Video Interviewing and Observed Differences in Mental Health Outcomes

The integration of video interviewing in survey research is relatively new and may offer similar benefits as telehealth visits in mental health research. Methodological evaluations of video interviewing are needed for large-scale surveys. Over 3,000 clinical interviews were conducted by video and over 1,500 by phone for a national study of U.S. adults, the Mental and Substance Use Disorders Prevalence Study (MDPS). Sociodemographic differences were observed among those who completed a clinical interview by video compared to phone respondents. Higher prevalence rates of all disorders, with the exception of schizophrenia spectrum disorders, were found for video respondents. Higher prevalence rates of generalized anxiety disorder (video 11.3% vs. 8.0%, p < .05), bipolar 1 (2.1% vs. 0.7%, p < .05) and obsessive compulsive disorder (OCD; 3.1% vs. 1.5%, p < .05) were observed among those completing an interview by video compared to those interviewed by phone. Individual logistic regression models were calculated for each disorder adjusting for sociodemographic characteristics to assess the difference in prevalence rate by mode of interview. Respondents interviewed by video had higher odds of having bipolar 1 (OR = 2.96, 95% CI [1.42, 6.17]), OCD (OR = 2.16, 95% CI [1.20, 3.90])and having two or more mental health disorders (OR = 1.64, 95% CI [1.23, 2.19]) than those interviewed by phone after adjusting for sociodemographic characteristics. While further investigation using experimental approaches is required, video interviewing may improve the ability to detect mental health conditions in large-scale survey research

End-user data analysis at the LHC

The Large Hadron Collider (LHC), located at CERN in Geneva, stands as one of the most monumental scientific experiments in human history. This remarkable machine facilitates approximately 40 million particle collisions every second, generating an astronomical amount of data. Even with rigorous filtering of collision events, the data retained for subsequent analysis remains staggering in scale. In addition to the recorded data, conducting a successful physics analysis demands an extensive set of simulations that can be compared to the recorded events. In this presentation, we will delve into our approach to incorporating external resources, such as the NEMO cluster in Freiburg, into our local batch system. This integration greatly enhances accessibility for the complex workflows required for physics data analyses

Mother Tongue or Non-Native Language? – The Influence of Language on Response Behavior in Surveys

Today, an increasing number of surveys offer respondents the choice of which language they want to answer the questionnaire. In later data analysis, however, the language in which the respondent answers the questions is often ignored, and no distinction is made regarding whether that language is the respondent’s mother tongue. Several psychological theoretical considerations and empirical observations indicate that respondents’ answering behaviors are influenced by whether the questions are presented in their mother tongue or a non-native language. Therefore, the extent to which these mechanisms and effects of language used are also applicable and relevant in social science studies remains unclear. Based on models of cognitive load, satisficing, and language-dependent memory, the influence of language nativeness on response behavior is explained from a theoretical point of view. The research question will be answered by analyzing the data from the refugee study ReGES (Refugees in the German Educational System). The results of the analyses show that there is a difference in response behavior depending on whether a question is answered in a mother tongue or a non-native language. The implications, both from a survey methodological point of view and for further research, will be discusse

A Survey Mode of the Future? Investigating Respondents’ Willingness to Participate in Self-Administered Video-Based Web Surveys

Web surveys convince through low costs, save interviewer time, and provide respondents with a high level of flexibility, but often struggle with achieving high response rates and data quality. One way to mitigate these problems might be to conduct video-based web surveys, in which at least one of the two interview components (i.e., questions or answers) is based on video communication technology. However, few studies have investigated respondents’ willingness to participate in such web surveys. Therefore, we examine the extent to which respondents are willing to participate in video-based web surveys and the reasons for their hypothetical (non)willingness. This also includes the investigation of characteristics of respondents and video-based web surveys that are associated with (non)willingness. The results of two experimental web surveys conducted in non-probability online panels in Germany reveal that a majority of respondents is willing to participate in web surveys with video-based questions and predefined answer options or open textual answers. Less respondents are willing to participate in web surveys with voice or video-based answers. While respondents mostly mention convenience and time flexibility as reasons for their willingness, the main reasons given for their nonwillingness are excessive effort regarding video-based questions and privacy and data security concerns regarding video-based answers. Video-based web surveys generally appeal to younger, male, and extraverted respondents as well as respondents who rate the survey as more interesting. This article provides researchers and practitioners with new insights into respondents’ hypothetical willingness when it comes to video-based web surveys, including empirical-driven survey design recommendations

cellular_raza – Novel Flexibility in Design of Agent-Based Models in Cellular Systems

This paper uses cellular_raza to develop a model with cell-type specific interactions whereby cells self-assemble into regions of similar species which is also known as cell-sorting. We use this model to asses the parallelization performance of the numerical backend at the core of cellular_raza and show that values of up to p = 97.78 ±} 0.14% parallelizable code can be achieved, which indicates a high level of parallelizability

From Clicks to Quality: Assessing Advertisement Design’s Impact on Social Media Survey Response Quality

