in Germany. Initial observations indicate active communication around cultural identity, migration experiences, and community issues. Analysis of content themes and engagement patterns is in progress, aiming to reveal how influencers connect members, shape narratives, and contribute to the formation of an online diaspora network.

Added Value

This study provides novel insights into an under-researched yet important digital community — the Afghanistan diaspora in Germany — enhancing understanding of online diaspora communication. By highlighting key actors, platforms, and narratives, it informs both academic research and policy discussions on integration, social cohesion, and misinformation mitigation. Methodologically, it demonstrates practical approaches for analyzing online communities, contributing to the broader field of digital social research. The findings have the potential to guide engagement with transnational publics and foster inclusive digital discourse.

Objectives - Which business question wanted the client to be answered?
Online surveys based on nonprobability samples have become increasingly popular due to their cost efficiency and rapid fieldwork. However, nonprobability samples have a poor reputation regarding their data quality compared to probability-based samples. This study investigates the extent to which probability and nonprobability samples differ with regard to data quality.
Method & Approaches & Innovation - How were the insights gathered?
We conduct one self-administered online probability recruited via postal invitation letters and four nonprobability online surveys administered by commercial online-access panel providers. All surveys use an identical questionnaire. We compare the resulting datasets in terms of key statistical indicators of data quality such as the survey duration, passing or failing of an attention check and specific screen times for longer item texts. In addition, we compare operational aspects of the fieldwork process between probability and nonprobability samples.
Results - What are striking and impactful insights?
Preliminary findings indicate not only substantial differences between the probability and nonprobability datasets, but also among the nonprobability panels themselves. These differences highlight the methodological and practical challenges of relying on nonprobability data for inferential research.
Impact - How did the project move the needle for the client? What was done differently afterwards?
The study adds value by providing a systematic, multi-panel comparison of online survey methods covering a broad target population based on IAB administrative data and provides a strong data base for developing and testing statistical adjustment techniques to correct for biases.

Relevance & Research Question

The literature identifies different types of masculinity. Hegemonic masculinity refers to the type of masculinity legitimating unequal gender relations (between men and women, between masculinity and femininity and among masculinities). Hegemonic masculine norms for the "ideal man" include traits and practices like being strong, successful, independent, unemotional, in control. Hypermasculinity, aggressivity, sexual prowess are generally glorified.

Differently, caring masculinity refers to the idea that, without rejecting masculinity, men are able to adopt what (traditionally) is seen as a feminine characteristic. Here, the emotional dimension is crucial.

Recent research refers of men negotiating masculinity e.g., not feeling challenged ‘as men’ because of their caregiving roles but reinforce interest in ‘manly’ hobbies/sports suggesting processes in which they negotiate what aspects of masculinity do not fit with their identity and what they do.

Extreme-right and manosphere ideologies tend to privilege hegemonic masculinity, which legitimizes unequal gender relations and anti-democratic stands. But how to measure these norms to investigate their spread in the public opinion?

This contribution describes the development, both conceptually and empirically, of the measurement of hegemonic masculinity norms and explore their spread among the general population of six European countries.

Methods & Data

The contribution reports on the development of the new “Hegemonic Masculinity Norms Scale” drafted in the context of the MEN4DEM (Masculinities for the future of European democracy – Horizon Europe, GA n. 101177356), on the cross-cultural comparability challenges (including use of advance translation), results from the online survey experiment conducted to assess reliability and validity. The final measurement is then used to show the spread of hegemonic masculinity norms in Greece, Germany, Italy, Netherlands, Poland, and Sweden. (N=800 in each of the 6 countries; representative samples randomly assigned to exp.setting)
Results
Fieldwork will finish in November 2025
Added Value
The “Hegemonic Masculinity Norms Scale” brings the following innovation: it combines a multidimensional perspective to masculinity with a continuum approach in its measurement, allowing for in-depth analysis of public opinion on masculinity, an aspect still largely unexplored. Methodology applied to develop the instrument in a cross-national setting can be of interest for cross-cultural survey scholars.

Relevance & Research Question

In emerging risk contexts and situations requiring contingent data collection, social media surveys can play a crucial role in analysing population needs and stress levels, offering an innovative monitoring tool. Bradyseism is a phenomenon of slow ground deformation that occurs in the Campi Flegrei area, in the Naples proximity (South of Italy). This phenomenon can cause shallow earthquakes, damage to structures, and an increase in seismic activity. The Campi Flegrei area is characterized by high population density and strong socioeconomic inequalities. This study aims to assess stress levels in the population affected by bradyseism in the Campi Flegrei area.

Methods & Data

An online survey was conducted during a period of increased bradyseism activity in the Campi Flegrei area. Meta advertisement platform was used to recruit participants exposed to the phenomenon. The survey, lasting 11 days, collected over 600 completed questionnaires with an investment of 720 Euros. This approach allowed for targeted spatial sampling of the population. The study included residents in the arounds of Pozzuoli (high-risk area) and Ottaviano (control area).

Results

The survey provided valuable information on stress levels among the affected population. The propensity to respond to a survey on social networks is proportional to the interest in the survey topic. This resulted in a higher number of responses from those affected by the earthquakes, despite our efforts to achieve a balanced sample. The data collected offered insights into the differential impacts of the bradyseism phenomenon across various demographic groups, with a particular focus on gender-based vulnerabilities. Moreover, the stress level, measured using a ten-item psychometric scale, showed a high correlation with both the number of perceived earthquake tremors and their intensity.
Added Value
This study demonstrates the effectiveness of social media-based surveys in rapidly gathering data during emerging risk situations in a specific area. It provides a cost-effective and time-efficient method for assessing population needs and stress levels in volcanic contexts. The findings contribute to a better understanding of the socio-economic and gender-based impacts of bradyseism, potentially informing more targeted and inclusive public policies for disaster preparedness and response.

Relevance & Research Question

This study addresses the growing need for transparent, theory-based methods to analyze political text using artificial intelligence. It asks whether Large Language Models (LLMs) can reliably identify political stances—such as ideological orientation, support for liberal democratic values, and populist rhetoric—without the need for manually labeled data.

Methods & Data

The paper employs a Natural Language Inference approach leveraging a retrained model, DEBATE (Burnham et al. 2024). In so doing, we build on expert survey codebooks from reputable sources in political science. The model is validated by comparing its outputs with expert-assigned scores to assess accuracy.

Results

The findings show no statistically significant differences between the model’s classifications and those of human experts. The model also demonstrates strong multilingual capabilities across English, German, Italian, Portuguese, and Spanish.

Added Value

This study introduces a cost-effective, replicable, and theoretically grounded approach for stance detection in political texts. By eliminating the need for data labeling and integrating the method into the forthcoming Automated Political Stance Identification (APSI) platform, it provides an accessible tool for researchers, policymakers, civil society, and the public seeking evidence-based insights into ideological and rhetorical patterns in politics.

Relevance & Research Question
Addressing complex market research questions often requires the integration of data from multiple sources, the development of reliable indices, and coordinated collaboration across organizations. In syndicated studies, even market competitors may work together within a shared research framework. Achieving high quality outcomes demands a deep understanding of the underlying data as well as the expectations and needs of all stakeholders. Only then can indices be developed that are robust, transparent, and capable of supporting strategic decision making.
Methods & Data

For a syndicated study, data from two competing companies have been pooled to date. A new questionnaire was developed specifically for the study in which internal employees were surveyed. In addition, data from various customer satisfaction studies were integrated. Financial performance indicators from the partner companies and their suppliers were also included.

From these inputs, distinct non-overlapping indicators were constructed, complemented by a single comprehensive index that synthesizes all individual measures. The development process took place in close collaboration with the syndicated study partners. Moreover, suppliers and customers were invited to contribute their perspectives and assessments, ensuring that the resulting indicators reflect a broad and balanced understanding of the market context.

Results
The analysis highlights several factors that are essential for the development of meaningful indicators. Although large volumes of data are often available, careful dimensionality reduction is necessary to maintain clarity and to avoid overly complex structures. Indicators tend to overlap, which can create unintended effects and lead to misplaced analytical focus. In addition, integrating data from various empirical surveys and KPIs requires appropriate statistical procedures. In our case, we apply, among other methods, inverse-variance weighting to ensure high reliability of the indicators.
Added Value
We illustrate how heterogeneous data, differing organizational perspectives, and collective development processes can be brought together to create a coherent and reliable measurement framework. The insights gained expand the understanding of how indices can be designed in collaborative settings and show how transparent methods increase acceptance and long-term usability. We offer practical guidance to develop robust indicators for complex syndicated studies.

Relevance & Research Question

When news of Ozzy Osbourne’s death broke in July 2025, millions of Facebook users brought collective grief together in a global digital ritual. This study examines how mourning, nostalgia, and fandom unfolded on social media, asking: how does celebrity death evolve into a networked media event? Building on media event theory (Dayan & Katz, 1992) and the concept of affective publics, the research explores how emotions, algorithms, and cultural memory intersect when a legendary figure dies "live" in the networked sphere.

Methods & Data

We collected 46,390 public Facebook posts published between 22 and 30 July 2025 that mentioned "Ozzy Osbourne". We then preprocessed the texts through tokenization, lemmatization, and stopword removal, and extracted bigrams using the gensim Phrases model. We applied Latent Dirichlet Allocation (LDA) topic modeling (Coherence = 0.73) to identify dominant themes and conducted sentiment analysis with TextBlob to measure polarity on a –1 to +1 scale. Finally, we visualized temporal and emotional dynamics to trace the evolution of discourse over time.

Results
Four dominant topics emerged: Death & Legacy, Memorial & Tribute, Music & Fan Tributes, and Tribute & Farewell. Posting peaked on 23 July (13,866 entries) and declined sharply thereafter, mirroring the lifecycle of an online event. Sentiment was predominantly positive (mean polarity = 0.105), with Tribute & Farewell (50% positive) and Music & Fan Tributes (49% positive) dominating. Frequent collocations such as "black_sabbath", "prince_darkness", "rest_peace", and "farewell_concert" illustrate how users merged grief, admiration, and mythology into a shared cultural narrative.
Added Value
This study conceptualizes celebrity death as a hybrid of media spectacle and participatory ritual. By integrating computational text analysis with cultural interpretation, it shows how algorithmic publics collectively reframe loss into commemoration, blending journalism, fandom, and emotional performance. The findings extend Dayan and Katz’s framework to the digital sphere, demonstrating how networked mourning transforms moments of death into global acts of mediated memory.

Relevance & Research Question
Online research methods are imperative for collecting accurate data on behaviors and cognitions. However, when respondents answer sensitive questions, they tend to provide socially desirable answers - overreporting desirable norms (e.g., exercising), underreporting norm violations (e.g., prejudicial attitudes), or skipping items. Reducing this social desirability bias (SDB) is essential for improving data validity. To reduce desirable responding, we conducted two survey experiments employing face-saving strategies: survey formulations that soften or justify norm violations. We addressed two questions: (1) To what extent does the effectiveness of face-saving answer options depend on the question format?; (2) Do respondents attend to face-saving preambles, and does engagement reduce desirable responses?
Methods & Data
Two online survey experiments were conducted among residents in a large Dutch city (N study 1 = 529; study 2 data collection currently ongoing). Study 1 examined face-saving strategies across four question formats: open; yes-no; apply-does not apply, and; very often-never. Experimental manipulations varied (a) the presence of a face-saving preamble and (b) the availability of face-saving answer options. Sensitive items were about volunteering, disturbances, safety, and poverty. Reading time of question introductions was recorded to assess respondents’ attention. Study 2 replicated question formats but introduced new items and different operationalizations of face-saving answer options and question introduction engagement. Similar reductions in SDB are expected for study 2.
Results
In study 1, face-saving answer options consistently increased the selection of socially undesirable responses across all closed question formats and domains, indicating reduced bias. In contrast, preambles did not meaningfully shift response patterns, even when participants spent sufficient time reading them. Data collection for study 2 will be completed in December 2025.
Added Value
This study offers the first systematic comparison of face-saving strategies across multiple online question formats. By demonstrating that face-saving answer options reduce SDB - while preambles did not - study 1 clarified the mechanisms behind face-saving techniques and offered practical guidance for online survey design. The oral presentation will integrate findings from both experiments, highlighting how face-saving strategies can strengthen the validity of online surveys on sensitive topics and thereby advance online research methodologies.

Relevance & Research Question:

High rates of item nonresponse to questions on income and expenditures compromise data quality, leading to sampling bias and limiting the generalizability of findings. This study investigates the effectiveness of follow-up (probing) questions in reducing nonresponse to financial questions within a Russian context and identifies the profiles of non-respondents.

Methods & Data:

Using data from the 6th wave of the HSE University's "Economic Behavior of Households" survey (N=6000), the analysis employs a two-stage approach combining the Random Forest method with the Boruta algorithm and logistic regressions.

