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Qualitative data sharing practices in social sciences

Jeng, Wei (2017) Qualitative data sharing practices in social sciences. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Social 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.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Jeng, Weiwej9@pitt.eduWEJ90000-0001-5560-511X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHe, Daqingdah44@pitt.eduDAH44
Committee MemberQin,
Committee MemberLyon, Lizelyon@pitt.eduELYON
Committee MemberCorrall, Sheilascorrall@pitt.eduSCORRALL0000-0001-5591-6524
Committee MemberOh, Jung Sunjsoh@pitt.eduJSOH
Date: 15 June 2017
Date Type: Publication
Defense Date: 12 January 2017
Approval Date: 15 June 2017
Submission Date: 24 April 2017
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 294
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Library and Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Research data sharing, knowledge infrastructure, social science data, qualitative data sharing, e-Research
Date Deposited: 15 Jun 2017 19:55
Last Modified: 30 Jun 2017 13:38


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