Researchers are increasingly using social media platforms for survey recruitment. However, empirical evidence remains sparse on how the content and design characteristics of advertisements used for recruitment affect response quality in surveys. Building on leverage-salience and self-determination theory, we assess the effects of advertisement design on response quality. We argue that different advertisement designs may resonate with specific social groups who vary in their commitment to the survey, resulting in differences in the observed response quality. We use data from a study conducted via ads placed on Facebook in Germany and the United States in June 2023. The survey, focusing on attitudes toward climate change and immigration, featured images with varying thematic associations with the topics (strong, loose, neutral). The sample consisted of 4,170 respondents in Germany and 5,469 respondents in the United States. We compare several data quality indicators, including break-off rate, completion time, non-differentiation, item non-response, passing an attention check question, and follow-up availability, across different advertisement features. Regression analyses indicate differences in response quality across advertisement designs, with a strong thematic design generally being associated with poorer response quality. Strongly themed ad designs are generally associated with higher attrition, non-differentiation, and item non-response, and with a lower probability of passing an attention check and providing an e-mail address for future survey inquiries. Our study advances the literature by highlighting the substantial impact of advertisement design on survey data quality, and emphasizing the importance of tailored decision-making in recruitment design for social media-based survey research

Researching the Moment of Truth: An Experiment Comparing In-the-Moment and Conventional Web Surveys to Investigate Online Job Applications

Understanding how people seek and apply for jobs online is crucial for addressing social inequality, discrimination, and aiding companies in attracting suitable candidates. Conventional surveys struggle to capture the nuances of online job searches that, as many online events, are characterized by repetition, low distinctiveness, and limited emotional impact. These characteristics can lead to memory-related errors, becoming more likely as the time between the event and the survey increases. Passively collected data, such as metered data provided by online panel members who install tracking software on their browsing devices, offer an alternative. While these data provide objective insights into online job searches, they suffer other types of errors, and cannot capture subjective information and all potential objective data of interest. This paper explores an alternative approach: sending surveys to individuals in a metered panel shortly after an event of interest is detected through metered data. These “in-the-moment” surveys aim to fill in missing information not obtainable through passive data collection while reducing memory-related errors that affect conventional surveys. To assess the feasibility and benefits of this method, an experiment comparing in-the-moment surveys triggered by online job applications with conventional surveys was conducted in an opt-in online panel in Spain to research how people apply for a job online. The results reveal that metered panelists accept well in-the-moment surveys, displaying high participation levels and positive evaluations regarding effort and satisfaction, without perceiving an increased privacy risk. Moreover, the data indicate positive impacts on data quality, with longer and more detailed responses to open-ended questions. However, not all aspects saw substantial improvements, with the reduction of non-recall being weaker than expected, possibly due to participants’ overconfidence in their memories. The significant disparities observed in substantive results between both types of surveys also suggest that participants are not fully aware of what they do not remember

Die Leidenschaft des Rezensierens: Ansichten eines Doktoranden zum Rezensionswesen

In diesem Beitrag plädiert der Autor für das vermehrte Lesen und Verfassen von Rezensionen insbesondere während der wissenschaftlichen Ausbildung, hebt die Vorteile hervor, die es den Rezensent*innen bietet, und spiegelt seine persönliche Meinung zum (deutschsprachigen) Rezensionswesen

A Scent of Strategy: Response Error in a List Experiment on Anti-Immigrant Sentiment

This Research Note reports on a list experiment regarding anti-immigrant sentiment (n=1,965) that was fielded in Spain in 2020. Among participants with left-of-center ideology, the experiment originated a negative difference-in-means between treatment and control. Drawing on Zigerell’s (2011) deflation hypothesis, we assess the possibility that leftist treatment group respondents may have altered their scores by more than one to distance themselves unmistakably from the sensitive item. We consider this possibility plausible in a context of intense polarization where immigration attitudes are closely associated with political ideology. This study’s data speak to the results of recent meta-analyses that have revealed list-experiments to fail when applied to prejudiced attitudes and other highly sensitive issues – i.e., precisely the kind of issues with regard to which the technique ought to work best. We conclude that the possibility of strategic response error in specific respondent categories needs to be considered when staging and interpreting list experiments

Augmenting Data Download Packages – Integrating Data Donations, Video Metadata, and the Multimodal Nature of Audio-visual Content

This research explores the potential of augmented Data Download Packages (aDDPs) as a novel approach to analyze digital trace data, using TikTok as a use case to demonstrate the broader applicability of the method. The study demonstrates how these data packages can be used in social science research to understand better user behavior, content consumption patterns, and the relationship between self-reported preferences and actual digital behavior.We introduce the concept of aDDPs, which extend the conventional Data Download Packages (DDPs) by augmenting the collected data with survey data, metadata, content data, and multimodal content embeddings, among other possibilities - rendering aDDPs an unprecedentedly rich data source for social science research. This work provides an overview and guidance on collecting, augmenting DDPs, and analyzing the resulting aDDPs.In a pilot study on 18 aDDPs, we use the combination of data components in aDDPs to facilitate research on user engagement behavior and content classification. We showcase the potential of the information breadth and depth that aDDPs depict by exploiting the combination of multimodal content embeddings, the users’ watch history, and survey data. To do so, we train and compare uni- and multimodal classifiers, classify the 18 aDDPs’ videos, and investigate the extent to which user engagement behavior impacts future content suggestions. Furthermore, we compare the users retrieved content with the users’ self-reported content consumption