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Hello, I am examining the relation between a CFA measurement model for depression and several binary and continuous predictors using WLSMV estimation. One of my factors consists of only 2 indicators. When I fixed the factor loadings of the 2 indicators to be equal, the model did not converge. When I fixed both the factor loadings to be equal and the variance of the factor to 1, the model converged. Question: Is it correct to apply both these constraints? Or should I just constrain the factor variance to 1 and freely estimate the 2 factor loadings? Many thanks in advance. Model: Dep by bcesd03 bcesd06 bcesd09 bcesd10 bcesd14 bcesd17 bcesd18; Pos by bcesd04 bcesd08 bcesd12 bcesd16; Som by bcesd01 bcesd02 bcesd05 bcesd07 bcesd11 bcesd13 bcesd20; Int by bcesd15* bcesd19 (eq1) ; Int @1; Dep on Bcode sex aedu bmage xnochrot bmalcnei bmmedad bmbenanx bpartner bal_dia B_CVD sex aedu bmage btotact smoke; Pos on Bcode sex aedu bmage xnochrot bmalcnei bmmedad bmbenanx bpartner bal_dia B_CVD sex aedu bmage btotact smoke; Som on Bcode sex aedu bmage xnochrot bmalcnei bmmedad bmbenanx bpartner bal_dia B_CVD sex aedu bmage btotact smoke; Int on Bcode sex aedu bmage xnochrot bmalcnei bmmedad bmbenanx bpartner bal_dia B_CVD sex aedu bmage btotact smoke; 


The model is not identified by holding loadings equal  you must fix the factor variance at one. 


Thank you. Are you saying that I should: (a) fix the factor variance to 1, as well as hold the factor loadings equal? or (b) set the variance to 1 and freely estimate the factor loadings? In both cases the model is identified  but how does the interpretation of the results differ in one case vs. the other? 


Either way identifies the model as you say. Choosing to hold loadings equal or not depends on theory and model fit. You should have a substantive reason for holding them equal  otherwise don't. 

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