{"id":4396,"date":"2014-10-11T01:05:33","date_gmt":"2014-10-11T00:05:33","guid":{"rendered":"http:\/\/emilkirkegaard.dk\/en\/?p=4396"},"modified":"2014-10-11T01:09:21","modified_gmt":"2014-10-11T00:09:21","slug":"international-general-factor-of-personality-yes-but","status":"publish","type":"post","link":"https:\/\/emilkirkegaard.dk\/en\/2014\/10\/international-general-factor-of-personality-yes-but\/","title":{"rendered":"International general factor of personality? yes, but…"},"content":{"rendered":"

I merged the dataset from Schmitt et al (2007)’s paper<\/a> about OCEAN traits in 56 countries with the rest of the megadataset. Then i extracted the first factor of the OCEAN means and SDs. These two are nearly uncorrelated (.07). As for factor strength, they are not too bad:<\/p>\n

> <\/span>DF.OCEAN.mean.omega\r\n<\/span>Omega \r\nCall: omega(m = DF.OCEAN.mean)\r\nAlpha:                 0.73 \r\nG.6:                   0.74 \r\nOmega Hierarchical:    0.54 \r\nOmega H asymptotic:    0.64 \r\nOmega Total            0.84 \r\n\r\nSchmid Leiman Factor loadings greater than  0.2 \r\n                                        g   F1*   F2*   F3*   h2   u2   p2\r\nExtraversionMeanSchmittEtAl2007      0.44        0.66       0.64 0.36 0.30\r\nAgreeablenessMeanSchmittEtAl2007     0.58  0.56             0.66 0.34 0.51\r\nConscientiousnessMeanSchmittEtAl2007 0.62  0.52             0.66 0.34 0.58\r\nNeuroticismMeanSchmittEtAl2007      -0.66  0.28  0.36 -0.36 0.76 0.24 0.56\r\nOpennessMeanSchmittEtAl2007          0.23        0.21  0.51 0.38 0.62 0.14\r\n\r\nWith eigenvalues of:\r\n   g  F1*  F2*  F3* \r\n1.40 0.69 0.62 0.40 \r\n\r\ngeneral\/max  2.04   max\/min =   1.7\r\nmean percent general =  0.42    with sd =  0.19 and cv of  0.46 \r\nExplained Common Variance of the general factor =  0.45<\/pre>\n
 <\/p>\n
and<\/p>\n
> <\/span>DF.OCEAN.SD.omega\r\n<\/span>Omega \r\nCall: omega(m = DF.OCEAN.SD)\r\nAlpha:                 0.79 \r\nG.6:                   0.78 \r\nOmega Hierarchical:    0.72 \r\nOmega H asymptotic:    0.86 \r\nOmega Total            0.84 \r\n\r\nSchmid Leiman Factor loadings greater than  0.2 \r\n                                      g   F1*   F2*   F3*   h2   u2   p2\r\nExtraversionSDSchmittEtAl2007      0.80                   0.64 0.36 0.99\r\nAgreeablenessSDSchmittEtAl2007     0.57        0.47       0.55 0.45 0.59\r\nConscientiousnessSDSchmittEtAl2007 0.57  0.35             0.48 0.52 0.68\r\nNeuroticismSDSchmittEtAl2007       0.78  0.52             0.87 0.13 0.69\r\nOpennessSDSchmittEtAl2007          0.43        0.24       0.25 0.75 0.74\r\n\r\nWith eigenvalues of:\r\n   g  F1*  F2*  F3* \r\n2.08 0.41 0.31 0.00 \r\n\r\ngeneral\/max  5.09   max\/min =   136.11\r\nmean percent general =  0.74    with sd =  0.15 and cv of  0.2 \r\nExplained Common Variance of the general factor =  0.74<\/pre>\n
 <\/p>\n
Compare with values in Table 5 in my just published paper<\/a>. GFP-mean is clearly weaker than g factor at individual level, GFP-SD is about the same.<\/p>\n\n<\/colgroup>\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n
Dataset
\n<\/b><\/td>\n		Var% MR
\n<\/b><\/td>\n		Var% MR SL<\/b><\/td>\nOmega h.<\/b><\/td>\nOmega h. a.<\/b><\/td>\nECV<\/b><\/td>\nR2<\/b><\/td>\n<\/tr>\n
NO Complete cases<\/td>\n0.68<\/td>\n0.65<\/td>\n0.87<\/td>\n0.91<\/td>\n0.78<\/td>\n0.98<\/td>\n<\/tr>\n
NO Impute 1<\/td>\n0.66<\/td>\n0.62<\/td>\n0.86<\/td>\n0.9<\/td>\n0.74<\/td>\n0.96<\/td>\n<\/tr>\n
NO Impute 2<\/td>\n0.64<\/td>\n0.6<\/td>\n0.85<\/td>\n0.89<\/td>\n0.75<\/td>\n0.95<\/td>\n<\/tr>\n
NO Impute 3<\/td>\n0.63<\/td>\n0.59<\/td>\n0.82<\/td>\n0.87<\/td>\n0.73<\/td>\n0.99<\/td>\n<\/tr>\n
DK complete cases<\/td>\n0.57<\/td>\n0.51<\/td>\n0.83<\/td>\n0.85<\/td>\n0.68<\/td>\n0.99<\/td>\n<\/tr>\n
DK impute 4<\/td>\n0.55<\/td>\n0.51<\/td>\n0.86<\/td>\n0.88<\/td>\n0.73<\/td>\n0.99<\/td>\n<\/tr>\n
Int. S. Factor<\/td>\n0.43<\/td>\n0.35<\/td>\n0.76<\/td>\n0.77<\/td>\n0.51<\/td>\n0.81<\/td>\n<\/tr>\n
Cognitive data<\/td>\n<\/td>\n0.33<\/td>\n0.74<\/td>\n0.79<\/td>\n0.57<\/td>\n0.78<\/td>\n<\/tr>\n
Personality data<\/td>\n<\/td>\n0.16<\/td>\n0.37<\/td>\n0.48<\/td>\n0.34<\/td>\n0.41<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Then i correlated these with national IQ, S factor and local S factors in Norway and Denmark.<\/p>\n
> <\/span>round(cor(DF.OCEAN.general.scores,use=\"pairwise.complete.obs\"),2)\r\n<\/span>             GFP.mean GFP.SD S.in.Norway S.in.Denmark Islam S.Int    IQ\r\nGFP.mean         1.00   0.07        0.09        -0.25  0.17 -0.21 -0.58\r\nGFP.SD           0.07   1.00        0.39         0.26 -0.14  0.36  0.24\r\nS.in.Norway      0.09   0.39        1.00         0.78 -0.72  0.73  0.60\r\nS.in.Denmark    -0.25   0.26        0.78         1.00 -0.71  0.54  0.54\r\nIslam            0.17  -0.14       -0.72        -0.71  1.00 -0.33 -0.27\r\nS.Int           -0.21   0.36        0.73         0.54 -0.33  1.00  0.86\r\nIQ              -0.58   0.24        0.60         0.54 -0.27  0.86  1.00<\/pre>\n
