Message/Author 

Yoon Young posted on Monday, October 08, 2018  6:44 am



Dear Dr. Muthen, Below is my EFA results. None of the pvalues are nonsignificant. 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 ChiSquare Freedom PValue 2factor 95 1143.772 229 0.0000 3factor 117 736.125 207 0.0000 4factor 138 540.906 186 0.0000 5factor 158 409.362 166 0.0000 6factor 177 315.571 147 0.0000 7factor 195 237.011 129 0.0000 8factor N/A Degrees of Models Compared ChiSquare Freedom PValue 2factor against 3factor 407.647 22 0.0000 3factor against 4factor 195.220 21 0.0000 4factor against 5factor 131.543 20 0.0000 5factor against 6factor 93.791 19 0.0000 6factor against 7factor 78.560 18 0.0000 


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 chisquare 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! 


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