Hi,
I found a discrepancy between results in R and Stata for a factor analysis
with a promax rotation. For Stata:
. *rotate, factor(2) promax*
(promax rotation)
Rotated Factor Loadings
Variable | 1 2 Uniqueness
-------------+--------------------------------
pfq_amanag~y | -0.17802 0.64161 0.70698
pfq_bwalk_~ø | 0.72569 0.05570 0.41706
pfq_cwalk_~s | 0.78938 -0.03497 0.41200
pfq_dkneel~g | 0.80165 -0.04188 0.39979
pfq_elifting | 0.58700 0.19396 0.46795
pfq_fhouse~e | 0.50086 0.38770 0.34323
pfq_gmeals | 0.03516 0.75884 0.38781
pfq_hwalki~s | 0.15942 0.52766 0.58543
pfq_istand~r | 0.46516 0.29058 0.52127
pfq_jget_i~d | 0.31819 0.43345 0.52934
pfq_kfork | 0.02458 0.48797 0.74549
pfq_ldress~g | 0.11193 0.63987 0.48377
pfq_mstand~s | 0.73177 0.07817 0.38311
pfq_nsitti~g | 0.49535 0.16943 0.61545
pfq_oreach~d | 0.34980 0.27156 0.67887
pfq_pgrasp~l | 0.26975 0.21778 0.80248
pfq_qgo_mo~s | 0.25753 0.65296 0.28598
pfq_rsocia~t | 0.14482 0.72348 0.31770
pfq_sleisu~e | -0.06316 0.69822 0.56654
For R:
*factanal(x = matrix, factors = 2, rotation = "promax")*
Loadings:
Factor1 Factor2
pfq_amanage_money 0.769
pfq_bwalk_mileø 0.925
pfq_cwalk_steps 0.977
pfq_dkneeling 0.802 0.152
pfq_elifting 0.812 0.114
pfq_fhouse_chore 0.884
pfq_gmeals 0.920
pfq_hwalking_rooms 0.963
pfq_istand_chair 0.927
pfq_jget_in_out_bed 0.951
pfq_kfork 0.846
pfq_ldressing 0.947
pfq_mstanding_hours 0.844
pfq_nsitting_long 0.795
pfq_oreach_over_head 0.856
pfq_pgrasp_small 0.814
pfq_qgo_movies 0.971
pfq_rsocial_event 0.930
pfq_sleisure_home 0.811
This is just one example -- all other comparisons with a different number of
factors, with and without rotation, generated different numbers. Any
thoughts from the list members on the reasons for the discrepancy?
thanks,
Ricardo Pietrobon, MD, PhD
Duke University Health System
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