Extension and Application of Local Structural Equation Modeling to Longitudinal Data

将教育作为一个终生过程来研究，并审查在多种情况下终身学习的累积和互动影响，前提是要有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(NINS)提供了这样高质量的、具有全国代表性的关于德国教育职业和学生和成年人能力发展的纵向数据。为了了解学习的基本条件和优化教育，有关学校背景或家庭环境的变量尤其相关。虽然纵向数据的结构方程建模(SEM)在过去几十年中得到了迅速发展，但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函数的发展。该项目的重点是最近开发的用于纵向数据分析的扫描电子显微镜技术的扩展。其目的是通过使用非政府组织的数据回答教育研究中关于终身能力发展的实质性问题来举例说明新的方法。局部结构方程模型(LSEM)允许研究受年龄或社会经济地位等连续背景变量调节的局部结构方程模型的参数。LSEM避免了对自然连续的调节变量的人为分类。虽然研究人员经常关注观察到的均值结构(即学习轨迹)，但有必要传达这样的问题不可避免地与总体测量有关，特别是关于方差和协方差的问题。这是因为研究平均趋势需要测量工具，这些工具总是能捕捉年龄、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可靠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。到目前为止，LSEM仅用于横断面设计；为了解决教育领域的实质性问题，它对纵向案例的扩展是悬而未决的，是值得的。在对NEP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。我们将首先在潜在增长曲线模型中检验社会经济地位(SES)作为一个连续调节变量对学业成绩的影响。其次，我们将父母的参与作为家庭背景的变量，研究学生在数学和信息通信技术素养方面的学习收获。第三，我们进一步扩展了LSEM方法，同时考虑了两个语境变量(SES和父母教育年限)来研究词汇的发展。

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.