Solved – Repeated measures structural equation modeling

I need to analyse a dataset of clinical rehabilitation data. I am interested in hypothesis-driven relationships between quantified "input" (amount of therapy) and changes in health status. Although the dataset is relatively small (n~70) we have repeated data reflecting temporal changes in both. I am familiar with non-linear mixed effects modelling in R however am interested in potential "causal" relationships between input and output here and thus am considering repeated measures applications of SEM

I'd appreciate advice on which if any of the SEM packages for R (sam, lavaan, openmx?) are best suited to repeated measures data, and particularly recommendations for textbooks (is there a "Pinheiro and Bates" of the field?).

I think you want a latent growth curve model. While I have only used LISREL for this, the lavaan package documentation indicates it can be used to fit this type of model.

I don't know of any books that specialise in this subject, the book I am working from for SEM covers a range of methods. Perhaps someone else can answer that aspect of your question.

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