Methodological Reflections on Online Data Collection during the Covid-19 Pandemic

The pandemic closed the door for the use of conventional, face-to-face data collection methods. At the same time, it built a momentum for the exploration and utilization of online data collection methods. However, the belief about superiority of the offline data collection persists. The literature review and the authors’ research experience reveal that offline and online data collection methods yield similar result in terms of data completion and quality. All data collection methods contain weaknesses and strengths. Nonetheless, the online data collection methods are very versatile. They allow the researchers to choose the tools that best align with their research objectives.

ANOSMIA AND AGEUSIA IN PATIENTS WITH COVID-19

Objective: To evaluate the prevalence and diagnostic significance of anosmia and ageusia among COVID-19 positive patients of Karachi, Pakistan. Study Design: Cross-sectional study. Place and Duration of Study: Dr Ruth K. M. Pfau Civil Hospital, (Dow University of Health Sciences), Karachi Pakistan, from Jan 2021 to Feb 2021. Methodology: The data were collected prospectively from 265 COVID-19 positive patients. Some patients were interviewed over the telephone, while for patient's ease, an online Google form was also formed, facilitating the online data collection. The patient's demographics, comorbidities, allergies, and COVID-19 associated characteristics were inquired. The statistical analysis was performed on SPSS version 23. Results: The observed frequency of anosmia and ageusia in COVID-19 patients was 49.1% & 43.8% respectively. The median time to recovery was 8-8.5 days (median) for both symptoms. We found no significant difference for gender, BMI, marital status, residential area, comorbidities and reason for long-standing breathing difficulties between patients with or without both anosmia and ageusia (p>0.05). Furthermore, most of the cigarette smokers reported none of the two symptoms (anosmia and ageusia), 24% and 25.2% of COVID-19 positive cases with smoking history were presented without anosmia and ageusia, respectively (p<0.05). Conclusion: Loss of sense of smell and taste was reported in almost half of the studied population infected by the SARS-CoV-2 virus. Therefore, screening for anosmia and ageusia must be considered while COVID-19 suspicion as an important diagnostic clue.

Bringing Home Baby Euclid: Testing Infants’ Basic Shape Discrimination Online

Online developmental psychology studies are still in their infancy, but their role is newly urgent in the light of the COVID-19 pandemic and the suspension of in-person research. Are online studies with infants a suitable stand-in for laboratory-based studies? Across two unmonitored online experiments using a change-detection looking-time paradigm with 96 7-month-old infants, we found that infants did not exhibit measurable sensitivities to the basic shape information that distinguishes between 2D geometric forms, as had been observed in previous laboratory experiments. Moreover, while infants were distracted in our online experiments, such distraction was nevertheless not a reliable predictor of their ability to discriminate shape information. Our findings suggest that the change-detection paradigm may not elicit infants’ shape discrimination abilities when stimuli are presented on small, personal computer screens because infants may not perceive two discrete events with only one event displaying uniquely changing information that draws their attention. Some developmental paradigms used with infants, even those that seem well-suited to the constraints and goals of online data collection, may thus not yield results consistent with the laboratory results that rely on highly controlled settings and specialized equipment, such as large screens. As developmental researchers continue to adapt laboratory-based methods to online contexts, testing those methods online is a necessary first step in creating robust tools and expanding the space of inquiry for developmental science conducted online.

National Health Interview Survey, COVID-19, and Online Data Collection Platforms: Adaptations, Tradeoffs, and New Directions

High-quality data are accurate, relevant, and timely. Large national health surveys have always balanced the implementation of these quality dimensions to meet the needs of diverse users. The COVID-19 pandemic shifted these balances, with both disrupted survey operations and a critical need for relevant and timely health data for decision-making. The National Health Interview Survey (NHIS) responded to these challenges with several operational changes to continue production in 2020. However, data files from the 2020 NHIS were not expected to be publicly available until fall 2021. To fill the gap, the National Center for Health Statistics (NCHS) turned to 2 online data collection platforms—the Census Bureau’s Household Pulse Survey (HPS) and the NCHS Research and Development Survey (RANDS)—to collect COVID-19‒related data more quickly. This article describes the adaptations of NHIS and the use of HPS and RANDS during the pandemic in the context of the recently released Framework for Data Quality from the Federal Committee on Statistical Methodology. (Am J Public Health. 2021;111(12):2167–2175. https://doi.org/10.2105/AJPH.2021.306516 )

From online data collection to identification of disease mechanisms: The IL-1beta, IL-6 and TNF-alpha; cytokine triad is associated with post-acute sequelae of COVID-19 in a digital research cohort

Post-acute sequelae of COVID-19 (PASC) emerge as a global problem with unknown molecular drivers. In a digital epidemiology approach, we rapidly recruited 8,077 individuals out of 129,733 households in Halle (Saale) to the cohort study for digital health research in Germany (DigiHero). These responded to a basic questionnaire followed by a PASC-focused survey and blood sampling in case of prior positive SARS-CoV-2 testing in their household. The presented analysis is based on the first 318 DigiHero participants, the majority thereof after mild infections. PASC were reported in 67.8% of cases, consisted predominantly in fatigue, dyspnea and concentration deficit, persisted in 60% over the follow-up period of on average eight months and their resolution was unaffected by post-infection vaccination. PASC was not associated with post-COVID-19 autoantibodies, but with elevated levels of IL-1beta, IL-6 and TNF-alpha. Blood profiling and single-cell data from validation cohorts with early infection suggested the induction of these cytokines in COVID-19 lung pro-inflammatory macrophages creating a self-sustaining feedback loop. Our data indicate a long-lasting cytokine triad - potentially underlying PASC symptoms - to be driven by macrophage primed during infection. We demonstrate how the combination of digital epidemiology with selective biobanking can rapidly generate hints towards disease mechanisms.

