Developments in Survey Research over the Past 60 Years: A Personal Perspective

Many developments have occurred in the practice of survey sampling and survey methodology in the past 60 years or so. These developments have been partly driven by the emergence of computers and the continuous growth in computer power over the years and partly by the increasingly sophisticated demands from the users of survey data. The paper reviews these developments with a main emphasis on survey sampling issues for the design and analysis of social surveys. Design‐based inference based on probability samples was the predominant approach in the early years, but over time, that predominance has been eroded by the need to employ model‐dependent methods to deal with missing data and to satisfy analysts' demands for survey estimates that cannot be met with design‐based methods. With the continuous decline in response rates that has occurred in recent years, much current research has focused on the use of non‐probability samples and data collected from administrative records and web surveys.     

Revisiting IS research practice in the era of big data

Through building and testing theory, the practice of research animates data for human sense-making about the world. The IS field began in an era when research data was scarce; in today's age of big data, it is now abundant. Yet, IS researchers often enact methodological assumptions developed in a time of data scarcity, and many remain uncertain how to systematically take advantage of new opportunities afforded by big data. How should we adapt our research norms, traditions, and practices to reflect newfound data abundance? How can we leverage the availability of big data to generate cumulative and generalizable knowledge claims that are robust to threats to validity? To date, IS academics have largely welcomed the arrival of big data as an overwhelmingly positive development. A common refrain in the discipline is: more data is great, IS researchers know all about data, and we are a well-positioned discipline to leverage big data in research and teaching. In our opinion, many benefits of big data will be realized only with a thoughtful understanding of the implications of big data availability and, increasingly, a deliberate shift in IS research practices. We advocate for a need to re-visit and extend traditional models that are commonly used to guide much of IS research. Based on our analysis, we propose a research approach that incorporates consideration of big data—and associated implications such as data abundance—into a classic approach to building and testing theory. We close our commentary by discussing the implications of this hybrid approach for the organization, execution, and evaluation of theory-informed research. Our recommendations on how to update one approach to IS research practice may have relevance to all theory-informed researchers who seek to leverage big data.     

高职院校课程设置的思考

高职院校的课程设置急需解决的问题包括:课程设置定位不准确、课程体系不完善、专业理论课与实训课课时比例差异大、课程设置追求多而不精等等。解决的对策主要包括:高职院校在课程设置时要以就业为导向,关注地方政府决策,设置贴近、服务于地方经济的特色专业课程。     

大数据及可信技术初探

随着信息技术的不断发展,数据产生途径越来越广泛,数据量日益增加,人们对于“大数据”的研究越来越深入,但数据的有效性、安全性和可信性方面的保证技术却不是特别完善。本文阐述了“大数据”的相关概念、特征和数据产生的渠道,详细介绍了“大数据”的处理技术以及数据的可信技术。     

大数据时代医院档案管理工作的创新及优化

在医疗体制改革日益深化、医疗卫生事业快速发展的背景下,医院档案种类、载体形式和数量有所增加,医院档案的重要性日益凸显。医院档案在医疗服务和医院各项工作开展中发挥着重要作用,医院管理部门要注重医院档案管理工作,为医院改革和发展提供基础保障。在大数据时代,医院需要树立新型档案管理理念,引进大数据技术,创新并优化现有的医院档案管理模式。论文主要对大数据时代医院档案管理工作的创新及优化进行了分析。     

Statistical Inference,Learning and Models in Big Data

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.     

