Common genetic basis of the five factor model facets and intelligence: A twin study


The main aim of this study was to explore the etiology of relations between general cognitive ability (g) and different hierarchical phenotypic levels of the Five Factor Model (FFM), including the General Factor of Personality (GFP), the Big Two, the five domains of the FFM, and their 30 facets. The second aim was to detect personality facets that contribute to the prediction of general intelligence. The sample consisted of 424 young adult twins (134 pairs of monozygotic twins) on whom the NEO-PI-R and Advanced Progressive Matrices were administered. The results did not support hierarchical solutions above the FFM. Thus, five-domain and facet level of personality were analyzed, showing that only Openness and Neuroticism had significant genetic or environmental correlations with intelligence. The several facets from all domains had significant associations, among which Ideas and Positive Emotions showed the highest positive correlations, while Order and Modesty showed the highest negative genetic correlations with intelligence. Furthermore, seven facets significantly predicted g factor (35%), with higher genetic (0.52) than environmental (0.13) correlations with intelligence. The results reveal the common genetic basis of narrow traits and intelligence, highlighting the importance of specific traits in the explanation of general cognitive abilities.
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8University of Novi Sad, Faculty of Medicine, Department of Psychology, Novi Sad, Serbia, Hajduk Veljkova 3, 21000 Novi Sad, Serbia
9University of Novi Sad, Faculty of Philosophy, Department of Psychology, Novi Sad, Serbia, Dr Zorana Đinđića 2, 21000 Novi Sad, Serbia
:Neurology Clinic, Clinical Center of Vojvodina, Novi Sad, Serbia, Hajduk Veljkova 3, 21000 Novi Sad, Serbia
;City University of New York, John Jay College of Criminal Justice, New York, NY, 524 W 59th St, New York, NY 10019, USA
<NYU School of Medicine, Department of Neurology, New York, NY, 240 E 38th St, New York, NY 10016, USA
=Institute of Forensic Medicine, Clinical Center of Vojvodina, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
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1. Introduction
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This is uncorrected proof, please cite this paper as: Nikolašević, Ž., Dinić, B. M., Smederevac, S., Sadiković, S., Milovanović, I.,…
Zgonjanin Bosić, D. (2021). Common genetic basis of the five factor model facets and intelligence: A twin study. Personality and
Individual Differences, 175, 110682.
Ž. Nikolašević et al. Personality and Individual DiBerences xxx (xxxx) xxx-xxx
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<?8M@FI8C ><E<K@: JKL;@<J ?8M< GIFM@;<; DFI< ;<K8@C<; @EJ@>?KJ @EKF
K?< @EK<IGC8P F= @EK<CC@><E:< 8E; G<IJFE8C@KP KI8@KJ $E GI<M@FLJ I<J<8I:?
><E<K@: =8:KFIJ <OGC8@E<; DFI< K?8E  F= K?< gM8I@8E:< <>
FL:?8I;  (:"L< +CFD@E <K 8C  N?@C< ?<I@K89@C@KP
F= !!( KI8@KJ I8E><; =IFD  FEJ:@<EK@FLJE<JJ KF  *G<EE<JJ
