Values in Europe: A Multiple Group
Comparison with 20 Countries Using
the European Social Survey 2003.
S.Schwartz/E.Davidov/P.Schmidt
How we proceed:
• 1) Short overview of the Value Theory of
Schwartz.
• 2) Goals of the analysis.
• 3) Overview of the ESS items.
• 4) Overview of the data in hand.
• 5) Results.
• 6) Conclusions.
Schwartz Value Theory
• The theory describes universals in the content and
structure of individual’s values.
• The human values are desirable goals, varying in
importance, that serve as guiding principles in people’s
lives.
• The values represent different three universal requirements
of human existence: biological needs, social interaction
and demand of group functioning.
• The theory derived 10 motivationally distinct types of
values postulated to be recognized implicitly in all
cultures.
The values:
•
•
•
•
•
Achievement (LE)
Hedonism (HE)
Power (MA)
Stimulation (ST)
Security (SI)
Self-Direction (SE)
Conformity (KO)
Universalism (UN)
Tradition (TR)
Benevolence (WO)
References:
• 1) Shalom H. Schwartz (1992). Universals in the
Content and Structure of Values: Theoretical
Advances and Empirical Tests in 20 Countries.
Advances in Experimental Social Psychology, 25,
1-65.
• 2) Shalom H. Schwartz and Lilach Sagiv (1995).
Identifying Culture-Specifics in the Content and
Structure of Values. Journal of Cross-Cultural
Psychology, 26(1), 92-116.
Relations among Values
• 1) The theory specifies dynamic relations among values.
Actions in pursuit of one type of value may conflict or be
compatible with the pursuit of other values. Seeking
personal success may obstruct actions aimed to promote
the welfare of others who need help.
• 2) Competing values correlate negatively and are in
opposing directions. Compatible values correlate
positively and are close.
• Although the theory discriminates 10 values, at a more
basic level they form a continuum which gives rise to a
circular structure.
Openness to Change
Self-transcendence
Self-direction
Universalism
Stimulation
Benevolence
Hedonism
Conformity
Tradition
Achievement
Power
Self-enhancement
Security
Conservation
Figure 1: Structural relations among the 10 values and the four higher values (see Devos, Spini, & Schwartz, 2002).
• In empirical studies values from adjacent types
may intermix rather than emerge in clearly distinct
regions.
• Values that express opposing motivations should
be discriminated clearly from one another.
• There are two dimensions: openness to change
versus conservation: favoring change vs.
submission. And: self enhancement versus selftranscendence: opposing acceptance of others as
equals vs. concern for others’ welfare.
Questions:
• 1) How do we assess the presence of the types of values
postulated by the theory?
• 2) How do we assess the similarity of the meanings of
single values and their meanings in other samples?
• 3)How do we assess the presence of the value structure?
Deviations in value types, meanings, and structure in
different samples invite interpretations. However, not all
deviations are meaningful.
• 4) How do we distinguish real cultural differences in
value meanings and structure from unreliability of
measurement?
• In the following study it is a first attempt to compare 20
countries.
Value Categories in the ESS 2003
• Now I will briefly describe some people. Please listen
to each description and tell me how much each person
is or is not like you.
• 1 Very much like me
• 2 Like me
• 3 Somewhat like me
• 4 A little like me
• 5 Not like me
• 6 Not like me at all
• 7 Refusal
• 8 Don't know
• 9 No answer
The 21 ESS Items for Each Value
• 1)
Power (MA):
• Imprich:Important to be rich, have money and
expensive things.
• Iprspot: Important to get respect from others
• 2) Achievement (LE):
• Ipshabt: Important to show abilities and be
admired.
• Ipsuces: Important to be successful and that
people recognize achievements
• 3)
Hedonism (HE):
• Ipgdtim: Important to have a good time
• Impfun: Important to seek fun and things that give
pleasure
• 4)
Stimulation (ST):
• Impdiff: Important to try new and different things
in life
• Ipadvnt: Important to seek adventures and have an
exiting life
•
•
•
•
•
5)
Self-Direcrtion (SE):
Ipcrtiv: Important to think new ideas and being creative
Impfree: Important to make own decisions and be free
6)
Universalism (UN):
Ipeqopt: Important that people are treated equally and
have equal opportunities
• Ipudrst: Important to understand different people
• Impenv: Important to care for nature and environment
• 7)
Benevolence (WO):
• Iphlppl: Important to help people and care for
others well-being
• Iplylfr: Important to be loyal to friends and devote
to close people.
• 8) Tradition (TR):
• Ipmodst: Important to be humble and modest, not
draw attention
• Imptrad: Important to follow traditions and
customs
• 9)
Conformity (KO):
• Ipfrule: Important to do what is told and follow
rules
• Ipbhprp: Important to behave properly
• 10) Security (SI):
• Impsafe: Important to live in secure and safe
surroundings
• Ipstrgv: Important that government is strong and
ensures safety
20 Countries (2 Missing)
• 20 countries: 1-AT (Austria), 2-BE (Belgium), 3CH (Switzerland), 4-CZ (Czech Republic), 5-DE
(Germany), 6-DK (Denmark), 7-ES (Spain), 8-FI
(Finland), 9-FR (France), 10-GB (Great Britain),
11-GR (Greece), 12-HU (Hungary), 13-IE
(Ireland), 14-IL (Israel), 15-IT(Italy, missing), 16LU (Luxemburg, missing), 17-NL (Netherlands),
18-NO (Norway), 19-PL (Poland), 20-PT
(Portugal), 21-SE (Sweden), 22-SI (Slovenia).
Table- N, Means and Std Deviation of
21 Items in 20 Countries
• Sweden has 6 of the weakest categories.
• Greece has 10 of the strongest categories.
• One can test for patterns of similarities
between Nordic countries (Norway,
Sweden), Mediterranean countries (Israel,
Greece, Spain), countries with a short
history of democracy (Spain, Germany,
Czech Republic, Hungary etc.).
CH-Switzerland
CH
imprich
imprich
iprspot
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ipshabt
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ipsuces
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AT-Austria
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GB-Great Britain
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ipsuces
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ipgdtim
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impfun
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impdiff
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ipadvnt
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ipcrtiv
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impfree
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ipeqopt - .
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iphlppl
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.
.
.
.
.
.
.
.
.
IL-Israel
IL
imprich
imprich
iprspot
.
ipshabt
.
ipsuces
.
ipgdtim
.
impfun
.
impdiff
.
ipadvnt
.
ipcrtiv
.
impfree
.
ipeqopt
.
ipudrst
-.
impenv - .
iphlppl
.
iplylfr
.
ipmodst - .
imptrad
.
ipfrule
.
ipbhprp
.
impsafe .
ipstrgv
.
iprspot
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
ipshabt
.
.
.
.
.
.
-.
.
.
.
.
.
ipsuces ipgdtim
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
-.
.
.
.
.
.
.
.
.
.
.
impfun
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
impdiff
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
ipcrtiv
.
.
.
.
.
.
.
.
.
.
.
ipadvnt
.
.
.
.
.
.
.
.
.
.
.
-.
-.
-.
-.
-.
.
.
.
.
.
.
.
.
.
.
.
.
.
impfree
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
-.
.
.
.
ipeqopt
.
.
.
.
.
.
.
.
.
.
.
.
.
ipudrst
-.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
impenv
-.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
iphlppl
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
iplylfr
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
ipmodst imptrad ipfrule
-.
.
.
.
.
.
-.
.
.
.
.
.
-.
.
.
.
.
.
.
.
.
-.
-.
-.
.
.
.
.
.
-.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Analyses
First Question
• How do we assess the presence of the types of values
postulated by the theory?
• We will start with analyses of 6 countries: three
German Speaking countries: Germany(DE),
Austria(AT), Switzerland(CH); 2 Mediterranean
countries: Spain(ES) and Israel(IL); and a country
with a long history of democracy: United Kingdom
(GB).
• Then we will try to do the same with the 20 countries.

