Структурная модель с латентными переменными
27/28 сентября 2021 г.
1 Роль ценностей в анти-иммигрантских настроениях
1.1 Гипотезы
- whether and to what extent country variations in the level of opposition to immigration and perceived threat can be accounted for by the level of universalism and conformity/tradition values,
- whether the effect of universalism and conformity/tradition is fully or only partially mediated by perceived threat, and
- the extent to which these patterns differ across countries.
1.2 Операционализация понятий
Мнение о въезде иммигрантов (Allow) – Позволить разным группам иммигрантов въезжать в страну
imsmetnAllow many/few immigrants of same race/ethnic group as majorityimdfetnAllow many/few immigrants of different race/ethnic group from majorityimpcntrAllow many/few immigrants from poorer countries outside Europe
Символическая угроза (Symbolic threat) – “Иммигранты ухудшают культуру и портят страну”
imuecltCountry’s cultural life undermined or enriched by immigrantsimwbcntImmigrants make country worse or better place to liverlgueimReligious beliefs and practices undermined or enriched by immigrants
“Равенство для всех” (Universalism)
ipeqoptImportant that people are treated equally and have equal opportunitiesipudrstImportant to understand different people
Конформность (Conformity)
ipfruleImportant to do what is told and follow rulesipmodstImportant to be humble and modest, not draw attentionipbhprpImportant to behave properlyimptradImportant to follow traditions and customs
Контрольные переменные:
ageaвозрастgndrгендерhincfelсубъективный доходeiscedуровень образованияlrscaleшкала политической ориентации (левый – правый)rlgdgrоценка собственной религиозности
1.3 Модель 1
Используя данные 7 раунда ESS, постройте такую модель для Австрии.
- Сначала постройте КФА для латентных переменных, а затем дополните его регрессионными связями и контрольными переменными.
- На каждом этапе проверяйте статистики согласия. Как можно улучшить факторную модель? А как можно улучшить структурную модель? И стоит ли это делать?
- Постройте диаграмму структурной модели и выведите таблицу с коэффициентами.
Для подготовки переменной образования используйте следующий код:
ess7$eisced[ess7$eisced == 4] <- 3
ess7$eisced[ess7$eisced == 5] <- 4
ess7$eisced[ess7$eisced == 6|ess7$eisced == 7] <- 5library("foreign")
ess7 <- read.spss(
"data/ESS7e02_1.sav",
use.value.labels = F,
use.missings = T,
to.data.frame = T)
ess7$eisced[ess7$eisced == 4] <- 3
ess7$eisced[ess7$eisced == 5] <- 4
ess7$eisced[ess7$eisced == 6| ess7$eisced == 7] <- 5library("lavaan")
cfa.AT <- cfa("Allow =~ imsmetn + imdfetn + impcntr;
Symbolic =~ imueclt + imwbcnt + rlgueim;
Universalism =~ ipeqopt + ipudrst ;
Conformity =~ ipfrule + ipmodst + ipbhprp + imptrad;
",
ess7[ess7$cntry == "AT",],
estimator = "ml",
missing = "listwise")
summary(cfa.AT, fit = T, est = T)> lavaan 0.6-9 ended normally after 55 iterations
>
> Estimator ML
> Optimization method NLMINB
> Number of model parameters 30
>
> Used Total
> Number of observations 1590 1795
>
> Model Test User Model:
>
> Test statistic 189.353
> Degrees of freedom 48
> P-value (Chi-square) 0.000
>
> Model Test Baseline Model:
>
> Test statistic 7925.694
> Degrees of freedom 66
> P-value 0.000
>
> User Model versus Baseline Model:
>
> Comparative Fit Index (CFI) 0.982
> Tucker-Lewis Index (TLI) 0.975
>
> Loglikelihood and Information Criteria:
>
> Loglikelihood user model (H0) -27737.590
> Loglikelihood unrestricted model (H1) -27642.913
>
> Akaike (AIC) 55535.179
> Bayesian (BIC) 55696.324
> Sample-size adjusted Bayesian (BIC) 55601.020
>
> Root Mean Square Error of Approximation:
>
> RMSEA 0.043
> 90 Percent confidence interval - lower 0.037
> 90 Percent confidence interval - upper 0.050
> P-value RMSEA <= 0.05 0.961
>
> Standardized Root Mean Square Residual:
>
> SRMR 0.038
>
> Parameter Estimates:
>
> Standard errors Standard
> Information Expected
> Information saturated (h1) model Structured
>
> Latent Variables:
> Estimate Std.Err z-value P(>|z|)
> Allow =~
> imsmetn 1.000
> imdfetn 1.247 0.030 41.657 0.000
> impcntr 1.189 0.031 38.531 0.000
> Symbolic =~
> imueclt 1.000
> imwbcnt 0.824 0.020 40.643 0.000
> rlgueim 0.737 0.022 32.753 0.000
> Universalism =~
> ipeqopt 1.000
> ipudrst 0.928 0.065 14.226 0.000
> Conformity =~
> ipfrule 1.000
> ipmodst 0.726 0.062 11.663 0.000
> ipbhprp 1.093 0.079 13.802 0.000
> imptrad 0.880 0.069 12.843 0.000
>
> Covariances:
> Estimate Std.Err z-value P(>|z|)
> Allow ~~
> Symbolic -1.147 0.058 -19.947 0.000
> Universalism 0.216 0.017 12.441 0.000
> Conformity -0.100 0.016 -6.277 0.000
> Symbolic ~~
> Universalism -0.732 0.060 -12.234 0.000
> Conformity 0.347 0.056 6.145 0.000
> Universalism ~~
> Conformity 0.100 0.019 5.156 0.000
>
> Variances:
> Estimate Std.Err z-value P(>|z|)
> .imsmetn 0.280 0.011 24.578 0.000
> .imdfetn 0.083 0.008 10.308 0.000
> .impcntr 0.206 0.010 20.229 0.000
> .imueclt 1.406 0.096 14.680 0.000
> .imwbcnt 1.460 0.077 18.928 0.000
> .rlgueim 2.666 0.109 24.445 0.000
> .ipeqopt 0.465 0.034 13.791 0.000
> .ipudrst 0.577 0.033 17.715 0.000
> .ipfrule 1.154 0.053 21.865 0.000
> .ipmodst 1.097 0.044 24.808 0.000
> .ipbhprp 0.674 0.043 15.592 0.000
> .imptrad 1.077 0.047 23.013 0.000
> Allow 0.439 0.024 18.183 0.000
> Symbolic 5.302 0.247 21.501 0.000
> Universalism 0.447 0.041 11.044 0.000
> Conformity 0.478 0.053 9.062 0.000sem.AT <- sem("Allow =~ imsmetn + imdfetn + impcntr;
Symbolic =~ imueclt + imwbcnt + rlgueim;
Universalism =~ ipeqopt + ipudrst;
Conformity =~ ipfrule + ipmodst + ipbhprp + imptrad;
# регрессии
Allow ~ Universalism + Conformity;
Symbolic ~ Universalism + Conformity;
# контрольные переменные
Allow + Symbolic ~ agea + gndr + hincfel + eisced + lrscale + rlgdgr ;
",
ess7[ess7$cntry == "AT",],
estimator = "ml",
missing = "listwise")
summary(sem.AT, fit = T, est = T)> lavaan 0.6-9 ended normally after 70 iterations
>
> Estimator ML
> Optimization method NLMINB
> Number of model parameters 42
>
> Used Total
> Number of observations 1475 1795
>
> Model Test User Model:
>
> Test statistic 684.271
> Degrees of freedom 108
> P-value (Chi-square) 0.000
