Hi everyone,
I am conducting a scale development study for my dissertation and I have run into quite a barrier. When I run the analysis in Jamovi, 6 factors are extracted. When I run SPSS, 14 are extracted. I have to figure out which program is correct (or at least better for the purpose of scale development) before I can proceed. I have 159 participants, no missing data, 126 items, and I am running Principle Axis Factoring with Oblimin rotation and using eigenvalues > 1 as my cutoff criteria. I ran the analysis in R as well to compare, and it is much closer to Jamovi compared to SPSS, though it is still a little bit different.
My overall question is why this is so drastically different, and which is best to use, but I understand it is not as simple as asking that and getting an answer. Any insight is helpful.
1. I checked to see if it the Kaiser normalization. It is not. When it is shut off, the results barely change and they are still drastically different from Jamovi. I ran the same EFA in R with Kaiser normalization and specified 14 factors, and things don’t align there with SPSS either. What else could be going on?
2. I ran an EFA (PAF, eigenvalues > 1) with no rotation so I can compare it to Jamovi. Should the number of eigenvalues > 1 ever change in total from the initial values to the total after extraction? For example, in SPSS it gives me 14 initial eigenvalues > 1, but then under “extraction sums of squared loadings” there are 8 eigenvalues > 1, and the eigenvalues got smaller (which is alright from what I learned about common variance vs all variance mentioned above). Which is appropriate at that point? Do I pay more attention to initial or after extraction?
3. How are eigenvalues calculated in SPSS? The initial eigenvalues are different in Jamovi and so I am wondering if this is where the problem might start.
4. Any other recommendations on where the issue might lie?
Thanks in advance for your help in building my understanding.
#SPSSStatistics#Support#SupportMigration