J<< 1LB8JFM@V  I8KBF  .KL;@<J =F:LJ@E> FE K?< <OGCFI8K@FE
F= :FDDFE ><E<K@: 8E; <EM@IFED<EK8C @E[L<E:<J F= @EK<CC@><E:< 8E;
G<IJFE8C@KP KI8@KJ ?8M< ;<DFEJKI8K<; J@>E@=@:8EK GFJ@K@M< ><E<K@: :FII<C8
K@FEJ 9<KN<<E M8I@FLJ D<8JLI<J F= @EK<CC@><E:< 8E; *G<EE<JJ 8E; >I<<
89C<E<JJ 8J N<CC 8J 8 E<>8K@M< :FII<C8K@FE N@K? )<LIFK@:@JD 8IK<CJ
<K 8C  'L:@8EF <K 8C  =FLE; K?8K ><E<K@: =8:KFIJ JFC<CP
D<;@8K<; K?< 8JJF:@8K@FE 9<KN<<E G<I=FID8E:<98J<; $, K<JKJ 8E; KNF
=8:<KJ F= FEJ:@<EK@FLJE<JJ  FDG<K<E:< 8E; LK@=LCE<JJ !LIK?<IDFI<
8DFE> OKI8M<IJ@FE =8:<KJ O:@K<D<EK .<<B@E> 8E; "I<>8I@FLJE<JJ ?8M<
J?FNE CFN E<>8K@M< I<C8K@FEJ N@K? 9FK? M<I98C 8E; G<I=FID8E:< @EK<CC@
><E:< <OGC8@E<; JFC<CP 9P 8;;@K@M< ><E<K@: @E[L<E:< +@E:FD9< <K 8C
 )<M<IK?<C<JJ @K @J @DGFIK8EK KF ?@>?C@>?K K?<@I G8IK@:LC8ICP JD8CC
J?8I<; M8I@8E:<  $E JKL;@<J LJ@E> KI8@KJ FLKJ@;< K?< !!( K?< I<
JLCKJ ?8M< @E;@:8K<; 8 GIFEFLE:<; ><E<K@: :FEKI@9LK@FE KF K?< I<C8K@FE
J?@G 9<KN<<E @EK<CC@><E:< 8E; G<IJFE8C@KP !8:<KJ K?8K :FLC; 9< I<C8K<;
KF *G<EE<JJ OKI8M<IJ@FE 8E; )<LIFK@:@JD ?8M< ?8; GFJ@K@M< ><E<K@:
8>>I<JJ@FE CFN >I<<89C<E<JJ ?8M< 9<<E E<>8K@M<CP I<C8K<; KF @EK<CC@
><E:< #8II@J <K 8C 
LE;<IJK8E;@E> F= K?< <K@FCF>P F= I<C8K@FEJ?@GJ 9<KN<<E ><E<I8C :F>E@
K@M< 89@C@KP > 8E; ;@==<I<EK ?@<I8I:?@:8C G?<EFKPG@: C<M<CJ F= K?< !!(
DF;<C @E:CL;@E> K?< "!+ K?< @> /NF K?< ZM< 98J@: KI8@KJ 8E; K?<@I
 =8:<KJ ::FI;@E> KF ILEJN@B JPDD<KIP GI@E:@GC< @= 8 D<8JLI< F=
@EK<CC@><E:< 8K K?< ?@>?<JK C<M<C F= K?< ?@<I8I:?P > =8:KFI @J @E:CL;<;
@K @J E<:<JJ8IP KF <O8D@E< @KJ I<C8K@FEJ N@K? ;@==<I<EK ?@<I8I:?@:8C C<M<CJ
F= G<IJFE8C@KP &I<KQJ:?D8I <K 8C  .G<:@=@:8CCP ;@==<I<EK ?@<I
8I:?@:8C G<IJFE8C@KP C<M<CJ 8I< <OG<:K<; KF ?8M< ;@==<I<EK ><E<K@:8CCP @E
=FID8K@M< I<C8K@FEJ?@GJ N@K? K?< > =8:KFI <JG@K< K?< ;@==<I<E:<J @E ?@
<I8I:?@:8C C<M<CJ K?< :FDGC<O@KP F= K?< 8JJF:@8K@FEJ 9<KN<<E G<IJFE8C@KP
8E; @EK<CC@><E:< 8E; :FEKIFM<IJ@8C GI<M@FLJ I<JLCKJ <> &I<KQJ:?D8I
<K 8C 'F<?C@E <K 8C  GIFM@;< 8 98J@J =FI 8JJLD@E> K?8K
K?< =8:<K C<M<C KF 9< DFJK @E=FID8K@M< @E K?< <OGC8E8K@FE F= K?< :FDDFE
><E<K@: 8E; <EM@IFED<EK8C @E[L<E:<J LE;<ICP@E> K?<J< I<C8K@FEJ /?<I<
=FI< K?< J<:FE; >F8C F= K?@J JKL;P N8J KF ;@J:FM<I I<C<M8EK =8:<KJ =IFD
;@==<I<EK G<IJFE8C@KP ;FD8@EJ K?8K :FLC; :FEKI@9LK< KF K?< GI<;@:K@FE F=
2. Materials and methods
2.1. Participants and procedures
+8IK@:@G8EKJ N<I<  KN@EJ =IFD K?< .<I9@8E /N@E -<>@JKIP J<<
/89C<  8E; =FI ;<K8@C<; I<:IL@KD<EK GIF:<;LI<J J<< LK?FIJ 
+8IK@:@G8K@FE N8J MFCLEK8IP 8E; <8:? G8IK@:@G8EK J@>E<; 8E @E=FID<;
Table 1
/N@E J8DGC< :?8I8:K<I@JK@:J
/N@EJ .<O ><
/FK8C (8C< !<D8C< @]<I<EKJ<O -8E>< M SD
(FEFQP>FK@:(4      
@QP>FK@:4       
Ž. Nikolašević et al. Personality and Individual DiBerences xxx (xxxx) xxx-xxx
:FEJ<EK =FID /?< JKL;P N8J 8GGIFM<; 9P K?< $EJK@KLK@FE8C -<M@<N
F8I; 4P>FJ@KP N8J ;<K<ID@E<; K?IFL>? K?< 8E8CPJ@J F= 9L::8C JN89
) J8DGC<J 8K K?< 5C@E;<;6
2.2. Measures
/?< ) * +<IJFE8C@KP $EM<EKFIP-<M@J<; ) *+$- FJK8  (:
I8<  =FI .<I9@8E 8;8GK8K@FE J<< &E<_<M@V <K 8C  @J 8
@K<D @EM<EKFIP D<8JLI@E> !!( KI8@KJ N@K? <8:? KI8@K :FEK8@E@E> 
=8:<KJ  @K<DJ G<I =8:<K <J:I@GK@M<J 8E; 8CG?8 I<C@89@C@K@<J 8I< >@M<E @E
