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Bringing Values Back In: A Multiple Group Comparison with 20 Countries Using the European Social Survey 2003 Measurement, causes and consequences To be Presented in Lugano, QMSS, 24.08.06. Eldad Davidov

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Bringing Values Back In:A Multiple Group Comparison with 20 Countries Using the European Social Survey 2003 Measurement, causes and consequencesTo be Presented in Lugano, QMSS, 24.08.06

Eldad Davidov

Together with Peter Schmidt and Shalom Schwartz (1st study), and with Jaak Billiet and Peter Schmidt (2nd study)


  • Why bringing values?

  • Weber;

  • Socio demographic variables may affect values, and values may affect attitudes and behavior. So values may be the black box in between.

  • This mediation can be different in different societies.


Outline
Outline

  • 1) Theory and research questions.

  • 2) Data from European Social Survey –ESS and items.

  • 3) Results and conclusions

    • Invariance issues

    • Possibilities to compare value means

    • Causes

    • Consequences


Questions we want to answer
Questions We Want To Answer:

  • 1) How many values from the theory do we find in Europe?

  • 2) Can we compare the values across the countries?

  • 3) How are values that we find influenced by social demographic variables: gender, education and age?

  • 4) Do values affect attitudes towards foreigners, in particular allowing foreigners into the country and granting them rights?


1 theory
1) Theory

  • Schwartz‘s measurement theory of values was first introduced in 1992. The theory describes universals in the content and the structure of individual values. It was measured previously by 10 distinct values and 40 items. The values are:


The values
The values:

  • Achievement (AC) Hedonism (HE)

  • Power (PO) Stimulation (ST)

  • Security (SEC) Self-Direction (SD)

  • Conformity (CO) Universalism (UN)

  • Tradition (TR) Benevolence (BE)


  • Some values are closer to other values, and some values may oppose one another. For example, tradition may oppose hedonism.

  • Close values are expected to correlate positively and opposing values are expected to correlate negatively or not at all.

  • The 10 values create a continuum, which can be expressed graphically.


Figure 1: Structural relations among the 10 values and the four higher values (see Devos, Spini, & Schwartz, 2002).



2 the data
2) The Data rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

  • The data we use is the first round of the European Social Survey on values, collected in 2003. It provides for the first time the opportunity to test Schwartz‘s value theory with representative and comparable across countries population surveys. Previously the theory had been tested by student surveys, or by representative data which was not comparable across countries.


20 countries 2 missing
20 Countries (2 Missing) rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

  • 20 countries: 1-AT (Austria), 2-BE (Belgium), 3-CH (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), 16-LU (Luxemburg, missing), 17-NL (Netherlands), 18-NO (Norway), 19-PL (Poland), 20-PT (Portugal), 21-SE (Sweden), 22-SL (Slovenia).


The 21 ess items for each value
The 21 ESS Items for Each Value rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

  • 1)      Power (PO):

  • Imprich/po1:Important to be rich, have money and expensive things.

  • Iprspot/po2: Important to get respect from others

  • 2)      Achievement (AC):

  • Ipshabt/ac1: Important to show abilities and be admired.

  • Ipsuces/ac2: Important to be successful and that people recognize achievements


  • 3)      Hedonism (HE): rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

  • Ipgdtim/he1: Important to have a good time

  • Impfun/he2: Important to seek fun and things that give pleasure

  • 4)      Stimulation (ST):

  • Impdiff/st1: Important to try new and different things in life

  • Ipadvnt/st2: Important to seek adventures and have an exciting life


  • 5)      Self-Direction (SD): rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

  • Ipcrtiv/sd1: Important to think new ideas and being creative

  • Impfree/sd2: Important to make own decisions and be free

  • 6)      Universalism (UN):

  • Ipeqopt/un1: Important that people are treated equally and have equal opportunities

  • Ipudrst/un2: Important to understand different people

  • Impenv/un3: Important to care for nature and environment


  • 7)      Benevolence (BE): rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

  • Iphlppl/be1: Important to help people and care for others well-being

  • Iplylfr/be2: Important to be loyal to friends and devote to close people

  • 8)      Tradition (TR):

  • Ipmodst/tr1: Important to be humble and modest, not draw attention

  • Imptrad/tr2: Important to follow traditions and customs


  • 9)      Conformity (CO): rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

  • Ipfrule/co1: Important to do what is told and follow rules

  • Ipbhprp/co2: Important to behave properly

  • 10) Security (SEC):

  • Impsafe/sec1: Important to live in secure and safe surroundings

  • Ipstrgv/sec2: Important that government is strong and ensures safety


The range of the items
The range of the items rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

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


3 descriptive results of items
3) Descriptive Results of Items rather than emerge in clearly distinct regions. So in empirical studies it may happen that we will not find always ten distinct values.

