Hi there,

I am working on a SEM relationship representation of three latent variables. The first one "Stres" [EN: Stress] has four indicators, the second "CAT" [a conceptual cognitive test] has three indicators, but the third latent factor "Fluencia" [EN: Verbal Fluency] has only two indicators. Because of the fact that the "Fluency" factor has only two manifest indicators in the measurement model, this factor has not enough degrees of freedom.

How can I correctly make constraints to deal with this issue?

PS: I set both loadings to "1" because in past research I found relative equal loadings between the variables - do I saved one degree of freedom here. One more thing, if I change the latent "Fluency" for a summative score (mere sum of #Z and #T), so there is no latent but manifest composit, the regression weight to "CAT" is almost exactly the same.

EDIT: the model fit is good: x2 = 0.2; CFI = 0,95; RMSEA = 0,069 (pclose > 0.3)

Thank you much,

best regards,

M.

Image - please see the red rectangle part.

I am working on a SEM relationship representation of three latent variables. The first one "Stres" [EN: Stress] has four indicators, the second "CAT" [a conceptual cognitive test] has three indicators, but the third latent factor "Fluencia" [EN: Verbal Fluency] has only two indicators. Because of the fact that the "Fluency" factor has only two manifest indicators in the measurement model, this factor has not enough degrees of freedom.

How can I correctly make constraints to deal with this issue?

PS: I set both loadings to "1" because in past research I found relative equal loadings between the variables - do I saved one degree of freedom here. One more thing, if I change the latent "Fluency" for a summative score (mere sum of #Z and #T), so there is no latent but manifest composit, the regression weight to "CAT" is almost exactly the same.

EDIT: the model fit is good: x2 = 0.2; CFI = 0,95; RMSEA = 0,069 (pclose > 0.3)

Thank you much,

best regards,

M.

Image - please see the red rectangle part.

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