Extension and Application of Local Structural Equation Modeling to Longitudinal Data

局部结构方程模型在纵向数据中的推广和应用

• 批准号: 390731750
• 负责人: Professorin Dr. Andrea Hildebrandt
• 金额: --
• 依托单位: Abteilung Pädagogisch-Psychologische Methodenlehre
• 依托单位国家: 德国
• 项目类别: Infrastructure Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/390731750?language=en
• 关键词: Extension; Application; Local; Structural; Equation

Studying education as a lifelong process and examining the cumulative and interactive effects of learning in multiple contexts across the lifespan presupposes a comprehensive database and flexible analytical tools for modeling change. The National Educational Panel Study (NEPS) offers such high-quality, nationally representative longitudinal data on educational careers and on developing competencies of students and adults in Germany. In order to understand the underlying conditions of learning and to optimize education, variables concerning the school context or family environment are especially relevant. Whereas Structural Equation Modeling (SEM) of longitudinal data has been rapidly advanced in the last decades, there is still need to develop flexible modeling techniques to study development as a function of continuous context variables. The focus of this project is the extension of a recently developed SEM technique for the analyses of longitudinal data. The aim is to exemplify the novel methodology by answering substantive questions of educational research concerning competence development across the lifespan using NEPS data. Local Structural Equation Models (LSEM) allow one to study the parameters of a SEM as being moderated by continuous context variables such as age or socio-economic status. LSEM avoids the artificial categorization of a naturally continuous moderator variable. Although researchers are often concerned with observed mean structures (i.e., learning trajectories), it is necessary to communicate that such questions are inevitably connected with measurement in general and questions on variances and covariances in particular. This is because studying average trends require measurement instruments that invariantly capture performance across age, time and other context variables. Therefore, research studying the variance-covariance structure of abilities are of particular importance for ensuring the soundness of potential mean effects and any substantive analyses. In a nutshell, LSEM is a non-parametric approach that relies on the idea of local, non-parametric regression analyses based on sample weights. LSEM has hitherto only been used in cross-sectional designs; its extension to the longitudinal case is pending and worthwhile in order to address substantive questions in the educational field. In a series of analyses of NEPS data, we will study the usability and utility of the newly developed method to describe competence development over shorter and longer time spans. We will first examine the influence of socioeconomic status (SES) as a continuous moderator variable on academic performance in a latent growth curve model. Second, we study students' learning gains in math and ICT literacy skills with parents' involvement as a variable of family context. Third, we further extend the LSEM method by considering two context variables simultaneously (SES and years of parental education) to study the development of vocabulary.

将教育作为一个终身过程进行研究，并在整个生命周期的多种背景下研究学习的累积和互动效应，这需要一个全面的数据库和灵活的分析工具来模拟变化。国家教育小组研究（NEPS）提供了关于德国学生和成人教育职业和能力发展的高质量、具有全国代表性的纵向数据。为了了解学习的基本条件和优化教育，与学校环境或家庭环境有关的变量尤其重要。而纵向数据的结构方程模型（SEM）在过去的几十年中已经迅速发展，仍然需要开发灵活的建模技术来研究作为连续上下文变量的函数的发展。该项目的重点是最近开发的SEM技术的纵向数据分析的扩展。其目的是通过使用NEPS数据回答关于整个生命周期能力发展的教育研究的实质性问题来验证新方法。局部结构方程模型（LSEM）允许人们研究SEM的参数，这些参数受连续背景变量（如年龄或社会经济地位）的调节。LSEM避免了自然连续调节变量的人工分类。虽然研究人员经常关注观察到的平均结构（即，学习轨迹），有必要沟通，这些问题不可避免地与一般的测量，特别是关于方差和协方差的问题有关。这是因为研究平均趋势需要测量工具，这些工具可以不变地捕捉年龄，时间和其他背景变量的表现。因此，研究能力的方差-协方差结构对于确保潜在均值效应和任何实质性分析的合理性具有特别重要的意义。简而言之，LSEM是一种非参数方法，它依赖于基于样本权重的局部非参数回归分析的思想。LSEM迄今为止只用于横截面设计，其延伸到纵向的情况下是悬而未决的，值得为了解决实质性的问题，在教育领域。在对NEPS数据的一系列分析中，我们将研究新开发的方法在较短和较长时间跨度内描述能力发展的可用性和实用性。首先，我们将在潜在增长曲线模型中检验社会经济地位（SES）作为连续调节变量对学业成绩的影响。其次，我们研究了学生在数学和信息通信技术素养技能的学习收益与家长的参与作为一个变量的家庭环境。第三，我们进一步扩展了LSEM方法，同时考虑两个语境变量（社会经济地位和父母受教育年限）来研究词汇的发展。