>
> Model Test Baseline Model:
>
> Test statistic 7693.810
> Degrees of freedom 138
> P-value 0.000
>
> User Model versus Baseline Model:
>
> Comparative Fit Index (CFI) 0.924
> Tucker-Lewis Index (TLI) 0.903
>
> Loglikelihood and Information Criteria:
>
> Loglikelihood user model (H0) -25573.083
> Loglikelihood unrestricted model (H1) -25230.948
>
> Akaike (AIC) 51230.166
> Bayesian (BIC) 51452.615
> Sample-size adjusted Bayesian (BIC) 51319.194
>
> Root Mean Square Error of Approximation:
>
> RMSEA 0.060
> 90 Percent confidence interval - lower 0.056
> 90 Percent confidence interval - upper 0.064
> P-value RMSEA <= 0.05 0.000
>
> Standardized Root Mean Square Residual:
>
> SRMR 0.065
>
> Parameter Estimates:
>
> Standard errors Standard
> Information Expected
> Information saturated (h1) model Structured
>
> Latent Variables:
> Estimate Std.Err z-value P(>|z|)
> Allow =~
> imsmetn 1.000
> imdfetn 1.292 0.035 37.173 0.000
> impcntr 1.229 0.035 34.635 0.000
> Symbolic =~
> imueclt 1.000
> imwbcnt 0.817 0.023 36.103 0.000
> rlgueim 0.727 0.025 29.385 0.000
> Universalism =~
> ipeqopt 1.000
> ipudrst 0.899 0.074 12.093 0.000
> Conformity =~
> ipfrule 1.000
> ipmodst 0.715 0.064 11.143 0.000
> ipbhprp 1.097 0.081 13.466 0.000
> imptrad 0.907 0.072 12.629 0.000
>
> Regressions:
> Estimate Std.Err z-value P(>|z|)
> Allow ~
> Universalism 0.437 0.041 10.728 0.000
> Conformity -0.309 0.034 -9.040 0.000
> Symbolic ~
> Universalism -1.498 0.142 -10.520 0.000
> Conformity 1.048 0.121 8.643 0.000
> Allow ~
> agea 0.002 0.001 1.786 0.074
> gndr 0.016 0.030 0.548 0.583
> hincfel 0.117 0.022 5.406 0.000
> eisced -0.011 0.006 -1.788 0.074
> lrscale 0.066 0.008 7.884 0.000
> rlgdgr -0.026 0.005 -4.848 0.000
> Symbolic ~
> agea -0.011 0.003 -3.388 0.001
> gndr -0.080 0.111 -0.724 0.469
> hincfel -0.413 0.080 -5.188 0.000
> eisced 0.032 0.022 1.491 0.136
> lrscale -0.236 0.031 -7.703 0.000
> rlgdgr 0.112 0.020 5.609 0.000
>
> Covariances:
> Estimate Std.Err z-value P(>|z|)
> Universalism ~~
> Conformity 0.094 0.020 4.744 0.000
> .Allow ~~
> .Symbolic -0.516 0.043 -12.069 0.000
>
> Variances:
> Estimate Std.Err z-value P(>|z|)
> .imsmetn 0.284 0.012 23.954 0.000
> .imdfetn 0.079 0.008 9.288 0.000
> .impcntr 0.207 0.011 19.374 0.000
> .imueclt 1.400 0.101 13.908 0.000
> .imwbcnt 1.490 0.081 18.409 0.000
> .rlgueim 2.645 0.112 23.546 0.000
> .ipeqopt 0.424 0.038 11.067 0.000
> .ipudrst 0.589 0.036 16.535 0.000
> .ipfrule 1.155 0.054 21.255 0.000
> .ipmodst 1.110 0.046 24.127 0.000
> .ipbhprp 0.661 0.044 15.040 0.000
> .imptrad 1.058 0.048 21.895 0.000
> .Allow 0.248 0.017 14.318 0.000
> .Symbolic 3.190 0.200 15.962 0.000
> Universalism 0.447 0.045 9.965 0.000
> Conformity 0.475 0.054 8.764 0.000> lhs op rhs mi epc sepc.lv sepc.all sepc.nox
> 193 Conformity ~ rlgdgr 107.884 -0.080 -0.116 -0.332 -0.116
> 176 Universalism ~ Allow 76.111 1.179 1.087 1.087 1.087
> 230 lrscale ~ Allow 74.277 1.633 1.006 0.553 0.553
> 231 lrscale ~ Symbolic 74.265 -0.477 -1.045 -0.574 -0.574
> 242 rlgdgr ~ Conformity 73.734 -1.109 -0.764 -0.266 -0.266
> 177 Universalism ~ Symbolic 68.738 -0.298 -0.976 -0.976 -0.976
> 188 Conformity ~ agea 62.288 -0.010 -0.014 -0.253 -0.014
> 183 Universalism ~ lrscale 60.023 0.093 0.139 0.253 0.139
library(semTable)
s.tab <- semTable(list(cfa.AT, sem.AT),
columns = "eststars",
type = "html",
print.results = FALSE)| Model 1 | Model 2 | |
| Estimate | Estimate | |
| Factor Loadings | ||
| Allow | ||
| imsmetn | 1.00+ | 1.00+ |
| imdfetn | 1.25*** | 1.29*** |
| impcntr | 1.19*** | 1.23*** |
| Symbolic | ||
| imueclt | 1.00+ | 1.00+ |
| imwbcnt | 0.82*** | 0.82*** |
| rlgueim | 0.74*** | 0.73*** |
| Universalism | ||
| ipeqopt | 1.00+ | 1.00+ |
| ipudrst | 0.93*** | 0.90*** |
| Conformity | ||
| ipfrule | 1.00+ | 1.00+ |
| ipmodst | 0.73*** | 0.72*** |
| ipbhprp | 1.09*** | 1.10*** |
| imptrad | 0.88*** | 0.91*** |
| Regression Slopes | ||
| Allow | ||
| Universalism | 0.44*** | |
| Conformity | -0.31*** | |
| agea | 0.00 | |
| gndr | 0.02 | |
| hincfel | 0.12*** | |
| eisced | -0.01 | |
| lrscale | 0.07*** | |
| rlgdgr | -0.03*** | |
| Symbolic | ||
| Universalism | -1.50*** | |
| Conformity | 1.05*** | |
| agea | -0.01*** | |
| gndr | -0.08 | |
| hincfel | -0.41*** | |
| eisced | 0.03 | |
| lrscale | -0.24*** | |
| rlgdgr | 0.11*** | |
| Residual Variances | ||
| imsmetn | 0.28*** | 0.28*** |
| imdfetn | 0.08*** | 0.08*** |
| impcntr | 0.21*** | 0.21*** |
| imueclt | 1.41*** | 1.40*** |
| imwbcnt | 1.46*** | 1.49*** |
| rlgueim | 2.67*** | 2.64*** |
| ipeqopt | 0.47*** | 0.42*** |
| ipudrst | 0.58*** | 0.59*** |
| ipfrule | 1.15*** | 1.16*** |
| ipmodst | 1.10*** | 1.11*** |
| ipbhprp | 0.67*** | 0.66*** |
| imptrad | 1.08*** | 1.06*** |
| agea | 316.25+ | |
| gndr | 0.25+ | |
| hincfel | 0.48+ | |
| eisced | 6.43+ | |
| lrscale | 3.31+ | |
| rlgdgr | 8.25+ | |
| Residual Covariances | ||
| agea w/gndr | -0.07+ | |
| agea w/hincfel | -0.02+ | |
| agea w/eisced | -1.03+ | |
| agea w/lrscale | 1.90+ | |
| agea w/rlgdgr | 10.16+ | |
| gndr w/hincfel | -0.00+ | |
| gndr w/eisced | 0.01+ | |
| gndr w/lrscale | -0.03+ | |
| gndr w/rlgdgr | 0.20+ | |
| hincfel w/eisced | -0.17+ | |
| hincfel w/lrscale | 0.04+ | |
| hincfel w/rlgdgr | 0.08+ | |
| eisced w/lrscale | -0.18+ | |
| eisced w/rlgdgr | -0.03+ | |
| lrscale w/rlgdgr | 0.61+ | |
| Latent Variances | ||
| Allow | 0.44*** | 0.25*** |
| Symbolic | 5.30*** | 3.19*** |
| Universalism | 0.45*** | 0.45*** |
| Conformity | 0.48*** | 0.48*** |
| Latent Covariances | ||
| Allow w/Symbolic | -1.15*** | -0.52*** |
| Allow w/Universalism | 0.22*** | |
| Allow w/Conformity | -0.10*** | |
| Symbolic w/Universalism | -0.73*** | |
| Symbolic w/Conformity | 0.35*** | |
| Universalism w/Conformity | 0.10*** | 0.09*** |
| Fit Indices | ||
| χ2 | 189.35(48)*** | 684.27(108)*** |
| CFI | 0.98 | 0.92 |
| TLI | 0.98 | 0.90 |
| RMSEA | 0.04 | 0.06 |
| +Fixed parameter | ||
| *p<0.05, **p<0.01, ***p<0.001 | ||
1.4 Модель 2
В исходной статье также рассматривается модель медиации, в которой эффект ценностей на положительное мнение о въезде иммигрантов опосредовано фактором символической угрозы.