信息化环境下企业内部审计模式思考

随着信息技术的高速发展,大数据技术呈现出爆发增长并逐渐渗入各行各业。基于信息化的大背景,大数据技术已被越来越多的企事业单位广泛运用,为企业发展带来更多可能性。大数据审计作为一种新型内部审计手段,具备独特的优势,将其运用到企业内部审计中可以较大程度提高内部审计的效率。但是它也为内部审计带来了一定不确定性,进而增加其审计风险。论文就大数据审计在企业内部审计中的应用进行了相关分析,并提出了相应的应对措施。     

Accessible Conceptions of Statistical Inference: Pulling Ourselves Up by the Bootstraps

With the rapid, ongoing expansions in the world of data, we need to devise ways of getting more students much further, much faster. One of the choke points affecting both accessibility to a broad spectrum of students and faster progress is classical statistical inference based on normal theory. In this paper, bootstrap‐based confidence intervals and randomisation tests conveyed through dynamic visualisation are developed as a means of reducing cognitive demands and increasing the speed with which application areas can be opened up. We also discuss conceptual pathways and the design of software developed to enable this approach.     

基于标签的数据挖掘技术的研究

近年来,数据呈指数级增长,人们对数据的利用能力越来越高,特别是近几年兴起的大数据、云计算、人工智能等新兴技术汇集了人类有史以来最多最全的数据,但是如何从数据中发现各种关系与规则,从海量数据中找到更有价值的数据,是人们急需解决的问题。数据挖掘技术是解决这一问题的根本方法,而基于标签的数据挖掘技术是完整刻画描述人物特征的基本方法。     

FROM THE LAB TO THE FIELD: A REVIEW OF TAX EXPERIMENTS

Tax experiments have been gaining momentum in recent years, although this literature dates back several decades. With new developments in methods and data availability, tax experiments have gradually moved away from lab settings and towards the field. This movement from the lab to the field has happened against the background of the ‘credibility revolution’ in applied economics, which has seen more rigorous methods applied to policy relevant questions, and of the availability to researchers of administrative data from tax returns. These developments have allowed significant advances in the experimental literature on tax compliance. This paper reviews this literature, giving particular attention to field experiments using administrative data, but putting them in the broader context of the compliance literature. A particular effort is made to take a global perspective, in a literature that is only recently seeing the emergence of evidence from Africa, Latin America and Asia.     

WHY IS THERE A LACK OF EVIDENCE ON KNOWLEDGE SPILLOVERS FROM FOREIGN DIRECT INVESTMENT?

Empirical analyses of knowledge spillovers from foreign direct investment (FDI) offer mixed results; they find positive, neutral and negative FDI spillover effects. This lack of evidence mainly comes from the results of firm‐level panel data analysis. This is important since this approach seems to be the most appropriate for estimating FDI spillovers. The paper takes a look at recent substantive and methodological developments in FDI spillover analysis, which have brought some more optimistic results with regard to FDI spillovers, and can help in further development in this field. The main substantive development relates to the introduction of a broad variety of sources of firm heterogeneity (foreign affiliates as well as local firms) in the analysis. Others include differentiation between vertical (inter‐industry) and horizontal (intra‐industry) spillovers, and host country absorptive capacity for knowledge spillovers. Methodological developments relate to distinguishing between technological/knowledge and productivity spillovers, improvement of modelling and estimation methods, and an increased amount and quality of data.     

Inductive reasoning: The promise of big data

Theory is a cornerstone of organizational research. Recently, however, some organizational scientists have argued that there is an overemphasis on theory development in our prominent publication outlets, calling for a rejuvenation of empirically driven research. To bring empirical research back to the forefront, the organizational sciences need a shock to the system: the advent of big data analytics in organizations provides just such a shock. The purpose of the following paper is to advocate for big data analytics as tools that can be used to support inductive research methods in the organizational sciences. We then highlight areas of organizational research and practice in which big data analytics can have an impact, provide readers with a tempered perspective on big data in the organizational sciences, and suggest a number of ways that researchers, reviewers, and editors can prepare themselves for the introduction of big data research in the organizational sciences.     