/89C<  @E .LGGC<D<EK CC K?< M8I@89C<J N<I< EFID8CCP ;@JKI@9LK<; I<
>8I;@E> JB<NE<JJ 8E; BLIKFJ@J N@K?@E K?< I8E>< F= U J<< /898:?
E@:B  !@;<CC 
/?< ;M8E:<; +IF>I<JJ@M< (8KI@:<J  .<K $$ +(  .<K $$ -8M<E <K
8C  D<8JLI<J EFEM<I98C [L@; @EK<CC@><E:< FI DFI< GI<:@J<CP K?<
I<8JFE@E> 89@C@KP N?@:? @J 8 :FDGFE<EK F= .G<8ID8EJ g=8:KFI F= ><E
<I8C @EK<CC@><E:< /?< +(  .<K $$ :FEJ@JKJ F=  K8JBJ N@K? K?< DLCK@
GC<:?F@:< =FID8K @E N?@:? G8IK@:@G8EKJ 8I< 8JB<; KF :?FFJ< 8DFE> 
GFJJ@9C< G8KK<IEJ 8E; @;<EK@=P K?< D@JJ@E> <C<D<EK K?8K :FDGC<K<J K?<
D8KI@O /?< +( N8J LJ<; N@K? 8 K@D< C@D@K F=  D@E CG?8 @E K?@J
JKL;P N8J 
2.3. Data analysis
!@IJK KF 8JJ<JJ K?< 8;<HL8:P F= ?@<I8I:?@:8C G?<EFKPG@: JFCLK@FEJ
F= K?< !!( KI8@KJ N< :FE;L:K<; 8 :FEZID8KFIP =8:KFI 8E8CPJ@J !
FE =8:<KJ N@K? K?< D8O@DLD C@B<C@?FF; <JK@D8KFI /NF DF;<CJ N<I<
K<JK<;  K?< @> /NF DF;<C N@K? KNF J<:FE;FI;<I =8:KFIJ  .K89@C
@KP )  8E;  8E; +C8JK@:@KP  8E; * J<< <3FLE>  
K?< "!+ N@K? FE< J<:FE;FI;<I =8:KFI (LJ<B  .<:FE; ;<J:I@G
K@M< 8E8CPJ@J 8E; G?<EFKPG@: :FII<C8K@FEJ N<I< :FDGLK<; J<< /89C< 
8E; ;<J:I@GK@FE F= K?< GIF:<;LI< @E .LGGC<D<EK /?@I; LE@M8I@8K< J<<
/89C<  @E .LGGC<D<EK 8E; DLCK@M8I@8K< 9@FD<KI@: 8E8CPJ@J N<I< :FE
;L:K<; 9P LJ@E> :LJKFD@Q<; - J:I@GKJ XFCFM@V  @E FI;<I KF <O
GCFI< G?<EFKPG@: 8JJF:@8K@FEJ 9<KN<<E !!( KI8@KJ 8E; =8:<KJ 8E; K?<
+( J:FI< .@E:< 8>< 8E; J<O <==<:KJ :8E 9@8J K?< KN@E :FII<C8K@FE
K?<@I <==<:KJ N<I< G8IK@8C@Q<; =IFD K?< J:FI<J (:"L<  FL:?8I;
 /?< G8KK<IEJ F= ><E<K@: 8E; <EM@IFED<EK8C :FII<C8K@FEJ N<I<
:FDG8I<; LJ@E> K?< ?FC<JBP ;<:FDGFJ@K@FE 'F<?C@E  !FLIK?
=FI K?< GI<;@:K@FE F= +( J:FI< 98J<; FE !!( =8:<KJ JK<GN@J< DLC
K@GC< I<>I<JJ@FE N8J G<I=FID<; 2< LJ<; :IFJJM8C@;8K@FE GI@E:@GC<J KF
8MF@; FM<IZKK@E> 9P ;@M@;@E> K?< J8DGC< @EKF ZM< G8IKJ N@K? KN@EJ
=IFD K?< J8D< =8D@CP @E J8D< JL9J8DGC< =FI ;<J:I@GK@FE F= K?< GIF:<
;LI< J<< .LGGC<D<EK 8K8 @J 8M8@C89C< 8K ?KKGJFJ=@FMKM@<N7
3. Results
#@<I8I:?@:8C G?<EFKPG@: JFCLK@FEJ =FI K?< "!+ 8E; K?< @> /NF DF;
<CJ J?FN<; GFFI DF;<C ZK @E K?< ! J<< /89C<  @E .LGGC<D<EK K?LJ
FECP K?< 98J@: !!( KI8@KJ 8E; =8:<KJ N<I< 8E8CPQ<; FII<C8K@FEJ 9<
KN<<E !!( KI8@KJ =8:<KJ 8E; G<I=FID8E:< FE K?< +( 8DFE> (4 8E;
4 KN@EJ 8J N<CC 8J I<JLCKJ F= 0E@M8I@8K< ><E<K@: 8E8CPJ<J 8I< GI<J<EK<;
@E .LGGC<D<EK J<< /89C<  8E; 
?FC<JBP 9<?8M@FI ><E<K@: 8E8CPJ<J N<I< G<I=FID<; @E FI;<I KF <O
GCFI< 8;;@K@M< ><E<K@: 8E; EFE J?8I<; <EM@IFED<EK8C :FII<C8K@FEJ F=
!!( KI8@KJ 8E; =8:<KJ 8E; K?< +( J:FI< *E K?< KI8@K C<M<C )<LIFK@
:@JD 8E; >I<<89C<E<JJ J?FN<; J@>E@=@:8EK 9LK E<>C@>@9C< J@Q< G?<EF
KPG@: :FII<C8K@FEJ N@K? K?< +( J:FI< /89C<  *G<EE<JJ J?FN<; 8
GFJ@K@M< ><E<K@: :FII<C8K@FE N@K? K?< +( J:FI< $;<8J =8:<K =IFD K?<
*G<EE<JJ ;FD8@E J?FN<; J@>E@=@:8EK GFJ@K@M< G?<EFKPG@: 8E; ><E<K@:
:FII<C8K@FEJ N@K? K?< +( J:FI< N?@C< !8EK8JP 8E; 18CL<J =8:<K J?FN<;
J@>E@=@:8EK ><E<K@: :FII<C8K@FEJ >I<<89C<E<JJ ;@; EFK 8:?@<M< J@>E@=@
:8EK ><E<K@: FI <EM@IFED<EK8C :FII<C8K@FEJ N@K? K?< +( J:FI< #FN
Table 2
+?<EFKPG@: ><E<K@: 8E; <EM@IFED<EK8C :FII<C8K@FEJ 9<KN<<E !!( KI8@KJ 8E; +( J:FI<
:FII<C8K@FE rG
:FII<C8K@FE rE
)<LIFK@:@JD 0.04*
(0.07; 0.01)
 
