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Survey Data Collection Methods Collaboration: Securing the Future of Social Surveys

Survey Data Collection Methods Collaboration: Securing the Future of Social Surveys
调查数据收集方法协作:确保社会调查的未来
批准号:
ES/X014150/1
负责人:
Peter Lynn
金额:
$304.55万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

项目摘要

项目成果

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中文摘要
翻译
调查数据收集界在采用大流行前方法实施调查方面面临严峻挑战。不同的数据收集技术和方法,如推送到网络、敲打到轻推和视频采访,特别是在混合模式背景下,存在着优缺点方面的知识差距。而且熟练的采访者和专业研究人员的能力都是有限的。最近的发展导致专员对面对面数据收集的要求发生变化,并对实地工作成本和采访者的作用产生影响。在调查方法的若干领域,迫切需要发展改进的方法和确定和交流最佳做法。调查数据收集方法协作(SDCMC)是对这些挑战的回应,旨在为英国收集人口调查数据的方法提供一个步骤变化,以确保它仍然有可能进行公众和学术部门所需的高质量社会调查,以监测和了解社会,并为政策提供证据基础。它将主要通过严格的研究计划来实现这一目标,重点是确保英国的大规模社会调查能够创新和适应不断变化的环境,并继续提供高质量和包容性的数据。工作方案的主要目的是评估与未来英国调查相关的最重要的调查设计选择的质量影响,并提供良好的实践指导和实用的培训材料,而次要目的是确定有希望的方法来提高采访者和研究专业人员的能力和技能,并采取措施进行这些改进。委员会将进行一系列的研究和培训工作,并进行一系列的宣传和推广活动。产出将具有很强的实践性,包括调查设计、调查执行、调查专员和调查数据使用者的良好做法指导,所有这些都得到严格和有充分文件的研究的支持,并辅以一系列相关活动,以确保将经验教训传播给所有相关利益攸关方,并酌情及时纳入机构实践。该项目还将寻求实现全社区对话和协作应对更广泛的战略挑战和问题,并纳入强有力的培训和能力建设内容。要实现调查委员会的愿景,需要广泛的持份者,包括委托调查的人、执行调查的人、使用调查数据的人,以及研究和发展调查方法的人,发挥领导作用,作出承诺,并积极参与。建设性对话与合作对于成功开展我们设想的一系列雄心勃勃的活动和产出至关重要。我们已经组建了一个经验丰富的项目团队,包括来自14个机构的学者和调查从业人员(39人),他们致力于必要的建设性合作,我们将在拨款过程中与更广泛的其他利益相关者合作,以确保我们的产出直接惠及广泛的受众。通过提高对问题和机会的认识和知识,不仅将对调查研究和调查实践产生影响,而且还将对社会科学内外的广泛学科产生影响,这些学科采用社会调查数据进行分析。
英文摘要
The survey data collection community is facing severe challenges in implementing surveys using pre-pandemic approaches. There are knowledge gaps regarding the advantages and disadvantages of different data collection techniques and approaches such as push-to-web, knock-to-nudge and video-interviewing, and particularly in the mixed-mode context. And there is limited capacity both of skilled interviewers and of research professionals. Recent developments are leading to changes in commissioner requirements for face-to-face data collection as well as having implications for fieldwork costs and the role of interviewers. In several areas of survey methodology, the need for development of improved methods and the need to identify and communicate best practice is urgent.The Survey Data Collection Methods Collaboration (SDCMC) is a response to these challenges and aims to deliver a step change in approaches to collecting population survey data in the UK to ensure that it will remain possible to carry out high quality social surveys of the kinds required by the public and academic sectors to monitor and understand society, and to provide an evidence base for policy. It will do this primarily through a rigorous programme of research focused on ensuring large-scale social surveys in the UK can innovate and adapt in a changing environment and continue to deliver high quality and inclusive data. The primary aim of the programme of work is to assess the quality implications of the most important survey design choices relevant to future UK surveys and provide good practice guidance and practical training materials, while a secondary aim is to identify promising ways to improve the capacity and skillset of both interviewers and research professionals and take steps towards making those improvements. The SDCMC will generate a range of research and training outputs and will engage in a programme of dissemination and promotion activities. Outputs will have a strong practical orientation, consisting of good practice guidance for survey design, survey implementation, survey commissioners and survey data users, all backed up by rigorous and well-documented research and with a range of associated activities to ensure that the lessons are disseminated to all relevant stakeholders and, where appropriate, embedded in institutional practice in a timely manner. The project will also seek to enable a whole community dialogue and collaborative response to wider strategic challenges and issues, as well as incorporating a strong training and capacity building component. To realise the vision of the SDCMC will require leadership, commitment and active participation of a broad range of stakeholders including those who commission surveys, those who implement them, those who use survey data and those involved in research and development of survey methods. Constructive dialogue and collaboration will be crucial to the successful delivery of the ambitious range of activities and outputs that we envisage. We have assembled an experienced project team including academics and survey practitioners (39 people from 14 institutions), who are committed to the necessary constructive collaboration and we will engage a wider range of other stakeholders during the course of the grant to ensure that our outputs directly benefit a wide range of audiences. Impact will be achieved not only on survey research and survey practice but also on a broad range of disciplines within the social sciences and beyond which employ social survey data for analysis through raised awareness and knowledge of issues and opportunities.
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Survey Resources Network
  • 批准号:
    ES/G034303/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $115.86万
  • 财政年份:
    2008
  • 负责人:
    Peter Lynn
  • 依托单位:
Understanding non-response and reducing non-response bias
  • 批准号:
    ES/E024246/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $29.45万
  • 财政年份:
    2007
  • 负责人:
    Peter Lynn
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位:
基于Linked Open Data的Web服务语义互操作关键技术
  • 批准号:
    61373035
  • 项目类别:
    面上项目
  • 资助金额:
    77.0万元
  • 批准年份:
    2013
  • 负责人:
    冯志勇
  • 依托单位: