臺灣社會科學學者資料再用行為之研究

本文摘要

近年來學術資料分享呼籲漸盛，學者的資料再用行為也開始受到關注，但相對於自然與應用科學，社會科學的資料再用研究相對較少。本文以深度訪談來瞭解臺灣社會科學學者的資料再用行為，共訪談14 位具有量化數據再用經驗的社會、政治、教育、經濟與心理學者。研究發現，在再用動機方面，學者取用既有資料，理由可能是因為無法自行蒐集大規模或長期性資料、取用來自權威單位的資料比較具有公信力、取用既有資料可免除研究倫理審查的煩擾、可探索潛在研究題目、能拓展既存的研究議題、學科領域文化的鼓勵或阻撓等。在獲知管道方面，學者可能透過五種管道來掌握資料的消息，包含學術文獻、同儕與指導教授、政府與學術機構網站、學會或調查機構的推廣活動、紙本統計資料等。在資料評估方面，學者常用的評估策略包含評估問卷工具品質、評估樣本品質、評估資料蒐集過程、評估資料新穎度、評估資料易得性、以及評估分析結果的發表潛力。在資料處理上，學者會先對資料集進行描述統計與信效度檢驗，確認資料基本品質後，再進行基本數據校正處理，有時還必須合併多組資料集並尋求或補足資料集缺乏的變數。

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.