 
EO@<KP 
 
 
 
#FJK@C@KP 
  0.22**
 
<GI<JJ@FE 
 
 
 
.<C=:FEJ:@FLJE<JJ 
 
 
$DGLCJ@M<E<JJ 
 
  
 
1LCE<I89@C@KP 0.18***
(0.29; 0.07)
 
 
OKI8M<IJ@FE 
 
  
 
28IDK? 
 
  
 
"I<>8I@FLJE<JJ 
 
 
JJ<IK@M<E<JJ 
 
  
 
:K@M@KP 
  
 
O:@K<D<EKJ<<B@E> 
 
  
 
+FJ@K@M<<DFK@FEJ 
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$;<8J 0.26***
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(F;<JKP 
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*I;<I 
  0.28**
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(LCK@GC<:FII<C8K@FEJ 0.35***
(0.27; 0.43)
(0.41; 0.61)
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pM8CL< J<K FE  K?< :FII<:K<; :I@K@:8C M8CL< N8J p 
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J@>E@=@:8EK E<>8K@M< <EM@IFED<EK8C :FII<C8K@FE N@K? K?< +( J:FI< )<L
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K?< +( J:FI< *E K?< =8:<K C<M<C =IFD )<LIFK@:@JD #FJK@C@KP 8E; 1LC
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:8EK :FII<C8K@FEJ FE K?< =8:<K C<M<C DFE> OKI8M<IJ@FE =8:<KJ O:@K<
D<EK .<<B@E> 8E; +FJ@K@M< DFK@FEJ J?FN<; GFJ@K@M< ><E<K@: :FII<C8
K@FEJ N?@C< 28IDK? J?FN<; 8 E<>8K@M< <EM@IFED<EK8C :FII<C8K@FE N@K?
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GFJ@K@M< :FII<C8K@FE 8E; *I;<I J?FN<; 8 E<>8K@M< ><E<K@: :FII<C8K@FE
J<M<E =8:<KJ N<I< =FLE; KF 9< GI<;@:K@M< F= G<I=FID8E:< FE K?< +(
$;<8J β  p  *I;<I β p  /<E;<I (@E;
<;E<JJ β p  :K@FEJ β p  +FJ@K@M<
DFK@FEJ β  p  #FJK@C@KP β p  8E;
(F;<JKP β p  /?< J<K F= GI<;@:KFIJ <OGC8@E<;  F=
K?< +( J:FI< F   p  +I<;@:K<; 8>>I<>8K< J:FI<
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9FKKFD IFN F= /89C< 
4. Discussion
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8E; K?< "!+ <O?@9@K<; GFFI ZK =8@C@E> KF GIFM@;< :FEM@E:@E> <DG@I@
:8C 8I>LD<EKJ =FI K?<@I I<C<M8E:< /?<J< I<JLCKJ :FEZID GI<M@FLJ G?<
EFKPG@: <> <3FLE> <K 8C  8E; 9<?8M@FI8C ><E<K@: 'F<?C@E
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!8:KFI (F;<C :FEK8@EJ 8 JL=Z:@<EK ;<>I<< F= ><E<I8C@KP KF D8B< I<C<M8EK
8JJF:@8K@FEJ N@K? K?< g=8:KFI 8E; K?LJ D<<K K?< :I@K<I@8 F= K?< ILEJN@B
JPDD<KIP GI@E:@GC< :B<ID8E  /?<I<=FI< G?<EFKPG@: ><E<K@:
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GFN<I F= =8:<KJ FE K?< +( G<I=FID8E:< N8J <OGCFI<;
M8IP ;<G<E;@E> FE N?<K?<I K?< KI8@K C<M<C FI K?< =8:<K C<M<C @J :FEJ@;
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=8:<K C<M<C J<M<I8C =8:<KJ =IFD 8CC ZM< G<IJFE8C@KP KI8@KJ J?FN J@>E@=@:8EK
$E 8::FI;8E:< N@K? <OG<:K8K@FEJ 8E; GI<M@FLJ I<JLCKJ <> 8IK<CJ
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Uncited references
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CRediT authorship contribution statement
Ž. Nikolašević et al. Personality and Individual DiBerences xxx (xxxx) xxx-xxx
Željka Nikolašević: FE:<GKL8C@Q8K@FE (<K?F;FCF>P $EM<JK@>8
K@FE 2I@K@E> FI@>@E8C ;I8=K Bojana M. Dinić: $EM<JK@>8K@FE (<K?F;
FCF>P 2I@K@E> FI@>@E8C ;I8=K Snežana Smederevac: .LG<IM@
J@FE 2I@K@E> I<M@<N  <;@K@E> !LE;@E> 8:HL@J@K@FE Selka Sadiković:
$EM<JK@>8K@FE 2I@K@E> FI@>@E8C ;I8=K !FID8C 8E8CPJ@J 8K8 :LI8
K@FE Ilija Milovanović: $EM<JK@>8K@FE !FID8C 8E8CPJ@J 2I@K@E> I<
M@<N <;@K@E> Vojislava Bugarski Ignjatović: .LG<IM@J@FE 2I@K@E>
I<M@<N <;@K@E> Mechthild Prinz: -<JFLI:<J $EM<JK@>8K@FE .LG<I
M@J@FE 2I@K@E> I<M@<N <;@K@E> Zoran Budimlija: -<JFLI:<J .L
G<IM@J@FE 2I@K@E> I<M@<N <;@K@E>Dragana Zgonjanin Bosić: -<
JFLI:<J .LG<IM@J@FE 2I@K@E> I<M@<N  <;@K@E>
/?@J JKL;P N8J JLGGFIK<; 9P K?< (@E@JKIP F= ;L:8K@FE .:@<E:<
8E; /<:?EFCF>@:8C <M<CFGD<EK F= K?< -<GL9C@: F= .<I9@8 5"I8EK
Appendix A. Supplementary data
.LGGC<D<EK8IP ;8K8 KF K?@J 8IK@:C< :8E 9< =FLE; FEC@E< 8K ?KKGJ;F@
9<C<    2FA:@JQB<   ><E:P 8E; :FDDLE@FE =IFD K?< G<IJG<:K@M< F=
J<C= M<IJLJ FK?<IJ %FLIE8C F= +<IJFE8C@KP 8E; .F:@8C +JP:?FCF>P  
:B<ID8E +  /?< J<8I:? =FI G<IJFE8C@KP@EK<CC@><E:< I<C8K@FEJ (<K?F;FCF>@:8C
8E; :FE:<GKL8C @JJL<J %FLIE8C F= $EK<CC@><E:<   ;F@
:B<ID8E +'   K?<FIP F= 8;LCK @EK<CC<:KL8C ;<M<CFGD<EK +IF:<JJ G<IJFE8C@KP
@EK<I<JKJ 8E; BEFNC<;>< $EK<CC@><E:<   ;F@
:B<ID8E +' #<>><JK8;   $EK<CC@><E:< G<IJFE8C@KP 8E; @EK<I<JKJ
M@;<E:< =FI FM<IC8GG@E> KI8@KJ +JP:?FCF>@:8C LCC<K@E  
LJK@E % #F=<I .( <8IP $%  9<I #2  $EK<I8:K@FEJ 9<KN<<E
$E;@M@;L8C @]<I<E:<J   ;F@J
LK?FIJ  /N@E I<J<8I:? 8E; ?LD8E ><E<K@:J.