So strangely, the correlation of GFP-mean x national IQ is very negative. It correlates weakly with S factors. Let’s try partialing out national IQ:<\/p>\n
> <\/span>DF.OCEAN.general.scores.no.IQ = partial.r(DF.OCEAN.general.scores,c(1:6),7)\r\n<\/span>> <\/span>DF.OCEAN.general.scores.no.IQ\r\n<\/span>partial correlations \r\n             GFP.mean GFP.SD S.in.Norway S.in.Denmark Islam S.Int\r\nGFP.mean         1.00   0.26        0.68         0.09  0.02  0.72\r\nGFP.SD           0.26   1.00        0.31         0.16 -0.08  0.32\r\nS.in.Norway      0.68   0.31        1.00         0.67 -0.73  0.53\r\nS.in.Denmark     0.09   0.16        0.67         1.00 -0.70  0.19\r\nIslam            0.02  -0.08       -0.73        -0.70  1.00 -0.21\r\nS.Int            0.72   0.32        0.53         0.19 -0.21  1.00<\/pre>\n
Even more strange. GFP-mean strongly correlates with 2 S factors, but not the one in Denmark. The Danish data are very good (25 variables) and so are the international data (42-54 variables). And all the S factors correlate strongly before partialing (.78, .73, .54) but mixed after removing IQ (.67, .53, .19). Again Denmark is odd. For GFP-SD, it is similar, but weaker (before: .39, .26, .36; after: .31, .16, .32).<\/p>\n
What to make of this? So i emailed some colleagues:<\/p>\n
\n
\n
Dear [NAMES]<\/p>\n<\/div>\n
Do you know if someone have looked at an international general factor of personality? Because I did it just now using a dataset of OCEAN trait scores (big five) from Schmitt et al 2007. There is indeed an international GFP in the data. It correlates negatively with national IQs (-.58). Strangely, partialing out national IQs, it correlates highly with general socioeconomic factors in Norway (.68) and internationally (.72), but not in Denmark (.09). Strange? Thoughts? I can send you the data+code if you like.<\/p>\n
Regards,
\nEmil<\/p><\/blockquote>\n
One of them had insider info:<\/p>\n
Emil,<\/p>\n
There is a paper about to appear in Intelligence in which an international GFP has been computed and analyzed.<\/div>\n
\n
Best,<\/p>\n<\/div>\n
[NAME].<\/div>\n<\/blockquote>\n
So i publish this here quickly so i establish priority and independence.<\/div>\n
What about OCEAN traits themselves?<\/h3>\n
(sorry, tables apparently not easy to make smaller)<\/div>\n
All correlations:<\/div>\n
\n\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\nE mean<\/td>\nE SD<\/td>\nA mean<\/td>\nA SD<\/td>\nC mean<\/td>\nC SD<\/td>\nN mean<\/td>\nN SD<\/td>\nO mean<\/td>\nO SD<\/td>\nMean SD<\/td>\nS.NO<\/td>\nS.DK<\/td>\nIslam<\/td>\nInt.S<\/td>\nIQ<\/td>\n<\/tr>\n