Results:

Results indicate that probing questions successfully converted 36-41% of initial nonresponses into substantive answers, decreasing the overall nonresponse rate from 6-17% to 4-10%. Key predictors of nonresponse were found to be unawareness of household expenditures (a 3- to 5-fold increase in odds), poor health, lack of savings, and residence in small towns. The technique proved most effective for respondents with lower education levels, no savings, and those from small towns.

Added Value:

The findings demonstrate that while probing is a valuable tool, its primary mechanism is reducing cognitive complexity rather than mitigating question sensitivity. Based on this evidence, the paper offers practical recommendations for improving the design of surveys that include financial questions.

Relevance & Research Question:

Careless or insufficient-effort responding (C/IER) is a major threat to data quality in online surveys. Existing detection approaches face substantial limitations: indicator-based methods (e.g., straightlining indices) require subjective threshold-setting, while model-based approaches rely on strong and often unrealistic assumptions and are difficult to implement in applied research.

Attention checks offer an objective-to-score and easy-to-use alternative. However, traditional attention checks, such as instructed-response items (Please select strongly agree) or bogus items (Orange is a fruit), suffer from limited validity, and high respondent reactivity (Daikeler et al., 2024; Gummer et al., 2021; Silber et al., 2022). This project addresses the lack of attention checks that would mimic ordinary questionnaire items to limit reactivity while reliably identifying inattentive respondents.

The central research question is: How can we design attention checks that outperform existing approaches in validity and non-reactivity?

Methods & Data:

First, 460 candidate ACES were developed across diverse domains (e.g., personality, technology, political attitudes), drawing on the concept of frequency/infrequency items (Kay & Saucier, 2023), and tested on 1498 respondents. Items were evaluated using distributional properties and socio-demographic invariance.

The selected ACES were validated in a between-subjects experiment (N = 880) comparing four conditions: (1) ACES, (2) IRIs, (3) bogus items, and (4) a no-check control group. The questionnaire included measures of perceived clarity and seriousness, as well as open-ended feedback on attention checks.

The findings were replicated in another survey (N = 1113) targeting less experienced online panel members. All studies used non-probability quota samples (age, gender, education) from Polish online research panels.

Results:

ACES showed stronger associations with independent indicators of inattentiveness (e.g., response times, straightlining indices) and demonstrated higher classification accuracy (careless-not careless) than IRIs and bogus items. Respondents generally did not recognize ACES as attention checks, despite being highly familiar with traditional checks.

Added Value:

This project delivers the first validated ACES set and provides empirical evidence that ACES improve detection of C/IER in comparison to other attention checks while minimizing respondent reactivity. This enables using ACES in probability samples or interviewer-based surveys where traditional attention checks were not used due to their coarseness and potential for reactivity.

Relevance & Research Question

The growth of nonprobability online panels and the emergence of synthetic survey respondents have created new opportunities and uncertainties for social measurement. While probability samples remain the reference standard, opt-in and synthetic data sources offer faster fieldwork and lower cost but may introduce unknown biases. This study asks: How comparable are attitudes measured across a probability panel, an opt-in panel, and a synthetic dataset?

Methods & Data

Three parallel surveys (≈500 completes each) were administered using identical instruments.

1. Probability sample: Lifepanel Netherlands, recruited via random-digit-dial/SMS.

2. Opt-in sample: Norstat’s proprietary nonprobability online panel.

3. Synthetic sample: Personas generated algorithmically to approximate Dutch demographic structures.

The questionnaire measured perceptions of the national situation, attitudes toward elections, and interest in sports. Analyses include demographic comparison with CBS benchmarks, item nonresponse, variance structures, and inter-item correlations. Calibration experiments test post-stratification and raking on age, gender, education, and region to evaluate alignment potential.

Results

The probability panel demonstrates the expected demographic balance and serves as the comparative baseline. The opt-in panel aligns closely with probability results after weighting, although unweighted data show overrepresentation of younger, higher-education groups. Attitudinal means are largely consistent across the two empirical samples, with modest discrepancies in political trust and evaluations of the national direction.

The synthetic dataset approximates mean values for several attitude items but exhibits compressed variance and weakened correlation patterns, indicating insufficient behavioral realism. Some synthetic respondents show inconsistent response structures not observed among human participants. Calibration improves demographic similarity but does not correct these structural limitations, suggesting that synthetic data are constrained more by model assumptions than by post-survey adjustment.

Added Value

This is one of the first empirical comparisons integrating probability, opt-in, and synthetic survey data within a single national framework. The study provides practical guidance on when synthetic respondents can complement empirical data (e.g., instrument testing) and where their limitations lie. It also clarifies the degree to which calibration can bridge differences between probability and nonprobability data but highlights fundamental constraints for synthetic datasets. The findings contribute to methodological best practices as synthetic data become increasingly visible in survey research.

Relevance & Research Question

High panel consent rates are essential for reducing panel attrition and limiting the risk of panel consent bias in panel surveys. This study investigates how panel consent rates can be optimized by applying three innovative survey design features covering a repeated request for panel consent within the questionnaire while experimentally varying the placement of requests (beginning vs. end) and the incentive for panel consent.

Methods & Data

Analyses are based on the recruitment wave of the third cohort of the "Online Panel for Labour Market Research" (OPAL) and cover about 7.200 cases (about 12% are classified as partial interviews). In our design of repeated requests for panel consent within the questionnaire, respondents who do not provide consent at the first request are followed up with a second request to reconsider their decision. The survey experiment comprises four experimental groups that differ in the placement of the first panel consent request (beginning vs. end) and the incentive for panel consent at the first (0€ vs. 5€) and the second request (5€ vs. 10€).

Results

Concerning the first request, an early request is more successful than a late placement: When offering no incentive, the panel consent rate is higher when asked at the beginning rather than asking at the end. When offering a 5€ incentive, the panel consent rate is higher when asking at the beginning rather than at the end. Due to the second request, the cumulated panel consent rate increases by 5 to 10 percentage points across experimental groups. The highest cumulative panel consent rate after two requests has the design with the first request at the beginning while offering 5€ and offering 10€ at the second request at the questionnaire end.

Added Value

This paper provides evidence, that the highest panel consent rates are realized with a placement at the beginning of the questionnaire, which questions the traditional placement at the end. The panel consent rate can be significantly improved when implementing a second request within the questionnaire. Results show that the placement of a panel consent request can be more relevant than incentivizing.

Relevance & Research Question

Survey nonresponse is one of the major challenges to survey data quality. While various treatments, e.g., monetary incentives, have been tested to increase response rates, the evidence regarding differential effects of the treatments for different population groups is rather thin. For example, it is known that incentives work well overall, but it is unclear whether a less costly form of appreciation (like a postcard) would achieve the same or a greater effect for certain population groups or whether some groups do not need any treatment at all.

Methods & Data

An experiment to increase survey response will be fielded in mid-December 2025 among the newly recruited participants of the German Internet Panel (GIP). After the first panel wave, before the second panel wave, panelists will randomly be assigned to four experimental groups:

1.) receiving a “Thank-you” postcard from the GIP team

2.) receiving a handwritten “Thank-you” postcard with the same text as 1.)

3.) receiving a postcard that states that they have been credited an extra of 5 Euro as a “Thank-you” for being in the panel

4.) control group

The postcards are not connected to the invitation to the second panel wave but are a general expression of appreciation for the panelist’s participation in the study.

Results

Results will be presented including:

1.) the overall effect of the treatment on nonresponse in wave 2

2.) possible interaction effects of personal characteristics and the treatment on nonresponse in wave 2. The personal characteristics include basic socio-demography, the BIG 5 personality traits, and the motivation to participate in the survey.

Added Value

Looking at heterogeneous effects of the treatment on nonresponse, the findings will inform survey practitioners on developing a targeted design to increase response rates and more specially, to increase response rates for specific population subgroups.

Relevance & Research Question:
The widespread use of generative AI tools such as ChatGPT is reshaping how students search for, evaluate, and apply information. This shift is especially critical for media students, who will become future communicators, journalists, and researchers. However, little is known about the specific psychological, social, and technical factors that influence their adoption of generative AI as an academic tool. This study investigates the behavioural intention of media students to use ChatGPT for academic information-seeking. It focuses on understanding how constructs such as performance expectancy, trust, perceived humanness, and availability influence their decision-making.

Methods & Data:
The study employed a concurrent mixed-methods design. Quantitative data were collected via an online survey (n = 103) distributed among media students at a German university of applied sciences. Constructs from the UTAUT2 model were adapted and extended to include trust, perceived humanness, and availability. The data were analysed using PLS-SEM. In parallel, six semi-structured interviews were conducted and analysed using concept-driven coding in MAXQDA 24. The integration of qualitative and quantitative data provides both generalisability and depth.

Results:
Quantitative findings reveal that performance expectancy and perceived humanness significantly predict behavioural intention to use ChatGPT, while effort expectancy, hedonic motivation, and trust (measured as privacy-related trust) do not. Availability was found to be a key practical driver but did not moderate use behaviour as expected. Qualitative results provide a nuanced picture: students appreciate ChatGPT’s speed and interactive design but consistently question its factual accuracy. Epistemic trust, not privacy, emerges as the decisive factor in usage patterns, leading to widespread verification practices. Human-like responses increase engagement but do not guarantee trust.

Added Value:
This research offers one of the first empirically grounded models of generative AI adoption among media students using an extended UTAUT2 framework. It introduces and validates the novel constructs of perceived humanness and availability in technology adoption theory. Practically, the study provides educators, developers, and policymakers with insights into how students use and critically assess AI tools in academic contexts. It highlights the urgent need for AI literacy initiatives that emphasise epistemic caution alongside technical competence.

Relevance & Research Question
Metered data—a type of digital trace data collected through a tracking application (a “meter”) installed by participants on their browsing devices (PCs, smartphones, or tablets), which records at least the URLs of visited web pages—have attracted growing interest due to their granularity and continuity. However, metered data are also subject to errors, which may differ from those found in survey data. Thus, the main goal of this study is to examine the extent to which results obtained from an online survey differ from those collected via a meter, providing new empirical evidence on a timely and relevant topic with a significant longitudinal dimension: ChatGPT adoption and engagement. Specifically, we aim to advance existing research by investigating the factors associated with variation in the discrepancies between survey-based and metered data—that is, to assess when such differences are likely to be larger or smaller (e.g., we expect larger discrepancies for behaviors that occurred further in the past).
Methods & Data
To achieve this, we use data from the Netquest opt-in online panel (www.netquest.com) in Spain, comparing various indicators of ChatGPT adoption and engagement between February 2023 and April 2025 across two independent samples of 2,100 panellists each. One sample responded to an online survey, while the other provided metered data, which was used to construct comparable variables to those in the survey. Profiling variables were available for both samples, including socio-demographics, technology ownership at home, and devices used. We employ both descriptive analyses and regression models to compare the two samples and examine whether different factors (e.g., the time elapsed since an event) influence the observed differences.
Results
Preliminary results suggest that differences between the survey and metered samples can sometimes be substantial, although the magnitude of these differences varies depending on the specific concept being measured. Different factors, such as the time elapsed since the event, also play a role. Final results are still forthcoming.
Added Value
This research advances understanding of how different data collection methods influence findings, particularly in longitudinal studies, while also offering new insights into ChatGPT’s use in Spanish society.

Relevance & Research Question

Commercial LLMs are now part of everyday online life, but we still know strikingly little about what people actually do with them in practice. Here, we present empirical insights upon the content of messages that people exchange with chatbots, such as ChatGPT.

There still are few consensual results in the area. A very recent study by OpenAI (Chatterji et al., 2025) found surprising results that partly contradicted earlier research, demonstrating limited gender and education differences and very few “personal” interactions between users and ChatGPT.

We use extensive GDPR-complaint data to address two questions:

RQ1: Can these recent findings regarding topic distribution and gender/education differences be replicated?

RQ2: How personal do conversations with LLMs get, and do they become more personal over time?

Methods & Data

Our data covers 5 months of conversation records from panel members in Brazil, Germany, Mexico and Spain who agreed to share their internet activity on laptop and/or mobile device (01.06.25–31.10.25; N = 45,200 participants). We collect both HTML streams and in-app contents for six major AI platforms: ChatGPT, Claude, Copilot, Gemini, Meta and Perplexity. We examine the context of the conversations using LLM-based classifiers. In particular, we reproduce the same prompts and data input as those used in Chatterji et al. (2025) for comparability (RQ1).
Results
Available mid-January 2026 – we are aware this is rather late but we thought the data was exciting enough to try and share at GOR!
Added Value

Our data uniquely combines multiple AI system sources and reliable sociodemographics information, putting us in a good position to better understand and assess the divergences in previous studies which were limited in terms of LLM sources and user qualification.

LLM-based classifiers enable fine-grained classification on high volumes of data, allowing for new approaches to RQ2 besides mere content classification. We believe the extent to which people get personal with LLMs is underestimated when it is assessed merely through topic classification, since topics other than “self expression” and “relationships”, such as practical guidance or multimedia topics, may also involve personal engagement with the systems.

