Troubleshooting problems with SEM models that have “Heywood” cases such as negative variance parameters and non-positive definite covariance matrices. Jeremy Yorgason Brigham Young University. Introduction. In SEM, it is fairly common to encounter Improper Solutions
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Troubleshooting problems with SEM models that have “Heywood” cases such as negative variance parameters and non-positive definite covariance matrices
Jeremy Yorgason
Brigham Young University
1. Specification error in the model
2. Model under-identified (negative degrees of freedom)
A. V(V+1)/2 minus parms (if estimating means/intercepts use V(V+3)/2)
3. Non-convergence
4. Outliers in the data
5. Too small of sample for the model being estimated
Kline, 2011; Kolenikov& Bollen, 2012; Chen et al., 2001; Newsome, 2012
6. Missing data
7. “Sampling fluctuations”
8. Two indicator latent variables
9. Non-normally distributed outcome or indicator variables in your model
10. Empirical under-identification
Kline, 2011; Kolenikov& Bollen, 2012; Chen et al., 2001; Newsome, 2012
Amos:
“XX: Default Model”
“The following variances are negative.”
“This solution is not admissible”
“The model is probably unidentified. In order to achieve identifiability, it will probably be necessary to impose 1 additional constraint.”
In place of estimates in the Amos output you see “unidentified”
Mplus:
THE MODEL ESTIMATION TERMINATED NORMALLY
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 -0.762D-17. PROBLEM INVOLVING PARAMETER 59.
MODIFICATION INDICES COULD NOT BE COMPUTED.
THE MODEL MAY NOT BE IDENTIFIED.
Mplus or other programs:
2. Researchers need to be attentive to model problems when there are latent variables with only 2 indicators (can be unstable)
A. Newsom (2012) suggests constraining the two factor loadings to be equal…
3. Caution is also warranted when estimating “higher order” latent variables with only two factors, and certain complex models (e.g., common fate models) that require specific constraints in order for the model to be identified
4. Either use a large sample , OR check the sample size and compare with the number of parameters being estimated.
5. If your model looks to be specified correctly, but you still have a problem with the model, it’s time to start looking at your data
6. Do you have any categorical or non-normally distributed dependent variables that are specified as continuous?
8. A start value is a number assigned to each estimated parameter when “iterations” begin for a model. Amos and Mplus automatically create start values for each parameter to be estimated, yet it is possible to assign start values if the program assigned ones don’t work. Researchers can provide start values for a model, which are essentially any known parameter estimates (e.g., regression weight or coefficient). You can get these by running simple linear regression with the variables in your model, and then plug in the coefficient from the simpler model.