摘要
【目的】在大数据时代,海量非结构化数据大规模兴起,有效运用质性数据分析工具的重要性日益凸显,本文系统综述Nvivo在中国社会科学研究的应用。【文献范围】以"Nvivo"为主题词在CNKI数据库全文检索,人工核校构建2008-2018年327篇样本文献数据库。【方法】采用内容分析法对样本文献逐篇编码,分析质性数据分析工具在中国社会科学研究的应用。【结果】(1)应用主体。近10年来,Nvivo应用主体以高校科研人员为主,跨校间科研合作薄弱。(2)应用过程。方法数据大多以内容分析法分析二手数据与访谈法收集访谈资料为主,Nvivo应用过程包括资料编码、编码检验、编码分析与理论建模四步骤,规范应用四步骤的研究占比远不足一成。(3)应用客体。研究热点聚焦于扎根理论、质性研究与内容分析法,研究前沿从理论层面向应用层面拓展呈现多元化趋势,其中公共管理学、图书情报学、新闻传播学是新兴应用学科。【局限】Nvivo应用研究在科研合作、步骤规范性、方法多样性、数据多源化等方面有待完善。【结论】未来质性数据分析工具将凭借其强大的数据编码、理论构建功能成为研究者开展混合研究的重要分析工具。
[Objective]At present,massive unstructured data has emerged on a large scale,which makes effective use of qualitative data analysis tools increasingly important.This paper systematically reviews Nvivo-applied research in Chinese social science.[Coverage]We used"Nvivo"as the keyword to search in CNKI database.A total of 327 sample articles were retrieved from 2008 to 2018 with manual cleaning.[Methods]We used the content analysis method to encode the sample literature,and then analyzed the application status of qualitative data analysis tools.[Results](I)Application subject.Over the last decade,Nvivo-applied research had been growing rapidly in China.However,the links between research teams and institutions were rather weak.(II)Application process.More than 80%of the methods were content analysis for non-obtrusive designs and interview for obtrusive designs.Less than 10%of Nvivo-applied research included four steps of data coding,coding test,coding analysis and theoretical modeling.(III)Application object.These researches focused on grounded theory,qualitative research and content analysis,and the leading researchers were from public administration,library and information science,and journalism communication.[Limitations]We should improve Nvivo-applied research from the perspectives of scientific research cooperation,step normalization,method diversification,and data diversity.[Conclusions]The future qualitative data analysis tools could play a better role in social science studies thanks to their powerful data coding and theoretical construction functions.
作者
潘虹
唐莉
Pan Hong;Tang Li(School of International Relations&Public Affairs,Fudan University,Shanghai 200433,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第1期51-62,共12页
Data Analysis and Knowledge Discovery
基金
教育部人文社会科学规划基金项目“我国科研诚信研究:现状、问题及政策建议”(项目编号:WKH3056005)的研究成果之一.
