yao lu posted on Tuesday, March 13, 2012 - 1:57 pm
Hi Dr. Muthen,
I have a structural regression model reads as:
fcom on ccom; ftrust on ctrust; ftrust on fcom; fatt on catt; fatt on fcom; fatt on ftrust; ctrust on ccom; catt on ccom ctrust; rec pur on fatt;
All the variables above are latent. ANALYSIS: Estimator = MLR;
The SEM solutions indicated a very good model fit. The results show that the coefficients of ftrust on fcom, rec on fatt, and pur on fatt, were all very high, all exceeding .85.
Although significant coefficient is desired, I hardly seen such high coefficients in other literature. I wonder should I be cautions that those high coefficients might indicate some technique errors I made in SEM? Model specification is theoretically sound.
There is no really good way around multicollinearity.
If the collinearity is among factors, you may want to investigate why they are so highly correlated, perhaps the fixed zero cross-loadings aren't all exactly zero, in which case inflated factor correlations appear.
How is it possible that the standardized path coefficients for boys are so extremely high (even higher than path coefficients relating the same construct over time). The model terminated normally but I guess something 'computational' is going on but I can't figure out what it is. I do not expect the paths to be the same for boys and girls but this difference is just too large.
I am currently trying to assess whether multicollinearity is a problem in my model.
In a nutshell, I have 3 emotions that affect 2 types of motivation. This motivation, in turn, influences 2 types of well-being. The emotions and the 2 types of motivation are both observed variables; 1 of the well-being variables is observed and the other is a latent variable.
The estimator is MLM.
The model is: OtherFocusedWell-Being by mastery growth self-acceptance purpose;
CommunionMotivation on gratitude compassion pride;
AgencyMotivation on gratitude compassion pride;
OtherFocusedWell-Being on AgencyMotivation CommunionMotivation;
SelfFocusedWell-Being on AgencyMotivation CommunionMotivation;
I have searched the mplus user guide and mplus discussions, but canít seem to find a solution. What would be the best way to see if multicollinearity is a problem?
Multicollinearity is caused by high correlations so I would look at the correlations among the variables. I don't know of any cutoff for how high constitutes a problem. You might want to ask this on a general discussion forum like SEMNET.
I am analysing fairly simple multilevel models, each having the following structure but with varying outcomes: %within% UO2 on predictor1 UO1; %between% UO2 on predictor2 UO1;
Some of my results have very high s.e.. For example an estimate of -2.5 and s.e. of 306 on the between level. If I inspect tech4 I do encounter a high correlation between between UO2 and predictor2 (r > .80).
1. Would this indicate problematic multicollinearity? 2. If so, would it be appropriate to exclude UO1 from the between level?
For one of the other outcomes using this model, I find a correlation of > .80 for predictor2 and UO2. Als the predictor is the variable of interest, I cannot simply delete this variable from the model.
3. Is there some other solution to take care of the high s.e.?