E mean<\/td>\n1<\/td>\n0.14<\/td>\n0.2<\/td>\n0.22<\/td>\n0.25<\/td>\n0.23<\/td>\n-0.49<\/td>\n0.17<\/td>\n0.27<\/td>\n0.09<\/td>\n0.23<\/td>\n0.06<\/td>\n-0.19<\/td>\n-0.02<\/td>\n0.09<\/td>\n-0.02<\/td>\n<\/tr>\n
E sd<\/td>\n0.14<\/td>\n1<\/td>\n-0.08<\/td>\n0.47<\/td>\n-0.07<\/td>\n0.48<\/td>\n0.13<\/td>\n0.66<\/td>\n0.3<\/td>\n0.34<\/td>\n0.81<\/td>\n0.45<\/td>\n0.35<\/td>\n-0.35<\/td>\n0.53<\/td>\n0.39<\/td>\n<\/tr>\n
A mean<\/td>\n0.2<\/td>\n-0.08<\/td>\n1<\/td>\n0.15<\/td>\n0.65<\/td>\n0.21<\/td>\n-0.48<\/td>\n0.21<\/td>\n0.26<\/td>\n-0.13<\/td>\n0.11<\/td>\n0.08<\/td>\n-0.26<\/td>\n0.26<\/td>\n-0.25<\/td>\n-0.53<\/td>\n<\/tr>\n
A SD<\/td>\n0.22<\/td>\n0.47<\/td>\n0.15<\/td>\n1<\/td>\n0.23<\/td>\n0.43<\/td>\n0<\/td>\n0.45<\/td>\n0.22<\/td>\n0.35<\/td>\n0.71<\/td>\n0.18<\/td>\n0.23<\/td>\n-0.18<\/td>\n0.12<\/td>\n-0.04<\/td>\n<\/tr>\n
C mean<\/td>\n0.25<\/td>\n-0.07<\/td>\n0.65<\/td>\n0.23<\/td>\n1<\/td>\n0.1<\/td>\n-0.57<\/td>\n0.07<\/td>\n0.2<\/td>\n-0.03<\/td>\n0.07<\/td>\n0.04<\/td>\n-0.19<\/td>\n0.14<\/td>\n-0.19<\/td>\n-0.6<\/td>\n<\/tr>\n
C SD<\/td>\n0.23<\/td>\n0.48<\/td>\n0.21<\/td>\n0.43<\/td>\n0.1<\/td>\n1<\/td>\n0.11<\/td>\n0.62<\/td>\n0.41<\/td>\n0.25<\/td>\n0.78<\/td>\n0.34<\/td>\n-0.03<\/td>\n0.04<\/td>\n0.19<\/td>\n0.04<\/td>\n<\/tr>\n
N mean<\/td>\n-0.49<\/td>\n0.13<\/td>\n-0.48<\/td>\n0<\/td>\n-0.57<\/td>\n0.11<\/td>\n1<\/td>\n0.22<\/td>\n-0.09<\/td>\n0.25<\/td>\n0.19<\/td>\n-0.1<\/td>\n0.13<\/td>\n-0.06<\/td>\n0.12<\/td>\n0.38<\/td>\n<\/tr>\n
N SD<\/td>\n0.17<\/td>\n0.66<\/td>\n0.21<\/td>\n0.45<\/td>\n0.07<\/td>\n0.62<\/td>\n0.22<\/td>\n1<\/td>\n0.41<\/td>\n0.28<\/td>\n0.83<\/td>\n0.23<\/td>\n0.19<\/td>\n0<\/td>\n0.24<\/td>\n0.18<\/td>\n<\/tr>\n
O mean<\/td>\n0.27<\/td>\n0.3<\/td>\n0.26<\/td>\n0.22<\/td>\n0.2<\/td>\n0.41<\/td>\n-0.09<\/td>\n0.41<\/td>\n1<\/td>\n0.07<\/td>\n0.4<\/td>\n-0.01<\/td>\n-0.07<\/td>\n0.04<\/td>\n-0.02<\/td>\n-0.06<\/td>\n<\/tr>\n
O sd<\/td>\n0.09<\/td>\n0.34<\/td>\n-0.13<\/td>\n0.35<\/td>\n-0.03<\/td>\n0.25<\/td>\n0.25<\/td>\n0.28<\/td>\n0.07<\/td>\n1<\/td>\n0.56<\/td>\n0.22<\/td>\n0.14<\/td>\n-0.07<\/td>\n0.25<\/td>\n0.37<\/td>\n<\/tr>\n
Mean SD<\/td>\n0.23<\/td>\n0.81<\/td>\n0.11<\/td>\n0.71<\/td>\n0.07<\/td>\n0.78<\/td>\n0.19<\/td>\n0.83<\/td>\n0.4<\/td>\n0.56<\/td>\n1<\/td>\n0.41<\/td>\n0.25<\/td>\n-0.15<\/td>\n0.36<\/td>\n0.25<\/td>\n<\/tr>\n
S.factor.in.Norway<\/td>\n0.06<\/td>\n0.45<\/td>\n0.08<\/td>\n0.18<\/td>\n0.04<\/td>\n0.34<\/td>\n-0.1<\/td>\n0.23<\/td>\n-0.01<\/td>\n0.22<\/td>\n0.41<\/td>\n1<\/td>\n0.78<\/td>\n-0.72<\/td>\n0.73<\/td>\n0.6<\/td>\n<\/tr>\n
