作者簡介: 林奇秀
Chi-Shiou Lin
國立臺灣大學圖書資訊學系暨研究所副教授
Associate Professor,
Department of Library and Information Science,
National Taiwan University
賴璟毅
Ching-Yi Lai
國立臺灣大學圖書資訊學研究所研究生
Graduate Student,
Graduate Institute of Library and Information Science,
National Taiwan University
This article describes the findings from a qualitative study on social scientists’ data reuse behavior in Taiwan. In recent years, data sharing has been a salient topic in the scholarly communities. Empirical studies on how scientists use existing data have also emerged. However, data reuse in social sciences is less studied than in natural and applied sciences. This study focused on the reuse of existing quantitative data by Taiwan’s social scientists. Semi-structured, in-depth interview was used to understand the experiences of 14 researchers from sociology, political science, education, economics, and psychology. The results show that, in regards to motivations, participants re-used existing data for the
following reasons: unable to collect large-scaled or long-term data, higher credibility of data released by authoritative sources, free from harassment of IRB reviews, exploring potential research questions, carrying on existing research directions, and the encouragement or discouragement of the disciplinary cultures. Participants had relied on five different channels to find existing data, i.e., research literatures, peers and advisors, government agencies or academic institutions’ websites, promotional activities of scholarly associations and survey institutes, and statistical publications. Participants also reported six evaluation strategies prior to the actual use of the located data, including the evaluation of survey instrument quality, sample quality, data collection procedures, timeliness of data, availability of data, and the potential value for publication of the re-use analysis. Finally, in data processing, participants first inspected the descriptive statistics and the validity and reliability of the dataset and then proceeded to correct, amend, or convert the data. They might also need to combine multiple datasets and fill in further needed variables from external sources or by using various techniques.