Model Comparison EFA
Message/Author
 Yoon Young posted on Monday, October 08, 2018 - 6:44 am
Dear Dr. Muthen,

Below is my EFA results. None of the p-values are non-significant. The parallel analysis suggested 4 factors, the eigenvalues suggested 6 factors, and the model fit comparison seems to extent this to 7 factors. Would you guide me which criteria I should consider as a priority in this case? Thanks a lot!

SUMMARY OF MODEL FIT INFORMATION

Number of Degrees of
Model Parameters Chi-Square Freedom P-Value

2-factor 95 1143.772 229 0.0000
3-factor 117 736.125 207 0.0000
4-factor 138 540.906 186 0.0000
5-factor 158 409.362 166 0.0000
6-factor 177 315.571 147 0.0000
7-factor 195 237.011 129 0.0000
8-factor N/A

Degrees of
Models Compared Chi-Square Freedom P-Value

2-factor against 3-factor 407.647 22 0.0000
3-factor against 4-factor 195.220 21 0.0000
4-factor against 5-factor 131.543 20 0.0000
5-factor against 6-factor 93.791 19 0.0000
6-factor against 7-factor 78.560 18 0.0000
 Bengt O. Muthen posted on Monday, October 08, 2018 - 10:46 am
These conflicting messages often happen in real data because the data may not represent a perfect factor model with a certain number of factors. For instance, you may have m factors but also several residual correlations. This then leads to chi-square saying you should have more factors than m - which is not the true model. This often shows up as some factors having only 2 significant loadings. Look at modification indices to see if residual correlation is present. In general, parallel analysis may be more robust to this, but I am not sure this has been thoroughly studied. And of course you need to see how any solution relates to theory for the topic.
 Yoon Young posted on Tuesday, October 09, 2018 - 11:23 am
Thank you so much! It is my first time examining the modification indices so would you please guide me how/what to look at?

Here is the part of the output.

MODIFICATION INDICES FOR ANALYSIS WITH 4 FACTOR(S)

MODIFICATION INDICES

THETA
FA1 FA2 FA3 FA4 FA5
________ ________ ________ ________ ________
FA1 0.000
FA2 0.237 0.000
FA3 24.441 1.142 0.000
FA4 0.092 1.364 0.604 0.000
FA5 7.303 1.460 0.452 5.285 0.000
FA6 0.481 0.205 1.330 0.652 0.759
FA7 4.265 0.017 1.732 0.009 0.121

I wrote a syntax as below:

DATA:
file =jordan EFA.csv;
VARIABLE:
Names are
FA1 FA2 FA3 FA4 FA5 FA6 FA7 FA8 FA9 FA10
FA11 FA12 FA13 FA14 FA15 FA16 FA17 FA18 FA19 FA20
FA21 FA22 FA23 FA24;

Missing = all (999);

Analysis:
type = efa 2 8;

Output: MODINDICES

Thank you so much!
 Bengt O. Muthen posted on Tuesday, October 09, 2018 - 6:03 pm
See the Topic 1 video and handout from our short courses on our website.