- Достройте модель и оцените непрямой и общий эффекты ценностей на мнение о въезде иммигрантов.
- Подтвердились ли гипотезы, отображенные на диаграмме выше?
- Совпадают ли результаты с приведенными в статье?
sem.AT2.full <- sem("Allow =~ imsmetn + imdfetn + impcntr;
Symbolic =~ imueclt + imwbcnt + rlgueim;
Universalism =~ ipeqopt + ipudrst;
Conformity =~ ipfrule + ipmodst + ipbhprp + imptrad;
# регрессии
Allow ~ A_U*Universalism + A_C*Conformity;
Symbolic ~ U_S*Universalism + C_S*Conformity;
# контрольные переменные
Allow + Symbolic ~ agea + gndr + hincfel + eisced + lrscale + rlgdgr ;
# новые строчки
Allow ~ A_S*Symbolic;
IND_U := A_S*U_S;
IND_C := A_S*C_S;
TOT_U := IND_U + A_U;
TOT_C := IND_C + A_C;
",
ess7[ess7$cntry == "AT",],
estimator = "ml",
missing = "listwise")
summary(sem.AT2.full, fit = T, est = T)> lavaan 0.6-9 ended normally after 76 iterations
>
> Estimator ML
> Optimization method NLMINB
> Number of model parameters 42
>
> Used Total
> Number of observations 1475 1795
>
> Model Test User Model:
>
> Test statistic 684.271
> Degrees of freedom 108
> P-value (Chi-square) 0.000
>
> Model Test Baseline Model:
>
> Test statistic 7693.810
> Degrees of freedom 138
> P-value 0.000
>
> User Model versus Baseline Model:
>
> Comparative Fit Index (CFI) 0.924
> Tucker-Lewis Index (TLI) 0.903
>
> Loglikelihood and Information Criteria:
>
> Loglikelihood user model (H0) -25573.083
> Loglikelihood unrestricted model (H1) -25230.948
>
> Akaike (AIC) 51230.166
> Bayesian (BIC) 51452.615
> Sample-size adjusted Bayesian (BIC) 51319.194
>
> Root Mean Square Error of Approximation:
>
> RMSEA 0.060
> 90 Percent confidence interval - lower 0.056
> 90 Percent confidence interval - upper 0.064
> P-value RMSEA <= 0.05 0.000
>
> Standardized Root Mean Square Residual:
>
> SRMR 0.065
>
> Parameter Estimates:
>
> Standard errors Standard
> Information Expected
> Information saturated (h1) model Structured
>
> Latent Variables:
> Estimate Std.Err z-value P(>|z|)
> Allow =~
> imsmetn 1.000
> imdfetn 1.292 0.035 37.173 0.000
> impcntr 1.229 0.035 34.635 0.000
> Symbolic =~
> imueclt 1.000
> imwbcnt 0.817 0.023 36.103 0.000
> rlgueim 0.727 0.025 29.385 0.000
> Universalism =~
> ipeqopt 1.000
> ipudrst 0.899 0.074 12.093 0.000
> Conformity =~
> ipfrule 1.000
> ipmodst 0.715 0.064 11.143 0.000
> ipbhprp 1.097 0.081 13.466 0.000
> imptrad 0.907 0.072 12.629 0.000
>
> Regressions:
> Estimate Std.Err z-value P(>|z|)
> Allow ~
> Unvrslsm (A_U) 0.195 0.033 5.839 0.000
> Confrmty (A_C) -0.139 0.028 -4.992 0.000
> Symbolic ~
> Unvrslsm (U_S) -1.498 0.142 -10.520 0.000
> Confrmty (C_S) 1.048 0.121 8.643 0.000
> Allow ~
> agea -0.000 0.001 -0.289 0.773
> gndr 0.003 0.025 0.138 0.890
> hincfel 0.050 0.018 2.745 0.006
> eisced -0.005 0.005 -1.075 0.282
> lrscale 0.028 0.007 3.877 0.000
> rlgdgr -0.008 0.005 -1.774 0.076
> Symbolic ~
> agea -0.011 0.003 -3.388 0.001
> gndr -0.080 0.111 -0.724 0.469
> hincfel -0.413 0.080 -5.188 0.000
> eisced 0.032 0.022 1.491 0.136
> lrscale -0.236 0.031 -7.703 0.000
> rlgdgr 0.112 0.020 5.609 0.000
> Allow ~
> Symbolic (A_S) -0.162 0.010 -16.477 0.000
>
> Covariances:
> Estimate Std.Err z-value P(>|z|)
> Universalism ~~
> Conformity 0.094 0.020 4.744 0.000
>
> Variances:
> Estimate Std.Err z-value P(>|z|)
> .imsmetn 0.284 0.012 23.954 0.000
> .imdfetn 0.079 0.008 9.288 0.000
> .impcntr 0.207 0.011 19.374 0.000
> .imueclt 1.400 0.101 13.908 0.000
> .imwbcnt 1.490 0.081 18.409 0.000
> .rlgueim 2.645 0.112 23.546 0.000
> .ipeqopt 0.424 0.038 11.067 0.000
> .ipudrst 0.589 0.036 16.535 0.000
> .ipfrule 1.155 0.054 21.255 0.000
> .ipmodst 1.110 0.046 24.127 0.000
> .ipbhprp 0.661 0.044 15.040 0.000
> .imptrad 1.058 0.048 21.895 0.000
> .Allow 0.165 0.011 14.648 0.000
> .Symbolic 3.190 0.200 15.962 0.000
> Universalism 0.447 0.045 9.965 0.000
> Conformity 0.475 0.054 8.764 0.000
>
> Defined Parameters:
> Estimate Std.Err z-value P(>|z|)
> IND_U 0.242 0.025 9.771 0.000
> IND_C -0.169 0.021 -8.073 0.000
> TOT_U 0.437 0.041 10.728 0.000
> TOT_C -0.309 0.034 -9.040 0.000sem.AT2.partial <- sem("Allow =~ imsmetn + imdfetn + impcntr;
Symbolic =~ imueclt + imwbcnt + rlgueim;
Universalism =~ ipeqopt + ipudrst;
Conformity =~ ipfrule + ipmodst + ipbhprp + imptrad;
# регрессии
# Allow ~ A_U*Universalism + A_C*Conformity;
Symbolic ~ U_S*Universalism + C_S*Conformity;
# контрольные переменные
Allow + Symbolic ~ agea + gndr + hincfel + eisced + lrscale + rlgdgr ;
# новые строчки
Allow ~ A_S*Symbolic;
IND_U := A_S*U_S;
IND_C := A_S*C_S;
# TOT_U := IND_U + A_U;
# TOT_C := IND_C + A_C;
",
ess7[ess7$cntry == "AT",],
estimator = "ml",
missing = "listwise")
lavTestLRT(sem.AT2.full, sem.AT2.partial)1.5 Модель 3
В статье используются также данные из других стран и особый вид модели - мультигрупповой КФА, в котором одновременной подсчитываются параметры для нескольких групп (стран).
