My model fit value for SRMR is 0.11. All the other parameters are good. I have checked for Multicollinearity, reliability, and validity. My question is how to counter this problem of high SRMR. Is there is any logical justification? Kindly suggest.
SRMR = .11 reflects that your model does not capture the data well, that it fits poorly. The SRMR is crudely the average of your residual correlations. You need to work on lower df and residual correlations. Check the normality of data.
I shared the following one in another question previously.
"Standardized Root Mean Square Residual (SRMR) The SRMR is an absolute measure of fit and is defined as the standardized difference between the observed correlation and the predicted correlation. It is a positively biased measure and that bias is greater for small N and for low df studies." (Baron & Kenny) Also, TLI and CFI (and others) incorporate the degrees of freedom, which means simpler models show high TLI and CFI, but SRMR does not. There is no benefit from a more parsimonious model. In your case, you do not have a measurement issue (that is, you do not need to examine whether there should be alternate factors or different groupings of the items per se). However, the results suggest that the observed correlations among the factors are not high although the items are grouped to the proposed factors. For measurement model itself, it may not be a serious problem per say. For the structural models, you may need to specify the models appropriately (e.g., correlating some measurement errors depending on the characteristics of your measures).