ach1

ach2

ach3

sd1

sd2

sd3







hed1


hed2


hed3









ben2

ben3


ben1
=1


=1

=1

=1


uni2


stm1


stm3

sec1


=1

=1



sec3

trad1
=1
trad2






pow1

pow2

=1



pow3




con1


con2


con3



Stimulation
Security



Universalism

sec2

Benevolence

=1

Hedonism

uni1
stm2
Self-direction



Achievement
=1



FIGURE 4: Measurement model with correlated latent variables
Tradition
Power
Conformity
Konfirmatorische Faktorenanalyse Switzerland (CH)
1.11
.88
d3
1
ips uces
1.00
.96
.89
d6
1
d1
im prich
.33
HE
.46
.67
1 1.09
1.00
im pdiff
d7
.30
1.05
ST
1 1.16
.13
.43
ipadvnt
.03
-.08
d20
im pfun
1.00
1.09
.63
.48
.05
.88
1
d21
.88
1
1
ipgdtim
LE
.13
1.36
.24
1
1.42
d2
iprs pot
-.07
1.07
1.00
1
MA
.92
d5
1
ips habt
.16
.55
d4
.37
.15 .26
.23
.24
.75
.04
.91
-.07
1.00
SI
-.09
.05
im psafe
.01
ips trgv
.21
.01
d8
.14 .18
.12
.30
1 .99
1.00 .15ipcrtiv
d9
.59
.82
.11
SE
1
.10
im
pfree
d10
.19
.00
.08
.20
.53 .16
.16
.21 .08 .39
1.27
1
1.12
d19
ipbhprp
1.39
1.00
1
d18
ipfrule
.53
.22
ipeqopt
1.00
1.08
UN
.96ipudrs t
.15
KO
.14
.41
.21
TR
1.17
im ptrad
d17
.23
.82
1.00
.18
ipm odst
1
1.63
im penv
WO
iplylfr
1
1.241 .36
d16
.28
d15
1.00
iphlppl
1 .54
d14
1
1
1
.79
d11
.52
d12
.59
d13
Modifications of the Model of Switzerland (CH)
Modification
Modification Index
0. Modification
Chi Square/DF
7.4
1. Modification
imprich - TR
65
6.6
2. Modification
D3-d11
51
6.2
3. Modification
D7-d9
38
5.9
4. Modification
Impdiff-TR
42
5.4
5. Modification
Imptrad-LE
36
5.0
6. Modification
D11-d17
28
4.8
7. Modification
D2-d13
26
4.6
8. Modification
D6-d8
25
4.3
9. Modification
D3-d21
21
4.2
10. Modification
D2-d21
21
4.0
Other Fit Measures: RMSEA=.039 ; P-CLOSE=1.0 ; CFI=.95 ; GFI=.98 ; AGFI =.96; AIC=730.9 (Sat.=462.0 ); CAIC=1,371.6 (Sat.=1987.8 )
Konfirmatorische Faktorenanalyse Switzerland (CH)
d3
d4
1
d5
1
ips habt
ips uces
d6
1
ipgdtim
1
1
1
d1
d21
d20
1
1
iprs pot
1
d18
1
1
ST
im pdiff
ipadvnt
1
1
SE
SI
im psafe
d19
HE
MA
im prich
ips trgv
1
im pfun
1
LE
d2
1
1
ipbhprp
ipfrule
1
KO
UN
WO
1
im ptrad
1
d17
ipm odst
1
d16
iplylfr
1
d15
1
iphlppl
1
d14
1
d7
d8
ipcrtiv
im pfree
ipeqopt
ipudrs t
im penv
TR
1
1
1
1
d11
d12
d13
1
d9
1
d10
Standardized Regression Weights (CH)
ipshabt
ipsuces
ipgdtim
impfun
impdiff
ipadvnt
imprich
iprspot
impsafe
ipstrgv
ipfrule
ipbhprp
ipmodst
imptrad
iphlppl
iplylfr
ipeqopt
ipudrst
ipcrtiv
impfree
impenv
imprich
impdiff
imptrad
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
Estimate
LE
LE
HE
HE
ST
ST
MA
MA
SI
SI
KO
KO
TR
TR
WO
WO
UN
UN
SE
SE
UN
TR
TR
LE
673.
686.
782.
627.
737.
635.
459.
551.
668.
660.
514.
598.
443.
439.
589.
584.
461.
550.
462.
527.
514.
301.272.
265.
Correlations(CH)
TR
LE
HE
HE
HE
HE
HE
HE
HE
HE
ST
ST
ST
ST
ST
ST
ST
LE
UN
WO
TR
KO
SI
MA
LE
WO
TR
KO
SI
MA
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
WO
HE
ST
SE
UN
WO
TR
KO
SI
MA
SE
UN
WO
TR
KO
SI
MA
ST
SE
SE
SE
SE
SE
SE
SE
UN
UN
UN
UN
UN
Estimate
572.
414.
618.
689.
340.
445.
033.081.
183.
379.
688.
122.
220.
538.314.227.210.
559.
644.
708.
101.171.075.
419.
453.
915.
660.
249.
363.
149.
KO
SI
MA
LE
KO
SI
MA
LE
SI
MA
LE
MA
LE
LE
d3
d7
d17
d2
d6
d3
d2
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
WO
WO
WO
WO
TR
TR
TR
TR
KO
KO
KO
SI
SI
MA
d11
d9
d11
d13
d8
d21
d21
383.
438.
528.
302.
978.
733.
203.
308.838.
803.
254.
757.
367.
850.
192.
188.
136.151.
191.
159.160.-
Modifications of the Model of Germany (DE)
Modification
Modification Index
0. Modification
