Volume 10, Issue s5 - Special Issue: Getting “good” data in a pandemic, part 2: more tools in the toolbox; Guest Editors: Kostadinova, Viktorija; Gardner, Matt Hunt

This is the second part of a two-part special issue that explores how linguistic researchers adapted their data collection methods during the COVID-19 pandemic. The collection draws from a series of workshops, Getting Data: Linguistic Data Collection in the Age of Pandemic, which served as a springboard for a range of small-scale studies investigating the validity of remote and modified data collection techniques. While the first issue examined the quality of data gathered through remote methods and strategies for maintaining research rigour under pandemic constraints, this second issue shifts focus to the development of alternative approaches that enabled researchers to collect linguistic data when traditional fieldwork was impossible. The articles explore various challenges and successes, including the use of videoconferencing, online platforms, and adjusted in-person protocols, while critically assessing the long-term applicability of these methods in linguistic research. This issue ultimately highlights the potential for remote methods to increase accessibility and safety for both participants and researchers, while also acknowledging the limitations and ethical concerns that arise from these new approaches. Together, these studies contribute to an evolving understanding of how data collection in linguistics can adapt to changing global conditions.

Attitudes to language and attitudes to ethnic groups have long been shown to be related to one another. In recent history, significant events have also been shown to negatively affect attitudes to specific groups who are deemed to be responsible. The current paper looks at how the COVID-19 pandemic has emboldened those who hold far right attitudes to migrants in an Irish context. Through a Twitter scraping exercise, conducted in August 2020, we show that far right framings of migrants as (a) contagion or disease, (b) criminals, and (c) favoured or elites are clearly evident and considerably on the rise in these Irish data. This would seem to run contrary to a concurrent study in Germany. Thus, we then pair this quantitative Twitter data with qualitative observations of anti-mask protests as indicative of a broadening of the allure of far right political groups, with COVID-19 as the “leading edge”. Taken together, these data seem to run contrary to European Social Survey and comparative data, leading us to question how attitudes are elicited, measured, and reported.

This study reports a preliminary exploration of the online collection of language assessment data in the author’s PhD project under the influence of the COVID-19 pandemic. It describes the challenges the author met, details the adaptations she made, and delineates its implications. Specifically, the data collection in this project involved remotely delivering three vocabulary tests and one speaking task to EFL learners in mainland China and collecting the results of these tests. In this process, the author trialed, pilot-tested, and compared three test-administering modes: first an asynchronous mode (Wenjuanwang), then a synchronous mode (Tencent Meeting), and finally a return to an asynchronous mode (Super Star Learning). It is hoped that this exploratory investigation into these technology-enhanced online methods will add a different perspective and present a multifaceted view for data collection of language assessment research during and after this pandemic.

Our research explores the relationship between cognition and language. The focus of this paper is to discuss how we embarked upon remote data collection with children during the COVID-19 pandemic. In this study we investigate cognitive processes of non-verbal intelligence, working memory, implicit statistical learning, and speed of automatization (measured with the multiple-trial Tower of Hanoi puzzle). Here we focus primarily on the speed of automatization, partly because of theoretical interest, and because it is more difficult to adapt to an online format due to the motor component of the task. We established a hybrid method of data collection where the researcher was present online to guide children through a battery of language and cognitive tasks. We used a videoconferencing platform, a digital visualiser, and a physical puzzle which we posted to each child prior to commencing the research sessions. We also designed an online version of the puzzle with support from the Getting Data project. We discuss the methodology of our study and the lessons learned during remote data collection.

The following paper examines the use of the stable sociolinguistic variable ( -ing ) across two different interview modalities: “classic” in-person sociolinguistic interviews and identical interviews conducted remotely over online video chat. The goal of this research was to test whether a change in modality results in style-shifting, as quantified by different rates of formal/standard [-ɪŋ] versus informal/non-standard [-ɪn]. Results show that when the internal linguistic constraints governing ( -ing ) variation are taken into account, there is not a significant difference between modalities, suggesting both modalities are equally formal (or informal). This suggests that remote online video chats are a viable method for collecting sociolinguistic data.

The Bear in a Window project captures Australian children’s experiences of the COVID-19 pandemic. We focused on children’s experiences of lockdown, or extended periods of home confinement, ranging from one to 100 days at a time between 2020 and 2021. Using the online experimental platform, Gorilla, we invited children aged 3–12 to record themselves telling stories about the positives and negatives of life in lockdown to our mascot, Covey Bear. Recordings were saved on the Gorilla server and orthographically and automatically transcribed using Sonix, with manual correction. Preliminary analyses of 18 children’s recordings illustrate several emergent topics, reflecting children’s experiences of the pandemic in the areas of health and wellbeing; education and online learning; digital engagement; family and friends; relationships; and mealtimes and food. We found that in their storytelling, children engaged in a wide variety of discourse strategies to hold the floor, indicate focus, and transition to different topics. The project will contribute to a national public collection of Australian children’s COVID-19 stories and create a digital repository of Australian children’s talk that will be available to researchers across different disciplines.

Italy was among the first countries in Europe to be heavily impacted by the outbreak of COVID-19. Northern Italy and Lombardy, in particular, were the most affected areas. In September 2021, the team of “AIS, the digital turn” (AISdt), a project whose objectives include collecting data in 50 locations in Italy, across Lombardy and Piedmont, resumed in-person data collection. Given the field of the investigation and the profile required for the informants (speakers over 60), the decision came only after a long examination of the right conditions and the necessary measures. The paper is intended as a reference text of field investigations amid the pandemic: based on the experience of the AISdt project, it recounts the preparation required by the new modality of fieldwork, the protocols put in place to ensure the safety of all participants before and during the interviews, and the response received from the informants.