- Достройте модель до мультигрупповой, добавив аргумент
group = "cntry"и убрав из формулы имена параметров и определения непрямых эффектов (последнее необходимо, чтобы избежать фиксирования параметров во всех группах). - Постройте таблицу с параметрами для всех групп, используя
semTable().
sem.MG <- sem("Allow =~ imsmetn + imdfetn + impcntr;
Symbolic =~ imueclt + imwbcnt + rlgueim;
Universalism =~ ipeqopt + ipudrst;
Conformity =~ ipfrule + ipmodst + ipbhprp + imptrad;
# регрессии
Allow ~ Universalism + Conformity;
Symbolic ~ Universalism + Conformity;
# контрольные переменные
Allow + Symbolic ~ agea + gndr + hincfel + eisced + lrscale + rlgdgr ;
# новые строчки
Allow ~ Symbolic;
",
ess7,
group = "cntry",
estimator = "ml",
missing = "listwise")
#summary(sem.AT3, fit = T, est = F)
stab <- semTable::semTable(sem.MG,
columns = "eststars",
type = "html",
print.results = FALSE)| AT | BE | CH | CZ | DE | DK | EE | ES | FI | FR | GB | HU | IE | IL | LT | NL | NO | PL | PT | SE | SI | |
| Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | Estimate | |
| Factor Loadings | |||||||||||||||||||||
| Allow | |||||||||||||||||||||
| imsmetn | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ |
| imdfetn | 1.29*** | 1.27*** | 1.51*** | 1.11*** | 1.46*** | 1.54*** | 1.47*** | 1.12*** | 1.35*** | 1.27*** | 1.18*** | 1.17*** | 1.12*** | 1.98*** | 1.19*** | 1.09*** | 1.30*** | 1.14*** | 1.15*** | 1.11*** | 1.22*** |
| impcntr | 1.23*** | 1.19*** | 1.55*** | 1.07*** | 1.36*** | 1.41*** | 1.28*** | 1.08*** | 1.22*** | 1.26*** | 1.07*** | 1.05*** | 1.03*** | 1.69*** | 0.91*** | 0.94*** | 1.12*** | 1.06*** | 1.04*** | 1.11*** | 1.12*** |
| Symbolic | |||||||||||||||||||||
| imueclt | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ |
| imwbcnt | 0.82*** | 0.90*** | 0.65*** | 0.82*** | 0.97*** | 0.87*** | 0.87*** | 0.92*** | 1.03*** | 0.80*** | 0.94*** | 0.90*** | 1.02*** | 0.97*** | 0.69*** | 0.76*** | 0.76*** | 0.86*** | 0.92*** | 1.05*** | 0.83*** |
| rlgueim | 0.73*** | 0.61*** | 0.56*** | 0.58*** | 0.64*** | 0.62*** | 0.62*** | 0.51*** | 0.73*** | 0.56*** | 0.60*** | 0.71*** | 0.48*** | 0.56*** | 0.60*** | 0.63*** | 0.69*** | 0.57*** | 0.54*** | 0.64*** | 0.59*** |
| Universalism | |||||||||||||||||||||
| ipeqopt | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ |
| ipudrst | 0.90*** | 1.35*** | 0.99*** | 1.09*** | 0.93*** | 0.76*** | 1.06*** | 1.34*** | 0.66*** | 1.20*** | 0.99*** | 1.67*** | 1.29*** | 1.07*** | 1.01*** | 1.05*** | 0.70*** | 1.26*** | 1.19*** | 0.82*** | 1.05*** |
| Conformity | |||||||||||||||||||||
| ipfrule | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ | 1.00+ |
| ipmodst | 0.71*** | 0.70*** | 0.60*** | 0.96*** | 0.50*** | 0.81*** | 0.77*** | 0.52*** | 0.64*** | 1.10*** | 0.56*** | 1.11*** | 0.82*** | 0.82*** | 0.84*** | 0.67*** | 0.60*** | 0.93*** | 0.88*** | 0.42*** | 1.13*** |
| ipbhprp | 1.10*** | 1.34*** | 0.98*** | 1.23*** | 1.07*** | 1.13*** | 0.99*** | 1.02*** | 0.94*** | 1.59*** | 1.04*** | 1.04*** | 1.10*** | 1.04*** | 1.06*** | 0.86*** | 1.04*** | 1.06*** | 1.26*** | 0.93*** | 1.28*** |
| imptrad | 0.91*** | 0.83*** | 0.95*** | 0.89*** | 0.80*** | 0.58*** | 0.69*** | 0.97*** | 0.72*** | 1.12*** | 0.89*** | 0.77*** | 0.95*** | 0.71*** | 0.84*** | 0.65*** | 0.75*** | 0.81*** | 1.14*** | 0.60*** | 1.12*** |
| Regression Slopes | |||||||||||||||||||||
| Allow | |||||||||||||||||||||
| Universalism | 0.19*** | 0.25*** | 0.12* | 0.66** | 0.18*** | 0.15*** | 0.18** | 0.12 | 0.13*** | 0.11** | 0.17*** | 0.31* | 0.26*** | 0.16*** | -0.27 | 0.16** | 0.14*** | 0.16 | 0.07 | 0.16*** | 0.08 |
| Conformity | -0.14*** | -0.11* | -0.08** | -0.48* | -0.09*** | -0.08*** | -0.13** | -0.08* | -0.06*** | -0.06 | -0.07** | -0.18 | -0.08** | -0.11*** | 0.17 | -0.06* | -0.06* | -0.03 | -0.03 | -0.03 | 0.00 |
| agea | -0.00 | 0.00* | 0.00*** | 0.00 | 0.00 | 0.00*** | 0.01*** | 0.00 | 0.00*** | 0.00*** | 0.00*** | 0.00** | 0.00*** | 0.00*** | 0.00 | -0.00* | 0.00*** | 0.01*** | -0.00 | 0.00* | 0.01*** |
| gndr | 0.00 | 0.04 | 0.01 | 0.04 | 0.05** | 0.00 | -0.02 | -0.04 | -0.01 | -0.01 | 0.03 | 0.03 | 0.00 | -0.03 | -0.02 | 0.02 | 0.01 | -0.02 | -0.06 | -0.03 | -0.04 |
| hincfel | 0.05** | 0.03 | 0.03* | 0.02 | 0.05*** | 0.03 | 0.04* | 0.03 | 0.03 | 0.03* | 0.04* | -0.00 | 0.01 | 0.02* | 0.01 | 0.04 | -0.01 | 0.04 | 0.02 | 0.02 | 0.01 |