  • The range of items across countries is not very large, but there are nevertheless differences.

  • In practice, social scientists often compare on the item level. Therefore, let’s look at some countries.



  • How do other Mediterranean countries do? scores for the values achievement, security, tradition, stimulation, universalism, power.

  • Israel for example scores most highly in Europe only for two values- power and stimulation.

  • Spain for example scores most highly in Europe in three values- universalism, benevolence and tradition. So geography does not tell us the whole story.


  • How is Germany doing? scores for the values achievement, security, tradition, stimulation, universalism, power.

  • In the middle golden way. Values tend to score around the average and there are no extreme items.

  • And Switzerland?

  • Switzerland is strongest in self-direction, hedonism and universalism, and weakest in conformity.


  • However, Scandinavia tells us a different story. scores for the values achievement, security, tradition, stimulation, universalism, power.

  • Sweden for example has the lowest scores for universalism, benevolence, security and conformity. Maybe people know that the state takes care of the people so they do not feel the need to do it themselves.

  • Norway has the lowest scores for universalism, security and also self-direction.

  • So at least for some Scandinavian countries geography and social system have a similar story to tell.


  • We would like to compare countries also on the scores for the values achievement, security, tradition, stimulation, universalism, power.value level, and not only on the item level as we are doing here. In such a way we can control for measurement error.

  • In order to be able to compare the means of the values (which are the constructs here), we first have to make sure the values mean the same thing all over Europe.

  • Ensuring that values mean the same can be done by showing measurement invariance, that the indicators are related to the values equally in all the countries.


3 data analysis
3) Data Analysis scores for the values achievement, security, tradition, stimulation, universalism, power.

1) Twenty separate analyses for each country.

2) A multiple sample analysis of all 20 countries together.


1 scores for the values achievement, security, tradition, stimulation, universalism, power.

  • At first we computed 20 correlation matrices for each country separately using pairwise deletion for missing values (see Browne 1994 and Schafer and Graham 2002, which demonstrate why pairwise is better than listwise and adequate if there is no more than 5% missing values).

  • The correlations ranged from negative values for indicators belonging to constructs, which are theoretically apart in the map of indicators, to highly positive values for adjacent value constructs and for indicators belonging to the same construct.


1 scores for the values achievement, security, tradition, stimulation, universalism, power.

  • Then we tested the theory for each country separately. In all countries some constructs correlated too highly. In order to solve the problem of non positive definite matrices caused, we had to unify such constructs.

  • As a result we identified 5-8 values in the 20 countries


2 scores for the values achievement, security, tradition, stimulation, universalism, power.

  • Then we ran the simultanuous analysis for 20 countries




Answer to first question
Answer to first Question improve the model

  • In simple words- we found a model which works for all the 20 European countries (configural invariance).

  • But- we have a model which has only 7 values and not 10.



Measurement Invariance: values between countries, we have to test for metric (measurement) invariance. Metric invariance will guarantee that the values mean the same over the 20 European countries

Equal factor loadings across groups

Group A

Group B

dB11

Item a

dA11

lB11=1

Item a

lA11=1

fB11

k B1

fA11

k A1

B1

A1

lB21

lA21

dB22

dA22

Item b

Item b

lB31

lA31

Item c

Item c

dB33

dA33

fB21

fA21

dB44

dA44

Item d

Item d

lB42=1

lA42=1

B2

A2

lB52

lA52

Item e

Item e

dB55

dA55

lB62

lA62

fB22

k B2

fA22

k A2

dB66

dA66

Item f

Item f


Steps in testing for Measurement Invariance values between countries, we have to test for metric (measurement) invariance. Metric invariance will guarantee that the values mean the same over the 20 European countries

  • Configural Invariance

  • Metric Invariance

  • Scalar Invariance

  • Invariance of Factor Variances

  • Invariance of Factor Covariances

  • Invariance of latent Means

  • Invariance of Unique Variances


Steps in testing for Measurement Invariance values between countries, we have to test for metric (measurement) invariance. Metric invariance will guarantee that the values mean the same over the 20 European countries

  • Configural Invariance

  • Metric Invariance

    • Equal factor loadings

    • Same scale units in both groups

    • Presumption for the comparison of latent means

  • Scalar Invariance

  • Invariance of Factor Variances

  • Invariance of Factor Covariances

  • Invariance of latent Means

  • Invariance of Unique Variances


Full vs. Partial Invariance values between countries, we have to test for metric (measurement) invariance. Metric invariance will guarantee that the values mean the same over the 20 European countries

  • 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

  • One of them can be the marker + one further invariant item



  • To conclude: we found also metric invariance: items are related to values equally in the different countries.

