Universität Mannheim: MAJOURNALS
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393 research outputs found
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Live Online Video Interviewing as a Complementary Mode to In-Person Interviews: Evidence from the European Social Survey
No full textLive video interviewing emerged as a method for collecting survey data during the COVID-19 pandemic, having rarely been used for survey data collection prior to this. There is now a need to assess experiences and outcomes from studies that utilised video interviewing, partly with a view to informing the future feasibility of the method in different contexts. This paper reports on the experience of the European Social Survey (ESS) with video interviewing, having used this approach as a complementary method to in-person interviewing at its 10th round (2020-2022). The ESS can provide a unique perspective, being the first cross-national survey to use video interviews. In total, 17 countries offered video interviewing alongside in-person interviewing at ESS Round 10. In this paper, we present a range of results based on ESS Round 10 in two main categories. We first look at the effectiveness of the implementation of video interviewing and then compare quality between video interviews and in-person interviews across various indicators, including interviewer effects. The results show that the prevalence of video interviews varied widely between countries, likely relating to national contextual factors. However, in countries where a large share of video interviews was carried out, we found that the interview experience was rated positively, and quality indicators were closely comparable with in-person interviews. These results suggest that future use of video interviewing may be more feasible in some countries that others, but in certain contexts it has the potential to offer an effective complementary option to in-person interviewing
End-user data analysis at the LHC
Full text linkThe 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
cellular_raza – Novel Flexibility in Design of Agent-Based Models in Cellular Systems
Full text linkThis 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
Improved Ensemble Predictive Modeling Techniques for Linked Social Media and Survey Data Sets Subject to Mismatch Error
Full text linkModern predictive modeling tools, such as random forests (and related ensemble methods), have become almost ubiquitous in research applications involving innovative combinations of survey methodology and data science. However, an important potential flaw in the widespread application of these methods has not received sufficient research attention to date. Researchers at the junction of computer and survey science frequently leverage linked data sets to study relationships between variables, where the techniques used to link two (or more) data sets may be probabilistic and non-deterministic in nature. If frequent mismatch errors occur when linking two (or more) data sets, the commonly desired outputs of predictive modeling tools describing relationships between variables in the linked data sets (e.g., variable importance, confusion matrices, RMSE, etc.) may be negatively affected, and the true predictive performance of these tools may not be realized. We demonstrate a new methodology based on mixture modeling that is designed to adjust modern predictive modeling tools for the presence of mismatch errors in a linked data set. We evaluate the performance of this new methodology in an application involving the use of observed Twitter/X activity measures and predicted socio-demographic features of Twitter/X users to accurately predict linked measures of political ideology that were collected in a designed survey, where respondents were asked for consent to link any Twitter/X activity data to their survey responses (exactly, based on Twitter/X handles). We find that the new methodology, which we have implemented in R, is able to largely recover results that would have been seen prior to the introduction of mismatch errors in the linked data set
Probing in Cognitive Interviews Can Promote Acquiescence
Full text linkCognitive interviewing is widely used to pretest survey questionnaires and is considered a best practice (e.g., Willis, 2005, 2018; Beatty & Willis, 2007). However, the method has been controversial because, among other concerns, it requires interviewers to probe respondents for more detail or clarity about their experience answering draft survey questions which may lead them to report “problems’’ they have not actually experienced (e.g., Conrad & Blair, 2009). The present study investigates this possibility from the perspective of Acquiescent Response Style (ARS) – the tendency for survey respondents to select positive responses such as “yes” or “strongly agree,” irrespective of the question’s content (e.g., Baumgartner & Steenkamp, 2001). For example, respondents in a cognitive interview might affirm experiencing a problem mentioned in or implied by an interviewer’s probe even if they have not actually experienced it. We embedded a probing experiment in a pretest of a health survey in which respondents participated in cognitive interviews that used either directive probes (n=41) or non-directive probes (n=26). Directive probes explicitly queried respondents about a specific, intentionally unlikely interpretation of each question in a draft questionnaire; non-directive probes were open-ended. Directive probe (DP) respondents affirmed the interpretation queried in the probes over five times more often than respondents in the non-directive probe (NP) group volunteered these interpretations. This pattern was reversed for interpretations of the questions that were volunteered, i.e., about which DP respondents were not asked: NP respondents volunteered alternative interpretations over four times more than DP respondents. These effects were particularly pronounced for respondents with lower levels of education and who were younger. The findings suggest that directive probing in cognitive interviewing can promote responding that is reminiscent of ARS – an affirmation bias – and likely harmful for the quality of evidence produced in cognitive interviews
How Does Video Interviewing Affect the Interviewers’ and Respondents’ Paralinguistic Behaviors? A First Exploration
Full text linkVideo interviews have been gradually adopted by survey organizations as an alternative to in-person interviews as a mode of data collection. Recent studies have shown that video and in-person interviews elicited similar levels of respondents’ rapport with interviewers and similar quality data. However, little is known about whether the presence and prevalence of interviewer and respondent paralinguistic behaviors, e.g., disfluencies such as “uh” and “um” or laughter, vary between the two modes, and when they do, how might this affect survey outcomes? To address these questions, we coded the presence of six paralinguistic behaviors in 710 question-answer (Q-A) sequences in 15 in-person interviews and 12 video interviews conducted by professional survey interviewers in a laboratory experiment. Most of the paralinguistic behaviors occurred equally often in the two interviewing modes except laughter which was significantly more prevalent in video than in-person interviews. We attributed the increased laughter in video interviews as a nervous response to greater communication difficulties in that mode. Nonetheless, this did not differentially impact the prevalence of respondents’ adequate responses, indicating (indirectly) that data quality was equivalent in the two modes. These findings bolster the emerging narrative that when interviewed via video, respondents’ experience and their answers are very similar to when they are interviewed in person.
