1. Yes. 2. Yes. 3. With WLSMV and a model with covariates, standard errors are not available.
young park posted on Wednesday, January 09, 2013 - 2:41 pm
Thank you so much.
I have one more question. When a SES (socio-economic status, a continuous variable, ranging from -2.25 to 3.00) variable was added to one of predictors (not a covariate), we only got std coefficients. There were no SE and p-values. WLSMV was used as an estimator. After the SES variable was removed from the predictors, we could get STD coefficients, SE, and p-values. What is the wrong?
A predictor is a covariate. This is why you don't get standard errors and p-values.
young park posted on Monday, January 14, 2013 - 11:30 am
Hello Linda, I always appreciate you. I have questions about a model with covariates, using WLSMV. Although you gave me explanations, I am still confused. Sorry about it.
My mplus syntax is as follow:
Usevarables are y3ses w cluster x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 c1 c2 c3 z1 z2 z3 z4; Categorical are x1-x10; Model !measurement model Pa_1 by x1 x2 x3 x4 x5; Pa_2 by x6 x7 x8 x9 x10; Lang by z1 z2 z3 z4; Read by c1 c2; Narra by c3 c4; !Path model Read Narra on pa_1 pa_2 Lang; !covariate Read Narra on y3ses;
As I told you, I did not get any std errors and p-values, adding ¡°y3ses (a continuous variable, ranging from -2.25 to 3.00) to a covariate in the model.
Here are my questions: Q1. Is the reason we did not get std error estimates and p-values that the ¡°y3ses¡± is a problematic variable? The y3ses ranges from -2.25 and 3.00 and is a negative and continuous variable. In our model, pa_1, pa_2, Lang, and y3ses are predictors and they are continuous.
Q2. If not so, does not a model with covariates and WLSMV provide any std errors and p-values regardless of types of variables (continuous, categorical, etc)?
Q3. If an observed item as a predictor is not applied, can we convert the observed item into a factor with single indicator?