8IK<CJ ( M8E 2<<><E !$ M8E <@AJK<IM<C;K  ( 8IC@<I ( +FC;<ID8E /%
#F<BJKI8 -  FFDJD8 $  /?< ZM< =8:KFI DF;<C F= G<IJFE8C@KP 8E;
8E; $E;@M@;L8C @]<I<E:<J   ;F@AG8@;
8K<J /  -F:B   +<IJFE8C@KP 8E; @E=FID8K@FE GIF:<JJ@E> JG<<; $E;<G<E;<EK
@E[L<E:<J FE @EK<CC@><EK G<I=FID8E:< $EK<CC@><E:<   ;F@
<CC  8N<J / 2<@EJ:?<EB  -@<D8EE -  &8E;C<I   +8KK<IEJ 8E;
JFLI:<J F= K?< 8JJF:@8K@FE 9<KN<<E @EK<CC@><E:< G8IKP @;<EK@Z:8K@FE 8E; GFC@K@:8C
FI@<EK8K@FEJ $EK<CC@><E:<  ;F@A@EK<CC 56
FL:?8I; /%  (:"L< (  "<E<K@: 8E; <EM@IFED<EK8C @E[L<E:<J FE ?LD8E
GJP:?FCF>@:8C ;@]<I<E:<J %FLIE8C F= )<LIF9@FCF>P   ;F@
8IIFC %  #LD8E :F>E@K@M< 89@C@K@<J  JLIM<P F= =8:KFI 8E8CPJ@J JKL;@<J
LJKI8C@8 8D9I@;>< 0E@M<IJ@KP +I<JJ
?8DFIIF+I<DLQ@: /  !LIE?8D    GFJJ@9C< DF;<C =FI LE;<IJK8E;@E>
K?< G<IJFE8C@KP@EK<CC@><E:< @EK<I=8:< I@K@J? %FLIE8C F= +JP:?FCF>P  
?8DFIIF+I<DLQ@: /  !LIE?8D   $EK<CC<:KL8C :FDG<K<E:< 8E; K?< @EK<CC@><EK
G<IJFE8C@KP  K?@I; N8P @E ;@]<I<EK@8C GJP:?FCF>P -<M@<N F= "<E<I8C +JP:?FCF>P
  ;F@
XFCFM@V +  +<K8I:FCFM@:-7H" <K8.4<EF;F  4<EF;F ;F@?KKGJ
FJK8 +/  (:I8< --  )FID8C G<IJFE8C@KP 8JJ<JJD<EK @E :C@E@:8C GI8:K@:<
/?< ) * G<IJFE8C@KP @EM<EKFIP +JP:?FCF>@:8C JJ<JJD<EK   ;F@
<3FLE> "  #@>?<IFI;<I =8:KFIJ F= K?< 9@> ZM< @E 8 DLCK@@E=FID8EK J8DGC<
%FLIE8C F= +<IJFE8C@KP 8E; .F:@8C +JP:?FCF>P   ;F@
<3FLE> "  $EK<CC@><E:< 8E; G<IJFE8C@KP $E .K<IE9<I> -%  &8L=D8E .
 ;J 8D9I@;>< ?8E;9FFBJ @E GJP:?FCF>P /?< 8D9I@;>< ?8E;9FFB F= @EK<CC@><E:<
GG  8D9I@;>< 0E@M<IJ@KP +I<JJ ;F@*
<3FLE> " +<K<IJFE %  #@>>@EJ (  .FLI:<J F= FG<EE<JJ@EK<CC<:K
F>E@K@M< 8E; E<LIFGJP:?FCF>@:8C :FII<C8K<J F= K?< Z=K? =8:KFI F= G<IJFE8C@KP %FLIE8C
F= +<IJFE8C@KP   ;F@AO
<3FLE> " +<K<IJFE % .R>L@E %-  /I<D9C8P -   OK<IE8C@Q@E>
9<?8M@FI 8E; K?< ?@>?<I FI;<I =8:KFIJ F= K?< 9@> ZM< %FLIE8C F= 9EFID8C +JP:?FCF>P
  ;F@8
<3FLE> " .?8DFJ? ) "I<<E   I8M<I /. "I8P %-  $EK<CC<:K 8J
;@JK@E:K =IFD FG<EE<JJ @]<I<E:<J I<M<8C<; 9P =(-$ F= NFIB@E> D<DFIP %FLIE8C F=
+<IJFE8C@KP 8E; .F:@8C +JP:?FCF>P   ;F@8
@>D8E %(  #@>?<IFI;<I =8:KFIJ F= K?< 9@> ZM< %FLIE8C F= +<IJFE8C@KP 8E; .F:@8C
+JP:?FCF>P   ;F@
L>8J (% !I<<JKFE (#  '8;FL:<LI -  F>E@K@M< /?<I8GP 8E; -<J<8I:?
  ;F@8
!FLEKFLC8B@J &) .@8DFLC@ ( (FPJ@;FL . +8EKFLC8  (FLKFL & +8E8>@FK@;@J
+ (:I8< -  .K8E;8I;@Q8K@FE F= K?< ) *+$ @E K?< "I<<B ><E<I8C
GFGLC8K@FE EE8CJ F= "<E<I8C +JP:?@8KIP   ;F@J
!LIE?8D   /?FD8J   +8I<EKJ><E;<I 8E; G<IJFE8C@KP 8E; <JK@D8K<J F= K?<@I
FNE 8E; K?<@I :?@C;I<EJ @EK<CC@><E:< +<IJFE8C@KP 8E; $E;@M@;L8C @]<I<E:<J 
 ;F@AG8@;
!LIE?8D  4?8E> %  ?8DFIIF+I<DLQ@: /  /?< I<C8K@FEJ?@G 9<KN<<E
GJP:?FD<KI@: 8E; J<C=<JK@D8K<; @EK<CC@><E:< :I<8K@M@KP G<IJFE8C@KP 8E; 8:8;<D@:
8:?@<M<D<EK $D8>@E8K@FE F>E@K@FE 8E; +<IJFE8C@KP   ;F@
"SKQ ! ( "FJC@E> .   -<EK=IFN %  %8EL8IP  .D8CC <==<:KJ /?< @E;@JG<EJ
89C< =FLE;8K@FE =FI 8 :LDLC8K@M< GJP:?FCF>@:8C J:@<E:< ;F@FJ=@F?QIO=
#8II@J % 1<IEFE +  %8E> &'   DLCK@M8I@8K< ><E<K@: 8E8CPJ@J F=
  ;F@
%<EJ<E -  /?< > =8:KFI 'FE;FE +I8<><I
&E<_<M@V " _8DFEA8$>EA8KFM@V /  YLI@V%FW@V   Petofaktorski model ličnosti
5ZM< =8:KFI DF;<C F= G<IJFE8C@KP6 <F>I8; <EK8I Q8 GI@D<EA<EL GJ@?FCF>@AL
&I<KQJ:?D8I  .G<E>C<I ( .:?L9<IK ' .K<@ED8PI -  4@<>C<I (  /?<
K<CC LJ %FLIE8C F= $EK<CC@><E:<   ;F@A@EK<CC@><E:<
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... Furthermore, studies have revealed gender-related differences in trait ASC, with boys and young men typically reporting higher general-school ASC than girls and young women regardless of their actual academic achievement (e.g., Parker et al., 2018). With respect to the Big Five, it has been found that conscientiousness and open-mindedness in particular are related to secondary school students' grades (Poropat, 2009), and twin studies have revealed significant genetic or environmental associations between reasoning ability and negative emotionality, open-mindedness, as well as a range of subfacets from all Big Five domains (Bartels et al., 2012;Nikolaševi c et al., 2021). PERSONALITY AND STATE ACADEMIC SELF-CONCEPT 5 This document is copyrighted by the American Psychological Association or one of its allied publishers. ...