Relevance & Research Question

Social science surveys frequently overestimate voter turnout due to measurement and nonresponse errors, which undermine the validity of research on the causes and consequences of political disengagement. As turnout bias may differ across countries and over time, both cross-national and longitudinal comparisons are challenged. Despite these concerns, there is no comprehensive longitudinal and cross-national comparison of turnout bias. Consequently, it remains unclear to what extent turnout bias is shaped by contextual factors or by survey design. To close this gap, we examine (1) the prevalence and development, (2) the contextual factors and (3) the survey design features associated with turnout bias in European social science surveys since 2000.

Methods & Data

We analyze data from the European Social Survey (ESS) and a unique data set of German Survey Programs (GSP) conducted between 2000 and 2023. First, we run separate OLS regression models using either absolute or relative turnout bias as the dependent variable and the year of data collection as the independent variable for each country in the ESS and for each survey program in the GSP. Second, we estimate fixed effects and mixed effects models using absolute or relative turnout bias in the ESS and GSP as the dependent variable. As independent variables, we include contextual factors. Third, we add variables capturing variations in survey design as independent variables.

Results

Our findings reveal that the extent of turnout bias varies between countries and has increased over time, posing a significant challenge for both cross-national and longitudinal research. We identify several survey design features that could mitigate turnout bias which are in line with previous literature. Moreover, we discuss methodological innovations aimed at reducing turnout bias by targeting nonvoters before or during data collection through tailored survey designs.

Added Value

The persistence of measurement and nonresponse errors, along with the lack of validated turnout data, are a constraint for social science surveys. This study offers the first comprehensive longitudinal and cross-national comparison of turnout bias. Our results underscore the urgent need for methodological innovations to ensure the validity and comparability of data on political disengagement.

Relevance & Research Question Response burden is a significant challenge in establishment surveys, threatening data quality and survey participation. However, the field lacks validated instruments to measure perceived response burden. This study addresses this gap by developing and validating a 6-item, binary (Yes/No) response burden scale. Our central research question is whether this scale achieves measurement equivalence across different levels of objective burden (questionnaire length) and stability over time (longitudinally).

Methods & Data We utilize data from an experiment embedded within three quarterly follow-up waves (2023-2024) of the IAB Job Vacancy Survey (IAB-JVS), a large-scale German establishment survey. Establishments (n=3,888) were randomly assigned to receive either a short (2-Page) or a longer (4-Page) follow-up questionnaire. We test for measurement invariance using multi-group Confirmatory Factor Analysis (CFA) adapted for binary indicators (WLSMV estimator), following Wu and Estabrook (2016). We assess both cross-sectional invariance (between experimental groups) and longitudinal invariance (across the three waves).

Results The scale demonstrates strong construct validity: respondents in the 4-page condition reported significantly higher perceived burden across all items (e.g., "High number of questions"). The analysis confirms full scalar invariance across the 2-page and 4-page experimental groups in each wave (e.g., Q1: ΔCFI < 0.01). This indicates the scale measures the same latent construct equivalently regardless of objective burden. Furthermore, the scale achieved full longitudinal scalar invariance across the three waves, demonstrating its temporal stability even as quarterly questionnaire content changed.

Added Value This study provides practicioners with a validated, concise instrument to monitor perceived burden in establishment surveys, Based on the confirmation of cross-sectional and longitudinal scalar invariance, researchers can now confidently use this scale to track burden trends over time and accurately evaluate the impact of questionnaire design interventions. Hopefully, our work provides a reliable tool for comparative analysis, supporting efforts to improve data quality and respondent engagement.

Research Question

Panel conditioning: changes in response behavior caused by repeated survey participation, is a central methodological concern in online panels. Research has identified both positive and negative conditioning effects, but little is known about how these processes unfold in egocentric social network surveys, where name-generator items create opportunities for satisficing. In this study I ask: (1) How does repeated participation affect the likelihood of motivated misreporting in these filter questions? (2) To what extent is this relationship mediated by respondents’ reported network size, that is, the number of alters named in the generator?

Methods

I use data from the 12th wave of the online probability-based LISS panel, drawing on the Core Discussion Network module, which includes a name generator and follow-up questions on alter characteristics. Panel experience operates as the independent variable, and Motivated misreporting in filter questions as the dependent variable, while network size serves as the mediator. I estimate causal mediation models using Poisson and logistic regression with 5,000 bootstrap resamples and control for sociodemographic and survey-evaluation variables associated with panel attrition.

Results

The results reveal two opposing mechanisms: a direct and indirect effect. Indirectly, respondents with greater survey experience report larger discussion networks, which increases their likelihood of misreporting in the filter questions, to avoid the tie-strength assessments. Directly, however, more experienced participants are less likely to engage in motivated misreporting when network size is held constant, suggesting reduced satisficing due to increased familiarity with online survey tasks. Because these pathways counteract each other, the total effect of panel experience on misreporting is small and statistically nonsignificant.

Added Value

This study demonstrates that panel conditioning in online surveys operates through simultaneous, opposing mechanisms that remain hidden to conventional response-quality diagnostics. The findings highlight the need to consider how question order, task burden, and instrument structure interact with respondent experience in modules involving name generators. By applying mediation analysis, the study provides a framework for detecting hidden behavioral mechanisms and offers practical guidance for improving the design and interpretation of longitudinal online surveys.

Relevance & Research Question:

In this contribution we will report on the 2025 recruitment of new respondents for the GIP. This includes a general overview of the recruitment procedure and results, unexpected obstacles we encountered along the way, lessons learned, and things to look out for when handling sample recruitment in-house. As we observe a general trend of moving more processes in-house among academic survey projects, often to reduce costs, this contribution will be of interest for a wide audience of survey practitioners who (plan to) handle their own recruitment of respondents.

Methods & Data:

In September and October 2025, we sent out 7,000 invitations to prospective new respondents for the GIP. About 2/3 went to a random sample from the population registers of 135 municipalities drawn by GESIS and 1/3 to addresses sampled from a commercial database. We report on the process of handling the recruitment of a probability sample in-house, obstacles we encountered and possible solutions to these, and recruitment results such as response rates.

Results:

During the recruitment process, we encountered a number of obstacles, such as uncooperative municipalities, coordinating the printing and sending of invitation letters and reminders in-house, print quality, handling the prepaid cash incentives, and a higher-than-expected number of invitations being returned by the postal service as undeliverable, presumably due to incorrect addresses, particularly in the commercial address database sample. While the fieldwork is still ongoing, preliminary results indicate that about 1,900 recruitment interviews have been started and about 1,100 to 1,200 new respondents will be recruited into the panel, while about 800 of the 7,000 invitations have been returned undeliverable.

Added Value:

We provide a hands-on report with concrete advice for conducting a survey recruitment and avoiding potential obstacles and costly errors. In addition, we report recent information on the quality of obtained address data, response rates, and recruitment success for a probability online panel in the ever-changing survey climate in Germany, which will be of interest to any practitioners planning the recruitment of a probability sample.

Relevance & Research Question

Panel consent is the permission given by respondents to be re-contacted for future panel waves. The lower the panel consent rate, the larger the initial panel attrition and the higher the risk of panel consent bias, which threatens data quality. This study investigates whether incentives for panel consent and repeated requests for panel consent can increase panel consent rates.

Methods & Data
We conducted an experiment in the first wave of the new online panel survey "International Mobility Panel of Migrants in Germany" (IMPa) on two innovative design features: (1) A repeated request for panel consent within the questionnaire: Respondents who do not provide consent at the first request are immediately followed up with a second request to reconsider their decision. (2) Incentivizing panel consent: Depending on the randomized experimental group, respondents are (not) offered an additional incentive (5€) for providing panel consent at the first and / or second request. In group 1, no incentive for panel consent is offered at neither request. In group 2, respondents are offered an incentive for providing panel consent at both requests. In group 3, panel non-consenters of the first request are offered an incentive for panel consent at the second request.

Results

Our analyses (N≈41.000) show that 1) panel consent rates at the first request are higher if incentives are offered; 2) the second request significantly increases the cumulated panel consent rate; 3) the second-request effect is highest for group 3, where incentives are offered only at the second request; 4) the cumulative panel consent rate is highest for groups 2 and 3. Regarding the cost effectiveness, group 3 leads to panel consent rates as high as for group 2, while requiring costs per panel consenting respondent is close to group 1. We will also provide results on how the experimental design affects wave 2 response rates.

Added Value

This paper introduces and assesses two innovative survey design features to maximize panel consent rates. Furthermore, we analyse the costs associated with each design and derive recommendations for panel consent request designs.

Relevance & Research Question:

In recent years, probability-based mixed-mode panels have become a common tool in empirical research. Most panels rely on continuous, regular surveys that ask participants about core topics at fixed intervals (in a similar way to classic longitudinal panels). In addition, some of these panels offer additional or ad hoc studies. These studies allow internal or external researchers to conduct additional and in-depth surveys. The Robert Koch Institute (RKI) Panel 'Health in Germany', set up in 2024, operates exactly in line with this logic. However, for research purposes, the question arises as to what extent additional studies (at irregular intervals on different topics) influence the non-response of further (regular) surveys. In this presentation we will present first findings within the RKI Panel.

Methods & Data:

The analysis draws on online survey response metrics from panel waves of the RKI Panel, complemented by information on invitations to and participation in an ad hoc survey conducted between the regular waves. A comparison is made between two randomly selected groups of panel members invited versus not invited to the analyzed ad hoc survey. Multivariate models control for sociodemographic characteristics to isolate potential effects attributable to the ad hoc survey.

Results:

Preliminary findings show that participants who were invited to the ad hoc survey exhibit marginally lower sub-group response rates for the following wave. However, controlling for sociodemographic characteristics in a multivariate model, we see no substantial effect regarding potential non-response for participants of the ad hoc survey. Thus, participants show stable response patterns overall. Data collection is still ongoing. Further results on more ad hoc surveys and following waves will be presented at the conference.

Added Value:

The findings contribute to an emerging evidence on the effects of additional survey burden in mixed-mode probability panels. By investigating invitation effects, the study offers practical insights for panel management, especially regarding contact frequency and respondent burden in newly established panels such as the RKI Panel.

1. Relevance & Research Question
This paper explores the potential of large-language-model–based “digital twins” as a novel instrument for empirical social research. The Last Interview investigates whether an AI agent, trained on an extended multi-stage quantitative survey complemented by a single qualitative session, can faithfully reproduce the attitudes and reasoning patterns of the original respondent. We address three central questions:
• Which types of data are most suitable for constructing such a digital twin?
• How accurately can a data-driven synthetic twin mirror a participant’s measurable opinions across multiple quantitative dimensions?
• What methodological and ethical implications arise when such an agent is used beyond its initial research scope?

2. Methods & Data
The study was conducted at HTW Berlin with volunteer students who consented to provide comprehensive personal data. Each participant completed eight sequential quantitative interview stages—covering demographics, personality traits, value orientations, decision preferences, and behavioral indicators—followed by one in-depth qualitative interview to contextualize the numeric responses. These combined data sets served as the training corpus for an advanced large-language-model architecture, producing individualized conversational agents (“digital clones”).

3. Results
We present the first practical implementation of this concept. Each digital twin was able to answer survey questions in a manner closely aligned with its human counterpart, demonstrating high agreement across quantitative measures. Notably, the clones occasionally produced reasoned extrapolations beyond the original questionnaire, suggesting emergent interpretative capacities despite being trained primarily on structured survey data.

4. Added Value
This project introduces a hybrid methodology that fuses extensive quantitative measurement with state-of-the-art generative modeling to create person-specific research agents. It illustrates how digital twins can support longitudinal analysis without repeated data collection while foregrounding critical issues of informed consent, data sovereignty, and identity representation. For the General Online Research community, The Last Interview provides a replicable framework and empirical evidence for extending quantitative social science into the era of personalized AI. Although we consider the current approach to be in an early developmental stage, the project has already prompted reflections far beyond its initial market-research intent, revealing broader scientific and ethical implications than originally anticipated.

Relevance & Research Question

Advances in computational methods, in particular recent advances in Artificial Intelligence (AI), have vastly reduced the manual effort required to derive results from any given survey dataset. This equally applies to structured, quantitative, interview-level data, but also to qualitative data. For the former, statistical and visualization methods may now be applied automatically; for the latter, sentiments, topics and codes are now easily extracted. As an industry, we’re thus experiencing the commoditization of results. This gives rise to the new questions of how to efficiently work with this overly abundant set of results, how to focus on what matters, how to tell signal from noise.
Methods & Data

In this talk, we introduce the concept of the result space, which we define as the set of all possible results that can be derived from a given dataset of interview-level raw survey data by (automatically) applying current analytical methods. Based on our practical work across dozens of surveys over the past years, we propose alternatives to structuring this space, e.g., by variable, by methodology, or by significance of result; we look into alternatives for sorting and ranking results; and we discuss ways for measuring relations between results.
Results

To strongly anchor the rather theoretical aspects of our work to every-day practical use, we illustrate the specific applicability of these concepts on real-world survey datasets, and on specific questions that we can now answer: What are the Top 3 things to know among all results relating a given sub-demographic? Of all the regression analyses, which ones stand out and why? Is there anything I overlooked in this summary that I wrote? We further demonstrate practicality by showcasing the system used to automatically derive the result spaces.
Added Value

Certain slices through the result space of a survey have already proven their practical value: Personas, for example, allow zeroing in on the particular wants, needs, and opinions of a sub-demographic of particular interest. With the toolkit presented in this talk, we generalize this concept, thereby providing the means to more deeply and more effectively investigate increasingly abundant survey results.