| eisced | -0.01 | -0.00 | -0.04*** | -0.01 | -0.01 | -0.01 | -0.02 | -0.03* | -0.00 | -0.06*** | 0.00 | -0.02* | -0.00 | -0.01* | -0.04* | -0.00 | 0.00 | -0.01 | 0.00 | 0.00 | -0.03 |
| lrscale | 0.03*** | 0.02*** | 0.04*** | -0.02* | 0.02*** | 0.01** | 0.00 | 0.03*** | 0.01** | 0.02*** | 0.00 | 0.02* | 0.02** | 0.03*** | 0.02** | 0.02 | 0.02*** | 0.01 | 0.01 | 0.01* | 0.01 |
| rlgdgr | -0.01 | 0.01* | -0.01* | -0.02** | -0.01*** | -0.00 | 0.01* | 0.00 | 0.01** | 0.00 | -0.01 | -0.01 | 0.01* | 0.01*** | -0.01 | 0.00 | 0.00 | 0.01 | 0.01 | 0.00 | 0.02** |
| Symbolic | -0.16*** | -0.19*** | -0.09*** | -0.17*** | -0.14*** | -0.11*** | -0.13*** | -0.25*** | -0.19*** | -0.16*** | -0.16*** | -0.18*** | -0.18*** | -0.09*** | -0.20*** | -0.23*** | -0.13*** | -0.19*** | -0.23*** | -0.17*** | -0.17*** |
| Symbolic | |||||||||||||||||||||
| Universalism | -1.50*** | -2.18*** | -1.89*** | -1.74** | -1.85*** | -1.50*** | -1.14*** | -1.33*** | -1.03*** | -1.66*** | -1.84*** | -1.05* | -1.51*** | -1.47*** | 0.58 | -1.55*** | -1.31*** | -2.22*** | -1.00*** | -1.40*** | -2.00*** |
| Conformity | 1.05*** | 1.10*** | 0.93*** | 1.52** | 0.78*** | 0.64*** | 0.63*** | 0.64*** | 0.53*** | 1.43*** | 0.95*** | 0.61 | 0.85*** | 1.05*** | -0.39 | 0.58*** | 0.70*** | 1.19*** | 0.71*** | 0.60*** | 1.55*** |
| agea | -0.01*** | -0.01*** | 0.00 | 0.00 | -0.01** | -0.00 | -0.03*** | -0.01 | -0.01*** | -0.01*** | -0.02*** | -0.01* | -0.01* | 0.02*** | -0.02*** | -0.00* | 0.00 | 0.00 | -0.01* | -0.01*** | -0.00 |
| gndr | -0.08 | -0.32*** | -0.22* | 0.26* | -0.23** | 0.13 | 0.12 | -0.46*** | 0.20** | -0.16 | -0.47*** | -0.06 | -0.18 | -0.10 | 0.26* | -0.07 | 0.06 | -0.00 | -0.30* | 0.15 | 0.02 |
| hincfel | -0.41*** | -0.60*** | -0.02 | -0.08 | -0.45*** | -0.43*** | -0.08 | -0.13 | -0.29*** | -0.34*** | -0.61*** | -0.24** | -0.44*** | -0.43*** | -0.30*** | -0.33*** | -0.32*** | -0.24* | -0.34*** | -0.42*** | -0.25** |
| eisced | 0.03 | 0.05*** | 0.37*** | 0.18** | 0.40*** | 0.23*** | 0.13** | 0.24*** | 0.16*** | 0.35*** | 0.01* | 0.01 | 0.06*** | 0.02 | 0.21*** | 0.03** | 0.07*** | 0.14** | 0.11*** | 0.07*** | 0.52*** |
| lrscale | -0.24*** | -0.10*** | -0.33*** | 0.09*** | -0.18*** | -0.15*** | 0.03 | -0.06* | -0.01 | -0.27*** | -0.12*** | -0.07** | 0.00 | 0.13*** | -0.04 | -0.17*** | -0.20*** | -0.03 | 0.03 | -0.08*** | -0.22*** |
| rlgdgr | 0.11*** | 0.09*** | 0.08*** | 0.01 | 0.08*** | 0.02 | 0.07*** | 0.02 | 0.05*** | 0.08*** | 0.12*** | 0.06** | 0.02 | -0.07*** | 0.02 | 0.04** | 0.04 | -0.04 | -0.01 | 0.08*** | 0.07** |
| Intercepts | |||||||||||||||||||||
| imsmetn | 1.70*** | 1.45*** | 1.70*** | 2.92*** | 1.43*** | 1.62*** | 1.73*** | 1.78*** | 1.85*** | 1.78*** | 1.53*** | 2.07*** | 1.62*** | 1.42*** | 1.98*** | 1.70*** | 1.47*** | 1.66*** | 1.94*** | 1.31*** | 1.78*** |
| imdfetn | 1.90*** | 1.51*** | 1.94*** | 3.27*** | 1.69*** | 1.84*** | 2.03*** | 1.87*** | 2.02*** | 1.94*** | 1.53*** | 2.62*** | 1.69*** | 2.22*** | 2.29*** | 1.73*** | 1.53*** | 1.81*** | 2.06*** | 1.32*** | 1.95*** |
| impcntr | 2.05*** | 1.70*** | 2.05*** | 3.25*** | 1.90*** | 2.15*** | 2.46*** | 1.95*** | 2.28*** | 2.13*** | 1.88*** | 2.90*** | 1.97*** | 2.61*** | 2.70*** | 2.05*** | 1.75*** | 1.94*** | 2.20*** | 1.42*** | 2.20*** |
| imueclt | 6.79*** | 7.81*** | 6.41*** | 2.59*** | 6.70*** | 6.39*** | 5.93*** | 6.89*** | 6.87*** | 6.81*** | 7.98*** | 5.94*** | 6.68*** | 5.26*** | 5.56*** | 7.56*** | 6.82*** | 6.56*** | 7.02*** | 8.21*** | 4.94*** |
| imwbcnt | 5.95*** | 6.62*** | 5.67*** | 2.84*** | 5.81*** | 6.19*** | 5.29*** | 5.95*** | 5.51*** | 5.96*** | 7.60*** | 5.03*** | 6.63*** | 4.82*** | 5.32*** | 6.45*** | 6.28*** | 5.92*** | 5.85*** | 7.61*** | 4.30*** |
| rlgueim | 5.81*** | 5.99*** | 5.42*** | 3.20*** | 5.53*** | 5.38*** | 5.27*** | 5.66*** | 5.84*** | 5.24*** | 6.19*** | 5.75*** | 5.56*** | 4.80*** | 5.12*** | 5.85*** | 5.69*** | 5.57*** | 5.93*** | 6.12*** | 4.81*** |
| ipeqopt | 1.95*** | 2.04*** | 1.93*** | 2.47*** | 1.87*** | 2.11*** | 2.36*** | 1.61*** | 1.95*** | 1.86*** | 2.01*** | 2.03*** | 2.01*** | 1.94*** | 2.36*** | 2.09*** | 2.13*** | 1.89*** | 2.13*** | 1.75*** | 1.67*** |
| ipudrst | 2.25*** | 2.32*** | 2.04*** | 2.76*** | 2.04*** | 2.26*** | 2.34*** | 2.00*** | 2.11*** | 2.34*** | 2.28*** | 2.60*** | 2.31*** | 2.05*** | 3.13*** | 2.43*** | 2.41*** | 2.26*** | 2.68*** | 2.18*** | 2.08*** |
