做CFA的时候是不能根据MI来修正的，

How to improve model fit

      Given the complexity of structural equation modelling, it is not uncommon to find that the fit of a proposed model  is  poor.  Allowing  modification  indices  to  drive  the  process  is not always a good practice however,  some modifications can be made locally that can substantially improve results. It is good practice to assess the fit of  each  construct  and  its  items  individually  to  determine  whether  there  are  any  items  that  are  particularly
weak. Items with low multiple r2 (less than .20) should be removed from the analysis as this is an indication
of very high levels of error. Following this, each construct should be modelled in conjunction with every other
construct  in  the  model  to  determine  whether  discriminant  validity  has  been  achieved.  The  Phi  (φ)  value
between two constructs is akin to their covariance, therefore a Phi of 1.0 indicates that the two constructs are
measuring  the  same  thing.  One  test  which  is  useful  to  determine  whether  constructs  are  significantly
different is Bagozzi et al’s (1991) discriminant validity test. The formula for this is: parameter estimate (phi
value) ±1.96 * standard error. If the value is greater than 1.0 discriminant validity has not been achieved and
further inspections of item cross-loadings need to be made. Items with high Lambda-Y modification indices
are possible candidates for deletion and are likely to be causing the discriminant validity problem. By deleting
indiscriminant  items  fit  is  likely  to  improve  and  is  advantageous  in  that  it  is  unlikely  to  have  any  major
theoretical repercussions.  
   
     A further way in which fit can be improved is through the correlation of error terms. This practice is generally
means that there is some other issue that is not specified within the model that is causing the covariation. If a researcher decides to correlate error terms there needs to be strong theoretical justification behind such a move. Correlating within-factor error is easier to justify than across latent variable correlations, however it is essential that the statistical and substantive impact are clearly discussed. If a researcher feels they can substantiate this decision, correlated error terms is acceptable, however it is a step that should be taken with caution.

是可以进行修正的吧，吴明隆的书上说，CFA测量模型允许测量变量的误差项与其他测量变量的误差项间存在共变关系（邱皓政，2005）