Relevance & Research Question:

Digital transformation, Evidence-based policymaking

Amid rapid technological innovation and digital transformation, EU-ALMPO addresses the need for more agile, inclusive, and responsive to evolving skill mismatches labour market interventions. Anchoring policy design in data, machine-learning analytics, and stakeholder co-creation, the project objective is the creation of the EU Active Labour Market Policy Observatory – an AI-enabled digital hub that enhances the design, implementation, and evaluation of ALMPs across Member States. By integrating advanced AI tools into a centralised digital platform, the Observatory supports evidence-based decisions and fosters knowledge exchange among policymakers, researchers, and labour-market actors.

Methods & Data

Analytical framework, Participatory validation, Comparative policy evaluation

Funded under Horizon Europe, EU-ALMPO has completed the WP1, which developed the analytical framework underpinning the Observatory. The framework analysed existing ALMP systems, identified structural gaps, and assessed the effectiveness of policies in addressing skills mismatches. It also provides a conceptual bridge - a translation layer - for its integration into the project’s AI-supported system for policy-makers across the EU and beyond. Methodologically, it combines a literature review, a meta-evaluation of ALMPs, and participatory validation with stakeholders from several EU countries.

Results

Skills mismatch analysis, Servuction model

Serving both diagnostic and prescriptive functions for skill mismatch analysis, the framework also deepens reflection on the implications of generative technologies for policy innovation. Inspired by the Servuction Model - bridging together front-end and back-end - it develops a user-oriented perspective and translate the knowledge and content developed in the project into actionable items that are valuable and useful for the policy-makers involved.

Added Value

AI-policy integration, Adaptive governance

EU-ALMPO represents a pioneering intersection between labour-market policy and AI. Its analytical framework and reflections around the ways to bridge policy design and AI tools, offers a data-driven, adaptive, and inclusive approach that strengthens Europe’s capacity to respond to future labour-market transformations. While the focus of the project is related to policy making in the area of skills and labour market, the project represents an innovative ground to bridge policy and technology and is thus relevant for potentially other policy areas as well.

Relevance & Research Question
Qualitative user and market research remains essential for customer-centric product development, yet traditional moderated interviews are time-intensive and expensive. A single 1-hour interview requires approximately 5 hours of researcher time for recruitment, coordination, preparation, execution, and analysis. At industry rates of €500-750 per interview, comprehensive research projects become prohibitively expensive, especially for SMEs. This study examines: How can AI-powered autonomous interviewing maintain research quality while achieving dramatic improvements in speed, cost, and scale?

Methods & Data

We developed Userflix, an end-to-end AI platform for qualitative research automation utilizing large language models fine-tuned for research methodology. The system implements: (1) AI-guided study setup through conversational project briefing, (2) real-time audio-to-audio interviews with dynamic follow-up questions, (3) visual stimuli presentation, (4) automatic transcription and analysis, and (5) automated insight extraction with traceability to source interviews.

Evaluation pilots (Q3-Q4 2025) are ongoing with Nielsen Norman Group (UX research methodology assessment), Innofact and Skopos (agency workflow integration), and IKEA (multilingual European research). Partners are systematically comparing AI versus human interview quality, transcript depth, and participant experience.

Results

Early evaluation feedback demonstrates 95% time reduction (weeks to hours) and 90% cost reduction (€36/hour vs €500-750/interview). The AI successfully conducts multilingual interviews, generates contextual follow-up questions, and enables unprecedented scale (50-500 interviews vs traditional 8-12), allowing statistical pattern recognition in qualitative data.

Nielsen Norman Group is assessing methodology soundness against established standards. Agency partners report high participant comfort, with some showing greater openness on sensitive topics with AI interviewers. The platform's 24/7 availability increased completion rates by 40% compared to scheduled interviews. Key advantages include parallel execution, elimination of interviewer bias, and consistent quality.

Added Value

This research demonstrates that AI can augment human researchers by handling routine execution, enabling focus on strategic interpretation. The "quantified qualitative" approach—conducting 50-500 interviews instead of 8-12—bridges qualitative depth with quantitative validation, addressing the longstanding trade-off between scale and depth.

For the online research community, this represents making comprehensive qualitative research accessible to broader audiences while elevating professional researchers to strategic roles. Evaluation results will provide evidence-based guidance for AI research tool adoption and quality standards.

Relevance & Research Question

Large Language Models (LLMs) increasingly shape qualitative and computational social science research, yet their use for text data analysis using topic modeling remains limited by low transparency, unstable outputs, and prompt sensitivity. Traditional approaches such as LDA often produce overlapping, generic topics, whereas LLM prompting lacks consistency and reproducibility. We introduce Agentic Retrieval-Augmented Generation (Agentic RAG) - a multi-step, agent based LLM pipeline designed to improve efficiency, transparency, consistency, and theoretical alignment in qualitative text analysis. Our study addresses two research questions: (1) How does Agentic RAG perform compared to LDA and LLM prompting in terms of topic validity, granularity, and reliability across datasets? and (2) How can Agentic RAG be extended to enable theory advancement through “lens-based” retrieval?

Methods & Data

We benchmark Agentic RAG against LDA and LLM prompting using three heterogeneous datasets: (i) the 20 Newsgroups corpus (online communication), (ii) the VAXX Twitter/X dataset (data on vaccine hesitancy), and (iii) a qualitative interview corpus from an organizational research context. Agentic RAG is implemented as a model-agnostic, agent-based pipeline that orchestrates retrieval, data analysis, and topic generation. In our analysis, Agentic RAG was applied to produce topics using different GPT models (GPT-3.5, GPT-4o, GPT-5). We evaluate all methods using standardized metrics: topic validity, topic overlap, and inter-round semantic reliability, computed via cosine similarity measures that extend prior topic quality metrics.

Results

Across datasets, Agentic RAG consistently yields high-validity topics with minimal redundancy compared to both LDA and LLM prompting. Whereas LDA and LLM prompting perform well only on specific datasets, Agentic RAG maintains performance across heterogeneous data architectures, while being more transparent and efficient. Based on these results, we derive a structured trade-off table that summarizes the strengths and limitations of all approaches, providing qualitative and computational scholars with clear guidance for selecting an appropriate text analysis method.

Added Value

Our findings demonstrate that Agentic RAG offers a scalable, transparent, and reproducible approach for qualitative text analysis. The method strengthens the rigor of LLM-based qualitative research by enabling more stable outputs, explicit retrieval reasoning, and broader options for assessing topic quality.

Relevance & Research Question

Loneliness has emerged as a growing social and public health concern that increasingly affects younger age groups. In response, the state government of North Rhine-Westphalia has initiated multiple initiatives and established a competence network to counteract loneliness. Against this backdrop, the present study examines the role of digital technologies in both the emergence and alleviation of loneliness. The research focuses on three interconnected key questions:

1) How do technological environments, ranging from face-to-face- communication tools to digital social platforms, shape experiences of social connectedness and emotional well-being, and to what extend may they contribute to or mitigate feelings of loneliness?

2) What role do so called third-places play in individuals’ perceptions of social belonging and connectedness?

3) How are digital media used to build and maintain social relationships and under what conditions are digitally mediated interactions transferred into offline, contexts?

Methods & Data

The study employs a qualitative research design based on more than 150 participants, and was conducted using BARI, the qualitative AI developed by Bilendi. Participants engaged via WhatsApp or Facebook Messenger over roughly one week. BARI supported almost the entire research process, including project flow, moderation, data analysis and reporting. The AI-based moderation is methodologically notable as the absence of a human interviewer may foster greater openness when discussing sensitive topics such as loneliness, potentially reducing social desirability bias. This setup allowed collecting rich narrative data while simultaneously enabling an empirical assessment of the methodological implications of AI supported qualitative research.

Results

Beyond substantive insights into perceptions and experiences of loneliness, the presentation will highlight methodological findings regarding the strengths, weaknesses and challenges of AI-assisted qualitative research. The integration of participant feedback and researcher reflection will be shown to play a central role in improving the AI´s performance and refining its methodological contribution to future research.

Added Value

The study provides dual added value: empirically, it offers new insights into how digital media and social spaces shape loneliness; methodologically, it delivers one of the first systematic assessments of AI-moderated qualitative fieldwork, demonstrating its potential and its limitations for scalable, participant-centered online research.

Relevance & Research Question

Respondents who do not go through the question answer process optimally and instead exhibit satisficing behavior are a longstanding problem for survey researchers. A growing body of studies examines stable, time-invariant, predictors of survey satisficing behavior, such as personality traits. These predictors introduce the possibility to measure the potential to satisfice before actual survey satisficing behavior occurs. This study wants to add to this research by introducing a potentially stable and reliable predictor of survey satisficing. Building on the notion that the question-answer process is a decision-making process and satisficing behavior is caused by low aspiration decisions, the effect of a decision-making tendency to maximize (in opposition to satisfice) on survey satisficing behavior is modelled.
Methods & Data

Data was gathered in October 2024 from 2,911 respondents within the Bilendi non-probability online access panel. Due to its short length, the generalizability of items and reduced dimensions of the scale, the modified maximizing scale by Lai (2010) is applied in this study to measure maximizing with its opposite pole satisficing. As dependent variables, “don’t know” responding and midpoint choosing in four single choice questions was measured. To explain each of them, two multilevel models with questions on level 1 and persons on level 2 were calculated to grasp four questions in one model. Langer’s (2020) extension of the McKelvey & Zavoina (1975) Pseudo R² for multilevel logistic regression models was obtained for the baseline models.
Results

Respondents who score medium to high on the maximizing scale exhibit significantly less “don’t know” responding and midpoint choosing than those scoring lower. However, the magnitude of the effect is small. The maximizing scale explains only a small amount of between-person variance in the examined satisficing behaviors.
Added Value

By affecting satisficing behavior in surveys, maximizing is not only a source of bias in survey data. The short and compact scale can be integrated in surveys to measure a respondent’s potential to exhibit satisficing behavior before it occurs. With the help of LLMs, consecutive survey questions could be tailored to said potential.

Relevance & Research Question
Assessing the quality of online survey data increasingly requires understanding how responses are produced rather than merely what is reported. Traditional outcome-based indicators such as nonresponse or internal consistency fail to capture the cognitive and motivational processes that shape respondents’ behaviour. This study therefore introduces a process-oriented framework that uses paradata—digital traces of respondent actions—to measure engagement and data quality. The research asks: How can behavioural traces such as response times, edits, and navigation patterns be transformed into valid indicators of response effort, satisficing, inattentiveness, and cognitive load?
Methods & Data
The analysis uses event-level Blaise 5 paradata from the CAWI component of CZ-GGS II, encompassing approximately 3,500 completed interviews. Raw XML logs containing timestamps, navigation events, and answer edits were parsed into event-, block-, and respondent-level datasets. Five standardised indices (0–100 scales) were constructed: Satisficing, Cognitive Load, Respondent Effort, Inattentive Responding, and Behavioural Process Validation (BPV). Each combines timing, editing, and navigation measures into interpretable dimensions of process quality. Regression models (OLS/Logit) examine associations between these indices, demographic and technological characteristics, and classical quality outcomes such as item nonresponse and completion time.
Results
The study builds directly on the author’s earlier device-effects analysis of the Czech GGS, extending it from technological determinants to behavioural mechanisms of response generation. Preliminary findings confirm the distinctiveness and validity of the indices. Higher Effort and BPV scores correlate with longer, more consistent response behaviour and lower item nonresponse, whereas higher Satisficing and Inattentiveness indicate faster, less engaged answering. Smartphone users show higher satisficing and reduced behavioural engagement compared to desktop users, while older and higher-educated respondents exhibit greater effort and more deliberate response trajectories. Final results will be completed by the end of 2025.
Added Value
By converting behavioural traces into quantifiable process indicators, the study redefines paradata as a methodological tool rather than a technical by-product. The resulting process-based framework offers a replicable blueprint for measuring engagement and response quality across survey waves, countries, and questionnaire designs implemented in Blaise 5. This approach enhances transparency, comparability, and methodological innovation within the GGP and broader demographic research.

Relevance & Research Question

Online surveys provide a great opportunity for researchers to collect response times alongside the participants’ answers. Extremely short response times—known as speeding—can indicate careless responding. Previous studies often identified speeding behavior using fixed cutoffs, for example those derived from average reading speeds reported in the literature. Although empirically motivated, these thresholds overlook other important cognitive demands of the questions and differences between respondents. This study introduces a probabilistic mixture modeling approach to identify speeding behavior in the “Health in Germany” probability-based panel of the Robert Koch Institute (RKI). We validate this approach by comparing its classifications against established methods and by analyzing correlations with other indicators of data quality.

Methods & Data

Response times from the CAWI participants of the 2024 regular panel (N~30.000) wave were analyzed using a shifted lognormal–uniform mixture model. As is standard practice in response-time analysis, the lognormal component represents regular, attention-based responses. The uniform component captures implausibly short response times (“speeding”). The shift parameter models the minimal realistic answering time. Hierarchical model specifications allow for variation across survey items and respondents. Fixed effects allow to estimate how model features such as speeding likelihood and minimal attention-based answering time vary as function of characteristics the participant (e.g., age) and the item (e.g., number of words).

Results

Preliminary results show that the model accurately reproduces the empirical distribution of response times and allows for the calculation of speeding probabilities per response, participant and question. Speeding probabilities vary substantially across participants, suggesting that individual differences are the dominant source of speeding behavior, while item-level differences are smaller but still substantial. Further analyses, to be completed before the conference, will examine how participant and item characteristics correlate with model parameters, how speeding behavior correlates with other indicators of data quality, and how the model performs in out-of-sample prediction.
Added Value

This study demonstrates the practical value of response-time modeling for improving data quality diagnostics in online panels. By quantifying speeding probabilities instead of applying fixed cutoffs, the method supports more data-driven cleaning and better understanding of response behavior.

Relevance & Research Question

Survey research aims to understand social issues and inform effective public policy. For results to be accurate and equitable, surveys must inclusively represent diverse population sub-groups. Excluding these groups can lead to biased data and policies that perpetuate inequalities. Consequently, inclusivity is now a core principle for major statistical bodies in the UK like the UK Statistics Authority. This has led to a “respondent-centred design” approach, which argues that making surveys accessible for marginalised groups often benefits all respondents (Wilson and Dickinson 2021). However, achieving greater inclusivity involves practical trade-offs, as measures like targeted procedures, alternative response modes, or survey questionnaire translations or adaptations, can often be resource intensive. Evidence of inclusivity practices implemented as part of probability-based self-administered surveys is scarce, and research is required to determine best practice recommendations.
Methods & Data

This evidence review highlights measures that aim to increase participation for harder-to-survey population sub-groups in self-administered surveys, while maintaining the goal of obtaining high-quality, representative data. We focus on two main population subgroups: (1) individuals with disabilities and impairments and (2) individuals with literacy and/or language limitations.

Results

This evidence review identifies general recommendations for recruitment practices to facilitate the inclusion of these frequently excluded sub-groups. It also highlights the cost trade-offs involved in implementing these methods, beyond the ethical imperative for inclusivity.

Added Value

This study addresses an under-researched area by providing evidence-based, practical recommendations for enhancing participation, accessibility and inclusivity in large-scale surveys.

Relevance & Research Question

Knock-to-nudge is an innovative method of household contact, first introduced during the COVID-19 pandemic when face-to-face interviewing was not possible. In this approach, interviewers visit households and encourage sampled units to participate in a survey through a remote survey mode (either web or telephone) at a later date. Interviewers also can collect contact information, such as telephone numbers or email addresses, or conduct within-household selection of individuals on the doorstep if required. This approach continued to be used post-pandemic in a number of surveys, but there remains a knowledge gap regarding its advantages and limitations. It is still unclear whether knock-to-nudge approach leads to improvements in sample composition and data quality.

Methods & Data

We analysed data from two UK surveys: the National Readership Survey (PAMCo) and the National Survey for Wales (NSW), each of which employed different versions of the knock-to-nudge approach. Our aim was to determine whether this method improves survey participation and sample composition, and to assess how incorporating participants recruited via knock-to-nudge impacts on data quality and responses to substantive questions. We investigate these effects using descriptive analyses, statistical tests, and logistic regression models.

Results

Our findings demonstrate that knock-to-nudge is associated with: (1) a significant increase in response rates, (2) improved sample composition, (3) higher item non-response, and (4) significant differences in responses to substantive survey questions.

Added Value

This study contributes to the under-researched area of knock-to-nudge methods. The results indicate that, when carefully designed and implemented, this approach can enhance recruitment efforts and improve sample composition of the resulting samples in surveys. However, its viability as a universal solution for mixed-mode surveys depends on whether these methodological benefits outweigh the potential compromises in data quality and the additional implementation costs.

Relevance & Research Question: Web surveys efficiently collect data on attitudes and behaviors, but often face challenges like satisficing behavior. The increasing prevalence of respondents using smartphones to answer surveys has brought about additional design challanges. The application of a messenger design as a web survey interface offers the opportunity to mitigate some of the drawbacks of a responsive web survey desing. Recent studies have demonstrated that a chatbot-like survey design may provide higher quality responses and greater engagement, albeit with longer response times. This study explores the respondents’ evaluation of using a messenger interface in a web survey setting.

Methods & Data: In 2025, a sample of 2.123 members of a non-probability online access panel in Germany answered a survey on the topic of “vacation”. The sample was cross-stratified by age and gender and limited to respondents aged 18-74. In a field-experiment employing a between-subjects designs respondents were randomly assigned to either a web survey design or to a chatbot design that mimics a messenger interface. In addition to survey duration, we assess the respondents’ evaluation concerning user experience, perceived social presence, perceived flow, ease of use and general satisfaction.

Results: Overall respondents in the chatbot condition were less satisfied with the survey and it took them longer to answer the questions. Also, they experienced lower levels of flow and ease of use. There was no significant difference in the user experience and the survey related social presence was lower only for respondents using a mobile device. Older respondents, females and respondents with a higher education degree seem to evaluate the chatbot design more preferable than younger, male and respondents with lower levels of education. However, the preference for the web survey design is generally confirmed for all respondent groups irrespective of age, education and gender and also for respondents using a desktop or a mobile device.

Added Value: This study contributes to an assessment of using a messenger interface for the administration of survey questions. We discuss the results in light of the recent trend towards an application of a chatbot-like interface in AI supported surveys.

Relevance & Research Question
To reduce respondent burden and increase data quality, survey researchers started to employ open questions with requests for voice instead of text answers. For example, respondents can record these answers through the microphone of their survey device by pressing a recording button facilitating narrations. Research indicates that voice answers, compared to text answers, are longer, consist of more topics, and result in higher validity. However, a key challenge remains the high item-nonresponse rate associated with voice answers. Item-nonresponse reduces the generalizability of results as it is frequently associated with respondent characteristics, such as use of voice input in daily life. This study investigates the following research question: Can we increase the voice answer rate and improve data quality by introducing extra incentives when asking open-ended questions in web surveys?

Methods & Data
We conducted a web survey (N = 2,271) in the Netquest opt-in online panel in Spain categorizing respondents into three groups based on their likelihood to provide voice answers: low, medium, and high. This grouping was done on the fly based on respondents’ answers to two questions related to their 1) use of voice input in daily life and 2) trust in the confidential treatment of their answers. Within each group, respondents were randomly assigned to either receive an extra five-point incentive for providing voice answers to two open questions (extra incentive condition) or not (no extra incentive condition).

Results
As expected, respondents categorized in the high likelihood group were most likely to provide voice answers, and the extra incentive did not affect the voice answer rate in the low and high likelihood groups. However, it reduced the voice answer rate in the medium likelihood group, contrary to our expectation. The extra incentive was partially associated with indicators of higher answer quality, including topic number.

Added Value
Our study provides new insights into the effectiveness of incentives in the context of additional web survey tasks, such as providing voice answers. By focusing on extra incentives and distinguishing between three likelihood groups of respondents with respect to voice answers, it stands out from previous studies.

Relevance & Research Question
In recent decades, there has been a shift from interviewer-administered surveys to self-administered modes. While this transition improves efficiency and reduces costs, it also raises concerns about response burden. Text-based web surveys are often perceived as tedious and burdensome due to the lack of social interaction. The growing presence of the Internet of Things (IoT) indroduces new opportunities to address these limitations. Voice assistants, in particular, enable an oral, conversational mode of data collection that may enhance social engagment while maintaining moderate costs. However, little is known about how respondents perceive and experience interviews conducted through voice assistants. This study uses a smart speaker to administer survey questions and compares respondents’ experiences in the smart speaker interview with those in a web survey.

Methods & Data
A laboratory experiment was conducted in summer 2025 with 245 participants recruited in the city center of Darmstadt. Using a within-subjects design, each participant completed both a web survey and a smart speaker interview containing the same questions. Immediately afterwards, each mode was evaluated separately. Paradata (e.g., response times, interruptions) and self-reported evaluation measures (e.g., flow, ease of use and user experience) were collected to assess participants’ experiences.
Results
Preliminary results indicate that respondents’ general satisfaction is significantly lower in the smart speaker interview than in the web survey. Paradata analyses show that responses take longer and interruptions are more likely in the smart speaker interview. These differences explain part of the variation in general satisfaction and also affect self-reports on evaluation items. Longer response times and a higher likelihood of interruptions are negatively associated with perceived flow, ease of use and user experience. Multilevel analyses suggest that the lower satisfaction with the smart speaker interview can largely be explained by these mediating factors.
Added Value
At this point smart speaker interviews are still in its infancies. This study provides initial insights into respondents’ perceptions of smart speaker interviews and identifies key aspects that require improvement to advance the development and successful implementation of smart speaker interviews in the future.

Relevance & Research Question

Smartphones offer opportunities for official statistics, promising improved user experience, reduced response burden, and higher data quality. We investigate whether respondents show higher activity and engagement in app-based diaries compared to traditional web-based diaries.

Methods & Data

We use Statistics Netherlands’ Household Budget Survey (HBS) as a case study. The HBS is a diary survey conducted every five years to capture household expenditure on goods and services. In 2020, Statistics Netherlands conducted a 4-week web-based survey (N = ~3,000). In 2021, they conducted a 2-week app-based survey on a smaller sample (N =~700). We compare participation and response behavior of respondents in the two modes. Among the indicators are the amount and spread of the reporting.

Results

First results show that initial dropout is higher in the web diary. During the first two weeks, dropout is gradual (~1% per day) and very similar across modes. % of registered respondents who submit at least one purchase and/or validate at least one day is higher for the app respondents. More results on objective burden (time spent in study) and reporting patterns will follow.

Added Value

App-based surveys are currently transitioning from the pilot phase to full implementation in panel surveys and official statistics. Our goal is to evaluate the expectation that app-based data collection enhances activity and user engagement in diary studies.

Relevance & Research Question
Digital data donations in online panels have become an additional avenue for researchers to collect health and fitness tracking data, social media data, and web search and viewing histories from panel participants. Initial studies investigating the general willingness to donate data reported encouraging hypothetical consent rates, underscoring the potential of this method. Nevertheless, actual participation rates in donation studies generally remain low and systematically biased across demographic groups, raising concerns about representativeness and generalizability. In contrast, data donations within online panels have demonstrated consistent participation rates, indicating that online panels might offer favorable conditions for successful data donations. In our study, we explored the conditions and participant characteristics that influence data donation success in online panels compared to other data donation studies conducted within survey contexts.

Methods & Data
We conducted a systematic review of studies that implemented digital data donation protocols in the context of surveys using comprehensive searches across major academic databases (see preregistration: https://osf.io/nad2y). Studies meeting eligibility criteria were included in a quantitative meta-analysis assessing key indicators for donation success. For this exploratory part of the study presented here, we assessed whether indicators for donation success differed between donation studies in online panels and those in cross-sectional recruitments.

Results

In general, donation probabilities varied largely across studies and were strongly influenced by study design and contextual factors, such as donation method, the requested data type, and the recruitment strategy. Studies with a lower mean age generally showed higher donation probabilities. However, online panels displayed more consistent donation probabilities compared to cross-sectional recruitments. Potential moderating factors, such as participants’ familiarity with online data collections and trust in the institution receiving the donation, warrant further consideration.

Added Value
Our results offer meta-analytic insights into key indicators of data donation probabilities for studies conducted in online panels compared to cross-sectional recruitments.

Relevance & Research Question

Data donations are increasingly discussed as a valuable source for social science research. However, little probability-based evidence exists on what drives individuals’ hypothetical willingness to donate personal communication data. We examine three components that could influence consent behavior: motivational framing (societal benefit, personal benefit, no benefit information), privacy (consent from chat partners required vs. consent not required), and the requested data type (aggregated metadata vs. full chat content).