| ipfrule | 3.15*** | 3.18*** | 3.37*** | 2.70*** | 3.57*** | 2.81*** | 3.33*** | 3.30*** | 3.04*** | 3.84*** | 3.36*** | 3.30*** | 3.09*** | 2.78*** | 3.21*** | 3.00*** | 2.74*** | 2.41*** | 3.51*** | 3.39*** | 3.06*** |
| ipmodst | 2.82*** | 2.44*** | 2.40*** | 2.74*** | 2.60*** | 3.24*** | 2.83*** | 1.91*** | 2.98*** | 2.39*** | 2.66*** | 2.56*** | 2.53*** | 2.16*** | 3.15*** | 3.16*** | 3.43*** | 2.59*** | 2.39*** | 2.66*** | 1.90*** |
| ipbhprp | 2.34*** | 2.53*** | 2.71*** | 2.53*** | 2.93*** | 2.60*** | 2.55*** | 2.24*** | 2.85*** | 2.70*** | 2.63*** | 2.34*** | 2.58*** | 2.47*** | 2.77*** | 2.87*** | 2.47*** | 2.30*** | 3.13*** | 3.06*** | 2.05*** |
| imptrad | 2.55*** | 2.74*** | 2.79*** | 2.59*** | 2.97*** | 2.75*** | 2.91*** | 2.70*** | 3.03*** | 3.34*** | 2.85*** | 2.41*** | 2.50*** | 2.41*** | 2.47*** | 2.89*** | 3.02*** | 2.11*** | 2.94*** | 3.17*** | 2.39*** |
| agea | 49.39+ | 46.86+ | 47.51+ | 47.36+ | 49.85+ | 47.78+ | 49.01+ | 46.65+ | 50.51+ | 49.41+ | 52.36+ | 50.19+ | 50.83+ | 45.79+ | 50.68+ | 50.78+ | 46.46+ | 45.71+ | 51.12+ | 48.93+ | 48.93+ |
| gndr | 1.51+ | 1.49+ | 1.49+ | 1.57+ | 1.49+ | 1.47+ | 1.59+ | 1.47+ | 1.50+ | 1.52+ | 1.53+ | 1.57+ | 1.53+ | 1.54+ | 1.59+ | 1.55+ | 1.46+ | 1.51+ | 1.52+ | 1.50+ | 1.51+ |
| hincfel | 1.85+ | 1.89+ | 1.52+ | 2.32+ | 1.72+ | 1.38+ | 2.19+ | 1.95+ | 1.90+ | 1.88+ | 1.80+ | 2.40+ | 2.00+ | 2.18+ | 2.18+ | 1.66+ | 1.44+ | 2.12+ | 2.32+ | 1.46+ | 1.78+ |
| eisced | 3.25+ | 3.55+ | 3.28+ | 3.32+ | 3.58+ | 3.57+ | 3.65+ | 2.84+ | 3.43+ | 3.20+ | 4.25+ | 3.25+ | 3.28+ | 3.79+ | 3.59+ | 3.56+ | 3.75+ | 3.10+ | 2.47+ | 3.59+ | 3.33+ |
| lrscale | 4.74+ | 5.05+ | 5.16+ | 4.94+ | 4.51+ | 5.48+ | 5.18+ | 4.38+ | 5.61+ | 5.07+ | 5.03+ | 5.28+ | 5.12+ | 5.53+ | 5.00+ | 5.09+ | 5.35+ | 5.68+ | 4.83+ | 5.08+ | 4.36+ |
| rlgdgr | 4.70+ | 4.45+ | 4.96+ | 2.21+ | 3.91+ | 3.89+ | 3.74+ | 3.97+ | 4.74+ | 4.66+ | 3.89+ | 3.64+ | 5.28+ | 4.80+ | 5.56+ | 4.20+ | 3.59+ | 6.25+ | 5.39+ | 3.16+ | 4.31+ |
| Residual Variances | |||||||||||||||||||||
| imsmetn | 0.28*** | 0.27*** | 0.22*** | 0.30*** | 0.24*** | 0.25*** | 0.35*** | 0.16*** | 0.22*** | 0.22*** | 0.20*** | 0.57*** | 0.23*** | 0.92*** | 0.31*** | 0.09*** | 0.19*** | 0.17*** | 0.20*** | 0.06*** | 0.19*** |
| imdfetn | 0.08*** | 0.10*** | 0.11*** | 0.16*** | 0.10*** | 0.09*** | 0.15*** | 0.09*** | 0.09*** | 0.12*** | 0.07*** | 0.21*** | 0.11*** | 0.31*** | 0.05*** | 0.05*** | 0.04*** | 0.11*** | 0.13*** | 0.02*** | 0.10*** |
| impcntr | 0.21*** | 0.28*** | 0.18*** | 0.18*** | 0.28*** | 0.25*** | 0.35*** | 0.20*** | 0.24*** | 0.26*** | 0.28*** | 0.24*** | 0.32*** | 0.39*** | 0.42*** | 0.31*** | 0.23*** | 0.22*** | 0.30*** | 0.15*** | 0.30*** |
| imueclt | 1.40*** | 1.95*** | 1.41*** | 1.25*** | 1.64*** | 1.28*** | 1.58*** | 2.24*** | 1.41*** | 1.85*** | 1.46*** | 1.80*** | 1.79*** | 2.00*** | 1.02*** | 1.47*** | 1.68*** | 1.86*** | 3.10*** | 1.45*** | 1.74*** |
| imwbcnt | 1.49*** | 2.01*** | 2.00*** | 1.91*** | 1.45*** | 1.05*** | 1.40*** | 1.62*** | 1.27*** | 1.36*** | 1.33*** | 1.43*** | 1.42*** | 2.04*** | 1.94*** | 1.81*** | 1.79*** | 1.70*** | 2.58*** | 1.18*** | 1.55*** |
| rlgueim | 2.64*** | 3.02*** | 2.63*** | 2.98*** | 2.71*** | 2.46*** | 2.55*** | 3.07*** | 2.74*** | 3.69*** | 3.12*** | 2.82*** | 3.38*** | 3.62*** | 2.06*** | 2.36*** | 2.33*** | 2.76*** | 4.11*** | 3.12*** | 3.35*** |
| ipeqopt | 0.42*** | 0.66*** | 0.68*** | 0.81*** | 0.66*** | 0.72*** | 0.88*** | 0.42*** | 0.40*** | 0.71*** | 0.72*** | 0.86*** | 0.63*** | 0.87*** | 0.84*** | 0.65*** | 0.64*** | 0.60*** | 0.74*** | 0.47*** | 0.40*** |
| ipudrst | 0.59*** | 0.50*** | 0.59*** | 0.81*** | 0.56*** | 0.71*** | 0.60*** | 0.52*** | 0.57*** | 0.79*** | 0.73*** | 0.75*** | 0.53*** | 0.66*** | 0.98*** | 0.59*** | 0.70*** | 0.62*** | 0.72*** | 0.73*** | 0.47*** |
| ipfrule | 1.16*** | 1.35*** | 1.52*** | 0.83*** | 1.33*** | 1.19*** | 1.21*** | 1.59*** | 1.09*** | 1.82*** | 1.39*** | 1.52*** | 1.23*** | 1.37*** | 1.06*** | 0.83*** | 0.91*** | 0.79*** | 1.52*** | 1.10*** | 1.74*** |
| ipmodst | 1.11*** | 0.91*** | 1.06*** | 0.97*** | 1.26*** | 1.51*** | 1.29*** | 0.73*** | 1.22*** | 1.01*** | 1.35*** | 0.73*** | 0.98*** | 0.93*** | 1.12*** | 1.18*** | 1.47*** | 1.05*** | 0.88*** | 1.23*** | 0.45*** |
| ipbhprp | 0.66*** | 0.65*** | 1.19*** | 0.73*** | 0.98*** | 0.94*** | 0.79*** | 0.74*** | 0.84*** | 1.08*** | 0.93*** | 0.72*** | 0.74*** | 0.79*** | 0.71*** | 0.73*** | 0.72*** | 0.50*** | 1.04*** | 1.06*** | 0.57*** |