S.factor.in.Denmark<\/td>\n-0.19<\/td>\n0.35<\/td>\n-0.26<\/td>\n0.23<\/td>\n-0.19<\/td>\n-0.03<\/td>\n0.13<\/td>\n0.19<\/td>\n-0.07<\/td>\n0.14<\/td>\n0.25<\/td>\n0.78<\/td>\n1<\/td>\n-0.71<\/td>\n0.54<\/td>\n0.54<\/td>\n<\/tr>\n
IslamPewResearch2010<\/td>\n-0.02<\/td>\n-0.35<\/td>\n0.26<\/td>\n-0.18<\/td>\n0.14<\/td>\n0.04<\/td>\n-0.06<\/td>\n0<\/td>\n0.04<\/td>\n-0.07<\/td>\n-0.15<\/td>\n-0.72<\/td>\n-0.71<\/td>\n1<\/td>\n-0.33<\/td>\n-0.27<\/td>\n<\/tr>\n
International.S.Factor<\/td>\n0.09<\/td>\n0.53<\/td>\n-0.25<\/td>\n0.12<\/td>\n-0.19<\/td>\n0.19<\/td>\n0.12<\/td>\n0.24<\/td>\n-0.02<\/td>\n0.25<\/td>\n0.36<\/td>\n0.73<\/td>\n0.54<\/td>\n-0.33<\/td>\n1<\/td>\n0.86<\/td>\n<\/tr>\n
LV2012estimatedIQ<\/td>\n-0.02<\/td>\n0.39<\/td>\n-0.53<\/td>\n-0.04<\/td>\n-0.6<\/td>\n0.04<\/td>\n0.38<\/td>\n0.18<\/td>\n-0.06<\/td>\n0.37<\/td>\n0.25<\/td>\n0.6<\/td>\n0.54<\/td>\n-0.27<\/td>\n0.86<\/td>\n1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
<\/div>\n
With IQ partialed out:<\/div>\n
\n\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\nE mean<\/td>\nE sd<\/td>\nA mean<\/td>\nA SD<\/td>\nC mean<\/td>\nC SD<\/td>\nN mean<\/td>\nN SD<\/td>\nO mean<\/td>\nO SD<\/td>\nMean SD<\/td>\nS.NO<\/td>\nS.DK<\/td>\nIslam<\/td>\nInt.S<\/td>\n<\/tr>\n
E mean<\/td>\n1<\/td>\n0.17<\/td>\n0.22<\/td>\n0.22<\/td>\n0.3<\/td>\n0.23<\/td>\n-0.52<\/td>\n0.18<\/td>\n0.27<\/td>\n0.1<\/td>\n0.24<\/td>\n0.09<\/td>\n-0.21<\/td>\n-0.02<\/td>\n0.21<\/td>\n<\/tr>\n
E sd<\/td>\n0.17<\/td>\n1<\/td>\n0.16<\/td>\n0.53<\/td>\n0.22<\/td>\n0.51<\/td>\n-0.02<\/td>\n0.65<\/td>\n0.35<\/td>\n0.23<\/td>\n0.8<\/td>\n0.29<\/td>\n0.18<\/td>\n-0.28<\/td>\n0.42<\/td>\n<\/tr>\n
A mean<\/td>\n0.22<\/td>\n0.16<\/td>\n1<\/td>\n0.15<\/td>\n0.49<\/td>\n0.28<\/td>\n-0.36<\/td>\n0.36<\/td>\n0.27<\/td>\n0.07<\/td>\n0.29<\/td>\n0.58<\/td>\n0.03<\/td>\n0.15<\/td>\n0.48<\/td>\n<\/tr>\n
A SD<\/td>\n0.22<\/td>\n0.53<\/td>\n0.15<\/td>\n1<\/td>\n0.25<\/td>\n0.43<\/td>\n0.02<\/td>\n0.47<\/td>\n0.21<\/td>\n0.4<\/td>\n0.74<\/td>\n0.26<\/td>\n0.3<\/td>\n-0.2<\/td>\n0.3<\/td>\n<\/tr>\n
C mean<\/td>\n0.3<\/td>\n0.22<\/td>\n0.49<\/td>\n0.25<\/td>\n1<\/td>\n0.15<\/td>\n-0.46<\/td>\n0.23<\/td>\n0.21<\/td>\n0.25<\/td>\n0.29<\/td>\n0.63<\/td>\n0.2<\/td>\n-0.02<\/td>\n0.82<\/td>\n<\/tr>\n
C SD<\/td>\n0.23<\/td>\n0.51<\/td>\n0.28<\/td>\n0.43<\/td>\n0.15<\/td>\n1<\/td>\n0.1<\/td>\n0.62<\/td>\n0.41<\/td>\n0.26<\/td>\n0.79<\/td>\n0.39<\/td>\n-0.05<\/td>\n0.06<\/td>\n0.31<\/td>\n<\/tr>\n
