SEM: How to report latent factor relationships (covariance vs. correlation)? I would like to report results from SEM I did in R using the lavaan package. I have gotten increasingly confused about how papers use different terms and units to describe latent factor relationships seemingly interchangeably.
Here is a picture of one of the models with a number for each relationship I would like to describe. I know they are not all latent, but I want to make sure I understand all concepts properly. The depicted coefficients are all standardized. I know I will need to report their significance levels in the final report.

I think that number...

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*1 with the double-headed arrow is a latent covariance. However, I have read that correlation is standardized covariance, which makes me think that this would have to be reported as correlation with the unit r (e.g., r = .30) because it is standardized. But I have also read that standardized values are reported without unit. Which of the options is correct?

*2 with the single-headed arrow is a latent correlation and should be reported as r = .86. However, I have seen many papers reporting this as regression instead and call it a beta coefficient (e.g., beta = .86). Is this the same thing?

*3 and all other coefficients on this level are factor loadings and should be reported as that without a unit (e.g., "the standardized factor loading of mz was .82").

*4 with the double-headed arrow is a covariance, but on a manifest level. I have the same question about this one as for 1, as this is also a standardized value and I might have to report it as correlation instead.

Can someone help me untangle this theoretical mess?

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*Doesn't really matter. A correlation is a covariance. It's more interpretable if you use the correlation. If you want to report the confidence intervan, you should use the covariance though.

*It's single headed, it's regression. I don't recall seeign a paper that says that. Your model is drawn strangely - WM is a mediator of the relationship between age and Gf, but that's not clear from the way it's drawn. Typically if it's later in the causal flow it should mov one level.

*Yes, but a factor loading is a regression coefficient.

*Same answer.

Also, why is age latent?
