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?).

**Contents**hide

#### Best Answer

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.

### Similar Posts:

- Solved – Is it possible to do a Tukey HSD test on a Two Way Repeated Measures ANOVA in SPSS
- Solved – How to interpret residual covariances in lavaan
- Solved – Power of Repeated-Measures ANOVA vs Mixed-Effects Model
- Solved – PCA/factor analysis of mixed (quantitative + qualitative) data: inconsistent results
- Solved – PCA/factor analysis of mixed (quantitative + qualitative) data: inconsistent results