  • Therefore at least statistically comparing the means of the values across countries is substantially meaningful (to be sure we should do cognitive pretests in different countries, but we do not have them)

  • According to results of the invariance test, factor covariances vary considerably across countries


  • The next test is scalar invariance. To guarantee scalar invariance, we have to set the intercepts to be equal across groups.

  • The global fit measures suggest we should reject this model.

  • Implication: Means of values cannot be compared meaningfully across groups.

  • Prospects for future possibilities to compare latent means (Little et al. 2006).


In a new study work in progress we test effects of gender education and age on values
In a new study (work in progress) we test effects of Gender, education and age on values

According to Kohn/Schoenbach (1993) :

  • people with higher education  more self directed

  • people with higher education  less conformist

    According to Steinmetz, Schmidt, Tina-Booh and Wieczorek (in progress)

  • men  less universalist, and score higher on power in Germany

    According to Heyder, 2003 and dissertation (in progress)

  • Higher age  more conformist


Gender Gender, education and age on values

7 Values:

Power and achievement

Security

Conformity and tradition

Universalism and benevolence

Self-Direction

Stimulation

Hedonism

Education

Age


Men Gender, education and age on values

Power and

Achievement

Security

Conformity and Tradition

Universalism and Benevolence

Self-Direction

Stimulation

Hedonism

Higher Education

Power and

Achievement

Security

Conformity and Tradition

Universalism and Benevolence

Self-Direction

Stimulation

Hedonism

Older Age

Power and

Achievement

Security

Conformity and Tradition

Universalism and Benevolence

Self-Direction

Stimulation

Hedonism

Muslim

Power and

Achievement

Security

Conformity and Tradition

Universalism and Benevolence

Self-Direction

Stimulation

Hedonism

Results

Dark blue: for all countries higher, light blue:for most countries higher

Dark gray: for all countries lower, light italic gray: for most lower

Green: effects in different directions in differernt countries.


In a new study work in progress
In a new study…(work in progress) Gender, education and age on values

  • We argue that values are more stable than attitudes (Ajzen/Fishbein, Eagly/Chaiken 1993)

  • This justifies using values to explain attitudes and opinions

  • Our intention is to explain two latent variables from the ESS 2003: Allowing immigrants into the country and Conditions to allow immigrants into the country


Indicators
Indicators Gender, education and age on values

  • Allow into country is measured by 4 indicators:

    • D5: Allow many/few immigrants of different race/ethnic group from majority

    • D7: Allow many/few immigrants from poorer countries in Europe

    • D8: Allow many/few immigrants from richer countries outside Europe

    • D9: Allow many/few immigrants from poorer countries outside Europe

    • Scale: 1=allow many, 4=allow none


Indicators 2
Indicators 2 Gender, education and age on values

  • Conditions to allow was measured by two indicators:

    • D10: Qualification for immigration: good educational qualifications

    • D16: Qualification for immigration: work skills needed in country

    • Scale: 0=extremely unimportant, 10=extremely important


The problem
The problem Gender, education and age on values

  • There is not much theory about these relations.

  • Ajzen Fishbein postulated for example a causal relation between conformism and attitudes towards immigrants.

  • Billiet postulated this relation too, and also the effect of security needs on attitudes to immigrants.

  • Theory is needed to further explain such relations.


  • We expect: Gender, education and age on values

  • People scoring high on Tradition, conformity and security to allow less immigrants in.

  • People scoring high on universalism and benevolence to allow more immigrants in.


Results
Results Gender, education and age on values

  • We guarnteed invariance across 21 countries to allow comparison of the effect of values on opinions

  • people scoring high on Hedonism, Universalism and benevolence, power and achievement want to allow more immigrants into the country.

  • People scoring high on stimulation and self direction, conformity and tradition, and on security want to allow less immigrants into country, and set more conditions for allowing them.


Conclusions
Conclusions Gender, education and age on values

  • What did we learn?

  • The model works well in Europe but for 7 values.

  • Maybe more items will solve this problem, and we may find out we can identify 10 values, but we cannot be sure.

  • We find meaningful relations between socio-demographic characteristics , opinions on immigration and values. Effects of gender and education postulated in previous studies were confirmed in many countries. Effects of confomity on attitudes towards immigrants as operationalized here was confirmed.


What next
What next? Gender, education and age on values

  • In the next steps we would like to:

  • 1) Conducting a full model simultanuously with socio dem. Variables, values and opinions to find direct and indirect relations.

  • 2) Doing it for several countries simultanuously to compare the structural effects

  • 3) Compare the means with the new method which does not require scalar invariance, and try to give meaningful explanations for differences, such as geographical, political and historical differences between countries

  • What we conducted here was a preliminary test for such comparisons

Soc.dem.

Charact.

Attitudes

opinions

Values

Behavior



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