Using new technologies to enhance the value of qualitative data in longitudinal studies: an application to health and well-being, and ageing

使用新技术提高纵向研究中定性数据的价值：在健康和福祉以及老龄化方面的应用

基本信息

批准号：
ES/N00650X/1
负责人：
Alissa Goodman
金额：
$ 26.56万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FN00650X%2F1
关键词：
Using new technologies enhance value

项目摘要

Qualitative data, such as essays and free response questions in surveys, are rich sources of psychological, social and behavioural information. Yet such information has traditionally been impossible to leverage at a large scale. Recent advances in computational linguistics and machine learning have produced automatic content analysis tools, which have started to be used in a variety of settings including in text used in social media settings such as Facebook and Twitter. We will apply these for the first time to the open responses collected longitudinally within a large national birth cohort study in order to make methodological and theoretical advances in the study of health, well-being, and successful ageing. Our work will contribute to an important policy debate about which social and emotional skills developed in childhood are vital for well-being throughout life, what interventions might support these, and the factors that underpin successful ageing. The techniques applied will also offer researchers working across a wide range of substantive topic areas a methodological model for extracting greater value out of rich but underutilised large-scale qualitative datasets, making this transformational research on both methodological and substantive scientific grounds. The project involves three major steps. The first step will be to digitally transcribe 13,000 age 11 essays contained within the National Child Development Study (NCDS), one of the UK's world-renowned birth cohort studies. The data include self-reported essays, written at age 11 and age 50, in response to the following questions: At age 11: "Imagine you are now 25 years old. Write about the life you are leading, your interests, your home life and your work at the age of 25" At age 50: "Imagine that you are now 60 years old...please write a few lines about the life you are leading (your interests, your home life, your health and wellbeing and any work you may be doing)". The responses (13,669 at age 11; 7,383 at age 50) provide a largely untapped source of psychological and behavioural information that can be linked longitudinally to outcomes for the same individuals. Automatic content analysis tools will next be applied to the transcribed essays in order to undertake quantitative analysis of the words and concepts expressed in essays at age 11 and 50. The words used by an individual will be classified into different categories, such as emotions, social relationships, and articles, allowing psycho-social content to be assessed. We will use both 'open vocabulary' and 'closed vocabulary' approaches. The classifications derived from open text through content analysis will then be used to quantitatively address a number of research questions, including: What psychological traits and behaviours are reflected in the language used in the essays of a large group of 11 year olds, collected in 1969? What is the association between such revealed psychological traits and behaviours and long-term trajectories of health and well-being across adult life, and early markers of ageing, captured up to the age of 55? How do future ambitions and expectations, as revealed in age-50 essays, relate to age-55 health statuses and practices What degree of persistence can be found in use of language used across the lifetime - between the ages of 11 and age 50, and how does the persistence of traits and behaviours revealed relate to health and well-being in adult life?The methods developed will be transformative - and will have the potential to unlock information contained open responses in many other national longitudinal data sources. The findings will have strong impact on policy, providing information relevant to schools, local community organisations and health practitioners as to the importance of developing social and emotional skills in childhood and throughout life for lifelong health and well-being.

定性数据，例如调查中的论文和免费回答问题，是心理，社会和行为信息的丰富来源。然而，传统上，这种信息是不可能大规模利用的。计算语言学和机器学习的最新进展生产了自动内容分析工具，这些工具已开始用于各种设置，包括在社交媒体设置（例如Facebook和Twitter）中使用的文本中。我们将首次将其应用于在一项大型国家出生队列研究中纵向收集的公开反应，以便在健康，福祉和成功衰老的研究中取得方法论和理论进步。我们的工作将有助于一项重要的政策辩论，即童年在童年时期发展哪些社会和情感技能至关重要，哪些干预措施可能支持这些技能以及成功衰老的因素。所应用的技术还将为在各种实质性主题领域的研究人员提供一种方法学模型，该模型可从丰富但未充分利用的大规模定性数据集中提取更大的价值，从而使这项对方法论和实质性科学理由的转型研究。该项目涉及三个主要步骤。第一步将是在国家儿童发展研究（NCDS）中包含的13,000年龄11年龄的论文，这是英国世界著名的出生队列研究之一。数据包括在11岁和50岁时撰写的自我报告的论文，以应对以下问题：“想象一下您现在已经25岁了。写下您所过的生活，兴趣，家庭生活，您的家庭生活和25岁年龄在50岁时的工作：回答（11岁时，年龄在50岁时的13,669年； 7,383年）提供了很大程度上未开发的心理和行为信息来源，可以纵向将其与同一个人的结果联系起来。自动内容分析工具接下来将应用于抄录的论文，以对11岁和50岁的论文中表达的单词和概念进行定量分析。个人使用的单词将分为不同的类别，例如情感，社会关系和文章，允许评估可以评估的心理社会内容。我们将使用“开放词汇”和“封闭词汇”方法。然后，通过内容分析得出的分类将用于定量解决许多研究问题，包括：哪些心理特征和行为反映在1969年收集的一群11岁儿童的论文中使用的语言？这种揭示的心理特征与行为与成人生活和福祉的长期轨迹与衰老的早期标记之间的联系是什么，直到55岁？未来的雄心和期望如何与55岁的健康状况和实践相关联，在使用整个生命周期之间使用的语言时可以找到什么程度的持久性 - 年龄在11至50岁之间，以及在成人生活中的特质和行为的持久性如何与发展中的发展 - 以及其他范围内的态度？纵向数据源。这些发现将对政策产生强大的影响，提供与学校，当地社区组织和卫生从业人员有关的信息，即在童年以及整个生活中发展社会和情感技能对终身健康和福祉的重要性。