In this case, can I do something for D or y4 to make it work?
(y4~~0.1 *y4 : To fix residual variance for y4 dosen't work)
In this case, can I do something for D or y4 to make it work?You need to put "y4" in quotation marks for the ordered= argument, like the other variables are.
(y4~~0.1 *y4 : To fix residual variance for y4 dosen't work)You need to set parameterization = "theta" for residual variances to be model parameters.
(y4~~0.1 *y4 : To fix residual variance for y4 dosen't work)You need to set parameterization = "theta" for residual variances to be model parameters.Thank you...but it doesn't help. Maybe because y4 isn't continuous and alone for D?!
(y4~~0.1 *y4 : To fix residual variance for y4 dosen't work)You need to set parameterization = "theta" for residual variances to be model parameters.Thank you...but it doesn't help. Maybe because y4 isn't continuous and alone for D?!Perhaps it is not allowed then. I can see why, because the residual variance is already fixed for identification even for multiple categorical indicators. So fixing a single categorical indicator's residual variance does not resolve the additional identifiability issue -- i.e., it will not imply that a certain proportion of the variance is (un)reliable. Ultimately, you would have to fix the factor D's variance or loading to a value that would sum the explained variance (effect of C on D, and indirect effects of A and B on D via C) and D's residual variance, and make that sum equal the proportion of reliable variance (easy if you use the default delta parameterization, which conveniently fixes the total variance of the latent item response to 1).I suspect it is simpler to simply model y4 itself. If it's reliability is really 0.9, then the effect of C on it won't be very attenuated.