Hi, I was wondering whether it makes sense to interpret p-values in BSEM the same way than in frequentist approach (rejection over a certain threshold). Similarly, standardized coefficients in BSEM are interpreted in the same way than in ML?
Thanks for your reply, I still have a doubt: can i make an interpretation about the proporion of cases on each side of the zero (that is the lower bound of th CI divided by the full range)? Or the credibility intervals give only range of the estimate, like confidence intervals? In other words, do the credibility intervals refer to number of observations or the possible values that the parameter can assume?
Dear Mplus team I am fitting an endogenous treatment model. All variables continuous, T binary. Sample size=6500
Define: ONE=1; Model: L1 BY s1@1s2@1;L2 BY d1@1d2@1; [L1 L2](m n); [s1 s2 d1 d2](is1 is2 id1 id2); s1 ON T (t1);s2 ON T (t1);d1 ON T (t2);d2 ON T (t2); T ON L1 L2 ONE (a b c);[T$1@0]; Model priors: t1~N(-17,3.2);t2~N(-15,2.25); is1~N(0,25);is2~N(0,25); cov(is1,is2)=-24.99999; id1~N(0,25);id2~N(0,25); cov(id1,id2)=-24.99999; m~N(110,100);n~N(90,100);
I used bayes to tackle underidentification using prior knowledge. Because of slow convergence, I fixed threshold at 0 and simulated an intercept with an auxiliary variable. The convergence improved (still slow).
1) Do you think that fitting an underidentified model using external information embedded in priors is a reasonable approach? 2) When I fit the model, I get an error message for singular sample covariance matrix. I understand why, but is there a way to obtain posterior p-values in this case? 3) Any suggestion to improve convergence?
The idea is that the observed values of the continuous indicators are affected by treatment status T, which, in turn, is not independent by the values of the underlying factors.
In the model, L1 and L2 represent blood pressure values (systolic and diastolic) in absence of treatment. With increasing L1 and/or L2, the probability of being treated increases, and this affects the observed values s1-d2. Treatment is endogenous in this sense.
That is like a reciprocal interaction model (in econometrics). It makes substantive sense, but seems hard to identify. Usually with reciprocal interaction you need exogenous variables to identify. Or you need longitudinal data.
I have various exogenous covariates (omitted from the model I posted, for simplicity). But I suppose you are thinking to instrumental variables, correlated with T but not with s1-d2. Am I interpreting correctly your comment?