Improving Assessments of Group-Based Appeals in Political Campaigns by Systematically Incorporating Visual Components of Ads
Full text linkExisting research on group-based appeals primarily uses text-based methods, and while many studies show the importance of visuals in implicitly cueing groups, this data is rarely captured in a systematic way. This paper seeks to make the first important step towards filling this gap by outlining a coding scheme to evaluate how group-based appeals are used multimodally in modern political campaigns. This paper builds categories from a qualitative sample of 182 images taken from 28 television and 63 Facebook ads from candidates running in the US 2020 House of Representatives elections. Direct appeals are captured as explicit group mentions and I present new categories for indirect and baseline appeals, which incorporate primarily visual indicators of groups. Intercoder reliability tests were conducted, and the schema was applied to a larger sample of 2480 images from 125 television ads from candidates running in the three most populous states (California, Texas, Florida). This paper finds that candidates use direct and indirect appeals at similar rates, often using them in combination. Capturing visual data therefore enables greater coverage of the range of group-based appeals that political campaigns conduct. Secondly, candidates are more likely to cue occupational groups indirectly, and capturing only direct cues may lead to skewed findings in terms of which groups candidates appeal to. I find that this new coding scheme may reduce bias in measures of both the prevalence of group-based appeals and the types of groups that campaigns appeal to in modern political discourse
Continuous Benchmarking of Numerical Algorithms Implemented in M++ via Gitlab CI/CD and Google Benchmark∗
Full text linkWe present an automated framework for benchmarking numerical algorithms that solve partial differential equations under consistent and reproducible conditions using the parallel finite element software M++. This framework integrates GitLab CI/CD, Google Benchmark, and the HoreKa supercomputing system to enable continuous integration and benchmarking. By incorporating ongoing software development, the framework supports improving performance and reliability, which are vital for various scientific computing applications, including wave propagation, cardiovascular simulations, dislocation dynamics, and uncertainty quantification. These applications motivate the two benchmarking examples presented in this text. We further outline the benchmarking workflow as well as the use of a research database storing comprehensive performance data, facilitating reproducibility for future studies
Effects of the Self-View Window in Live Video Survey Interviews
Full text linkThe studies reported here explore how the “self-view” window (a live video feed of oneself) affects live video survey respondents’ likelihood of disclosing sensitive information and their feelings about the interview. In Study 1 (2012), 124 laboratory respondents answered sensitive and nonsensitive questions taken from US government and social scientific surveys over Skype, either with or without a self-view window. Respondents randomly assigned to having a self-view disclosed no less sensitive information than those without a self-view, and on a few questions, they disclosed more (more frequent alcohol use and more sex partners). Self-view respondents also perceived the interview as less sensitive, and they reported less copresence with the interviewer, reduced self-consciousness, and greater comfort answering many of the sensitive questions. Study 2 (2017) replicates these findings in a second sample of 133 respondents by (a) tracking where video survey respondents look on the screen—at the interviewer, at the self-view, or elsewhere—while answering the same survey questions and (b) examining how gaze location and duration differ for sensitive vs. nonsensitive questions and for more and less socially desirable answers. Findings include that self-view respondents looked less at the self-view while answering sensitive (vs. nonsensitive) questions, and that respondents who looked more at the self-view window reported feeling less self-conscious and less worried about how they presented to the interviewer. Results demonstrate that the self-view can change respondents’ experience and where they look during a video interview. They also document, for the first time in video surveys, surprising individual variability in looking at the self-view, with some respondents never once looking and others looking at their self-view as much as 50% of the time. Attending to how self-view and respondents’ choices (e.g., turning it on or off) affect respondent experience and data quality will be important as live video surveys are increasingly deployed
Creating Design Weights for a Panel Survey With Multiple Refreshment Samples: A General Discussion With an Application to a Probability-Based Mixed-Mode Panel
Full text linkPanel surveys suffer from attrition, where participants drop out over time. To maintain generalizability, refreshment samples are frequently employed, bringing in new individuals, increasing the number of panelists, and balancing sample composition. Although refreshment samples offer numerous advantages, the inclusion of new panel members may introduce bias into the analysis if the design weights are not appropriately tailored to these new members and adjusted to align with existing panel members. If not correctly accounted for, their inclusion may bias results. This paper addresses the issue of designing proper weights by applying the multiple-frame weighting approach proposed by Kalton and Anderson, which is generally used for cross-sectional surveys, to ongoing panel studies with refreshment samples. We demonstrate its application to a synthetic data set and a probability-based mixed-mode panel with an initial sample and two refreshment samples. We compare estimates obtained using multiple-frame weighting with those obtained using unweighted and naively weighted methods (where design weights are used as calculated for the respective samples without adjusting for the fact that some members of the population have a chance of being sampled more than once due to the refreshments). These comparisons showcase the potential for bias introduced by neglecting proper weighting and underscore the importance of both a multiple-frame weighting approach and meticulous sample documentation