Dear Lavaan and SEM experts,
I am new to lavaan (and SEM in general), so please bear with me.
I basically have a design where I am looking at the effect of invasive species on soil nutrients and microbial communities across a bunch of sites (see data attached and R code). I already know that the invasives definitely alter the communities in terms of composition as well as altering some soil nutrients. Now I would really like to see whether the invasives alter the microbe communities directly, i.e. potentially because of altered mutualistic associations, or indirectly, i.e. via a changed soil nutrient pathway (since the invasives can change soil nutrient levels, and the bacteria can then respond to this).
At first I made a latent variable model (model 1) where I grouped the soil variables as one latent variable (i.e. "soil"), and used it as predictor for change in community composition (either the of the first or second axes of an NMDS ordination as composition; the same idea would go for determining the effects of invasives on bacterial species richness and diversity). But I keep getting very significant chi-square values, so I thought my model was wrong. So the other option I have is based upon comments from an author of a paper who did something similar who told me that he did not include any latent variables in his model, which is why I dropped them and used direct paths. But I still get highly significant chi-square values and high RMSEA. Does this mean that none of these models are usable? Or is there a way to remedy the situation?
Many thanks in advance!
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