Using multi-level multi-source auxiliary data to investigate nonresponse bias in UK general social surveys

使用多层次多源辅助数据调查英国一般社会调查中的无回应偏差

• 批准号: ES/L013118/1
• 负责人: Rory Fitzgerald
• 金额: $ 32.9万
• 依托单位: City, University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FL013118%2F1
• 关键词: Using; multi; level; source; auxiliary

This project will explore the extent to which the predictive power of various forms of "Big Data" can be harnessed to overcome the impact of poor response to surveys - one of the major challenges facing social research today. Social surveys are a key tool used by the media, policy makers, and academics to understand more about public attitudes and behaviour. However, the value of surveys is put at risk by the fact that a large and growing number of those selected to take part in surveys do not respond. As non-respondents may be very different from respondents, nonresponse can introduce significant bias into the conclusions drawn from survey data. There is a pressing need therefore to understand more about the extent and sources of nonresponse bias. This requires having information about both respondents and non-respondents. In the absence of interview data being available for non-respondents, this information must be obtained from other, external, sources. The growth in "Big Data" i.e. routinely generated data arising from commercial transactions, online communication or public administration provides exciting new opportunities to supplement survey data with data from other sources. As opportunities for data linkage increase, there is a need for a detailed investigation into how such data can be used to understand and hopefully correct for nonresponse bias in general social surveys. This project will conduct such an investigation by adding pre-existing data from multiple sources to UK data from the European Social Survey (ESS), a methodologically rigorous survey of public attitudes. The project, drawing on the expertise of an inter-disciplinary team of survey researchers, statisticians and geographic information (GI) specialists, has three strands: First, the project will explore the opportunities that exist for matching data from three different sources to survey data. These include: small-area administrative data; commercial marketing data and geocoded information from the Ordnance Survey. Each data source will be evaluated in terms of: what information it can provide which may be matched to the survey records of respondents and non-respondents; the accuracy and completeness of this information; and the challenges that matching data presents in terms of the increased risk of individuals or households being identified from the combination of data held about them. Second, we will see how the matched data can provide information about potential biases that may be present in the survey data as a result of nonresponse. This will involve identifying any external variables associated both with the likelihood of nonresponse and the attitudes and behaviour the survey intendeds to measure. The project will consider how sources of nonresponse bias may vary geographically across the UK. Finally, we will assess whether using these external variables to create nonresponse weights to adjust for the possible over or under representation of certain types of respondent in the dataset has a significant effect on survey estimates and reduces bias in the data. This project has the potential to contribute significantly to our understanding not only of survey nonresponse bias but also the statistical tools available to remedy this bias, to improve survey data collection and generate more robust data to better understand public attitudes and behaviour. Lessons learnt will enhance general social surveys in the UK and internationally. This will have considerable benefits for the wide range of stakeholders involved in the funding, collection, and analysis of survey data and those who rely on the insights it provides. This includes academics, government agencies and other publically funded bodies, third sector organisations, policy makers and, ultimately, the general public.

该项目将探讨各种形式的“大数据”的预测能力可以在多大程度上被利用，以克服对调查反应不佳的影响-这是当今社会研究面临的主要挑战之一。社会调查是媒体、政策制定者和学术界用来更多地了解公众态度和行为的一个关键工具。然而，被选中参加调查的人中有越来越多的人没有作出答复，这使调查的价值受到威胁。由于不答复者可能与答复者有很大不同，不答复可能给从调查数据得出的结论带来重大偏差。因此，迫切需要更多地了解不回答偏见的程度和来源。这就需要有关于答复者和非答复者的信息。在没有未答复者的访谈数据的情况下，必须从其他外部来源获得这一信息。 “大数据”，即商业交易、在线通信或公共管理产生的常规数据的增长，为用其他来源的数据补充调查数据提供了令人兴奋的新机会。随着数据联系的机会增加，有必要详细调查如何利用这些数据来了解并希望纠正一般社会调查中的无答复偏见。该项目将通过将来自多个来源的预先存在的数据添加到来自欧洲社会调查（ESS）的英国数据来进行这样的调查，ESS是一项对公众态度的严格调查。 该项目利用了由调查研究人员、统计人员和地理信息专家组成的跨学科小组的专门知识，有三个方面：首先，该项目将探讨将三个不同来源的数据与调查数据相匹配的机会。这些数据包括：小地区行政数据;商业营销数据和来自Orlando Survey的地理编码信息。将对每个数据来源进行以下方面的评价：它能够提供哪些信息，这些信息可以与答复者和非答复者的调查记录相匹配;这些信息的准确性和完整性;匹配数据带来的挑战，即个人或家庭被从所掌握的有关他们的数据组合中识别出来的风险增加。 其次，我们将看到匹配的数据如何提供有关调查数据中可能存在的潜在偏差的信息。这将涉及确定与不答复的可能性以及调查打算衡量的态度和行为有关的任何外部变量。该项目将考虑如何来源的无应答偏差可能会有所不同的地理在英国。 最后，我们将评估是否使用这些外部变量来创建非响应权重，以调整数据集中某些类型的受访者可能的过度或不足的代表性，对调查估计有显着影响，并减少数据中的偏差。 该项目有可能大大有助于我们不仅了解调查无答复偏见，而且了解可用于纠正这种偏见的统计工具，以改进调查数据收集，并生成更可靠的数据，从而更好地了解公众的态度和行为。吸取的经验教训将加强在英国和国际上的一般社会调查。这将为参与调查数据的供资、收集和分析的广泛利益攸关方以及依赖调查数据提供的见解的人带来相当大的好处。这包括学术界、政府机构和其他财政资助的机构、第三部门组织、政策制定者，最终包括公众。

项目成果

ADDResponse: Auxiliary Data Driven nonResponse Bias Analysis Technical report on appending geocoded auxiliary data to Round 6 of European Social Survey (UK)

ADDResponse：辅助数据驱动的无响应偏差分析有关将地理编码辅助数据附加到欧洲社会调查（英国）第 6 轮的技术报告

• 发表时间: 2016
• 作者: Butt, S

Supporting Theoretically-grounded Model Building in the Social Sciences through Interactive Visualisation

通过交互式可视化支持社会科学的理论模型构建

• 发表时间: 2017
• 期刊: Neurocomputing
• 影响因子: 6
• 作者: Turkay, C.