Download presentation

Presentation is loading. Please wait.

Published byGage Pardy Modified over 3 years ago

1
Values in Europe: A Multiple Group Comparison with 20 Countries Using the European Social Survey 2003. S.Schwartz/E.Davidov/P.Schmidt

2
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.

3
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.

4
The values: Achievement (LE) Hedonism (HE) Power (MA) Stimulation (ST) Security (SI) Self-Direction (SE) Conformity (KO) Universalism (UN) Tradition (TR) Benevolence (WO)

5
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.

6
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.

7
Figure 1: Structural relations among the 10 values and the four higher values (see Devos, Spini, & Schwartz, 2002).

8
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 self- transcendence: opposing acceptance of others as equals vs. concern for others’ welfare.

9
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.

10
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

11
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

12
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

13
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

14
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

15
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

16
20 Countries (2 Missing) 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-SI (Slovenia).

17
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.).

18
CH-Switzerland

19
DE-Germany

20
AT-Austria

21
GB-Great Britain

22
ES-Spain

23
IL-Israel

24
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.

25
FIGURE 4: Measurement model with correlated latent variables

27
Modifications of the Model of Switzerland (CH) ModificationModification IndexChi Square/DF 0. Modification 7.4 1. Modificationimprich - TR656.6 2. ModificationD3-d11516.2 3. ModificationD7-d9385.9 4. ModificationImpdiff-TR425.4 5. ModificationImptrad-LE365.0 6. ModificationD11-d17284.8 7. ModificationD2-d13264.6 8. ModificationD6-d8254.3 9. ModificationD3-d21214.2 10. ModificationD2-d21214.0 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 )

29
Standardized Regression Weights (CH) Estimate ipshabt<---LE.673 ipsuces<---LE.686 ipgdtim<---HE.782 impfun<---HE.627 impdiff<---ST.737 ipadvnt<---ST.635 imprich<---MA.459 iprspot<---MA.551 impsafe<---SI.668 ipstrgv<---SI.660 ipfrule<---KO.514 ipbhprp<---KO.598 ipmodst<---TR.443 imptrad<---TR.439 iphlppl<---WO.589 iplylfr<---WO.584 ipeqopt<---UN.461 ipudrst<---UN.550 ipcrtiv<---SE.462 impfree<---SE.527 impenv<---UN.514 imprich<---TR-.301 impdiff<---TR.272 imptrad<---LE.265

30
Correlations(CH) KO WO.383 SI WO.438 MA WO.528 LE WO.302 KO TR.978 SI TR.733 MA TR.203 LE TR-.308 SI KO.838 MA KO.803 LE KO.254 MA SI.757 LE SI.367 LE MA.850 d3 d11.192 d7 d9.188 d17 d11-.136 d2 d13.151 d6 d8.191 d3 d21-.159 d2 d21-.160

31
Modifications of the Model of Germany (DE) ModificationModification IndexChi Square/DF 0. Modification 9.8 1. ModificationImpdiff-UN998.7 2. ModificationIprspot-TR927.7 3. ModificationImptrad-MA517.1 4. ModificationD17-d13486.8 5. ModificationD3-d21456.5 6. ModificationD5-d10386.2 7. ModificationD6-d17346.0 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)

32
Standardized Regression Weights (DE) Estimate ipshabt<---LE.683 ipsuces<---LE.715 ipgdtim<---HE.766 impfun<---HE.791 impdiff<---ST.633 ipadvnt<---ST.746 imprich<---MA.649 iprspot<---MA.685 impsafe<---SI.674 ipstrgv<---SI.647 ipfrule<---KO.627 ipbhprp<---KO.735 ipmodst<---TR.575 imptrad<---TR.520 iphlppl<---WO.597 iplylfr<---WO.583 ipeqopt<---UN.482 ipudrst<---UN.599 ipcrtiv<---SE.632 impfree<---SE.561 impenv<---UN.543 impdiff<---UN.271 iprspot<---TR.338 imptrad<---MA.259

33
Correlations(DE) WO UN.936 TR UN.454 KO UN.274 SI UN.409 MA UN-.244 LE UN.178 KO WO.396 SI WO.536 MA WO-.167 LE WO.246 KO TR.801 SI TR.616 MA TR-.506 LE TR-.362 SI KO.772 MA KO.082 LE KO.204 MA SI.073 LE SI.336 LE MA.956 d17 d13.152 d3 d21-.181 d5 d10.163 d6 d17.156

34
Modifications of the Model of Austria (AT) ModificationModification IndexChi Square/DF 0. Modification 9.7 1. ModificationImpfun-UN858.8 2. ModificationImpdiff-UN887.8 3. ModificationImprich-TR836.6 4. ModificationD5-d10626.1 5. ModificationD6-d2355.9 6. ModificationIpfrul-ST275.6 7. ModificationIpfrul-UN365.3 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)

35
Standardized Regression Weights (AT)

36
(AT)Correlations KO WO.333 SI WO.460 MA WO.218 LE WO.312 KO TR1.010 SI TR.793 MA TR.250 LE TR-.045 SI KO.857 MA KO.584 LE KO.143 MA SI.471 LE SI.269 LE MA.854 d5 d10.247 d6 d2-.188

37
Correlation over 1 between KO and TR Solution would be to unite them. They seem to be very close and unseparable in Austria.

