说明你的模型有问题。原因有很多。模型是否 underidentified? 检查一下是否有共线性问题。
从理论上看看模型是否可以重新设置。另外可试试用贝叶斯估计方法。
请看看下面的解释－挺有帮助的，虽然是英文（
http://faculty.chass.ncsu.edu/ga ... tm#negativevariance）
What does it mean when I get negative error variance estimates?
When this occurs, your solution may be arbitrary. AMOS will give an error message saying that your solution is not admissable. LISREL will give an error message "Warning: Theta EPS not positive definite." Because the solution is arbitrary, modification indices, t-values, residuals, and other output cannot be computer or is arbitrary also.
There are several reasons why one may get negative variance estimates.
1. This can occur as a result of high multicollinearity. Rule this out first.
2. Negative estimates may indicate Heywood cases (see below)
3. Even though the true value of the variance is positive, the variability in your data may be large enough to produce a negative estimate. The presence of outliers may be a cause of such variability. Having only one or two measurement variables per latent variable can also cause high standard errors of estimate.
4. Negative estimates may indicate that observations in your data are negatively correlated. See Hocking (1984).
5. Least likely, your SEM program may be flawed. To test this, factor analyze your observed variance/covariance matrix and see if the determinant is greater than zero, meaning it is not singular. If it is singular, you may have used the pairwise option for missing values or used wrong missing data substitution. Assuming the observed matrix is not singular, then factor analyze the implied variance/covariance matrix. If the output contains negative eigenvalues when the observed matrix is not singular, there is a flaw in how the SEM program is computing implied variances and covariances.
For more on causes and handling of negative error variance, see Chen, Bollen, Paxton, Curran, and Kirby (2001).
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