

NonPositive Definite Between Level M... 

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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 nonpositive definite matrix at the between level. Any suggestions as to why and how to fix this problem? My level2 n=29 and my level1 n=447. There are two variables at level2 and eight level1 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 NONPOSITIVE DEFINITE FIRSTORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.142D16. 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? Your help is very much appreciated. Thanks a lot! 


Dear Linda, dear Bengt, 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 5pointLikert 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? Thanks & kind regards! 


Your run probably has more than 16 parameters which is the number of clusters  this gives the message. If you have less than 16 Betweenlevel parameters you should be ok and can use MLR or WLSMV. 

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