Joseph posted on Thursday, March 27, 2014 - 2:18 pm
I need to run a growth model over 7 time points to explore the relationship between the predictor at time t and the outcome at time t+1. My variables are categorical and are all observed.
I'd like to observed whether the type of service received (home; hospital; GP practice) at time t is associated with satisfaction (yes, partly, no) at time t+1 across the 7 time points.
Could you direct me to some relevant reference/material on how to conduct growth analysis with observed and categorical data while accounting for clustering at ID level? can MPLUS handle missing data across time points?
See the introduction to Chapter 9 which discusses how clustered data can be handled in Mplus. See Chapter 6 examples particularly Example 6.10 where you would add the CATEGORICAL option and lag the covariates.
Yes, Mplus can handle missing data across time.
You may find the Topic 3 and 4 course videos and handouts on the website helpful.
However, I now receive a series of warning messages telling me that there are a number of empty cells. Looking at the output I noticed that this must be down to the fact that only 70 cases have information on (all?) the x-variables (out of the original 600 (!).
Is there a way to handle/impute missing data within MPlus when using the WLSMV estimator for categorical variables? Or do I need to impute the data in a different program and then import it into MPlus?