Chi Square/DF
9.8
1. Modification
Impdiff-UN
99
8.7
2. Modification
Iprspot-TR
92
7.7
3. Modification
Imptrad-MA
51
7.1
4. Modification
D17-d13
48
6.8
5. Modification
D3-d21
45
6.5
6. Modification
D5-d10
38
6.2
7. Modification
D6-d17
34
6.0
Other Fit Measures: RMSEA=.042 ; P-CLOSE=1.0 ; CFI=.96 ; GFI=.97 ; AGFI =.96; AIC=1,004.7 (Sat.=462 ); CAIC=1,657.4 (Sat.=2,065.9)
Standardized Regression Weights (DE)
ipshabt
ipsuces
ipgdtim
impfun
impdiff
ipadvnt
imprich
iprspot
impsafe
ipstrgv
ipfrule
ipbhprp
ipmodst
imptrad
iphlppl
iplylfr
ipeqopt
ipudrst
ipcrtiv
impfree
impenv
impdiff
iprspot
imptrad
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
Estimate
LE
LE
HE
HE
ST
ST
MA
MA
SI
SI
KO
KO
TR
TR
WO
WO
UN
UN
SE
SE
UN
UN
TR
MA
683.
715.
766.
791.
633.
746.
649.
685.
674.
647.
627.
735.
575.
520.
597.
583.
482.
599.
632.
561.
543.
271.
338.
259.
Correlations(DE)
TR
LE
HE
HE
HE
HE
HE
HE
HE
HE
ST
ST
ST
ST
ST
ST
ST
LE
UN
WO
TR
KO
SI
MA
LE
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
Estimate
WO
HE
ST
SE
UN
WO
TR
KO
SI
MA
SE
UN
WO
TR
KO
SI
MA
ST
SE
SE
SE
SE
SE
SE
SE
614.
604.
769.
527.
224.
247.
250.108.
206.
462.
594.
006.
055.560.159.183.692.
689.
660.
589.
146.036.123.
417.
617.
WO
TR
KO
SI
MA
LE
KO
SI
MA
LE
KO
SI
MA
LE
SI
MA
LE
MA
LE
LE
d17
d3
d5
d6
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
UN
UN
UN
UN
UN
UN
WO
WO
WO
WO
TR
TR
TR
TR
KO
KO
KO
SI
SI
MA
d13
d21
d10
d17
936.
454.
274.
409.
244.178.
396.
536.
167.246.
801.
616.
506.362.772.
082.
204.
073.
336.
956.
152.
181.163.
156.
Modifications of the Model of Austria (AT)
Modification
Modification Index
0. Modification
Chi Square/DF
9.7
1. Modification
Impfun-UN
85
8.8
2. Modification
Impdiff-UN
88
7.8
3. Modification
Imprich-TR
83
6.6
4. Modification
D5-d10
62
6.1
5. Modification
D6-d2
35
5.9
6. Modification
Ipfrul-ST
27
5.6
7. Modification
Ipfrul-UN
36
5.3
Other Fit Measures: RMSEA=.043 ; P-CLOSE=1.0 ; CFI=.96 ; GFI=.97 ; AGFI =.95; AIC=907.6 (Sat.=462); CAIC=1,539.3 (Sat.=2,014.3)
Standardized Regression Weights (AT)
ipshabt
ipsuces
ipgdtim
impfun
impdiff
ipadvnt
imprich
iprspot
impsafe
ipstrgv
ipfrule
ipbhprp
ipmodst
imptrad
iphlppl
iplylfr
ipeqopt
ipudrst
ipcrtiv
impfree
impenv
impfun
impdiff
imprich
ipfrule
ipfrule
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
Estimate
LE
LE
HE
HE
ST
ST
MA
MA
SI
SI
KO
KO
TR
TR
WO
WO
UN
UN
SE
SE
UN
UN
UN
TR
ST
UN
741.
758.
709.
761.
671.
789.
593.
603.
716.
704.
705.
751.
537.
547.
662.
693.
658.
673.
728.
642.
658.
326.263.
337.200.
174.-
(AT)Correlations
TR
LE
HE
HE
HE
HE
HE
HE
HE
HE
ST
ST
ST
ST
ST
ST
ST
LE
UN
WO
TR
KO
SI
MA
LE
WO
TR
KO
SI
MA
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
WO
HE
ST
SE
UN
WO
TR
KO
SI
MA
SE
UN
WO
TR
KO
SI
MA
ST
SE
SE
SE
SE
SE
SE
SE
UN
UN
UN
UN
UN
Estimate
510.
581.
751.
707.
486.
481.
187.175.053.
461.
601.
072.
016.
528.380.318.388.
540.
669.
614.
206.178.019.
415.
580.
956.
369.
164.
364.
042.
KO
SI
MA
LE
KO
SI
MA
LE
SI
MA
LE
MA
LE
LE
d5
d6
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
WO
WO
WO
WO
TR
TR
TR
TR
KO
KO
KO
SI
SI
MA
d10
d2
333.
460.
218.
312.
1.010
793.
250.
045.857.
584.
143.
471.
269.
854.
247.
188.-
Correlation over 1 between KO
and TR
• Solution would be to unite them. They seem
to be very close and unseparable in Austria.
Konfirmatorische Faktorenanalyse Austria
(modified and unified factors)
d3
d4
1
d5
1
ips habt
ips uces
d6
1
ipgdtim
1
1
1
d1
d21
d20
1
1
iprs pot
1
d18
1
1
ST
im pdiff
ipadvnt
1
1
SE
