Applications of multilevel modelling: An evaluation of the assumption of no correlation between explanatory variables and random effects

多级建模的应用：解释变量与随机效应之间不相关的假设的评估

• 批准号: 2604266
• 金额: --
• 依托单位: University of Stirling
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2604266
• 关键词: Applications; multilevel; modelling; evaluation; assumption

'Multilevel models', defined here as statistical models that include random effects parameters in order to analyse a data structure that features clustering of individual cases within higher level units, are popular and widely used analytical tools across the social sciences. Nevertheless, when multilevel models are deployed in applied research scenarios, they frequently violate an assumption associated with their statistical estimation, namely that the estimated random effects are uncorrelated with explanatory variables (hereafter 'NCRX'). If there is a correlation, it has been demonstrated that parameter estimates could be biased and/or inefficient. Although the statistical issues of the NCRX assumption have already been demonstrated, there remain many research applications where the assumption is not fully explored, and there are notable divergences between social science disciplines in how seriously the assumption is taken.This PhD project will review methodological and applied research literature on the NCRX assumption across social science disciplines; analyse simulated data to explore the importance of the assumption in different plausible contexts; and pursue secondary survey analyses (using 'Understanding Society', special licence versions) on two case studies that will illustrate varying consequences of NCRX and its violation (on socio-economic inequalities by occupations, and on health inequalities by localities).The aims of the PhD are to communicate the issue and its relevance; to compare and contrast responses to the NCRX assumption in research applications; and to develop and promote appropriate methodological recommendations, both theoretical and operational. It will make an original contribution by comparing applied research across disciplines, and by examining the full spectrum of activities that are associated with preparing, estimating and interpreting statistical models. The outcomes of the research will have an impact on how we understand and support applied social research that uses complex statistical models.

“多级模型”在这里定义为包含随机效应参数的统计模型，用于分析具有较高级别单元内个体案例聚类特征的数据结构，是整个社会科学中流行且广泛使用的分析工具。然而，当在应用研究场景中部署多级模型时，它们经常违反与其统计估计相关的假设，即估计的随机效应与解释变量（以下简称“NCRX”）不相关。如果存在相关性，则已证明参数估计可能存在偏差和/或效率低下。尽管 NCRX 假设的统计问题已经得到证明，但仍有许多研究应用尚未充分探讨该假设，并且社会科学学科之间在对该假设的重视程度方面存在显着差异。该博士项目将审查方法论和关于 NCRX 假设的跨社会科学学科的应用研究文献；分析模拟数据，探索假设在不同合理背景下的重要性；并对两个案例研究进行二次调查分析（使用“理解社会”，特殊许可版本），这将说明 NCRX 及其违规行为（按职业划分的社会经济不平等和按地区划分的健康不平等）的不同后果。博士将传达该问题及其相关性；比较和对比研究应用中对 NCRX 假设的响应；制定和推广适当的理论和操作方法建议。它将通过比较跨学科的应用研究，并检查与准备、估计和解释统计模型相关的全方位活动，做出原创性贡献。研究结果将影响我们如何理解和支持使用复杂统计模型的应用社会研究。