Methods & Data

The study was fielded in the May 2025 wave of the German Internet Panel (GIP), a probability-based online panel of the German adult population. Respondents were randomly assigned to one of 18 conditions in a 3×2×2 experimental design. The dependent variable was a binary measure of hypothetical willingness to donate WhatsApp chats, and we controlled for characteristics such as WhatsApp usage intensity and perceived data sensitivity.
Results

Overall, 11% of respondents (approx. 350 individuals) reported willingness to donate their chat data. Willingness was higher when societal benefit was emphasized (13%) compared with personal benefit or no benefit information (each 10%). Requiring consent from chat partners showed no significant effect (12% vs. 10%). Participants were more willing to donate when full chat content was requested (15%) rather than aggregated metadata (8%). Multivariate analyses indicated that willingness increased among heavy WhatsApp users and decreased with higher perceived data sensitivity. No significant interaction effects across experimental factors were found.

Added Value

This study provides one of the first probability-based examinations of willingness to donate WhatsApp chat data—an especially sensitive and understudied data type. The results indicate that, in this hypothetical context, variation in content sensitivity influenced stated willingness less than expected..These findings offer empirically grounded guidance for implementing future data donation infrastructures and highlight which informational cues and design choices may reduce barriers, increase trust, and support responsible integration of chat-based digital trace data into social research. For example, trying to limit the content sensitivity of a data donation request may not be as promising to increase data donation rates as simply emphasizing the research’s societal benefit.

Relevance & Research Question
Data protection regulations in the EU, Brasil, and California give users the right to access the information online platforms hold about them. Data donation studies capitalize on this requirement by asking web survey respondents to request and donate their data at the end of the survey. However, these studies often suffer from modest donation rates. In this study, we experimentally test whether using different motivational appeals in the data donation request and persuading respondents who initially decline the request increase participation.

Methods & Data
In summer of 2025, we conducted an experiment involving over 2,000 participants from a German online access panel. Panel members were asked at the end of a web survey to donate data from LinkedIn, Instagram, or YouTube. Here, we randomly varied the motivation appeal, either (1) emphasizing respondents' ability to quantify their online platform behavior (personal appeal), (2) emphasizing respondents' ability to learn what online platforms know about them (prosocial appeal), and (3) no appeal. Those who declined were asked for their reason for not donating, followed by a persuasion message that either provided (1) an argument tailored to their reason for declining or (2) a random argument not tailored to their reason for declining.

Results
We found that the motivational appeals did not significantly affect respondents' willingness to donate data or their actual donation rates. However, the inclusion of motivational appeals had some effects on donation bias, for example, the prosocial appeal led to an overrepresentation of respondents with high trust in science among the donors. While providing a persuasion message led about 7% (60 out of 876) of decliners to reconsider their initial decision, only 3 additional respondents actually donated their data after receiving the persuasion message. The type of persuasion argument (tailored vs. random) did not affect participation.

Added Value
Our study shows that basic motivational appeals and persuasion messages have little effect on the participation behavior in data donation studies, but can influence donation bias. As our persuasion message was rather static, future studies investigating such adaptive survey designs could adopt a more tailored approach based on conversational agents.

Relevance & Research Question

Social networking sites have become popular tools for recruiting survey respondents through targeted advertisements. Ad design is crucial, as it must capture users’ attention within seconds.While previous studies highlight the relevance of ad design in recruitment performance, sample composition, and data quality, they have almost exclusively focused on static images. However, static images represent only one possible design format, and the potential effects of animated visuals remain underexplored. This study extends prior research by systematically comparing the effects of static and animated ad images on two key aspects of survey recruitment: sample composition and response quality. It addresses the following research questions:

How are different visual elements (static vs. animated) related to sample composition?

How are different visual elements (static vs. animated) related to response quality?

Methods & Data

Data stem from the recruitment campaign for the new online panel GP.dbd, which combines survey data with digital behavior data (e.g., web tracking and app data). The target population comprised adults living in Germany, and recruitment was conducted in 2023 via Facebook and Instagram. Four static images and their animated counterparts were tested. Differences between the two ad formats were examined using descriptive and comparative analyses focusing on respondent demographics and data quality indicators.

Results

Findings show that the visual format of an ad image influences both who participates and how attentively respondents engage with the survey. Static images tend to attract women and highly educated individuals, whereas animated ads appeal more to men, those with lower or middle education, and older respondents. Analyses of extreme response times, item nonresponse, and break-offs yielded mixed findings, suggesting that animation influences attentiveness in complex and context-dependent ways.

Added Value

These results highlight that ad design choices can subtly shape both the composition and engagement of recruited samples. Rather than favoring one format over the other, the findings suggest that static and animated ads serve different purposes in recruitment. A practical implication is to combine both formats strategically. Together, these insights provide nuanced, evidence-based guidance for researchers and practitioners seeking to optimize recruitment on social media platforms.

Relevance & Research Question

Social media platforms, such as Facebook and Instagram, are increasingly used for survey recruitment, particularly for targeting hard-to-reach populations. Previous research has shown that the visual design of advertisements plays a key role in the effectiveness and costs of the recruitment and the data quality of the resulting samples (e.g., Donzowa et al., 2025; Höhne et al., 2025). Pictures are typically used in the advertisements to attract the social media users’ attention and motivate them to click on the survey invitation. However, there is yet a limited understanding of how other visual formats, in particular videos, influence survey recruitment and data quality. In this study, we examine the effectiveness of pictures vs. short videos for the survey recruitment of young adults with social media. We hypothesize that videos are more engaging than pictures, resulting in a larger number of completed surveys at a lower cost, but do not expect differences in sample composition and response quality.

Methods & Data

We will conduct an online survey in December 2025 among young people aged 18-25 from Germany who traveled with Interrail in the last year. The survey includes questions about their travel behavior, attitudes towards the European Union, and European identity. Survey respondents are recruited through Meta (Facebook and Instagram). Using snowball sampling, respondents are also asked to forward the survey invitation to other people who fit the target criteria. In the ad campaign, we use pictures showing different contents, such as a person within a train, trains in front of different landscapes, and railway stations. We create corresponding videos with the AI software Midjourney by setting the respective pictures as the starting frame.
Results

We compare the effects of the two advertisement formats on survey recruitment regarding their effectiveness, measured by the number of completed surveys and referrals through snowballing sampling, and their cost efficiency. Furthermore, we evaluate differences in sample balance across sociodemographics, in particular age and gender, and response quality, measured by completion time and item-nonresponse.
Added Value

The findings will be highly relevant for survey practitioners who plan to recruit respondents through social media.

Relevance & Research Question

Traditional survey methods consistently face critical challenges in achieving adequate coverage and response rates among migrant populations, leading to significant sampling bias in market and opinion research. Understanding these diverse groups is vital for both commercial and public sector decision-making.

Research Question: Can targeted, non-probability sampling methods utilizing social media platforms (SMs) effectively recruit demographically diverse and representative samples of specific migrant populations in European countries, and how do the resulting data quality and efficiency metrics compare to surveys via online access panels?

Methods & Data

We’ve run 5 online surveys between January and October 2025 focusing on first- and second-generation migrants from Turkish and Arabic origin countries residing in France, Germany and Belgium. We used stratified recruitment campaigns across Meta platforms (Facebook/Instagram), utilizing the advertising API for targeting based on age, gender and language. The surveys were run in local languages + Arabic + Turkish.

The collected data was compared with online access panel data regarding metadata on response behaviour (survey speed, devices used, drop-outs…) as well as survey results including a deeper analysis and comparison of language impacts on survey results.

Results

The Social Media Sampling (SMS) method demonstrated clear advantages in efficiency and reach. Crucially, SMs proved highly effective at accessing younger and lower-assimilation migrant cohorts who are severely underrepresented in standard frames.

The results showed, for example, that participants had significantly better knowledge of Turkish and Arabic compared to panel members. In addition, the proportion of Muslims was on average 20 percentage points higher than in the online access panels. Furthermore, we observed that many of the participants via social media were 1st generation migrants and an overrepresentation of people who recently moved to the respective country.

Added Value

This research provides an essential, validated framework for survey practitioners, demonstrating that social media can be leveraged as a rapid and reasonably cost-effective primary recruitment tool for hard-to-reach, mobile populations. It offers a robust, tested procedure for mitigating sampling biases compared to online panels. Ultimately, this study promotes greater inclusivity and accuracy in survey results by ensuring the reliable representation of migrant voices.

TBA

TBA

TBA

Relevance & Research Question: Although online probability-based panels aim for accuracy, they can exhibit a left-leaning bias in public opinion research due to the overrepresentation of politically and civically engaged individuals. Researchers employ weighting techniques to correct sample imbalances relative to the population. This study aims to assess the extent to which diverse adjustment variables and weighting techniques can mitigate this left-leaning bias and enhance the accuracy of estimates from probability-based panels.
Methods & Data: This research examines samples from KnowledgePanel Europe (KP Europe), Ipsos' random probability online panel, which has been operational in the US since 1999 and the UK since 2020, with expansion across Europe since 2022. The evaluation focuses on the effectiveness of different weighting techniques, including raking, propensity score adjustments and generalized regression estimation (GRE). These methods are implemented using two sets of adjustment variables: basic demographics (age, sex, education and geographic region) and a more comprehensive set that includes demographics and variables linked to political attitudes and engagement.

To gauge the relative advantages of various adjustment procedures and variables, each was evaluated based on its success in reducing bias for different benchmarks from high-quality, "gold-standard" surveys. These benchmarks cover a range of topics, like civic engagement, living situation and technology use. Besides biases, the variance or precision of estimates is crucial. The "margin of error" (MOE) describes the expected variance in survey estimates if repeated multiple times under identical circumstances. The MOE is calculated for estimates from all benchmark variables to see how different weighting procedures and variables affect variability.

Results: Initial findings reveal variability in left-leaning bias in KP Europe samples. While various weighting methods effectively reduce bias and align results with population distributions, the choice of adjustment variables significantly affects the accuracy of the estimates. Additionally, incorporating political variables alongside basic demographics has a different impact on the MOE across KP Europe's countries.

Added Value: This study highlights the critical role of adjustment variables in improving the accuracy of estimates and provides valuable insights into the effectiveness of weighting techniques for reducing bias in political and public opinion research across diverse European contexts.

Relevance & Research Question
Gaining insights into how different sampling methods may affect the generalizability and validity of survey-embedded experiments is essential for online survey actors. Previous studies have found that social psychological experiments appear to generate comparable experimental treatment effects across sampling (probability and nonprobability) and stratification schemas (prestratified or not). This study replicates and expands such research to survey embedded experiments from political science (Krishnarajan, 2023) and media and communication studies (Van Erkel & Soontjens, 2022) and assesses whether effect sizes vary over types of sampling and stratification strategies.
Methods & Data
Two survey-embedded experiments were administered to participants of the Swedish Citizen Panel (SCP), an academically run non-commercial online access panel. Participants were 2,000 panelists recruited to the SCP using probability sampling and 2,000 panelists who self-selected into the panel. These panelists were then selected either through prestratified random sampling or simple random sampling, yielding a total of four independent groups of invited panelists: (1) a nonprobability-based sample stratified on age, education, and gender, (2) a nonprobability-based sample that was not selected using prestratification criteria, (3) a probability-based sample stratified on age, education, and gender, and (4) a probability-based sample that was not selected using prestratification criteria.
Results
The results are not yet available; the present abstract will be revised with the results amended in January 2026. The data will be analyzed by comparing the outcomes of the political and media and communication survey embedded experiments in the four sample groups.
Added Value
This study adds to the research on survey methodology and the impact sampling has in treatment effects. A better understanding of how nonprobability and probability samples might affect data quality is crucial to be able to conduct more precise and valuable research in the future.

Relevance & Research Question
The expansion of internet access in many countries raises the question of whether it is now feasible to conduct web-only surveys of the general population without compromising representativeness. This presentation will examine the representativeness of web-only surveys of the general population in the United Kingdom. The analysis addresses the following three research questions:

• RQ1: How have internet exclusion and intensity of internet use changed over time?

• RQ2: What are the characteristics of different types of internet users and non-users? How representative are these groups? How has this changed over time?

• RQ3: How does the representativeness of web respondents compare to the representativeness of different groups of internet users? How has this changed over time?

Methods & Data
We use data from the United Kingdom Household Longitudinal Study (UKHLS). The main survey (2009-2022), a probability-based sample of the UK household population, is used to explore the evolution of internet exclusion over time (RQ1 and RQ2). Moreover, we benefit from a mixed-mode experiment embedded in the Innovation Panel (2012-2023), a probability-based sample of the Great Britain household population. In the experiment, a random sample of households was assigned to a web-first and CAPI sequential design. This enables us to evaluate the representativeness of web respondents and compare it with that of internet users (RQ3).

We use coefficients of variation of the response propensities to estimate the representativeness of internet users and web respondents with regard to a set of auxiliary variables.
Results
The results show a significant decrease in internet exclusion. Internet users are increasingly representative of the general population, although gaps still remain among older adults, those with lower education levels, and other disadvantaged groups. Web survey respondents have also become more representative of the general population over the past decade, but they remain less representative than internet users.
Added Value

The results offer valuable empirical evidence about the quality of web-only surveys in the past and present, which will assist survey practitioners in understanding the opportunities and risks of conducting web-only surveys now and in the near future.