| imptrad | 1.06*** | 1.34*** | 1.29*** | 1.29*** | 1.47*** | 1.75*** | 1.42*** | 1.60*** | 1.47*** | 2.02*** | 1.56*** | 1.07*** | 1.09*** | 1.58*** | 1.10*** | 1.24*** | 1.50*** | 0.77*** | 1.22*** | 1.71*** | 1.13*** |
| agea | 316.25+ | 354.05+ | 347.33+ | 262.53+ | 326.99+ | 335.86+ | 340.73+ | 312.44+ | 332.97+ | 337.18+ | 321.85+ | 299.61+ | 304.00+ | 364.51+ | 289.21+ | 297.69+ | 335.82+ | 322.16+ | 345.44+ | 372.24+ | 322.99+ |
| gndr | 0.25+ | 0.25+ | 0.25+ | 0.24+ | 0.25+ | 0.25+ | 0.24+ | 0.25+ | 0.25+ | 0.25+ | 0.25+ | 0.25+ | 0.25+ | 0.25+ | 0.24+ | 0.25+ | 0.25+ | 0.25+ | 0.25+ | 0.25+ | 0.25+ |
| hincfel | 0.48+ | 0.70+ | 0.53+ | 0.59+ | 0.50+ | 0.39+ | 0.56+ | 0.72+ | 0.45+ | 0.58+ | 0.65+ | 0.56+ | 0.68+ | 0.92+ | 0.64+ | 0.59+ | 0.44+ | 0.36+ | 0.71+ | 0.47+ | 0.63+ |
| eisced | 6.43+ | 14.72+ | 1.08+ | 0.67+ | 1.02+ | 3.60+ | 1.21+ | 2.15+ | 3.08+ | 1.64+ | 54.94+ | 3.57+ | 10.05+ | 14.88+ | 1.38+ | 15.36+ | 7.27+ | 1.43+ | 7.81+ | 8.49+ | 1.08+ |
| lrscale | 3.31+ | 4.32+ | 3.87+ | 5.12+ | 3.61+ | 5.42+ | 3.75+ | 4.32+ | 3.87+ | 5.80+ | 3.99+ | 5.69+ | 3.47+ | 8.88+ | 4.99+ | 3.57+ | 4.38+ | 5.34+ | 6.90+ | 5.73+ | 5.62+ |
| rlgdgr | 8.25+ | 10.06+ | 8.58+ | 7.73+ | 8.87+ | 7.28+ | 8.22+ | 8.18+ | 7.76+ | 11.34+ | 9.03+ | 7.64+ | 7.05+ | 10.91+ | 6.82+ | 9.57+ | 7.56+ | 6.52+ | 7.63+ | 7.59+ | 9.04+ |
| Residual Covariances | |||||||||||||||||||||
| agea w/gndr | -0.07+ | 0.18+ | 0.10+ | -0.02+ | 0.02+ | 0.29+ | 1.02+ | -0.04+ | 0.14+ | 0.13+ | -0.26+ | 0.40+ | -0.42+ | 0.28+ | 0.76+ | -0.71+ | 0.06+ | -0.05+ | 0.22+ | 0.02+ | -0.15+ |
| agea w/hincfel | -0.02+ | 1.65+ | 0.60+ | 1.54+ | -0.39+ | -0.88+ | 3.15+ | 0.37+ | -0.51+ | -0.06+ | -2.65+ | 1.01+ | -1.01+ | -0.22+ | 3.19+ | -0.69+ | -2.13+ | 2.42+ | 2.39+ | -0.05+ | 2.30+ |
| agea w/eisced | -1.03+ | -5.86+ | 0.48+ | 0.32+ | 1.72+ | 1.12+ | -0.77+ | -7.19+ | -3.61+ | -5.56+ | 6.04+ | -1.60+ | -8.55+ | -7.76+ | -2.85+ | -3.26+ | 2.42+ | -4.77+ | -10.25+ | -3.86+ | -4.15+ |
| agea w/lrscale | 1.90+ | 0.37+ | 3.64+ | -10.17+ | 2.08+ | 2.56+ | -3.44+ | 4.10+ | 3.50+ | 2.62+ | 5.95+ | -8.35+ | 5.41+ | 1.64+ | -2.44+ | 0.83+ | 1.25+ | -0.06+ | 3.72+ | 3.01+ | -0.17+ |
| agea w/rlgdgr | 10.16+ | 9.10+ | 9.80+ | 8.08+ | 2.96+ | 9.90+ | 2.79+ | 12.21+ | 12.99+ | 9.51+ | 11.33+ | 12.41+ | 14.38+ | -2.54+ | 15.17+ | 7.28+ | 11.00+ | 8.98+ | 14.72+ | 9.19+ | 2.35+ |
| gndr w/hincfel | -0.00+ | 0.00+ | 0.01+ | 0.04+ | 0.02+ | 0.01+ | 0.04+ | 0.01+ | 0.01+ | 0.04+ | 0.03+ | 0.00+ | 0.03+ | 0.02+ | 0.04+ | -0.00+ | 0.03+ | 0.02+ | 0.04+ | 0.02+ | 0.03+ |
| gndr w/eisced | 0.01+ | 0.03+ | -0.07+ | -0.02+ | -0.05+ | 0.08+ | 0.06+ | 0.02+ | 0.07+ | -0.03+ | -0.24+ | 0.01+ | 0.04+ | -0.08+ | 0.06+ | -0.01+ | 0.05+ | 0.06+ | -0.01+ | 0.04+ | 0.00+ |
| gndr w/lrscale | -0.03+ | -0.06+ | -0.13+ | -0.04+ | -0.03+ | -0.15+ | -0.02+ | 0.06+ | -0.03+ | -0.07+ | -0.09+ | -0.12+ | -0.02+ | 0.00+ | 0.07+ | -0.10+ | -0.10+ | -0.02+ | -0.01+ | -0.07+ | -0.00+ |
| gndr w/rlgdgr | 0.20+ | 0.19+ | 0.19+ | 0.17+ | 0.14+ | 0.22+ | 0.21+ | 0.24+ | 0.26+ | 0.26+ | 0.15+ | 0.23+ | 0.11+ | 0.08+ | 0.25+ | 0.18+ | 0.23+ | 0.24+ | 0.24+ | 0.18+ | 0.22+ |
| hincfel w/eisced | -0.17+ | -0.22+ | -0.13+ | -0.16+ | -0.16+ | -0.05+ | -0.14+ | -0.33+ | -0.15+ | -0.28+ | -0.25+ | -0.20+ | -0.20+ | -0.51+ | -0.25+ | -0.15+ | 0.00+ | -0.18+ | -0.42+ | -0.11+ | -0.20+ |
| hincfel w/lrscale | 0.04+ | -0.20+ | -0.10+ | -0.23+ | -0.10+ | -0.14+ | -0.22+ | -0.08+ | -0.19+ | -0.05+ | -0.22+ | -0.07+ | -0.13+ | 0.16+ | -0.16+ | -0.16+ | -0.10+ | -0.01+ | -0.17+ | -0.23+ | 0.10+ |
| hincfel w/rlgdgr | 0.08+ | 0.23+ | 0.27+ | 0.18+ | -0.12+ | 0.01+ | 0.15+ | 0.05+ | 0.02+ | 0.22+ | -0.08+ | 0.02+ | 0.00+ | 0.31+ | 0.16+ | 0.18+ | 0.08+ | 0.08+ | 0.26+ | 0.09+ | 0.24+ |
| eisced w/lrscale | -0.18+ | 0.37+ | -0.17+ | 0.30+ | -0.10+ | -0.38+ | 0.23+ | -0.16+ | 0.30+ | -0.18+ | 0.85+ | 0.06+ | 0.05+ | 0.30+ | 0.08+ | -0.17+ | -0.08+ | -0.13+ | -0.21+ | 0.21+ | -0.30+ |
| eisced w/rlgdgr | -0.03+ | -0.63+ | -0.26+ | 0.12+ | 0.03+ | -0.13+ | 0.33+ | -0.61+ | -0.01+ | -0.30+ | -0.55+ | -0.15+ | -0.85+ | 0.11+ | -0.19+ | 0.10+ | -0.43+ | -0.53+ | -1.17+ | -0.15+ | -0.52+ |
| lrscale w/rlgdgr | 0.61+ | 0.37+ | 0.27+ | 0.02+ | 1.10+ | 0.64+ | -0.28+ | 2.26+ | 0.93+ | 1.37+ | 0.91+ | -0.11+ | 0.94+ | 1.94+ | -0.12+ | 0.44+ | 0.52+ | 1.69+ | 1.52+ | 0.71+ | 2.57+ |
