数据可视化素养量表设计研究  

Design of Data Visualization Literacy Scale

作　　者：吴晓伟[1] 龙青云[1] 易艳红[1] 黄务兰 Wu Xiaowei;Long Qingyun;Yi Yanhong;Huang Wulan(Shanghai Business School, Shanghai 200235)

机构地区：[1]上海商学院,上海200235

出　　处：《情报杂志》2022年第7期181-188,共8页Journal of Intelligence

基　　金：国家社会科学基金项目“面向智库建设的图书馆知识服务模式和创新路径研究”(编号:18BTQ058)研究成果之一。

摘　　要：[研究目的]数据可视化素养是大数据社会公众必备的基本素养之一。创建数据可视化素养评价框架和量表对科学评价数据可视化能力,实现国家对大数据环境下人才培养具有重要意义。[研究方法]通过对国内外文献的梳理,提出了基于人力资源的KSAO模型的数据可视化素养框架,该框架由数据可视化相关知识、数据可视化相关技能、数据可视化相关通用能力、数据可视化相关其他特质四个维度构成。在此框架指引下,设计了面向大学新生的数据可视化量表,该量表由80个题项构成。通过专家调查法,对量表题项内容效度进行分析。[研究结论]量表总体内容效度比CVR=0.91,专家意见一致效度系数B=0.08,克隆巴赫Alpha=0.891,具有较好的内容效度和信度。该量表设计思路对数据可视化素养评价具有一定的参考价值,为不同场景下,如专业特点、学生分类、职业分类的数据可视化素养量表开发提供了解决思路。[Research purpose]Data visualization literacy is one of the essential basic literacy for the public in the big data society.Creating a data visualization literacy evaluation framework and scale are of great significance to scientifically evaluate the data visualization ability and realize the national requirements for talent training in the big data environment.[Research method]By combing the literature at home and abroad,this paper puts forward a data visualization literacy framework based on KSAO.The framework is composed of four dimensions:data visualization related knowledge,data visualization related skills,data visualization related general abilities,and data visualization related other characteristics.Under the guidance of this framework,a data visualization scale for freshmen is designed,which consists of 80 items.Through the expert investigation method,the content validity of the items of the scale is analyzed.[Research conclusion]the overall content validity ratio of the scale CVR=0.91,expert consensus validity coefficient B=0.08,Cronbach's Alpha=0.891,which has good content validity and reliability.The design idea of the scale has a certain reference value for the evaluation of data visualization literacy,and provides a solution for the development of data visualization Literacy Scale in different scenarios,such as professional characteristics,student classification and occupation classification.

关 键 词：数据可视化素养 KSAO模型 大学新生 素养量表 题项设计 专家调查法 

分 类 号：G254[文化科学—图书馆学]