39
Correlations(AT modified unified factors) FIT- is a bit worse Estimate LE HE.582 HE ST.745 HE SE.702 HE UN.488 HE WO.483 HE KO-.172 HE SI.063 HE MA.557 ST SE.603 ST UN.074 ST WO.014 ST KO-.440 ST SI-.317 ST MA.584 LE ST.546 UN SE.669 WO SE.615 KO SE-.183 SI SE.020 MA SE.509 LE SE.580 WO UN.958 KO UN.239 SI UN.362 MA UN-.073 LE UN.250 KO WO.393 SI WO.461 MA WO.082 LE WO.312 SI KO.841 MA KO.199 LE KO.074 MA SI.269 LE SI.269 LE MA.933 d5 d10.247 d6 d2-.183

40
Modifications of the Model of Great Britain (GB) ModificationModification IndexChi Square/DF 0. Modification 8.1 1. ModificationImpdiff-UN777.3 2. ModificationD8-d20656.6 3. ModificationIprspot-KO565.7 4. ModificationD17-d13425.4 5. ModificationD3-d20345.1 6. ModificationD2-d15304.9 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)

41
Standardized Regression Weights(GB) Estimate ipshabt<---LE.723 ipsuces<---LE.807 ipgdtim<---HE.776 impfun<---HE.770 impdiff<---ST.569 ipadvnt<---ST.776 imprich<---MA.602 iprspot<---MA.454 impsafe<---SI.585 ipstrgv<---SI.626 ipfrule<---KO.665 ipbhprp<---KO.762 ipmodst<---TR.419 imptrad<---TR.453 iphlppl<---WO.667 iplylfr<---WO.634 ipeqopt<---UN.482 ipudrst<---UN.623 ipcrtiv<---SE.618 impfree<---SE.562 impenv<---UN.573 impdiff<---UN.292 iprspot<---KO.327

42
Correlations(GB) WO UN.781 TR UN.629 KO UN.442 SI UN.530 MA UN-.106 LE UN.238 KO WO.565 SI WO.623 MA WO.009 LE WO.273 KO TR1.036 SI TR.922 MA TR.031 LE TR.141 SI KO.770 MA KO.121 LE KO.190 MA SI.243 LE SI.336 LE MA.951 d8 d20-.338 d17 d13.191 d3 d20.186 d2 d15.173

43
Modifications of the Model of Spain (ES) ModificationModification IndexChi Square/DF 0. Modification 7.5 1. ModificationImpdiff-UN916.4 2. ModificationImprich-TR695.4 3. ModificationIpfrule-WO414.7 4. ModificationImptrad-UN383.9 5. ModificationD15-d13293.6 6. ModificationD2-d15223.5 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)

44
Standardized Regression Weights (ES)

45
Correlations(ES) WO UN.888 TR UN.867 KO UN.591 SI UN.564 MA UN.325 LE UN.222 KO WO.658 SI WO.568 MA WO.482 LE WO.321 KO TR.998 SI TR.873 MA TR.545 LE TR.195 SI KO.816 MA KO.771 LE KO.300 MA SI.664 LE SI.310 LE MA.904 d15 d13.187 d2 d15.151

46
Modifications of the Model of Israel (IL) ModificationModification IndexChi Square/DF 0. Modification 6.8 1. ModificationIpadvnt-TR636.2 2. ModificationImprich-TR435.7 3. ModificationIpstrgv-UN305.1 4. ModificationImptrad-HE274.8 5. ModificationD3-d16244.7 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)

47
Standardized Regression Weights (IL)

48
Correlations(IL) WO UN.931 TR UN.526 KO UN.414 SI UN.365 MA UN.229 LE UN.410 KO WO.561 SI WO.515 MA WO.509 LE WO.582 KO TR.819 SI TR.567 MA TR.238 LE TR.141 SI KO.664 MA KO.626 LE KO.308 MA SI.649 LE SI.526 LE MA.859 d3 d16-.141

49
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.

50
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.

51
Multiple-Group Comparison 6 countries: CH, DE, AT, GB, ES, IL Basic model: we look at the fit measures- can we believe this model is correct?

52
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)

53
CHDEATGBES ipshabt<---LE1.000 ipsuces<---LE.9601.021.9751.1191.2191.146 ipgdtim<---HE1.000 impfun<---HE1.0871.142.866.970.963.986 impdiff<---ST1.000 ipadvnt<---ST1.0551.0901.0441.2401.0941.043 imprich<---MA1.000 iprspot<---MA1.417.9291.0201.0401.047.999 impsafe<---SI1.000 ipstrgv<---SI.909.9731.006.9701.042.750 ipfrule<---KO1.000 ipbhprp<---KO1.1221.0691.1971.0541.1131.229 ipmodst<---TR1.000 imptrad<---TR1.171.8051.0311.2291.036.932 iphlppl<---WO1.000 iplylfr<---WO.817.838.905.9091.058.642 ipeqopt<---UN1.000 ipudrst<---UN1.0771.2451.1191.2211.1521.103 ipcrtiv<---SE1.000 impfree<---SE.822.786.794.808.904.910 Unstandardized Regression Coefficients- 6 Countries IL

