I am trying to run a two-level growth model for a continuous outcome (3 level analysis). In addition to the clustering in the data, we also have sampling weights to account for unequal probability of selection. The outcome is measured at 7 time points, and the sampling weights are different for each time point. Is there any way to use all 7 weights in m-plus?
bmuthen posted on Tuesday, November 01, 2005 - 9:17 am
Can you tell me why the sampling weights are different across time? Is it due to poststratification?
The study was not originally set up to be longitudinal. It is looking at the effectiveness of a program for teachers. There were 7 years of data collection, but the samples and response rates were not necessarily equivalent at each point. For example, one year we may have sampled 500 out of 2000 teachers in the program and got responses from 450 of them, while another year we may have sampled 300 and got responses from 225 of them. There are several teachers with multiple time points, but the data are very sparse over time (this is another problem-- no one has all 7 data points, but many have at least 2 or 3 time points). Is it possible to incorporate different probabilities of selection for each time point?
Mplus doesn't allow multiple weight variables in the analysis; however I think that this would not be appropriate for the situation you describe anyway. Missing data generally is not a reason to introduce weights. Simply including type=missing in the analysis section should be sufficient. Maximum likelihood estimation for multivariate models will be able to extrapolate the missing observations to some extent from the observed data. Weights included in the analysis simply because of non-response may actually lead to incorrect estimates.
The weights were not primarily intended for non-response. Not all teachers were sampled at each time point (because it was not originally set up as a longitudinal study). Because different number of teachers were sampled at each timepoint, there were unequal probabilities of selection across time.
bmuthen posted on Thursday, November 03, 2005 - 9:09 am
This sounds like an example of missing by design. This can be handled via a multiple-group analysis where group corresponds to pattern of missing data by design.