Existing 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.
If survey respondents do not interpret a question as it was intended, they may, in effect, answer the wrong question, increasing the chances of inaccurate data. Researchers can bring respondents' interpretations into alignment with what is intended by defining the terms that respondents are at risk of misunderstanding. This article explores strategies to increase alignment between researchers' intentions and respondents' answers by taking advantage of the unique affordances of online surveys compared to paper or other analog formats. Web surveys are often text-based, but allow for the seamless integration of embedded audio material so that users may read, listen to, or both read and listen to survey instructions. Unimodal definitions are either spoken or textual, while multimodal definitions are both spoken and textual. Further, definitions can be designed to take advantage of the affordances of each mode. While mode-invariant definitions contain the same words irrespective of whether they are textual or spoken, mode-optimized definitions are designed to take advantage of the affordances of written and spoken communication. For example, definitions optimized for textual presentation use fewer words than corresponding mode-invariant definitions and are designed so the key information is visually salient, while definitions optimized for spoken presentation are shorter and more colloquial than corresponding mode-invariant definitions. In this study, both mode-optimized and mode-invariant formats improved alignment. Multimodal, mode-optimized definitions produced improved alignment over both types of unimodal definitions. This study suggests that multimodal definitions, when thoughtfully designed, can improve data quality in online surveys without negatively impacting respondents.
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.
The 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 selfconsciousness, 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.
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.
