Dear Mike and Others,
I am trying to estimate a random effects tssem for my dissertation.I have read your book and related papers. I am following the wonderful resources provided by you and your team. My goal is to perform some moderator analyses using categorical variables, after I successfully run the tssem model.
I am attaching my R script and the structural model image. In this data and model, I found 2 issues, and have 2 clarifications.
Some of the 95% likelihood based CI’s are shown as “NA”. This happens mainly for the indirect effects – for example for my main tssem2 model – the first one in my R code. This issue is more pronounced when I run moderator analyses and estimate two tssem2 models (split based on the categorical moderator )after I perform the moderator analysis. In these cases, the lbound and ubound values of even the direct effects are showing as “NA”. Can you please let me know if I have set up anything wrong with respect to my model specification or data. Please let me know if and how I have to use starting values from the prior estimation?
I get the following warning message when I run some of the tssem2 models. For example, in my first moderator analysis with variable “tc”.
In .solve(x = object$mx.fit@output$calculatedHessian, parameters = my.name) :
Error in solving the Hessian matrix. Generalized inverse is used. The standard errors may not be trustworthy.
I assume I can ignore this warning given that I am primarily using 95% likelihood based CI’s, and provided R can estimate these 95% likelihood based CI’s for all my parameters.
- I tried to not use the intervals="LB" option and see if I atleast get standard errors. Though I was successful in getting the standard errors and the CI for the direct effects, I could not get them for the indirect effects. Moreover, due to the following warning message, I was not sure if I can report them for review to a top journal.
In vcov.wls(object, R = R) :
Parametric bootstrap with 50 replications was used to approximate the sampling covariance matrix of the parameter estimates. A better approach is to use likelihood-based confidence interval by including the intervals.type="LB" argument in the analysis.
My question is , can I report these std errors? Or can I increase the replications? How do I obtain the std errors for indirect effects when we do not specify the intervals="LB" option?
- This is a clarification regarding my setup of the S matrix. In my model (please see the figure attached), since I am not explicitly modeling the link between T to J or vice versa, I wanted to correlate them. Can you please verify if my S matrix makes sense in this regard? Is it okay if I do not correlate them?
Your response will greatly help me in completing my manuscript. Thanks in advance for your help.