SI
im psafe
1
HE
MA
im prich
ips trgv
d19
im pfun
1
LE
d2
1
1
ipbhprp
ipfrule
1
KO
UN
im ptrad
1
d17
ipm odst
1
d16
iplylfr
1
d15
1
iphlppl
1
d14
1
d7
d8
ipcrtiv
im pfree
ipeqopt
ipudrs t
im penv
WO
1
1
1
1
d11
d12
d13
1
d9
1
d10
Correlations(AT modified unified factors)
FIT- is a bit worse
LE
HE
HE
HE
HE
HE
HE
HE
ST
ST
ST
ST
ST
ST
LE
UN
WO
KO
SI
MA
LE
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
Estimate
HE
ST
SE
UN
WO
KO
SI
MA
SE
UN
WO
KO
SI
MA
ST
SE
SE
SE
SE
SE
SE
582.
745.
702.
488.
483.
172.063.
557.
603.
074.
014.
440.317.584.
546.
669.
615.
183.020.
509.
580.
WO
KO
SI
MA
LE
KO
SI
MA
LE
SI
MA
LE
MA
LE
LE
d5
d6
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
UN
UN
UN
UN
UN
WO
WO
WO
WO
KO
KO
KO
SI
SI
MA
d10
d2
958.
239.
362.
073.250.
393.
461.
082.
312.
841.
199.
074.
269.
269.
933.
247.
183.-
Modifications of the Model of Great Britain (GB)
Modification
Modification Index
0. Modification
Chi Square/DF
8.1
1. Modification
Impdiff-UN
77
7.3
2. Modification
D8-d20
65
6.6
3. Modification
Iprspot-KO
56
5.7
4. Modification
D17-d13
42
5.4
5. Modification
D3-d20
34
5.1
6. Modification
D2-d15
30
4.9
Other Fit Measures: RMSEA=.047 ; P-CLOSE=0.92 ; CFI=.95 ; GFI=.97 ; AGFI =.94; AIC=865 (Sat.=462); CAIC=1,468.0 (Sat.=1960.0)
Standardized Regression Weights(GB)
ipshabt
ipsuces
ipgdtim
impfun
impdiff
ipadvnt
imprich
iprspot
impsafe
ipstrgv
ipfrule
ipbhprp
ipmodst
imptrad
iphlppl
iplylfr
ipeqopt
ipudrst
ipcrtiv
impfree
impenv
impdiff
iprspot
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
Estimate
LE
LE
HE
HE
ST
ST
MA
MA
SI
SI
KO
KO
TR
TR
WO
WO
UN
UN
SE
SE
UN
UN
KO
723.
807.
776.
770.
569.
776.
602.
454.
585.
626.
665.
762.
419.
453.
667.
634.
482.
623.
618.
562.
573.
292.
327.
Correlations(GB)
TR
LE
HE
HE
HE
HE
HE
HE
HE
HE
ST
ST
ST
ST
ST
ST
ST
LE
UN
WO
TR
KO
SI
MA
LE
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
Estimate
WO
HE
ST
SE
UN
WO
TR
KO
SI
MA
SE
UN
WO
TR
KO
SI
MA
ST
SE
SE
SE
SE
SE
SE
SE
867.
650.
759.
437.
146.
318.
175.
083.
298.
760.
578.
175.
182.
028.
097.140.
746.
671.
643.
555.
310.
134.
335.
364.
526.
WO
TR
KO
SI
MA
LE
KO
SI
MA
LE
KO
SI
MA
LE
SI
MA
LE
MA
LE
LE
d8
d17
d3
d2
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
UN
UN
UN
UN
UN
UN
WO
WO
WO
WO
TR
TR
TR
TR
KO
KO
KO
SI
SI
MA
d20
d13
d20
d15
781.
629.
442.
530.
106.238.
565.
623.
009.
273.
1.036
922.
031.
141.
770.
121.
190.
243.
336.
951.
338.191.
186.
173.
Modifications of the Model of Spain (ES)
Modification
Modification Index
0. Modification
Chi Square/DF
7.5
1. Modification
Impdiff-UN
91
6.4
2. Modification
Imprich-TR
69
5.4
3. Modification
Ipfrule-WO
41
4.7
4. Modification
Imptrad-UN
38
3.9
5. Modification
D15-d13
29
3.6
6. Modification
D2-d15
22
3.5
Other Fit Measures: RMSEA=.038 ; P-CLOSE=1.0 ; CFI=.97 ; GFI=.98 ; AGFI =.96; AIC=662.9 (Sat.=462.0); CAIC=1,262.2 (Sat.=1,950.6)
Standardized Regression Weights (ES)
ipshabt
ipsuces
ipgdtim
impfun
impdiff
ipadvnt
imprich
iprspot
impsafe
ipstrgv
ipfrule
ipbhprp
ipmodst
imptrad
iphlppl
iplylfr
ipeqopt
ipudrst
ipcrtiv
impfree
impenv
impdiff
imprich
ipfrule
imptrad
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
Estimate
LE
LE
HE
HE
ST
ST
MA
MA
SI
SI
KO
KO
TR
TR
WO
WO
UN
UN
SE
SE
UN
UN
TR
WO
UN
644.
798.
808.
791.
595.
763.
647.
528.
739.
697.
767.
713.
589.
934.
724.
719.
631.
675.
670.
681.
661.
322.
402.372.626.-
Correlations(ES)
TR
LE
HE
HE
HE
HE
HE
HE
HE
HE
ST
ST
ST
ST
ST
ST
ST
LE
UN
WO
TR
KO
SI
MA
LE