... Gender differences were found only for aggregated state general-school ASC, with young men indicating higher general-school ASC than young women. In a similar vein, the correlations between the Big Five and the covariates were mostly consistent with previous research (Bartels et al., 2012;Nikolaševi c et al., 2021;Poropat, 2009;Soto & John, 2017; see Table S1 in the online supplemental materials). Regarding the Big Five, high open-mindedness, conscientiousness, and agreeableness but low negative emotionality significantly related to higher GPA. ...
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A positive academic self-concept (ASC) relates to many desirable educational outcomes. Research on which student characteristics relate to the formation of ASC is therefore crucial. To examine the importance of personality for ASC, we investigated the relation between Big Five traits and mean level as well as within-person variability in state general-school ASC for the first time using intensive longitudinal data. The sample comprised N = 291 German ninth and 10th graders who completed a 3-week e-diary after filling in a 60-item Big Five questionnaire assessing extraversion, agreeableness, conscientiousness, negative emotionality, and open-mindedness as well as their respective subfacets (15 subfacets overall). To assess state ASC, students completed three items after each lesson in four different subjects (resulting in Mlessons = 21.32). We ran six mixed-effects location scale models: one with all broad Big Five domains and five (one for each Big Five domain) with the subfacets as predictors of state ASC. Higher scores in the domains and in at least one subfacet of open-mindedness, conscientiousness, and extraversion but lower scores in negative emotionality were related to higher mean levels of state ASC. Higher scores in depression (subfacet of negative emotionality) were related to greater within-person variability in state ASC. These findings suggest that Big Five traits are predictors of mean level and within-person variability in students’ state ASC, thus contributing to a more complete map of the formation of students’ ASC and the role of personality therein. Theoretical and practical implications of these findings are discussed.
... For the majority of individuals, gender identity develops in early childhood . Need for cognition has been found to be related to fluid intelligence (Hill et al., 2013) for which genetics accounts for approximately 52% of individual variation (Nikolašević et al., 2021). Research has suggested that the approximately 50% of variation in Big Five personality traits are determined by genetics (Bouchard & Loehlin, 2001). ...
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The use of strong passwords is viewed as a recommended cybersecurity practice, as the hacking of weak passwords led to major cybersecurity breaches. The present research investigated whether nudging with messages based on participants’ self-schemas could lead them to create stronger passwords. We modeled our study on prior health-related research demonstrating positive results using messages based on self-schema categories (i.e., True Colors categories -compassionate, loyal, intellectual, and adventurous). We carried out an online study, one with 256 (185 women, 66 men, 5 other) undergraduates and one with 424 (240 men, 179 women, 5 other) Amazon Mechanical Turk (MTurk) workers, in which we randomly assigned participants to receive messages that matched or mismatched their self-schema. We also investigated whether differences across the Big Five personality traits, secure password knowledge, attitudes and behavior, need for cognition, and general risk-taking predicted the strength of passwords that participants created during the study. Multiple individual difference variables predicted password strength (i.e., conscientiousness, emotional stability, need for cognition, self-reported secure password knowledge, attitude, and behavior, and general risk-taking). MTurk workers had higher levels of cybersecurity knowledge and created stronger passwords than college students. The nudging messages did not lead to stronger passwords. Implications for strategies to increase the use of secure passwords are discussed.
