Methods of analysis and inference for social survey data within the framework of latent variable modeling and pairwise likelihood

在潜变量建模和成对似然框架内分析和推断社会调查数据的方法

基本信息

项目摘要

Research in Social and Medical Sciences, among others, is based on the analysis of data gathered by sample surveys which are usually carried out with the help of a complex questionnaire or the administration of tests. The answers to the individual questions are treated as indicators of latent (unobserved) constructs such as respondents' skills, beliefs, attitudes or state of health. Often the interest is on how the aforementioned constructs are related to each other and to covariates also measured in the surveys. The results of data analysis are often used by policy makers, educators and economists and are of interest to the general public. There is a well-established modelling framework called latent variable modelling (LVM), or structural equation modelling (SEM), for the analysis of such social data. However, the standard statistical methods for estimation and inference in LVM and SEM are often not computationally feasible for large problems. This has led to the use of ad hoc approaches, but even these have computational limitations under certain conditions. As a feasible alternative, the project proposes the use of pairwise likelihood (PL) because it is computationally practical, shows good statistical properties and there is a sound theoretical background to support it. There are three main methodological objectives of the project. The first is to study the performance of PL under realistic conditions, i.e. whether accurate and reliable results are obtained when the model size and complexity and the data type match those encountered in real applications. The performance of PL will also be compared with the existing approaches using both simulated data and the PIAAC (Programme for the International Assessment of Adult Competencies) and ESS (European Social Survey) data. The second goal is to develop, within the SEM and PL framework, methodology for assessing the goodness-of-fit of a hypothesized model and for selecting among competing models. The proposed methods will be compared with their counterparts developed under the existing approaches. The third objective is to extend the models to handle non-ignorable item nonresponse within the SEM and PL framework. Omitting cases with item non-response can affect the reliability and validity of data analysis and subsequently the conclusions. All methodological advances will be general enough to cover the cases of both continuous and categorical (ordinal, binary, and ranking) data.Variables from the PIAAC and ESS survey data will be analysed using the proposed methods to demonstrate their potential and to answer the main research questions posed by researchers who designed these surveys. These include cross-national comparisons of adult skills (PIAAC) and public trust in criminal justice and criteria for immigrants to enter a country (ESS). The overarching aim is to keep a good balance between statistical theory and practice. For this, any methodological development will be "translated" to practical statistical tools readily available to researchers and practitioners to facilitate the analysis of their own data. The proposed techniques and tools will be accessible through the free open source R package lavaan. Online manuals and tutorials will explain when and how the proposed approaches can be used. These will give computing instructions and illustrative examples of data analysis, as well as discussing the interpretation of the results. The methodological and substantive findings of the project will be disseminated through academic publications and presentations at seminars and international conferences. The exact means within these communication channels will be carefully selected in order to reach statisticians, social researchers from both academic and non-academic environments, and practitioners such as educators, psychometricians, policy makers.
社会科学和医学研究,除其他外,是基于对抽样调查收集的数据的分析,这些调查通常是在复杂的问卷调查或测试管理的帮助下进行的。对个别问题的回答被视为潜在(未观察到的)结构的指标,如受访者的技能,信仰,态度或健康状况。通常,人们感兴趣的是上述结构如何相互关联,以及如何与调查中测量的协变量关联。数据分析的结果经常被决策者、教育工作者和经济学家使用,并引起公众的兴趣。有一个成熟的建模框架,称为潜在变量建模(LVM)或结构方程建模(SEM),用于分析此类社会数据。然而,在LVM和SEM中用于估计和推断的标准统计方法对于大型问题通常在计算上不可行。这导致了使用特设的方法,但即使是这些在某些条件下有计算限制。作为一个可行的替代方案,该项目提出使用成对似然(PL),因为它在计算上是实用的,显示出良好的统计特性,并有一个健全的理论背景来支持它。首先是研究PL在现实条件下的性能,即当模型大小和复杂性以及数据类型与真实的应用中遇到的那些匹配时,是否获得准确和可靠的结果。PL的性能也将使用模拟数据和PIAAC(成人竞争力国际评估计划)和ESS(欧洲社会调查)数据与现有的方法进行比较。第二个目标是开发,SEM和PL框架内,方法评估的拟合优度的假设模型和竞争模型之间的选择。所提出的方法将与根据现有方法开发的对应方法进行比较。第三个目标是扩展模型来处理SEM和PL框架内的非可验证项目无应答。遗漏项目无应答的案例会影响数据分析的信度和效度,进而影响结论。所有的方法学进展将是一般性的,足以涵盖连续和分类(有序,二进制,和排名)data.Variables从PIAAC和ESS调查数据的情况下,将使用所提出的方法进行分析,以证明其潜力,并回答设计这些调查的研究人员提出的主要研究问题。其中包括成人技能的跨国比较(PIAAC)和公众对刑事司法的信任以及移民进入一个国家的标准(ESS)。总体目标是在统计理论和实践之间保持良好的平衡。为此,任何方法上的发展都将“转化”为研究人员和从业人员随时可用的实用统计工具,以便利分析他们自己的数据。所提出的技术和工具将通过免费的开源R包lavaan访问。在线手册和教程将解释何时以及如何使用所提出的方法。这些将给出计算指令和数据分析的说明性例子,以及讨论结果的解释。该项目的方法和实质性结论将通过学术出版物和在研讨会和国际会议上的发言加以传播。将仔细选择这些传播渠道的确切手段,以便接触到来自学术和非学术环境的统计学家、社会研究人员以及教育工作者、心理测量学家和决策者等从业人员。

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random
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