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
Estimate
WO
HE
ST
SE
UN
WO
TR
KO
SI
MA
SE
UN
WO
TR
KO
SI
MA
ST
SE
SE
SE
SE
SE
SE
SE
862.
664.
816.
655.
379.
481.
104.
054.
142.
491.
500.
069.
102.
287.230.248.308.
642.
736.
695.
413.
318.
370.
519.
593.
WO
TR
KO
SI
MA
LE
KO
SI
MA
LE
KO
SI
MA
LE
SI
MA
LE
MA
LE
LE
d15
d2
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
UN
UN
UN
UN
UN
UN
WO
WO
WO
WO
TR
TR
TR
TR
KO
KO
KO
SI
SI
MA
d13
d15
888.
867.
591.
564.
325.
222.
658.
568.
482.
321.
998.
873.
545.
195.
816.
771.
300.
664.
310.
904.
187.
151.
Modifications of the Model of Israel (IL)
Modification
Modification Index
0. Modification
Chi Square/DF
6.8
1. Modification
Ipadvnt-TR
63
6.2
2. Modification
Imprich-TR
43
5.7
3. Modification
Ipstrgv-UN
30
5.1
4. Modification
Imptrad-HE
27
4.8
5. Modification
D3-d16
24
4.7
Other Fit Measures: RMSEA=.040 ; P-CLOSE=1.0 ; CFI=.95 ; GFI=.97 ; AGFI =.96; AIC=833.2 (Sat.=462.0); CAIC= 1,451.9 (Sat.=2,015.4)
Standardized Regression Weights (IL)
ipshabt
ipsuces
ipgdtim
impfun
impdiff
ipadvnt
imprich
iprspot
impsafe
ipstrgv
ipfrule
ipbhprp
ipmodst
imptrad
iphlppl
iplylfr
ipeqopt
ipudrst
ipcrtiv
impfree
impenv
ipadvnt
imprich
ipstrgv
imptrad
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
---<
Estimate
LE
LE
HE
HE
ST
ST
MA
MA
SI
SI
KO
KO
TR
TR
WO
WO
UN
UN
SE
SE
UN
TR
TR
UN
HE
605.
732.
764.
744.
719.
656.
600.
584.
743.
339.
553.
755.
583.
440.
626.
511.
505.
517.
448.
506.
529.
249.296.305.
155.
Correlations(IL)
TR
LE
HE
HE
HE
HE
HE
HE
HE
HE
ST
ST
ST
ST
ST
ST
ST
LE
UN
WO
TR
KO
SI
MA
LE
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
Estimate
WO
HE
ST
SE
UN
WO
TR
KO
SI
MA
SE
UN
WO
TR
KO
SI
MA
ST
SE
SE
SE
SE
SE
SE
SE
727.
637.
710.
706.
312.
420.
050.148.
320.
574.
866.
451.
512.
065.
145.
147.
495.
571.
779.
755.
087.
088.
248.
515.
777.
WO
TR
KO
SI
MA
LE
KO
SI
MA
LE
KO
SI
MA
LE
SI
MA
LE
MA
LE
LE
d3
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
>--<
UN
UN
UN
UN
UN
UN
WO
WO
WO
WO
TR
TR
TR
TR
KO
KO
KO
SI
SI
MA
d16
931.
526.
414.
365.
229.
410.
561.
515.
509.
582.
819.
567.
238.
141.
664.
626.
308.
649.
526.
859.
141.-
Answer to First Question
• 1) We could assess the presence of the types
of values postulated by the theory by a
confirmatory factor analysis.
• 2) Items were highly related to their factors.
The types of values could be assessed for
the countries.
2nd Question
• How do we assess the similarity of the
meanings of single values and their
meanings in other samples?
• We will conduct a multiple-group
comparison and test for invariance between
countries.
Multiple-Group Comparison
• 6 countries: CH, DE, AT, GB, ES, IL
• Basic model: we look at the fit measurescan we believe this model is correct?
Model Fit:
•
•
•
•
•
•
•
Chi square/DF=8.2
GFI=.95
AGFI=.92
CFI=.91
RMSEA=.024
Pclose=1.0
AIC=8,148.8 (Sat=2,772.0)
Unstandardized Regression Coefficients- 6 Countries
CH
DE
AT
GB
ES
IL
ipshabt
<---
LE
1.000
1.000
1.000
1.000
1.000
1.000
ipsuces
<---
LE
.960
1.021
.975
1.119
1.219
1.146
ipgdtim
<---
HE
1.000
1.000
1.000
1.000
1.000
1.000
impfun
<---
HE
1.087
1.142
.866
.970
.963
.986
impdiff
<---
ST
1.000
1.000
1.000
1.000
1.000
1.000
ipadvnt
<---
ST
1.055
1.090
1.044
1.240
1.094
1.043
imprich
<---
MA
1.000
1.000
1.000
1.000
1.000
1.000
iprspot
<---
MA
1.417
.929
1.020
1.040
1.047
.999
impsafe
<---
SI
1.000
1.000
1.000
1.000
1.000
1.000
ipstrgv
<---
SI
.909
.973
1.006
.970
1.042
.750
ipfrule
<---
KO
1.000
1.000
1.000
1.000
1.000
1.000
