Training Workshops on Modelling Mechanisms of Change Using Longitudinal Archived Data

使用纵向存档数据建模变革机制培训研讨会

• 批准号: ES/M005771/1
• 负责人: Mark Shevlin
• 金额: $ 3.63万
• 依托单位: University of Ulster
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FM005771%2F1
• 关键词: Training; Workshops; Modelling; Mechanisms; Change

Many social scientists are interested in investigating longitudinal change. For example, a educational researcher may be interested in how academic ability develops during childhood, an economist may be interested in how levels of personal debt changes during a recession, or a political scientist may be interested in how attitudes towards political parties change. There are now statistical methods available which are useful in describing the patterns of longitudinal change, both at the group level of the individual level. Latent growth models provide a powerful and flexible approach to describing longitudinal change. Current developments in statistical modelling now allow social science researchers to move beyond simply describing longitudinal change and model the mechanisms and processes that underlie such changes. So, for example, a researcher could examine those individual (e.g. self-esteem, personality), social (e.g. socioeconomic status, parental educational achievement), and contextual (e.g. school and teacher characteristics) factors that may influence, or predict, how academic ability develops during childhood. This allows for the mechanisms and processes that determine change to be better understood. Such models can be formulated and tested using a flexible and powerful statistical framework known as latent variable modelling (LVM). Although UK social scientists can avail of training in descriptive models of longitudinal change using LVM, there is no provision for comprehensive training in the use of those models that allow for the mechanisms and processes to be examined. This project aims to provide high quality training for UK social scientists in the application of these models. Workshops will be delivered three times a year, for three years, in three different locations (Belfast, London, and Stirling). Each workshop will last two days and comprise lectures and practical sessions. One important aspect of the workshops is that they will be based on datasets from the UK Data Archive which provides the largest collection of data in the social sciences in the UK. The benefits of using secondary data analysis have been demonstrated when there is clear integration of statistics and substantive issues. In order to maximise the impact of the workshops there will be follow-up webinars, the training materials from the workshops will also be archived on a dedicated website, and the specialist software will remain with the host institutions.The programme of training will provide UK researchers and research students the opportunity to take part in training in state of the art statistical methods that allow complex hypotheses relating to the nature and mechanisms of change to be formulated and tested.

许多社会科学家对纵向变化感兴趣。例如，教育研究人员可能对儿童时期的学术能力发展感兴趣，经济学家可能对经济衰退期间个人债务水平的变化感兴趣，或者政治学家可能对政党态度的变化感兴趣。现在有一些统计方法可以用来描述纵向变化的模式，无论是在群体层面还是在个人层面。潜在增长模型为描述纵向变化提供了一种强有力的、灵活的方法。目前统计建模的发展使社会科学研究人员能够超越简单地描述纵向变化，并对这些变化背后的机制和过程进行建模。因此，例如，研究人员可以检查那些可能影响或预测儿童时期学术能力发展的个人（例如自尊，个性），社会（例如社会经济地位，父母教育成就）和背景（例如学校和教师特征）因素。这使人们能够更好地理解决定变革的机制和过程。这些模型可以使用一个灵活而强大的统计框架来制定和测试，该框架被称为潜在变量模型（LVM）。虽然英国的社会科学家可以利用培训的描述性模型的纵向变化使用LVM，有没有提供全面的培训，在使用这些模型，允许的机制和过程进行检查。该项目旨在为英国社会科学家提供应用这些模型的高质量培训。讲习班将在三个不同的地点（贝尔法斯特、伦敦和斯特林）每年举办三次，为期三年。每个工作坊为期两天，包括讲座和实践环节。研讨会的一个重要方面是，它们将基于英国数据档案馆的数据集，该档案馆提供了英国社会科学领域最大的数据集。在统计数据与实质性问题明确结合的情况下，使用二手数据分析的好处已得到证明。为了最大限度地扩大讲习班的影响，将举行后续网络研讨会，讲习班的培训材料也将在一个专门的网站上存档，专业软件将保留在主办机构中。培训计划将为英国研究人员和研究学生提供参加最先进统计方法培训的机会，这些方法允许与性质相关的复杂假设和改变的机制有待制定和测试。