Repository logo
  • English
  • 中文
Log In
Have you forgotten your password?
  1. Home
  2. College of Liberal Arts / 文學院
  3. Library and Information Science / 圖書資訊學系
  4. A Study of Quantitative Data Reuse in the Social Sciences – the Case of the TSSCI Journal Articles, 2011-2015
 
  • Details

A Study of Quantitative Data Reuse in the Social Sciences – the Case of the TSSCI Journal Articles, 2011-2015

Date Issued
2016
Date
2016
Author(s)
Lai, Ching-Yi
DOI
10.6342/NTU201602283
URI
http://ntur.lib.ntu.edu.tw//handle/246246/276839
Abstract
With the increasing calls for data sharing, governments, academic institutions and journal publishers have mandated or encouraged scholars to share their research data. Data sharing is a complicated issue involving technical and social rearrangement. There are also calls for empirical examination of research data reuse activities to evaluate the outcome and benefit of data sharing. This study examined the state of data reuse in the Taiwan social sciences journals as well as the data reuse behavior of social sciences scholars. This study employed a content-analysis approach to analyze journal articles indexed in TSSCI. Five TSSCI domains were chosen for the analysis, including sociology, political sciences, education, economics and psychology. Journal articles from 2011 to 2015 were used as the sample for this study. The analyses focused on: (1) the characteristic of the data reuse papers, i.e., proportions of data reuse papers, publishing year, amount of datasets used in each reuse paper, and data-reporting state of paper; (2) the characteristic of the reused data, i.e., the subject distribution of datasets, subject distribution of variables, origination of data, types of data, and year gap between reuse papers and used data. It also used semi-structured in-depth interviews to examine 14 social scientists’ data reuse behavior. The interviews focused on (1) the reason for data reusing; (2) channels for data seeking and discovery; (3) principles governing assessment of the found data; and (4) the preparation treatment of the data prior to its reuse. Based on the analysis, this study found 511 reuse papers published in the said period (17.33% of the total empirical papers), most of which used one dataset. Almost half of the economics papers had used two or more datasets, making it distinct from other social sciences domains. Less than half of the papers had reported data in abstracts, tables, acknowledgements and references. However, most of reuse papers had provided sufficient identification information for the data, e.g., titles, collectors, and year of data. This study also identified 875 reused datasets. It was found that half of the datasets were in economics, political sciences and education. As to the variables used in the reuse papers, sociology, political sciences and education papers tended to use variables related to social characteristics, e.g., race, salary, and gender. On the contrary, economics papers had tended to use macro-level variables relating to country or institutional phenomenon. 46.86% of the reused datasets were originated from governments, followed by academic institutions (23.77%) and corporations (21.14%). Less than 6% of the datasets were from previous individual research. More than half of the datasets were business-transaction data, followed by series surveys (34.63%) and one-time study (8.34%). The year gap between reuse papers and datasets were relatively long in economics and sociology, but shorter in political sciences and education. The interviews revealed that scholars were motivated to reuse data mainly because of the barrier to collect data on their own, good credibility of existing data, ability to extend existing research, explore potential research questions, and the influences of subject disciplines. Scholars sought data through journal articles, colleagues and advisors, websites of government agencies and academic institutions, the promotion of academic institutes and hard copy statistics data. Prior to data use, a researcher would assess the usability and quality of the data, including the collection processes, representativeness of samples, timeliness and accessibility of data. Prior to data reanalysis, researchers may also observe the descriptive statistics of the datasets and conduct the necessary data cleaning and re-processing activities.
Subjects
Social Sciences
Quantitative Data
Data Reuse
Data Citation
Data Seeking Behavior
Type
thesis
File(s)
Loading...
Thumbnail Image
Name

ntu-105-R02126003-1.pdf

Size

23.54 KB

Format

Adobe PDF

Checksum

(MD5):0ff53f5104e76afaa02fa539e5e0f1af

臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

總館學科館員 (Main Library)
醫學圖書館學科館員 (Medical Library)
社會科學院辜振甫紀念圖書館學科館員 (Social Sciences Library)

開放取用是從使用者角度提升資訊取用性的社會運動,應用在學術研究上是透過將研究著作公開供使用者自由取閱,以促進學術傳播及因應期刊訂購費用逐年攀升。同時可加速研究發展、提升研究影響力,NTU Scholars即為本校的開放取用典藏(OA Archive)平台。(點選深入了解OA)

  • 請確認所上傳的全文是原創的內容,若該文件包含部分內容的版權非匯入者所有,或由第三方贊助與合作完成,請確認該版權所有者及第三方同意提供此授權。
    Please represent that the submission is your original work, and that you have the right to grant the rights to upload.
  • 若欲上傳已出版的全文電子檔,可使用Open policy finder網站查詢,以確認出版單位之版權政策。
    Please use Open policy finder to find a summary of permissions that are normally given as part of each publisher's copyright transfer agreement.
  • 網站簡介 (Quickstart Guide)
  • 使用手冊 (Instruction Manual)
  • 線上預約服務 (Booking Service)
  • 方案一:臺灣大學計算機中心帳號登入
    (With C&INC Email Account)
  • 方案二:ORCID帳號登入 (With ORCID)
  • 方案一:定期更新ORCID者,以ID匯入 (Search for identifier (ORCID))
  • 方案二:自行建檔 (Default mode Submission)
  • 方案三:學科館員協助匯入 (Email worklist to subject librarians)

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science