ipbhprp
<---
KO
1.122
1.069
1.197
1.054
1.113
1.229
ipmodst
<---
TR
1.000
1.000
1.000
1.000
1.000
1.000
imptrad
<---
TR
1.171
.805
1.031
1.229
1.036
.932
iphlppl
<---
WO
1.000
1.000
1.000
1.000
1.000
1.000
iplylfr
<---
WO
.817
.838
.905
.909
1.058
.642
ipeqopt
<---
UN
1.000
1.000
1.000
1.000
1.000
1.000
ipudrst
<---
UN
1.077
1.245
1.119
1.221
1.152
1.103
ipcrtiv
<---
SE
1.000
1.000
1.000
1.000
1.000
1.000
impfree
<---
SE
.822
.786
.794
.808
.904
.910
Covariances- 6 countries
CH
DE
AT
GB
IL
ES
TR
<-->
WO
.244
.276
.244
.342
.317
.399
LE
<-->
HE
.533
.516
.533
.706
.683
.493
HE
<-->
ST
.796
.688
.796
.702
.978
.743
HE
<-->
SE
.631
.375
.631
.366
.620
.409
HE
<-->
UN
.233
.098
.233
.088
.270
.169
HE
<-->
WO
.243
.142
.243
.240
.368
.291
HE
<-->
TR
-.187
-.074
-.187
.102
-.040
.072
HE
<-->
KO
-.145
.093
-.145
.090
-.022
.122
HE
<-->
SI
.010
.164
.010
.249
.123
.292
HE
<-->
MA
.365
.358
.365
.578
.403
.495
ST
<-->
SE
.667
.456
.667
.446
.528
.494
ST
<-->
UN
.175
.083
.175
.160
.165
.202
ST
<-->
WO
.131
.058
.131
.180
.190
.274
ST
<-->
TR
-.335
-.257
-.335
.057
-.181
.005
ST
<-->
KO
-.247
-.092
-.247
-.015
-.126
.007
ST
<-->
SI
-.221
-.080
-.221
.057
-.096
.087
ST
<-->
MA
.409
.449
.409
.405
.309
.409
LE
<-->
ST
.588
.592
.588
.586
.603
.426
UN
<-->
SE
.422
.240
.422
.267
.371
.235
WO
<-->
SE
.399
.252
.399
.303
.397
.294
TR
<-->
SE
-.151
-.048
-.151
.129
.098
.075
KO
<-->
SE
-.144
-.027
-.144
.090
.130
.041
SI
<-->
SE
.020
.074
.020
.198
.236
.174
MA
<-->
SE
.339
.251
.339
.212
.252
.236
LE
<-->
SE
.525
.399
.525
.404
.450
.337
WO
<-->
UN
.471
.292
.471
.304
.377
.335
TR
<-->
UN
.183
.187
.183
.218
.266
.221
KO
<-->
UN
.059
.126
.059
.216
.208
.176
SI
<-->
UN
.218
.166
.218
.222
.263
.231
MA
<-->
UN
-.025
-.049
-.025
.018
.032
.046
LE
<-->
UN
.175
.087
.175
.130
.137
.165
KO
<-->
WO
.161
.216
.161
.366
.271
.313
SI
<-->
WO
.286
.262
.286
.333
.297
.367
MA
<-->
WO
.044
-.018
.044
.121
.139
.200
LE
<-->
WO
.216
.135
.216
.197
.208
.304
KO
<-->
TR
.598
.602
.598
.534
.479
.522
SI
<-->
TR
.509
.431
.509
.397
.441
.381
MA
<-->
TR
-.022
-.093
-.022
.131
.122
.065
LE
<-->
TR
-.049
-.141
-.049
.072
.067
.075
SI
<-->
KO
.624
.567
.624
.555
.440
.457
MA
<-->
KO
.245
.213
.245
.268
.265
.302
LE
<-->
KO
.149
.173
.149
.169
.169
.191
MA
<-->
SI
.176
.146
.176
.243
.229
.340
LE
<-->
SI
.234
.213
.234
.299
.216
.349
LE
<-->
MA
.675
.724
.675
.694
.609
.540
Variances-6 countries
LE
HE
ST
SE
UN
WO
TR
KO
SI
MA
d3
d4
d5
d6
d7
d8
d1
d2
d20
d21
d18
d19
d16
d17
d14
d15
d11
d12
d9
d10
d13
DE
.834
.880
.817
.502
.267
.360
.508
.828
.640
.600
1.013
.807
.598
.706
1.041
.935
1.001
1.366
.766
.850
1.278
.807
1.182
1.676
.657
.485
.872
.738
.798
.629
.740
CH
.834
.880
.817
.502
.267
.360
.508
.828
.640
.600
1.013
.807
.598
.706
1.041
.935
1.001
1.366
.766
.850
1.278
.807
1.182
1.676
.657
.485
.872
.738
.798
.629
.740
AT
.976
.900
1.053
.837
.498
.494
.538
.709
.779
.534
.794
.697
.703
1.346
.911
.936
1.244
1.193
.758
.783
1.263
.787
1.219
1.305
.632
.439
.634
.730
.830
.671
.585
GB
1.006
1.172
.723
.595
.301
.495
.298
.858
.566
.497
.868
.699
.777
.752
.994
.878
1.097
1.286
.947
1.043
1.081
.694
1.472
1.782
.627
.593
1.004
.726
.979
.826
.811
ES
.823
1.286
.933
.697
.352
.459
.304
.508
.585
.425
1.157
.703
.689
.707
1.220
1.068
1.548
1.477
.491
.666
1.376
.593
.985
1.405
.462
.437
.589
.628
.856
.659
.555
IL
.582
1.025
1.012
.324
.270
.473
.627
.664
.577
.643
.989
.679
.722
.817
1.070
1.428
1.601
1.284
