Anonymous posted on Saturday, April 23, 2005 - 2:53 pm
I am trying to develop multiple level path analysis. When I have a restricted number of variables (n=6), I am able to perform the analysis. However, I discovered theoretical reasons to disaggregate a few of the measures, but not enough for Multilevel SEM. When I do this, I end up with a non-positive definite matrix at the between level. Any suggestions as to why and how to fix this problem?
My level-2 n=29 and my level-1 n=447. There are two variables at level-2 and eight level-1 variables. I can't understand why the disaggregation would generate such problems. Any thoughts?
Anonymous posted on Saturday, April 23, 2005 - 5:24 pm
I was able to figure out a proper start value that did the trick.
I have a model with four factors with three indicators each (one with four), collected in 16 organisations.
I am using TYPE=COMPLEX to account for the nesting of the data.
I have received the following warning: "THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.142D-16. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 16, PSC BY PSC1_3
THIS IS MOST LIKELY DUE TO HAVING MORE PARAMETERS THAN THE NUMBER OF CLUSTERS MINUS THE NUMBER OF STRATA WITH MORE THAN ONE CLUSTER."
This happens both when I use all cases (i.e. including missing values, N = 1730) and complete cases (i.e. restricted to those with complete data, N = 1521).
What does this mean - what kind of "strata" does Mplus refer to?
referring to my post from May, 27th - do you have a suggestion of why this happens? To what kind of "strata" does Mplus refer to?
I have meanwhile run the model applying WLSMV because my indicators are 5-point-Likert type items. In this model, I do not get the above message. Can I consider this an indication that WLSMV is more appropriate for my data? Would this answer my above question?