大数据及其对地矿分析测试工作的启示——以贵州地质矿产中心实验室为例

大数据已经受到越来越多的关注,大数据时代即将到来。在介绍大数据的内涵基础上,分析大数据的特征和时代价值,探讨其带给社会经济发展的意义。大数据时代将对地质矿产实验室的分析测试数据库建设和管理工作带来了新的机遇。     

大数据技术融入财务管理课程的教学设计

在财务管理课程教学中,嵌入大数据技术能够提高学生的学习兴趣与学习效果.文章从教学目标、教学实施、教学评价三个角度,对财务管理与大数据技术交叉融合的教学设计进行了探讨,有助于跨学科课程教学的具体实施与操作推广.     

移动网络数据中大数据处理的关键技术探索

最近几年以来,我国科技水平持续提升,计算机网络技术也逐渐在各行各业中获得了广泛运用,这也使得信息技术的深度融合发展受到一定的促进,数据规模愈发庞大,在此背景下,开始演变成大数据供不应求的状况。为此,国内陆续推出了一系列相关的管理法规与条例,同时扩大了对大数据的探究创新力度,有效提升了移动通信网络处理数据的速度,这同时促使国内移动通信网络中有关大数据的研究内容更为明晰,由此可知,针对大数据的相关研究具有一定必要性。     

Searching for big data: How incumbents explore a possible adoption of big data technologies

Big data is often described as a new frontier of IT-enabled competitive advantage. A limited number of exemplary firms have been used recurrently in the big data debate to serve as successful illustrations of what big data technologies can offer. These firms are well-known, data-driven organizations that often, but not always, are born digital companies. Comparatively little attention has been paid to the challenges that many incumbent organizations face when they try to explore a possible adoption of such technologies. This study investigates how incumbents handle such an exploration and what challenges they face. Drawing on a four-year qualitative field study of four large Scandinavian firms, we are able to develop a typology of how incumbents handle the exploration of and resistance to adopting big data technologies. Directly affecting the incumbents’ exploration are two aspects that separate the adoption of big data technologies from that of other technologies. First, being an elusive concept, big data technologies can mean different things to different organizations. This makes the technologies difficult to explain before an investing body, while it simultaneously opens up possibilities for creative definitions. Second, big data technologies have a transformative effect on the organization of work in firms. This transformative capability will make managers wary as it might threaten their position in the firm, and it will create ripple effects, transforming other systems besides those directly connected to the technology.     

论新医改背景下大数据分析对医疗资源配置的促进作用

以医疗服务机构改革、医保体制改革和药品流通体制改革为核心的三医联动医疗服务体系改革是解决百姓“看病难”“看病贵”问题的重要途径。资源的优化配置已成为此次改革中实现医疗资源公平性的着眼点之一。论文从大数据的特征入手,从医疗保障、药品供应保障体制改革、医联体的建设及医疗机构内部改革三个层次梳理大数据分析运用现状,并通过大数据分析的预测性及分析范围的高维度论述其在医疗卫生服务体系改革中的促进作用。     

新农村金融:理论、成因及中国相关制度安排——基于建设社会主义新农村视角

发展农村经济、建设社会主义新农村离不开农村金融的支持,我国现存农村金融体制的种种缺陷已经成为阻碍农村进步的绊脚石。本文对我国农村金融体制进行考察,对其农村金融组织体系及配套的扶持政策进行评介,找出我国农村金融的不足,提出与社会主义新农村建设相适应的农村金融体制改革路径选择。主要结论有:我国农村金融体制改革,要发挥市场机制的导向作用,建立农村资金良性循环机制;政府应该承担主导作用,建立多层次、全方位的农村金融体制等。     

大数据时代下企业市场营销策略研究

随着线上网络平台在市场上占据着越来越多、越来越重要的地位,更多企业意识到大数据带来商机的同时,也注意到自身的发展面临的困境。本文以大数据时代为调查背景,中小企业为企业代表研究对象,探究大数据在新时代的关键性作用,以及处于大数据时代下企业的市场营销现状,通过swot分析和企业营销五大困境的分析,从产品、服务、人才、宣传、技术五个方面提出新策略,以往在大数据时代下为不同行业的中小企业提供有效借鉴。