Qualitative data sharing practices in social sciencesJeng, Wei (2017) Qualitative data sharing practices in social sciences. Doctoral Dissertation, University of Pittsburgh. (Unpublished)
AbstractSocial scientists have been sharing data for a long time. Sharing qualitative data, however, has not become a common practice, despite the context of e-Research, information growth, and funding agencies’ mandates on research data archiving and sharing. Since most systematic and comprehensive studies are based on quantitative data practices, little is known about how social scientists share their qualitative data. This dissertation study aims to fill this void. By synergizing the theory of Knowledge Infrastructure (KI) and the Theory of Remote Scientific Collaboration (TORSC), this dissertation study develops a series of instruments to investigate data-sharing practices in social sciences. Five sub-studies (two preliminary studies and three case studies) are conducted to gather information from different stakeholder groups in social sciences, including early career social scientists, social scientists who have deposited qualitative data at research data repositories, and eight information professionals at the world’s largest social science data repository, ICPSR. The sub-studies are triangulated using four dimensions: data characteristics, individual, technological, and organizational aspects. The results confirm the inactive data sharing practices in social sciences: the majority of faculty and students do not share data or are unaware of data sharing. Additional findings regarding social scientists’ qualitative data-sharing behaviors include: 1) those who have shared qualitative data in data repositories are more likely to share research tools than their raw data; and 2) the perceived technical supports and extrinsic motivations are both strong predictors for qualitative data sharing. These findings also confirm that preparing qualitative data sharing packages is time- and labor-consuming, because both researchers and data repositories need to spend extra effort to prevent sensitive data from disclosure. This dissertation makes contributions in three key aspects: 1) descriptive facts regarding current data-sharing practices in social sciences based on empirical data collection, 2) an in-depth analysis of determinants leading to qualitative data sharing, and 3) managerial recommendations for different stakeholders in developing a sustainable data-sharing environment in social sciences and beyond. Share
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