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Extension and Application of Local Structural Equation Modeling to Longitudinal Data

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PIs:
Prof. Dr. Andrea Hildebrandt, University of Greifswald
Prof. Dr. Ulrich Schroeders, University of Kassel

Staff:

Project Summary:
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 competence development across the lifespan.
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. In a nutshell, LSEM is a non-parametric approach that relies on the idea of local, non-parametric regression analyses based on sample weights, thus, avoiding 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. 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 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.