.870
.833
1.513
.754
1.049
1.897
.710
.566
.776
.935
1.278
.788
1.050
Descriptive Comparison
• Countries seem to vary in factor loadings,
covariances and variances.
• So far we have found configural invariance.
A statistical comparison of invariance
would provide an answer, if models in
different countries are fully invariant,
partially invariant or only configurally
invariant.
Steps in testing for Measurement Invariance
• Configural Invariance
• Factor loadings Invariance
• Invariance of Factor Variances
• Invariance of Factor Covariances
• Invariance of latent Means (given there is factor
loading invariance)
• Invariance of measurement errors
Steps in testing for Measurement Invariance
• Configural Invariance
• Same model structure in both groups
• Most important test (apples & oranges)
• Factor loadings Invariance
• Invariance of Factor Variances
• Invariance of Factor Covariances
• Invariance of latent Means (given there is factor
loading invariance)
• Invariance of measurement errors
Steps in testing for Measurement Invariance
• Configural Invariance
• Factor loadings Invariance
•
Equal factor loadings
•
Presumption for the comparison of latent means
• Invariance of Factor Variances
• Invariance of Factor Covariances
• Invariance of latent Means (given there is factor loading
invariance)
• Invariance of measurement errors
Full vs. Partial Invariance
•
Concept of ‘partial invariance’ introduced by Byrne, Shavelson & Muthén
(1989)
•
Procedure
•
Constrain complete matrix
•
Use modification indices to find non-invariant parameters and then
relax the constraint
•
Compare with the unrestricted model
•
Steenkamp & Baumgartner (1998): Two indicators with invariant
loadings and intercepts are sufficient for mean comparisons
•
In our case there are only two items for each construct, so we cannot
test for partial invariance.
Fit measures of different models
Chi
square/D
F
(ind=56.6
3)
GFI
(sat=1.0,
ind=.5)
AGFI
(ind=.45
)
CFI
(sat=1.0)
RMSEA
(ind=.066
)
P-Close
(ind=0.0
)
AIC (sat=
2,772.0)
Unconstraine
d model
8.223
.949
.919
.911
.024
1.000
8148.847
Factor
Loadings
equal across
countries
8.008
.947
.921
.908
.023
1.000
8293.283
FL and
covariances
equal across
countries
7.883
.931
.920
.883
.023
1.000
9796.401
FL,
covariances
and
measurement
errors equal
across
countries
9.133
.916
.910
.849
.025
1.000
12038.08
7
Nested Model Comparisons
Assuming model Unconstrained to be correct:
Model
DF
CMIN
P
NFI
Delta-1
IFI
Delta-2
RFI
rho-1
TLI
rho2
Measurement weights
55
254.436
.000
.004
.004
-.004
-.004
Structural covariances
330
2307.554
.000
.032
.033
-.006
-.006
Measurement residuals
435
4759.240
.000
.067
.068
.016
.016
Assuming model Measurement weights to be correct:
Model
DF
CMIN
P
NFI
Delta-1
IFI
Delta-2
RFI
rho-1
TLI
rho2
Structural covariances
275
2053.118
.000
.029
.029
-.002
-.002
Measurement residuals
380
4504.803
.000
.063
.064
.020
.020
Assuming model Structural covariances to be correct:
Model
DF
CMIN
P
NFI
Delta-1
IFI
Delta-2
RFI
rho-1
TLI
rho2
Measurement residuals
105
2451.685
.000
.034
.035
.022
.022
Fir Measures for 20 countries
Chi
square/DF
(ind=49.7
68)
GFI
(sat=1.0,
ind=.49
5)
AGFI
(ind=.44
4)
CFI
(sat=1.0
)
RMSEA
(ind=.06
6)
P-Close
(ind=1.
0)
AIC (sat=
9,240.0;
ind=209,867.
6)
Unconstraine
d model
7,782
,946
,913
.911
,013
1.000
25892,180
Factor
Loadings
equal across
countries
7,606
,943
,915
.908
,013
1.000
26557,360
FL and
covariances
equal across
countries