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Πολλοί ερευνητές έχουν στραφεί στη μελέτη του εγκεφάλου ως τη βάση της θεώρησης της νοημοσύνης και της μέτρησής της. Πολλές πτυχές της ανατομίας του εγκεφάλου και της Φυσιολογίας έχουν προταθεί ως ενδεχομένως σχετικές με τη νοημοσύνη. Οι διακλαδώσεις των νευρώνων στον εγκεφαλικό φλοιό, ο μεταβολισμός της εγκεφαλικής γλυκόζης, η ταχύτητα μεταφοράς νευρικών σημάτων αποτελούν κάποιους από τους παράγοντες της Φυσιολογίας που έχει βρεθεί ότι σχετίζονται με τη νοημοσύνη. Σχετικά με τη γενετική προέλευση της νοημοσύνης, αποτελέσματα ερευνών δείχνουν κυρίως ότι η κληρονομικότητα ασκεί μία ισχυρότερη επίδραση σε σχέση με το περιβάλλον στις νοητικές διαφορές. Επικεντρώνονται σε ποικίλες περιπτώσεις διδύμων, όπως σε μονοζυγωτικούς διδύμους και διζυγωτικούς διδύμους, σε βιολογικά και ετεροθαλή αδέλφια, κάθε ζεύγος από τα οποία ανατράφηκε μαζί ή χωριστά από τη βρεφική ηλικία ή αργότερα στη ζωή. Τέλος, τα τελευταία χρόνια, ορισμένες έρευνες διερευνούν την έκθεση στο αλκοόλ κατά την εγκυμοσύνη ως παράγοντα για την κατοπινή μείωση της νοημοσύνης των παιδιών, ενώ άλλες απενεχοποιούν τη μέθοδο της εξωσωματικής γονιμοποίησης και συγκεκριμένα της ICSI, αφού τα αποτελέσματα σχεδόν απέκλεισαν οποιαδήποτε καθυστέρηση στη νοητική ανάπτυξη του παιδιού.
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Review of the research on General Factor of Personality.
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Personality and intelligence are defined as hierarchical constructs, ranging from broad g-factors to (domain-)specific constructs. The present study investigated whether different combinations of hierarchical levels lead to different personality-intelligence correlations. Based on the integrative data analysis approach, we combined a total of five data sets. The focus of the first study (N = 682) was an elaborated measurement of personality (NEO-PI-R), which was applied with a relatively short intelligence test (Intelligence Structure Test 2000 R). In the second study (N = 413), a comprehensive measurement of intelligence (Berlin Intelligence Structure test) was used with a shorter personality questionnaire (NEO-FFI). In line with the Brunswik symmetry principle, the findings emphasize that personality-intelligence correlations varied greatly across the hierarchical levels of constructs considered in the analysis. On average, Openness showed the largest relation with intelligence. We recommend for future studies to investigate personality-intelligence relations at more fine-grained levels based on elaborated measurements of both personality and intelligence.
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The correlations between the measures of cognitive abilities and personality traits are known to be low. Our data based on the popular Big Five model of intelligence show that the highest correlations (up to r = 0.30) tend to occur with the Openness to Experience. Some recent developments in the studies of intelligence (e.g., emotional intelligence, complex problem solving and economic games) indicate that this link may become stronger in future. Furthermore, our studies of the processes in the “no-man’s-land” between intelligence and personality suggest that the non-cognitive constructs are correlated with both. These include the measures of social conservatism and self-beliefs. Importantly, the Big Five measures do not tap into either the dark traits associated with social conservatism or self-beliefs that are known to be good predictors of academic achievement. This paper argues that the personality domain should be broadened to include new constructs that have not been captured by the lexical approach employed in the development of the Big Five model. Furthermore, since the measures of confidence have the highest correlation with cognitive performance, we suggest that the trait of confidence may be a driver that leads to the separation of fluid and crystallized intelligence during development.
