Recent Developments in Structural Equation Modeling with Panel Data

Causal Analysis and Change over Time in Attitude Research
  • Jochen MayerlEmail author
  • Henrik Andersen


This article outlines the various applications of longitudinal models using the structural equation modeling (SEM) framework. Two classical approaches of longitudinal analysis in SEM are the autoregressive cross-lagged models and the latent growth curve models. Hybrid longitudinal models in SEM attempt to combine the two strands of techniques. Recently, these hybrid models have been expanded upon to properly separate between- and within-effects. This article demonstrates the application of these models by empirically examining the relationship between environmental values and attitudes towards the so-called Energy Transition in Germany over time. We show that a variety of research questions can be examined using longitudinal models in SEM. The new developments in longitudinal models in SEM furthermore make it possible for researchers to more accurately test theories.


Longitudinal Models Structural Equation Modeling Measurement Invariance Latent Growth Curve Models Autoregressive Cross-Lagged Models Structured Residuals Hybrid Panel Models 


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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.Technische Universität KaiserslauternKaiserslauternDeutschland
  2. 2.KaiserslauternDeutschland

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