Qualitative Data Sharing: Participant Understanding, Motivation, and Consent

VandeVusse, Alicia; Mueller, Jennifer; Karcher, Sebastian

doi:10.1177/10497323211054058

Cited by 25 publications

(29 citation statements)

References 33 publications

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“…Moreover, senior faculty in the social sciences are not necessarily familiar with best practices in information security and may sometimes provide outdated advice. Finally, and most relevant to the possibilities of future use and sharing of qualitative data is that advice by senior researchers, who were rarely subject to data-sharing mandates, may be overly restrictive and prevent future sharing of data, e.g., due to lack of consent to data sharing by participants (see, e.g., VandeVusse, Mueller, and Karcher 2022;Mozersky et al 2020 on consent for data sharing in qualitative research).…”

Section: Discussionmentioning

confidence: 99%

Protecting Sensitive Data Early in the Research Data Lifecycle

Karcher¹,

Secen²,

Weber³

2022

Preprint

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How do researchers in fieldwork-intensive disciplines protect sensitive data in the field, how do they assess their own practices, and how do they arrive at them? This article reports the results of a qualitative study involving 36 semi-structured interviews with qualitative and multi-method researchers in political science and humanitarian aid/migration studies. We find that researchers frequently feel ill-prepared to handle the management of sensitive data in the field and that formal institutions provide little support. Instead, they use a patchwork of sources to devise strategies for protecting their informants and data. We argue that this carries substantial risks for the security of the data as well as their potential for later sharing and re-use. We conclude with some suggestions for effectively supporting data management in fieldwork-intensive research without unduly adding to the burden on researchers conducting it.

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Section: Discussionmentioning

confidence: 99%

Protecting Sensitive Data Early in the Research Data Lifecycle

Karcher¹,

Secen²,

Weber³

2022

Preprint

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“…The respondents of their survey reported key concerns about protecting data that may be sensitive and that they had not obtained permission from the participants to share data. A recent study supports these concerns: VandeVusse et al [91] found that participants in qualitative studies volunteer to share data to be helpful. However, their participants misunderstood "sharing" as disseminating research findings instead of sharing the interview transcripts [91].…”

Section: Transparency Of Datamentioning

confidence: 95%

“…A recent study supports these concerns: VandeVusse et al [91] found that participants in qualitative studies volunteer to share data to be helpful. However, their participants misunderstood "sharing" as disseminating research findings instead of sharing the interview transcripts [91]. HCI research has looked into challenges in Research Data Management [34,35] and has come up with an innovative approach to facilitate sharing despite these challenges [63].…”

Section: Transparency Of Datamentioning

confidence: 95%

Changes in Research Ethics, Openness, and Transparency in Empirical Studies between CHI 2017 and CHI 2022

Niksirat¹,

Goswami²,

Rao³

et al. 2023

Preprint

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In recent years, various initiatives from within and outside the HCI field have encouraged researchers to improve research ethics, openness, and transparency in their empirical research. We quantify how the CHI literature might have changed in these three aspects by analyzing samples of 118 CHI 2017 and 127 CHI 2022 papers—randomly drawn and stratified across conference sessions. We operationalized research ethics, openness, and transparency into 45 criteria and manually annotated the sampled papers. The results show that the CHI 2022 sample was better in 18 criteria, but in the rest of the criteria, it has no improvement. The most noticeable improvements were related to research transparency (10 out of 17 criteria). We also explored the possibility of assisting the verification process by developing a proof-of-concept screening system. We tested this tool with eight criteria. Six of them achieved high accuracy and F1 score. We discuss the implications for future research practices and education.

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“…Public qualitative data could potentially be beneficial when individuals or groups are particularly difficult to access (Fielding, 2004) or groups that might face trauma from multiple rounds of research participation (Ruggiano & Perry, 2019). Some research has revealed participants are generally willing to allow researchers to archive and share their qualitative data (e.g., Cummings et al, 2015;Mozersky et al, 2020;VandeVusse et al, 2022) out of a desire to help others and improve research.…”

Section: Common Concerns About the Quantitative Open Science View Of ...mentioning

confidence: 99%

Rethinking Transparency and Rigor from a Qualitative Open Science Perspective

Steltenpohl¹,

Lustick²,

Meyer³

et al. 2022

Preprint

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Discussions around transparency in open science focus primarily on sharing data, materials, and coding schemes, especially as these practices relate to reproducibility. This fairly quantitative perspective of transparency does not align with all scientific methodologies. Indeed, qualitative researchers also care deeply about how knowledge is produced, what factors influence the research process, and how to share this information. Explicating a researcher’s background and role allows researchers to consider their impact on the research process and interpretation of the data, thereby increasing both transparency and rigor. Researchers may engage in positionality and reflexivity in a variety of ways, and transparently sharing these steps allows readers to draw their own informed conclusions about the results and study as a whole. Imposing a limited, quantitatively-informed set of standards on all research can cause harm to researchers and the communities they work with if researchers are not careful in considering the impact of such standards. Our paper will argue the importance of avoiding strong defaults around transparency (e.g., always share data) and build upon previous work around qualitative open science. We explore how transparency in all aspects of our research can lend itself toward projecting and confirming the rigor of our work.

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Qualitative Data Sharing: Participant Understanding, Motivation, and Consent

Cited by 25 publications

References 33 publications

Protecting Sensitive Data Early in the Research Data Lifecycle

Protecting Sensitive Data Early in the Research Data Lifecycle

Changes in Research Ethics, Openness, and Transparency in Empirical Studies between CHI 2017 and CHI 2022

Rethinking Transparency and Rigor from a Qualitative Open Science Perspective

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