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Prior to empirical investigation of trait level measures, it had been suggested that, on balance, well-adjusted individuals tended to have a higher level of intelligence than poorly adjusted individuals. The underlying inference was that there should be positive correlations found between personality traits associated with "adjustment" and intelligence, at least at the level of general mental abilities. Over the last several decades, empirical research has suggested that, while there are sources of common variance among personality and intellectual ability measures, the relations are more scattered and provide few general findings (other than broad assessments of neuroticism and so-called engagement traits and intellectual abilities). The status of the empirical research foundation is briefly reviewed. Conceptual and methodological issues, such as non-linear relations, typical and maximal behaviors, contextualized assessment, and missing linkages are discussed in an effort to explore personality and intelligence traits in a manner that might better reveal underlying relations between these domains.
We draw on genetics research to argue that complex psychological phenomena are most likely determined by a multitude of causes, with any individual cause likely to have only a small effect. Building upon this, we highlight the dangers of a publication culture that continues to demand large effects: First, it rewards inflated effects that are unlikely to be real and encourages practices likely to yield such effects. Second, it overlooks the small effects that are most likely to be real, hindering attempts to identify and understand the actual determinants of complex psychological phenomena. We then explain the theoretical and practical relevance of small effects, which can have substantial consequences, especially when considered at scale and over time. Finally, we suggest ways in which scholars can harness these insights to advance research and practices in psychology (i.e., leveraging the power of big data, machine learning and crowdsourcing science; promoting rigorous pre-registration, including pre-specifying the smallest effect size of interest (SEOI); contextualizing effects; changing cultural norms to reward accurate and meaningful, rather than exaggerated and unreliable effects). It is only once we accept small effects as the norm, rather than exception, that we can build a reliable and reproducible cumulative psychological science.
We examined the association between intelligence, party identification, and political orientations using genetically informative data gathered from German twins and their families (n = 9553 individuals including 1524 adolescent and young-adult twin pairs). The results indicated that supporters of the Pirate Party and the Green Party had levels of intelligence that were significantly higher than those of other party identifiers, with the exception of individuals who preferred the Free Democrats. These findings were remarkably similar to results based on British research participants that were reported in a previous study. We also observed a significant, albeit modest, negative correlation between intelligence and political conservatism, and our data indicated that this correlation could be accounted for by common genetic influences on those two variables. The findings of the study were interpreted in light of competing theories on the nature of the relationship between intelligence, party identification, and political orientations.
This study examines cultural differences in genetic and environmental influences on Five-Factor Model (FFM) across Croatian, German and Serbian cultures. Participants were 1021 monozygotic and 722 dizygotic twin pairs and NEO Five-Factor Inventory– NEO-FFI is used to assess FFM personality traits. Results show a similar pattern of genetic and environmental contribution to the variance of all FFM dimensions, indicating that culture has no significant effect on the genetic and environmental variance of personality traits. The best fitted common factor - common AE pathway models show that FFM dimensions are accounted for by the common latent factor. Although FFM dimensions clearly share some common sources of variance, the effects of specific genetic and environmental factors are more pronounced than common ones. Different patterns of genetic and environmental correlations across three samples may reflect the way that the synergy of personality traits responds to the specificities of a particular culture, as well as possible subtle differences in item translation, testing conditions, and measurement error.