7,768
,921
,912
.883
,013
1.000
33083,803
FL,
covariances
and
measurement
errors equal
across
countries
10,026
,894
,892
.849
,016
1.000
45621,392
Model Comparison
Assuming model Unconstrained to be correct:
Model
DF
CMIN
P
NFI
Delta1
IFI
Delta2
RFI
rho1
TLI
rho2
Measurement weights
209
1083,179
00,
0
005,
005,
004,-
004,
Structural covariances
125
4
9699,622
00,
0
046,
047,
000,
000,
Measurement
residuals
165
3
23035,21
2
00,
0
110,
112,
045,
046,
Assuming model Measurement weights to be correct:
Model
DF
CMIN
P
NFI
Delta1
IFI
Delta2
RFI
rho1
TLI
rho
2
Structural covariances
104
5
8616,443
00,
0
041,
042,
003,
003,
Measurement
residuals
144
4
21952,03
3
00,
0
105,
107,
049,
050,
Assuming model structural covariances to be correct:
Model
DF
CMIN
P
NFI
Delta1
IFI
Delta2
RFI
rho1
TLI
rho
2
Measurement
residuals
39
9
13335,59
0
00,
0
064,
065,
045,
046,
To answer Question 2
• Countries seem to differ in the meaning of
values due to variance in the factor loadings
across countries. AIC goes up in the
invariance tests, therefore we reject
invariance.
To answer Question 3
• We asked: How do we assess the presence of the value
structure? Deviations in value types, meanings, and
structure in different samples invite interpretations.
However, not all deviations are meaningful.
• Possible answer: Covariances between values differ
across countries. So we do have configural invariance,
but “distances” in terms of covariances seem to differ in
European countries. One has to find the reasons for the
differences: can we base these differences on
geographical dispersion: mediterranean vs. north
Europe? Long vs. shorter history of democracy?
• Some values in some countries correlate
very highly, and in later analyses we will
need to unify them into a smaller number of
values. These correlations are different
across countries.
To answer Question 4
• We asked: How do we distinguish real cultural
differences in value meanings and structure from
unreliability of measurement?
• Answer: if factor loadings are equal we can
guarantee equal meaning of constructs.
Differential measurement errors in the different
countries is corrected by correction for attenuation
in the SEM approach. In our case f”l are different,
so the meaning of the factors may be different
between some countries.
Conclusions/Next Steps
• Considering to modify the models in the multiple
group comparison to improve the fit.
• Finding reasons for differences: geographical,
historical, political.
• When we tried to do it with the 20 countries-as is
in the next slide, the model did not converge, and
it is not clear why. Therefore, one should still be
cautious as to the substantial meaning of the
results so far.
Konfirmatorische Faktorenanalyse Deutschland
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Gender Mean Comparison (by Steinmetz,
Schmidt, Tina-Booh and Wieczorek)
Men
Women
Achievement
--
--
Self-Direction
3.26
3.26
Hedonism
3.38
3.47
Benevolence
3.40
3.57
Universalism
3.29
3.50
Stimulation
2.24
2.11
Security
--
--
Tradition
--
--
Power
2.53
2.33
Conformity
3.21
3.31
Data collected in a telephone survey in Germany on part time jobs,
N=1,677.
• Consider variance/invariance across gender
groups. Maybe socio-demographic
characteristics are responsible for more
variance than cultural differences.
• Maybe people are more different within
countries than countries themselves in
Europe?
Thank you very much for your
attention!!!!