N mean<\/td>\n-0.52<\/td>\n-0.02<\/td>\n-0.36<\/td>\n0.02<\/td>\n-0.46<\/td>\n0.1<\/td>\n1<\/td>\n0.17<\/td>\n-0.07<\/td>\n0.13<\/td>\n0.11<\/td>\n-0.45<\/td>\n-0.1<\/td>\n0.05<\/td>\n-0.44<\/td>\n<\/tr>\n
N SD<\/td>\n0.18<\/td>\n0.65<\/td>\n0.36<\/td>\n0.47<\/td>\n0.23<\/td>\n0.62<\/td>\n0.17<\/td>\n1<\/td>\n0.43<\/td>\n0.23<\/td>\n0.83<\/td>\n0.16<\/td>\n0.11<\/td>\n0.05<\/td>\n0.18<\/td>\n<\/tr>\n
O mean<\/td>\n0.27<\/td>\n0.35<\/td>\n0.27<\/td>\n0.21<\/td>\n0.21<\/td>\n0.41<\/td>\n-0.07<\/td>\n0.43<\/td>\n1<\/td>\n0.1<\/td>\n0.42<\/td>\n0.03<\/td>\n-0.04<\/td>\n0.03<\/td>\n0.06<\/td>\n<\/tr>\n
O sd<\/td>\n0.1<\/td>\n0.23<\/td>\n0.07<\/td>\n0.4<\/td>\n0.25<\/td>\n0.26<\/td>\n0.13<\/td>\n0.23<\/td>\n0.1<\/td>\n1<\/td>\n0.52<\/td>\n0.01<\/td>\n-0.07<\/td>\n0.03<\/td>\n-0.14<\/td>\n<\/tr>\n
Mean SD<\/td>\n0.24<\/td>\n0.8<\/td>\n0.29<\/td>\n0.74<\/td>\n0.29<\/td>\n0.79<\/td>\n0.11<\/td>\n0.83<\/td>\n0.42<\/td>\n0.52<\/td>\n1<\/td>\n0.33<\/td>\n0.15<\/td>\n-0.09<\/td>\n0.3<\/td>\n<\/tr>\n
S.factor.in.Norway<\/td>\n0.09<\/td>\n0.29<\/td>\n0.58<\/td>\n0.26<\/td>\n0.63<\/td>\n0.39<\/td>\n-0.45<\/td>\n0.16<\/td>\n0.03<\/td>\n0.01<\/td>\n0.33<\/td>\n1<\/td>\n0.67<\/td>\n-0.73<\/td>\n0.53<\/td>\n<\/tr>\n
S.factor.in.Denmark<\/td>\n-0.21<\/td>\n0.18<\/td>\n0.03<\/td>\n0.3<\/td>\n0.2<\/td>\n-0.05<\/td>\n-0.1<\/td>\n0.11<\/td>\n-0.04<\/td>\n-0.07<\/td>\n0.15<\/td>\n0.67<\/td>\n1<\/td>\n-0.7<\/td>\n0.19<\/td>\n<\/tr>\n
IslamPewResearch2010<\/td>\n-0.02<\/td>\n-0.28<\/td>\n0.15<\/td>\n-0.2<\/td>\n-0.02<\/td>\n0.06<\/td>\n0.05<\/td>\n0.05<\/td>\n0.03<\/td>\n0.03<\/td>\n-0.09<\/td>\n-0.73<\/td>\n-0.7<\/td>\n1<\/td>\n-0.21<\/td>\n<\/tr>\n
International.S.Factor<\/td>\n0.21<\/td>\n0.42<\/td>\n0.48<\/td>\n0.3<\/td>\n0.82<\/td>\n0.31<\/td>\n-0.44<\/td>\n0.18<\/td>\n0.06<\/td>\n-0.14<\/td>\n0.3<\/td>\n0.53<\/td>\n0.19<\/td>\n-0.21<\/td>\n1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
<\/div>\n
R code (load in the megadataset as DF.mega3 first):<\/div>\n
DF.interest = cbind(DF.mega3[2:12],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 DF.mega3[14],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 DF.mega3[40],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 DF.mega3[42],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 DF.mega3[64],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 DF.mega3[76])\r\nDF.interest.cor = rcorr(as.matrix(DF.interest))\r\nround(DF.interest.cor$r,2)\r\nwrite.csv(round(DF.interest.cor$r,2),file=\"OCEANCors.csv\")\r\n\r\n#remove IQ\r\nDF.interest.cor.without.IQ = partial.r(DF.interest, c(1:15),16)\r\nwrite.csv(round(DF.interest.cor.without.IQ,2), file=\"OCEANCors_no_g.csv\")\r\n\r\nDF.OCEAN.full = cbind(DF.mega3[2:12])\r\nDF.OCEAN.full.omega = omega(DF.OCEAN.full)\r\nDF.OCEAN.full.mr = fa(DF.OCEAN.full)\r\n\r\nDF.OCEAN.mean = cbind(DF.mega3[c(2,4,6,8,10)])\r\nDF.OCEAN.mean.omega = omega(DF.OCEAN.mean)\r\nDF.OCEAN.mean.mr = fa(DF.OCEAN.mean)\r\n\r\nDF.OCEAN.SD = cbind(DF.mega3[c(3,5,7,9,11)])\r\nDF.OCEAN.SD.omega = omega(DF.OCEAN.SD)\r\nDF.OCEAN.SD.mr = fa(DF.OCEAN.SD)\r\n\r\nDF.OCEAN.general.scores = cbind(DF.OCEAN.mean.mr$scores,DF.OCEAN.SD.mr$scores,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 DF.mega3[14],DF.mega3[40],DF.mega3[42],DF.mega3[64],DF.mega3[76])\r\ncolnames(DF.OCEAN.general.scores) = c(\"GFP.mean\",\"GFP.SD\",\"S.in.Norway\",\"S.in.Denmark\",\"Islam\",\"S.Int\",\"IQ\")\r\nround(cor(DF.OCEAN.general.scores,use=\"pairwise.complete.obs\"),2)\r\nDF.OCEAN.general.scores.no.IQ = partial.r(DF.OCEAN.general.scores,c(1:6),7)<\/pre>\n
Megadataset is in the OSF repository, version 1.6b.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
I merged the dataset from Schmitt et al (2007)’s paper about OCEAN traits in 56 countries with the rest of the megadataset. Then i extracted the first factor of the OCEAN means and SDs. These two are nearly uncorrelated (.07). As for factor strength, they are not too bad: > DF.OCEAN.mean.omega Omega Call: omega(m = […]<\/p>\n","protected":false},"author":17,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1839,2591,1921],"tags":[2025,2592],"class_list":["post-4396","post","type-post","status-publish","format-standard","hentry","category-psychometics","category-intelligence-iq-cognitive-ability","category-sociology","tag-gfp","tag-s-general-socioeconomic-factor","entry"],"_links":{"self":[{"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/posts\/4396","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/comments?post=4396"}],"version-history":[{"count":11,"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/posts\/4396\/revisions"}],"predecessor-version":[{"id":4408,"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/posts\/4396\/revisions\/4408"}],"wp:attachment":[{"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/media?parent=4396"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/categories?post=4396"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/emilkirkegaard.dk\/en\/wp-json\/wp\/v2\/tags?post=4396"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}