A small # of cases had very large values for the loglikelhood. However, when I re-estimated the model excluding these cases (using subpopulation command b/c of the complex sampling design), model fit was *unchanged* even though the N reflected the reduction in cases. This was unexpected. My questions:
1. how are the individual LL values related to individual contributions to model chi square, which is what I'm after
2. is there a better way to assess person contribution to model misfit in Mplus?
3. might my use of complex sample (weights/strata) be contributing to lack of changes in model fit despite exclusion of cases w/ large LL values?
4. can you recommend reading for how the individual LL and influence statistics are calculated in Mplus?
In working w/ this longer, I discovered that I had not requested correlations b/w time varying covariates. When I did this, there were no longer cases w/ large contributions to the LL (only highly influential cases). So my problem is solved.
However, I'm still interested in whether you conceive of the individual contribution to LL to be analogous in function to individual contribution to chi square (i.e., large positive values indicate individuals for who the model does not fit well)?