Challenging issues of integrity and identity of participants in non-synchronous online qualitative methods. (December 2021)

• Record Type: Journal Article
• Authors: Jones, Abigail; Caes, Line; Rugg, Tessa; Noel, Melanie; Bateman, Sharon; Jordan, Abbie
• Is Part Of: Methods in psychology. Volume 5(2021)
• Publication Date: 2021-12
• Subjects: Quality -- Fraud -- Online; Psychology -- Periodicals; 150.5
• DOI: 10.1016/j.metip.2021.100072
• Languages: English
• ISSNs: 2590-2601

Abstract

Abstract: Qualitative data collection is increasingly occurring online, with data collection methods often lacking the synchronous contact between researchers and participants present in more traditional methods of qualitative data collection such as face-to-face interviews. Despite numerous benefits of non-synchronous online methods of qualitative data collection, such methods also pose unique challenges concerning participant eligibility and data quality in the qualitative domain. Due to a longer tradition of conducting non-synchronous quantitative online data collection, researchers have discussed issues related to data quality for use within quantitative research, and developed techniques to address such issues. However, such discussions have not taken place within qualitative research and due to the differences in types of data and theoretical underpinnings, only some of the techniques developed in quantitative research can be appropriately applied in qualitative research. In this paper, we address this knowledge gap by providing an important 'how to guide', presenting techniques to help address threats to data quality and integrity in non-synchronous online qualitative research. We start by outlining techniques developed for use in quantitative research that can be appropriately transferred to qualitative paradigms, before proposing techniques to manage challenges faced specifically by non-synchronous online qualitative research. We go on to discuss some of the potential pitfalls which can prevent the implementation of these techniques and how to overcome them. Finally, we urge researchers to be transparent about the techniques they implement to optimise data quality and to adopt a proactive rather than reactive approach to maximising data quality in qualitative research studies.