Getting “good” data in a pandemic, part 1: assessing the validity and quality of data collected remotely

Journal article

The articles presented in this special issue contribute to recent scholarship on remote data collection. The topics covered can be described in terms of two focal areas. The first focus is on the ways in which research can be adapted to remote data collection, and the second on the ways in which data collected remotely should be considered alongside data collected using “traditional” methods. The overarching epistemological question uniting these focal areas is whether remote data collection yields data of substantive quality. While varied in their aims and approaches, the studies presented suggest that remote data collection methods can be used on a par with traditional approaches, thus aligning with the findings of already existing scholarship on remote data collection methods. The detailed findings presented in the papers provide valuable methodological information for further development of methods in sociolinguistics and related areas. Though these studies originated in conditions when remote data collection was the only option, they reveal the suitability of remote data collection methods beyond the COVID-19 pandemic. Remote methods can get “good” data; the experience of conducting fieldwork during the pandemic, while a challenge, was a catalyst for creativity, innovation, and enrichment in the field

Authors: Kostadinova, V; Gardner, MH

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: De Gruyter
• Journal: Linguistics Vanguard: A Multimodal Journal for the Language Sciences
• Volume: 9
• Issue: s4
• Pages: 329-334
• Publication date: 2023-12-22
• DOI: 10.1515/lingvan-2023-0170
• EISSN: 2199-174X
• ISSN: 2199-174X
• Language: English
• Keywords: Epistemology; Coronavirus disease 2019 (COVID-19); Political science; Sociology; Medicine; Data collection; Data quality; Business; Marketing; Social science; Computer science; Remote sensing; Pandemic; Scholarship; Data science; Geography; Quality (philosophy)