Reviewers comments on Modification indices in AMOS and how to deal with it.

[Avantika Bakshi]

Dear Sir and group members. 

I recently received a comment from one of the reviewers regarding the use of modification indices (MI) so as to improve my model fit. This is the comment i received ' 

"Although the authors have used the method of Modification Indices (MI) to correct the model data, this approach generally lacks interpretability. The operational premise of structural equation modeling techniques is the assumption that residuals are independent. Furthermore, during the specific correction process, there should be a reference to the literature regarding the specific threshold above which corrections are necessary, such as the standard proposed by Bagozzi & Yi (1988) of 3.84. Lastly, from a statistical data perspective, regarding the correlation of residuals, the article should provide corresponding theoretical explanations, for example: Do these two items have semantic similarities?"

Now I understand that error covariances should be used to the minimum. In my work I have kept the threshold limit for MI (above 20) which led to me introduce 3 error covariances. Prior to this I had also checked for low factor loadings (0.5) and also the standardised residual values (below 2) and none of the items fell in that front. The error variances were drawn between similiar meaning items from the same factor, For example- item corresponding e3 statement was 'I have the technological skills needed to use AI platforms for my teaching' and item corresponding e4 was 'I know how to solve technical problems by myself while using AI platforms' .

I have also found a few papers that use MI for improving model fit in similiar studies.

The original model fit as indicated by the following fit indices: χ²(164) = 393.6, χ² /df= 2.4, CFI = 0.895 , GFI=0.805,  RMSEA = 0.091. 

After introducing MI it was χ² /df=2.061, CFI= 0.927, GFI=.841, AGFI=.793, TLI=.914, RMSEA= 0.077

I would be grateful on your suggestions on two things:

1) Should I introduce the MI considering the model fit before that was close to average in the first place.

2) If yes, what would be the best way to address the reviewers comment and make changes to the paper so as to get a positive response.

I would really appreciate any help in this regard.

Thankyou in anticipation.

[Neeraj Kaushik]

Dear Avantika

You've presented a nice summary of your work.

Yes, MIs shd be used to minimum. Some experts criticize this method of improving Model fit  and they call this as forced Model fitness.

However, you've correctly used MI for a bare min of 3 error covar. Now you shd give some studies that this is permissible and advisable. Also give arguments in favor of similarity of 2 items and why and how the residuals of 2 items may be connected.

If it's a per then report the results only after incorporating the MIs. Had it been these wor, then both models may be given (as space or word limit is not a problem in the thesis).

Best wishes

Neeraj

--
The members of this group are expected to follow the following Protocols:
1. Please search previous posts in the group before posting the question.
2. Don't write the query in someone's post. Always use the option of New topic for the new question. You can do this by writing to dataanaly...@googlegroups.com
3. It’s better to give a proper subject to your post/query. It'll help others while searching.
4. Never write Open-ended queries. This group intends to help research scholars, NOT TO WORK FOR THEM.
5. Never write words like URGENT in your posts. People will help when they are free.
6. Never upload any information about National Seminars/Conferences. Send such information
in personal emails and feel free to share any RESEARCH-related information.
7. No Happy New Year, Happy Diwali, Happy Holi, Happy Birthday, Happy Anniversary, etc. allowed in this group.
8. Asking or sharing Research Papers is NOT ALLOWED.
9. You can share your questionnaire only once.
---
You received this message because you are subscribed to the Google Groups "DataAnalysis" group.
To unsubscribe from this group and stop receiving emails from it, send an email to dataanalysistrai...@googlegroups.com.
To view this discussion visit https://groups.google.com/d/msgid/dataanalysistraining/d945dcc7-f7c9-4fb9-8d13-137f932bcc14n%40googlegroups.com.