Relevance & Research Question

Survey researchers often allow respondents to fill out questionnaires both online and by paper-and-pencil forms in a mixed mode fashion. However, respondents who choose to complete questionnaires on paper tend to submit their questionnaires with fewer unanswered questions than respondents who choose to complete questionnaires online. Furthermore, many respondents who choose to fill out a questionnaire online do so without ever submitting their questionnaire. The aim of the present study is to evaluate whether sending digital push-to-complete reminders to respondents who have chosen to fill out a questionnaire online but have not yet submitted it are more likely to submit their questionnaires and submit them with fewer unanswered questions than similar respondents who do not get a digital push-to-complete reminder.

Methods & Data

The assessment will be made on a self-administered push-to-web mixed-mode survey (web and sequential paper-and-pencil questionnaire) distributed to a random sample of 9,000 individuals residing in Gothenburg, Sweden. In the experiment, potential respondents were randomly assigned to one of two groups: One group was sent digital push-to-complete reminders a few days after they had started but not submitted the questionnaire online, whereas the other group did not receive such a reminder.

Results

Data collection began in August 2025 and will be completed in early January 2026. Empirical results from the experiment will not be available until January 2026. Upon completing the data collection, this present abstract will be amended with the results of the experiment. The data will be analyzed by comparing response rates (RR1) and data quality between the treatment group and the control group.

Added Value

The present study contributes to existing research on survey reminders by examining whether digital push-to-complete reminders can increase response rates and data quality in push-to-web mixed-mode questionnaires specifically, and online questionnaires generally.

Relevance & Research Question
Panel attrition remains a challenge in longitudinal survey research. Methodologists discuss several mechanisms affecting respondents’ motivation to continuously participate in panel surveys. We focus on two of them: On the one hand, we test the idea that more frequent contact may increase respondents’ engagement with the panel and hence reduce attrition. On the other hand, we address the question of how respondent burden in terms of questionnaire topic and complexity may impact future panel participation.

Methods & Data

We use data from the German Longitudinal Environmental Study (GLEN), a large-scale, nationwide randomly sampled panel on environmental topics launched in 2024. The experiments were implemented in an inter-wave survey in September 2025, followed by a panel wave in November 2025, used to measure participation effects.

Experiment 1

The first experiment investigates the effect of time interval between survey waves by not inviting a random 10% (N = 1,727) of the eligible sample (N = 16,772) to the inter-wave survey. We expect longer intervals between panel waves to reduce participation rates, as a higher survey frequency creates habituation and increases familiarity and engagement with the panel.

Experiment 2

In the second experiment, we examine the effect of complexity and thematic content of the questionnaire. In a first random split, 25% of participants received a long item battery on climate change skepticism, expected to increase burden due to its repetitive nature, while the rest answered a more diverse module on internet use. In a second random split, 90% were assigned a complex factorial survey experiment on CO₂ pricing policies, expected to increase burden through topic complexity, while the rest answered questions on cultural participation. The assumed completion time was kept constant across groups, allowing us to isolate the effect of the questions on subsequent participation.
Results

For both experiments, we analyze the effect on participation in the next panel wave. Data collection will be completed by the end of 2025. We will present results at the conference.
Added Value

We contribute to the literature on panel nonresponse and survey experience by providing experimental evidence from a nationwide randomly sampled panel.

Relevance & Research Question

Many surveys have transitioned to online data collection. To minimize the risk of nonresponse bias, surveys often adopt a web-first mode with follow-up of nonrespondents via face-to-face or telephone interviewing. Evidence suggests such designs may reduce costs and may produce datasets of higher quality than web only designs.

However, with proportions of populations using the internet increasing markedly and people becoming less willing to welcome interviewers, in recent years the contributions of web with face-to-face or telephone modes to minimizing non-response biases that justify such a design may have changed. This paper addresses this issue. The main research questions are: Do we still need to follow up web-non-respondents in a second mode to

RQ1: maximise response rates?

RQ2: maximise dataset representativeness?

RQ3: maximise response by under-represented hard-to-reach population subgroups?

RQ4: minimise non-response biases remaining after non-response weighting?

and how has this changed over time?

Methods & Data

This study uses data from Understanding Society (UK Household Longitudinal Study, UKHLS). We focus on the Innovation Panel component of the study, in which a subset of sample members has been offered web interviews with face-to-face or telephone follow-ups of non-respondents.

For each survey wave, we use Coefficients of Variation of response propensities to quantify the representativeness of web only and web plus face-to-face or telephone respondents. In addition, we use the UKHLS main survey, which enables investigation of hard-to-reach population groups.

Results

Key findings are: 1) follow-ups are still required to maximise response rates and dataset sizes, though impacts have declined; 2) the impact of follow-ups on representativeness has declined, with web and web plus face-to-face datasets not differing; 3) impacts of follow-ups on the under-representation of hard-to-reach population subgroups have become negligible; and 4) impacts of follow-ups on non-response biases remaining after non-response weighting, have similarly declined and are now negligible.

Added Value

We discuss the implications for survey practice. This paper is the first to investigate if follow-ups are still needed in web surveys in the UK context. If follow-ups are not needed any more, this could potentially have large cost-saving implications for survey agencies.

Relevance & Research Question

4chan is an anonymous online forum known for ephemeral content and internet subcultures. This research examines how masculinities are constructed and contested in such spaces, where gender norms are negotiated through irony, confrontation, and subcultural slang. Combining theories of masculinity with computational text analysis, it bridges qualitative research and data-driven modeling of online discourse. It asks how different types of masculinities (hegemonic, hybrid, negotiating, caring) are constructed and circulated online and how theory-driven annotation and lexicon development can support computational identification.
Methods & Data

This study employs a mixed-method design integrating computational text analysis and qualitative annotation. It uses a comprehensive 2.5-year dataset of over 328M 4chan posts, encompassing textual data and metadata across all boards collected via a 4chan text collection tool. The platform’s unfiltered nature allows gender and politics to frequently intersect across diverse contexts. The empirical pipeline includes preprocessing (cleansing, fixing misspelled, broken, or repeated words), identifying masculinity-related discourse through SentenceBert and extracting candidate terms for a theory-driven annotation schema grounded in gender studies and discourse theory. The schema operationalizes masculinity types through parameters such as dominance, emotionality, caregiving, and norm stance. Human annotators use a web-based interface (Doccano) supported by automated term suggestions and inter-annotator agreement metrics. The resulting annotations form the basis for a masculinity lexicon and subsequent computational modeling and clustering to explore discursive variations across online communities.

Results

This study generates a dataset and lexicon capturing linguistic construction of different masculinity types. These resources support qualitative interpretation and large-scale computational modeling, revealing patterns in identity performance and negotiation of gender norms. Methodologically, it demonstrates how theory-driven annotation combined with embedding-based term extraction and automated quality control, providing a scalable framework for future studies of digital gender expression.
Added Value

Conceptually, this study illuminates how different types of masculinities are performed and negotiated in online communities, linking these patterns to broader social and political discourses. Methodologically, it demonstrates how theory-driven annotation and embedding-based lexicon development can create scalable tools for analyzing gendered language. The resulting dataset, annotation framework, and lexicon provide reusable resources for future research across platforms and contexts.

Relevance & Research Question
As survey self-reports on digital behavior are often inaccurate, social and behavioral researchers increasingly augment web surveys with digital traces. Existing research has mostly focused on collecting these traces, leaving open the question of how to further analyze them. The present study utilize traces in the form of metered data to measure one digital behavior: online information-seeking in the context of (expectant) parenthood. We investigate the following research question: How can traces of website visits, online searches, and app usage be transformed into measurements of online information-seeking behavior?
Methods & Data
We obtained metered data and self-reports on (expectant) parenthood through the Netquest opt-in online panel in Spain. The sample (n = 800) includes both participants who self-reported being and not being (expectant) parents. In 2024, Netquest extracted participants’ metered data to previously specified websites, search terms, apps, and URL keywords over a period of 12 to 24 weeks. Before data extraction, an external ethics advisor reviewed and approved the study. As the large volume of extracted traces (approx. 184,000) renders manual, human-based coding infeasible, we classify the traces using locally deployed Large Language Models (LLMs).
Results
Preliminary results indicate that about 85% of parents engaged in website visits, 70% in online searches, and 30% in app usage related to online information-seeking behavior. Next, we will investigate the impact of key analytical decisions on the measurements generated. Specifically, we will systematically vary the type of trace considered (e.g., website visits, search terms, or app usage), the LLM used (e.g., Llama or Gemma), and the aggregation rules applied (e.g., total duration or frequency). To evaluate the impact of these decisions, we will determine criterion validity by using participants’ self-reports on (expectant) parenthood.
Added Value
Our study stands out from previous methodological research, which mostly focused on the collection of traces, and builds an important knowledge basis for future studies analyzing traces (or metered data). By shedding light on key analytical decisions and their impact on measurement outcomes, it contributes to informed research workflows and ultimately more accurate measurements of digital behavior, including online information-seeking.

Relevance & Research Question
This study aimed to evaluate the effectiveness of social media platforms, specifically Meta and TikTok, as data collection tools and to explore their influence on body image perception, with a focus on gender differences. The goal was to assess these platforms' efficacy in gathering data on sensitive topics like body image.
Methods & Data

Advertising campaigns were conducted on Meta and TikTok to recruit participants for an online survey about body image perception. Throughout the campaign, various images were used for the advertisements, comparing and testing the performance. Additionally, an experiment was conducted to assess the impact of an incentive as a motivation for engaging with the questionnaire. Data analysis focused on campaign performance metrics and survey responses, employing logistic regression and cluster analysis to examine the relationship between social media use and body satisfaction.
Results
The campaigns demonstrated social media's potential to reach a large audience quickly, with TikTok generating high visibility and Meta platforms, especially Facebook, ensuring acceptable participation rates. However, self-selection bias and sample representativeness emerged as concerns. The survey results revealed a clear link between social media use and body perception. Logistic regression showed that perceiving social media content as realistic and influential significantly increased the likelihood of body dissatisfaction, up to four times higher. Age emerged as a protective factor, while gender differences were less pronounced than expected. Cluster analysis identified three distinct user profiles based on perceived social media influence and content realism.

Added Value
This study provides insights into the effectiveness of social media as a data collection tool for sensitive topics and contributes to understanding the complex relationship between social media use and body image perception. It highlights the need for cautious interpretation of data collected through these platforms due to potential biases. The findings suggest that perceived realism of social media content plays a crucial role in body dissatisfaction, offering valuable implications for promoting more conscious social media use and personal well-being. Future research directions are proposed to address limitations and expand on these findings.

Relevance & Research Question

Declining response rates in traditional probability-based surveys have prompted researchers and survey practitioners to increasingly explore alternative recruitment strategies, such as via Social Networking Sites (SNS) and piggybacking (i.e., re-using respondents) from established surveys. While these strategies offer faster and more cost-efficient access to respondents, they also raise questions about the quality of the resulting data. SNS may threaten response quality through different motivational motives and an increased risk of satisficing. On the other hand, piggybacked samples may benefit from respondents’ experience and commitment but could suffer from conditioning effects. This research provides a comparative assessment of response quality across different recruitment strategies.

Methods & Data
We examine response quality across different recruitment strategies using data from the newly established multi-source panel study GESIS Panel.dbd in Germany. The GESIS Panel.dbd combines several recruitment approaches, sampling respondents through a) SNS from Meta, b) piggybacking from the German General Social Survey (ALLBUS), the German Longitudinal Election Study (GLES), and the GESIS Panel.pop, and c) traditional probabilistic sampling from population registers. To assess response quality, we analyze a range of indicators, such as item nonresponse, break-off rates, straightlining in item batteries, and response times. Our analyses control for sociodemographic composition across the different recruitment groups to disentangle effects of recruitment mode from compositional differences.
Results

Results will be presented at the conference in February. Preliminary analyses reveal notable sociodemographic differences (e.g., in age and education) across recruitment groups, pointing to potential disparities in response behaviors and data quality.

Added Value

Altogether, this study provides one of the first comparative assessments of response quality across recruitment strategies increasingly used in survey practice. By controlling for differences in sample composition, we disentangle compositional from recruitment-driven effects, offering insights into how integrated recruitment designs affect data quality.

Relevance & Research Question
What if the success of your survey depended on a single piece of paper?
As survey researchers, we are now competing harder than ever for people’s attention and time. With the rise of online, self-administered online surveys, interviewers play a smaller role in motivating participation. For these online studies in the Netherlands, households can only be invited by traditional mail — making the invitation letter our sole opportunity to connect. Its wording, tone, access options and layout can decide whether someone visits the link or ignores the request. Over the past decade, Statistics Netherlands has continuously refined its invitation strategy in search of the perfect letter for a diverse population.
Methods & Data

Our research combines qualitative pre-testing with large-scale field experiments. After many rounds of testing, a standard letter was designed that performs consistently well — until three new experiments challenged our assumptions. We examined (1) the effect of adding a QR code for easier access, (2) a shorter version of the letter, and (3) the response to a refreshed, more visually appealing layout. Each experiment used a fresh, representative sample and a corresponding control group.