Careless responding in crowdsourced alcohol research: a systematic review and meta-analysis of practices and prevalence

Crowdsourcing — the process of using the internet to outsource research participation to ‘workers’ — has considerable benefits, enabling research to be conducted quickly, efficiently, and responsively, diversifying participant recruitment, and allowing access to hard-to-reach samples. One of the biggest threats to this method of online data collection however is the prevalence of careless responders who can significantly affect data quality. The aims of this preregistered systematic review and meta-analysis were to: i), examine the prevalence of screening for careless responding in crowdsourced alcohol-related studies; ii), examine the pooled prevalence of careless responding; and iii) identify any potential moderators of careless responding across studies. Our review identified 96 eligible studies (~126,130 participants), of which 51 utilised at least one measure of careless responding (53.2%: 95% CI 42.7% to 63.3%; ~75,334 participants). Of these, 48 reported the number of participants identified by careless responding method(s) and the pooled prevalence rate was ~11.7% [95% CI: 7.6% to 16.5%]. Studies using the MTurk platform identified more careless responders compared to other platforms, and the number of careless response items was positively associated with prevalence rates. The most common measure of careless responding was an attention check question, followed by implausible response times. We suggest that researchers plan for such attrition when crowdsourcing participants and provide practical recommendations for handling and reporting careless responding in alcohol research.

Online data collection to address language sampling bias: Lessons from the COVID-19 pandemic

The COVID-19 pandemic has massively limited how linguists can collect data, and out of necessity, researchers across several disciplines have moved data collection online. Here we argue that this rising popularity of remote web-based experiments also provides an opportunity for widening the context of linguistic research by facilitating data collection from understudied populations. We discuss collecting production data from adult native speakers of Tagalog using an unsupervised web-based experiment. Compared to equivalent lab experiments, data collection went quicker, and the sample was more diverse, without compromising data quality. However, there were also technical and human issues that come with this method. We discuss these challenges and provide suggestions on how to overcome them.

Comment on “Which Earthquake Accounts Matter?” by Susan E. Hough and Stacey S. Martin

In their analysis of the U.S. Geological Survey’s (USGS) “Did You Feel It?” (DYFI) data Hough and Martin (2021) claim, among other assertions, that the following: Socioeconomic and geopolitical factors can introduce biases in the USGS’ characterization of earthquakes and their effects, especially if online data collection systems are not designed to be broadly accessible;These biases can, in turn, potentially cascade in myriad ways, potentially shaping our understanding of an earthquake’s impact and the characterization of seismic hazard; andCaution should be urged when relying on data from the DYFI system to characterize the distribution of shaking from large earthquakes in India and other parts of the world (outside of the United States). Claims of inequity in access, systematic data biases, or urging caution in the usage of data from critical governmental earthquake information systems should not be made, nor taken, lightly. Several assertions made by Hough and Martin (hereafter, H&M) about the nature of DYFI contributors—and the data they provide—leave a false narrative concerning DYFI system accessibility and quality that H&M have not adequately substantiated. I describe several shortcomings of H&M’s demographic statistics and methodology, focusing on four main concerns. First, DYFI has revolutionized and greatly facilitated access to reporting intensities, in contrast to H&M claims to the contrary. Second, because DYFI does not directly collect demographic data other than the observer’s location, any demographic analyses require extraordinary inferences, well outside the normal bounds of sociodemographic analyses. Third, independent of accessibility and the geographic distribution of contributions from the public, the macroseismic data collected are nonetheless representative of the shaking and impact at each location, of quality, rapid, and thus extremely useful. Lastly, H&M fail to cite critical and pertinent prior, highly relevant scholarly studies, and as such, they misrepresent the novelty of their own work as well as miss key practical matters detailed in those prior studies. Prior to rebutting what H&M claim DYFI does not do, I will remind the reader the ways in which DYFI excels.

Moderated Online Data-Collection for Developmental Research: Methods and Replications

Online data collection methods are expanding the ease and access of developmental research for researchers and participants alike. While its popularity among developmental scientists has soared during the COVID-19 pandemic, its potential goes beyond just a means for safe, socially distanced data collection. In particular, advances in video conferencing software has enabled researchers to engage in face-to-face interactions with participants from nearly any location at any time. Due to the novelty of these methods, however, many researchers still remain uncertain about the differences in available approaches as well as the validity of online methods more broadly. In this article, we aim to address both issues with a focus on moderated (synchronous) data collected using video-conferencing software (e.g., Zoom). First, we review existing approaches for designing and executing moderated online studies with young children. We also present concrete examples of studies that implemented choice and verbal measures (Studies 1 and 2) and looking time (Studies 3 and 4) across both in-person and online moderated data collection methods. Direct comparison of the two methods within each study as well as a meta-analysis of all studies suggest that the results from the two methods are comparable, providing empirical support for the validity of moderated online data collection. Finally, we discuss current limitations of online data collection and possible solutions, as well as its potential to increase the accessibility, diversity, and replicability of developmental science.