54
Covariances- 6 countries CHDEATGBES 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 IL

55
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.149.173.149.169.191 MA SI.176.146.176.243.229.340 LE SI.234.213.234.299.216.349 LE MA.675.724.675.694.609.540

56
DECHATGBES LE.834.9761.006.823.582 HE.880.9001.1721.2861.025 ST.817 1.053.723.9331.012 SE.502.837.595.697.324 UN.267.498.301.352.270 WO.360.494.495.459.473 TR.508.538.298.304.627 KO.828.709.858.508.664 SI.640.779.566.585.577 MA.600.534.497.425.643 d31.013.794.8681.157.989 d4.807.697.699.703.679 d5.598.703.777.689.722 d6.706 1.346.752.707.817 d71.041.911.9941.2201.070 d8.935.936.8781.0681.428 d11.001 1.2441.0971.5481.601 d21.366 1.1931.2861.4771.284 d20.766.758.947.491.870 d21.850.7831.043.666.833 d181.278 1.2631.0811.3761.513 d19.807.787.694.593.754 d161.182 1.2191.472.9851.049 d171.676 1.3051.7821.4051.897 d14.657.632.627.462.710 d15.485.439.593.437.566 d11.872.6341.004.589.776 d12.738.730.726.628.935 d9.798.830.979.8561.278 d10.629.671.826.659.788 d13.740.585.811.5551.050 Variances-6 countries IL

57
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.

58
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

59
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

60
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

61
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.

62
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 8.223.949.919.911.0241.0008148.847 Factor Loadings equal across countries 8.008.947.921.908.0231.0008293.283 FL and covariances equal across countries 7.883.931.920.883.0231.0009796.401 FL, covariances and measurement errors equal across countries 9.133.916.910.849.0251.000 12038.08 7 Fit measures of different models

63
Nested Model Comparisons Assuming model Unconstrained to be correct: ModelDFCMINP NFI Delta-1 IFI Delta-2 RFI rho-1 TLI rho2 Measurement weights55254.436.000.004 -.004 Structural covariances3302307.554.000.032.033-.006 Measurement residuals4354759.240.000.067.068.016 Assuming model Measurement weights to be correct: ModelDFCMINP NFI Delta-1 IFI Delta-2 RFI rho-1 TLI rho2 Structural covariances2752053.118.000.029 -.002 Measurement residuals3804504.803.000.063.064.020 Assuming model Structural covariances to be correct: ModelDFCMINP NFI Delta-1 IFI Delta-2 RFI rho-1 TLI rho2 Measurement residuals1052451.685.000.034.035.022

64
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 7,782,946,913.911,0131.00025892,180 Factor Loadings equal across countries 7,606,943,915.908,0131.00026557,360 FL and covariances equal across countries 7,768,921,912.883,0131.00033083,803 FL, covariances and measurement errors equal across countries 10,026,894,892.849,0161.00045621,392 Fir Measures for 20 countries

65
Model Comparison Assuming model Unconstrained to be correct: ModelDFCMINP NFI Delta- 1 IFI Delta- 2 RFI rho- 1 TLI rho2 Measurement weights2091083,179, 000,005 -,004 Structural covariances 125 4 9699,622, 000,046,047,000 Measurement residuals 165 3 23035,21 2, 000,110,112,045,046 Assuming model Measurement weights to be correct: ModelDFCMINP NFI Delta- 1 IFI Delta- 2 RFI rho- 1 TLI rho 2 Structural covariances 104 5 8616,443, 000,041,042,003 Measurement residuals 144 4 21952,03 3, 000,105,107,049,050 Assuming model structural covariances to be correct: ModelDFCMINP NFI Delta- 1 IFI Delta- 2 RFI rho- 1 TLI rho 2 Measurement residuals 39 9 13335,59 0, 000,064,065,045,046

66
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.

67
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?

68
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.

69
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.

70
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.

72
MenWomen Achievement-- Self-Direction3.26 Hedonism3.383.47 Benevolence3.403.57 Universalism3.293.50 Stimulation2.242.11 Security-- Tradition-- Power2.532.33 Conformity3.213.31 Gender Mean Comparison (by Steinmetz, Schmidt, Tina-Booh and Wieczorek) Data collected in a telephone survey in Germany on part time jobs, N=1,677.

73
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?

74
Thank you very much for your attention!!!!

Similar presentations

OK

1 A Continental Divide? Social capital in the US and Europe Pippa Norris and James Davis Harvard University and NORC.

1 A Continental Divide? Social capital in the US and Europe Pippa Norris and James Davis Harvard University and NORC.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on paintings and photographs related to colonial period Ppt on surface area and volume of sphere and hemisphere Ppt on remote control robot car Urinary system anatomy and physiology ppt on cells Ppt on earth damn Ppt on domestic tourism in india Ppt on network load balancing Ppt on job rotation evaluation Ppt on bluetooth communication protocol Ppt on creativity and innovation