Results

Adding a QR code had no significant effect (no code: 35.1% vs. QR code: 34.8%, n.s.).
However, the redesigned “fancy” letter, praised in qualitative pre-testing, led to a significant drop in response (35.4% → 32.2%, p = 0.0003).
The shortened letter, by contrast, increased participation (23,7% → 26,8%, p = 0,000) – but also changed the way respondents answered the questions, resulting in fewer short trips being reported in the travel survey.

Added Value
Our findings reveal how intuitive design choices can have unintended consequences. While respondents claim to prefer modern, appealing letters, with QR-codes, actual behavior tells a different story and may subtly affect measurement. This presentation offers ten years of lessons — and a few humbling surprises — from the ongoing search for the holy grail of survey invitations.

Relevance & Research Question
Amid declining survey response rates, innovative methods are needed to gather representative data. This presentation evaluates a novel "push-to-web" methodology. The core research question is: Can Short Message Service (SMS) text messages effectively replace traditional contact modes to recruit a representative, probability-based sample for mobile-friendly web surveys?
Methods & Data
This methodology has been tested in many Greek surveys since the 2019 Hellenic National Election Voter Study (ELNES 2019). The target population is Greek citizens, leveraging the country's near-universal (99%) mobile phone penetration. A probability-based sample is generated using a Random Digit Dialing (RDD) approach for mobile numbers. Selected individuals received a sequence of text messages: a pre-notification, an invitation with a unique survey URL, and reminders. The web survey is optimized for a seamless mobile experience.
Results
The SMS "push-to-web" approach is a feasible and effective method for large-scale data collection. Although the final response rate is between 6% and circa 10%, the high population coverage of mobile phones ensures a comprehensive and representative sampling frame. The study demonstrates the viability of conducting large-scale, probability-based web surveys using SMS as the primary contact mode.
Added Value
This study provides a proven, practical framework for using SMS in survey recruitment. It highlights the method's potential for researchers to recruit representative samples in populations with high mobile phone access, even without a traditional sampling frame. This presentation will discuss the implementation, advantages, and key considerations for broader application, such as cross-national differences in mobile costs and legal restrictions.

Relevance & Research Question

Telephone surveys have historically been a popular form of data collection in labor market research and continue to be used to this day. Yet, telephone surveys are confronted with many challenges, including imperfect coverage of the target population, low response rates, risk of nonresponse bias, and rising data collection costs. To address these challenges, many telephone surveys have shifted to online and mixed-mode data collection to reduce costs and minimize the risk of coverage and nonresponse biases. However, empirical evaluations of the intended effects of introducing online and mixed-mode data collection in ongoing telephone surveys are lacking.

Methods & Data

We address this research gap by analyzing a telephone employee survey in Germany, the Linked Personnel Panel (LPP), which experimentally introduced a sequential web-to-telephone mixed-mode design in the refreshment samples of the 4th and 5th waves of the panel. By utilizing administrative data available for the sampled individuals with and without known telephone numbers, we estimate the before-and-after effects of introducing the web mode on coverage and nonresponse rates and biases.

Results

We show that the LPP was affected by known telephone number coverage bias for various employee subgroups prior to introducing the web mode, though many of these biases were partially offset by nonresponse bias. Introducing the web-to-telephone design improved the response rate but increased total selection bias, on average, compared to the standard telephone single-mode design. This result was driven by larger nonresponse bias in the web-to-telephone design and partial offsetting of coverage and nonresponse biases in the telephone single-mode design. Significant cost savings (up to 50% per respondent) were evident in the web-to-telephone design.

Added Value

Using a unique experimental design we showed the potential for known telephone number coverage bias in telephone surveys. However, while introducing the web mode eliminates this coverage error, there is potential for other error trade-offs. For practitioners, this underscores the importance of carefully weighing the potential trade-offs between costs and multiple sources of error when designing a specific study.

Relevance & Research Question

The web survey mode offers a high degree of flexibility while also being highly cost-efficient. However, in the context of web surveys, potential coverage issues are repeatedly raised as a problem, as target respondents without internet access cannot participate in an internet-based survey. In addition, there are respondents with internet access for whom participating online would be possible from a formal perspective, but who do not want to participate via the internet. I want to know: Are the reasons for abstaining from participating online-based also influenced by internet-related characteristics, apart from simply having or not having internet access?
Methods & Data

I use data from the GESIS Panel.pop Population Sample, a probability-based self-administered mixed-mode panel of the German general population, surveyed via web and paper mode (push-to-web design with paper mode as alternative mode). I perform logistic regressions with mode selection as the dependent variable and various internet-related characteristics as independent variables (frequency of internet use, internet skills, variety of internet use, and number of internet-enabled devices). I put particular emphasis on the key challenge of identifying causal effect directions between internet-related characteristics to construct my models with appropriate control variables. For all regressors, I use not only the quasi-metric scale level frequently used in the literature, but I also examine different threshold levels through varying thresholding.
Results
Data analysis is ongoing. I am going to have results in January 2026.
Added Value

With my analyses, I want to shed light on (internet-related characteristics of) those individuals who are not reached by online-only. Knowledge about those not reached by online-only also contributes to evaluating a target group-oriented use of paper questionnaires when conducting surveys.

Relevance & Research Question
Longitudinal studies are transitioning from face-to-face to push-to-web designs for cost-efficiency and timeliness. However, for ageing populations, concerns persist about mode effects on data quality and measurement equivalence. The English Longitudinal Study of Ageing (ELSA) Wave 12 dress rehearsal provides empirical evidence addressing: Does push-to-web compromise data quality in aging surveys? What mode effects emerge comparing web-first to traditional face-to-face approaches?
Methods & Data
Wave 12 implemented sequential web-first then CAPI design for 375 individuals (June-August 2025). Web-first participants (n=260 households) received online invitations with CAPI follow-up for non-responders after 5 weeks. We assessed mode effects across multiple dimensions: item non-response, break-off patterns, scale reliability, occupation/industry coding quality (CASCOT confidence scores), and measurement consistency versus Wave 11 single-mode CAPI. Respondent feedback (n=31) and interviewer reports provided qualitative insights.
Results
Push-to-web showed mixed outcomes. Individual web completion reached 33.6%, but household completion was only 21.5%, requiring 78% of web-first households to receive CAPI follow-up. Web median duration (87.6 minutes) exceeded estimates (60 minutes), with break-offs concentrated at household grid and financial modules. However, web mode demonstrated improved data quality: item non-response was lower compared to CAPI Wave 11 CAPI; transformed web questions improved the data quality for job and accupation responses; scale reliabilities remained excellent (CASP-19 α=0.90). Mode preference split: 50.7% respondents preferred web for flexibility and self-pacing; 18.8% valued face-to-face interaction. Telephone-administered cognitive assessments proved challenging, with hearing difficulties affecting completion quality.
Added Value

Evidence from ELSA study provides critical evidence for longitudinal surveys implementing push-to-web among older populations. Key contributions: (1) household-level completion metrics are essential—individual response rates mask significant household non-completion, which has big implications on fielwork costs; (2) structured web design reduces measurement error in complex variables despite completion burdens; (3) sequential mixed-mode designs must balance mode-specific strengths—web excels at structured data while face-to-face remains valuable for cognitively demanding tasks and panel engagement; (4) realistic duration expectations and improved re-entry protocols are crucial. Findings demonstrate push-to-web viability for ageing populations when supported by tailored strategies and hybrid approaches for mode-sensitive content.

Relevance & Research Question:

As digital communication increasingly unfolds on online platforms, behavioral data have become central to understanding media exposure, polarization, and social interaction. Yet computational approaches necessary to analyze such data often remain inaccessible to many communication and social science researchers who lack extensive programming expertise or institutional resources. As a result, many research-driven tools remain scattered across personal repositories, supplementary materials, or project websites, reducing their visibility, reusability, and long-term sustainability. This presentation addresses the question of how a community-driven infrastructure can lower entry barriers for computational methods and support transparent, reproducible online research.

Methods & Data:

The Methods Hub is designed as an open platform that curates computational resources relevant to social science research. It integrates three core components: (1) open-source tools ranging from lightweight scripts to fully developed software packages, (2) tutorials explaining both general principles of reproducible computational workflows and concrete methodological applications, and (3) containerized interactive coding environments that can be executed directly in the browser without local installation. All contributions follow open licensing and reproducibility standards and are reviewed accordingly. The platform architecture supports interoperability with complementary infrastructures (e.g., KODAQS) to facilitate cross-linking between datasets, tools, and training materials. The development process combines community submissions, expert curation, and iterative user testing to ensure methodological relevance and usability.

Results:

Preliminary implementation demonstrates that the platform successfully bridges gaps between computational tooling and social science workflows. Initial contributions include tools for digital trace data collection, automated preprocessing pipelines, validation and reliability routines, and visualization templates. Tutorials and browser-based execution environments have proven effective in enabling researchers to test methods without configuring complex software environments. User feedback from pilot workshops indicates substantial reductions in setup time, increased willingness to experiment with computational approaches, and improved understanding of reproducible research practices.

Added Value:

The platform lowers entry barriers to behavioral data analysis, strengthens methodological knowledge transfer, and promotes long-term visibility and reuse of tools otherwise confined to fragmented project repositories. Through openness, interoperability, and executable documentation, the Methods Hub contributes to building a robust ecosystem for computational communication science.

Relevance & Research Question

Clearly communicating data quality limitations is essential for transparent research. Data quality frameworks and reporting guidelines support researchers in identifying and documenting potential data quality concerns, but it is unclear how well this translates to reporting practices. In this project, we analyze reports of data quality limitations in substantive social science publications. Thus, we provide insights into typical limitations that reoccur but also highlight underrepresented areas where researchers might require additional guidance.

Methods & Data

We analyze the “Limitations” sections and limitation-related paragraphs in “Discussion” sections of substantive survey-based research published in the journals including American Sociological Review and Public Opinion Quarterly. We use a large language model to extract data-quality-related aspects of these sections and paragraphs and assign them to the measurement and representation sides as defined in Total Data Quality error frameworks. We then cluster the excerpts into themes and compare the themes to components of the error frameworks. Based on this, we discuss which data quality dimensions are commonly and rarely mentioned, and what possible reasons for these differences may be. Through comparisons with reporting guidelines (e.g., AAPOR transparency initiative, datasheets for datasets), we highlight areas where researchers might require additional support. We also analyze areas where current guidelines might be adapted to better represent researchers’ needs in reporting.

Results

Initial findings show a prevalence of discussions on measurement validity and on coverage of the target population in contrast to only few mentions of limitations related to data processing. We also find that limitations are often communicated implicitly, adding a challenge for readers from other disciplines. For example, briefly mentioning the concrete implications of using an “online non-probability sample” would increase interdisciplinary validity assessments.

Added Value

We contribute to the transparent and well-structured communication of data quality as a crucial step for validating research by providing an overview of current reporting practices and directions for improved reporting.

Relevance & Research Question
Synchronous online qualitative data collection as a research method, such as interviews or focus groups via digital video conferencing platforms, has been experiencing a major upswing. Consequently, researchers are challenged to consider if and how the traditional qualitative research process from preparation to follow-up, could be adapted for online contexts, especially concerning quality, distance versus proximity and privacy. Existing research, however, is usually limited to advantages and disadvantages. We thus ask which strategies and quality practices qualitative researchers use to collect qualitative research in an online context and how these can be interpreted through existing methodological and quality frameworks, situating our project within the topic “survey innovations”.
Methods & Data

We conducted a systematic literature review (Tranfield et al. 2003) of academic journal articles reporting experiences and reflections of qualitative online data collection from 2000 to 2024. A literature search was carried out in the databases Springer Link, Science Direct and Emerald Insight, using among others, the following terms: “digital OR virtual OR online data collection” AND “qualitative research OR method”. Following a three-stage selection process, 44 articles were selected and systematically coded in MaxQDA, combining a deductive framework (according to “before, during and after the survey”) with an inductive coding process.

Results
The findings indicate that successful qualitative online research relies on well-coordinated teams with clearly distributed roles, careful platform selection, and transparent communication with participants. Core strategies include developing digital and interpersonal competencies, fostering trust and interactional dynamics, and ensuring secure and ethical data handling. Rather than adhering to fixed standards, researchers maintain quality through adaptive, context-sensitive, and collaboratively negotiated practices.
Added Value

The online context will continue to play an increasingly important role in qualitative social research. We fulfil the need for practical guidance on conducting qualitative research projects online while maintaining quality standards. Furthermore, we relate research practices to existing debates on research quality. In doing so, it not only offers practical guidance but also theoretical connections for developing a reflexive methodology of digital social research.