General SEM Discussions

mat = data.frame(matrix(c(
"B1_1", "B1_2", 1, .2,
"B1_2", "B1_3", 1, .2, #### biomarkers 1 cause themselves across time
"B2_1", "B2_2", 1, .2,
"B2_2", "B2_3", 1, .2, #### biomarkers 2 cause themselves across time
"B1_1", "B2_2", 1, .2,

but in sem the arrow have one head i dont want covariance between latent variable it is a regression i think

i use matrix method
how can i change cov between latents variable in matrix

please help me
thanks
emmy

I am new here and I would like to start of by asking you a question regarding a design I made in AMOS. It is meant as part of my graduation thesis in Supply Chain Management.

I am following the structural model of one paper, which uses first- and second-order factors, however, it reports only a simplistic version of this model and it is hard for me to identify if I replicated the model well.

Information about my model:
- My independent latent variables are IC, CO and IG;
- My dependent latent variables are C, Q, FLEX, INN and FIN;

Would anyone recommend creating a factor in a CFA or Full SEM model that is only made up of a single indicator variable? I am thinking that this is as good as using the indicator directly in the model (as it is) since the loading will be very high (close to 1). Any thoughts on this will be highly appreciated.

Thanks in advance!

I am new to Structural Equation Modeling but know that it is a confirmatory technique and one has to specify his\her own model and check if the data is supportive of the same. However, I have run into some illogical results using some very logical linkages in my model. In my model, one of the most logical linkages has a low negative regression weight which will be difficult to explain to the client. In such cases, would anyone recommend fixing the regression weight for this linkage to a low positive value (make the results face-valid)?