| Latent Intercepts | |||||||||||||||||||||
| Allow | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ |
| Symbolic | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ |
| Universalism | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ |
| Conformity | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ | 0.00+ |
| Latent Variances | |||||||||||||||||||||
| Allow | 0.16*** | 0.17*** | 0.10*** | 0.19*** | 0.10*** | 0.09*** | 0.13*** | 0.32*** | 0.13*** | 0.13*** | 0.21*** | 0.17*** | 0.28*** | 0.09*** | 0.33*** | 0.26*** | 0.16*** | 0.28*** | 0.21*** | 0.15*** | 0.17*** |
| Symbolic | 3.19*** | 1.96*** | 2.09*** | 2.55*** | 2.21*** | 2.67*** | 2.96*** | 2.70*** | 1.68*** | 2.80*** | 3.58*** | 2.88*** | 3.11*** | 2.90*** | 3.54*** | 1.61*** | 2.87*** | 2.50*** | 2.87*** | 1.82*** | 2.66*** |
| Universalism | 0.45*** | 0.21*** | 0.24*** | 0.33*** | 0.25*** | 0.64*** | 0.29*** | 0.22*** | 0.66*** | 0.37*** | 0.39*** | 0.22*** | 0.33*** | 0.37*** | 0.41*** | 0.30*** | 0.42*** | 0.25*** | 0.38*** | 0.36*** | 0.22*** |
| Conformity | 0.48*** | 0.32*** | 0.53*** | 0.43*** | 0.64*** | 0.59*** | 0.52*** | 0.55*** | 0.84*** | 0.31*** | 0.67*** | 0.45*** | 0.65*** | 0.74*** | 0.59*** | 0.67*** | 0.55*** | 0.47*** | 0.42*** | 0.81*** | 0.21*** |
| Latent Covariances | |||||||||||||||||||||
| Universalism w/Conformity | 0.09*** | 0.11*** | 0.09*** | 0.32*** | 0.07*** | 0.11*** | 0.25*** | 0.11*** | 0.13*** | 0.14*** | 0.11*** | 0.25*** | 0.22*** | 0.34*** | 0.43*** | 0.13*** | 0.17*** | 0.24*** | 0.20*** | 0.07** | 0.11*** |
| Fit Indices | |||||||||||||||||||||
| χ2 | 16144.14(2268)*** | ||||||||||||||||||||
| CFI | 0.89 | ||||||||||||||||||||
| TLI | 0.86 | ||||||||||||||||||||
| RMSEA | 0.06 | ||||||||||||||||||||
| _BOML10_+Fixed parameter | |||||||||||||||||||||
| _BOML10_*p<0.05, **p<0.01, ***p<0.001 | |||||||||||||||||||||
2 Дополнительное задание для энтузиастов – Политическое участие в Испании
В этом упражнении мы а) воспроизведем пошагово модель из статьи Фернандеса и Франсеса о политическом участии в Испании, а затем улучшим ее.
2.1 Воспроизведем модель
Данные из первого раунда ESS для модели можете найти в файле Spain.Rdata, его можно загрузить командой load(‘Spain.Rdata’). Содержание переменных приведено в таблице ниже
| label | Nº variable in questionnaire |
|---|---|
| contact | B15 Contacted politician or government official last 12 months(Recode 0=No / 1= Yes) |
| workorg | B17 Worked in another organisation or association last 12 months (Recode 0=No / 1= Yes) |
| petition | B19 Signed petition last 12 months (Recode 0=No / 1= Yes) |
| demonstr | B20 Taken part in lawful public demonstration last 12 months (Recode 0=No / 1= Yes) |
| boycott | B21 Boycotted certain products last 12 months (Recode 0=No / 1= Yes) |
| tv | A2 TV watching, news/ politics/current affairs on average weekday |
| radio | A4 Radio listening, news/ politics/current affairs on average weekday |
| trust | A8 Most people can be trusted or you can’t be too careful |
| fairness | A9 Most people try to take advantage of you, or try to be fair |
| interest | B1 How interested in politics (Recode – reverse range) |
| close.party | B25a Feel closer to a particular party than all other parties (Recode 0=No / 1= Yes) |
| soc.activity | C4 Take part in social activities compared to others of same age |
| participate | Generated new variable. Association participate. in E1 to E12. (Recoded new variable 0=No / 1= Yes) |
| voluntary | Generated new variable. Voluntary work in associations. in E1 to E12. (Recoded new variable 0=No / 1= Yes) |
2.2 Просмотрите статью.
Ознакомьтесь с теоретической моделью на Рисунке 1.
2.3 Построим первую факторную модель
Постройте первую факторную модель двух типов индивидуального политического участия в Испании: институционально оформленное и институциональное неоформленное (Figure 2). Институционально оформленное участие может выражаться в таких индикаторах, как контакты с политиками или чиновниками, и взаимодействие с общественными организациями. Институционально не оформленное политическое участие предполагает подписание петиций, участие в демонстрациях, бойкотирование продукции. Обратите внимание, что для идентификации модели используется не привычная фиксация одной из факторных нагрузок (это видно на диаграмме), а фиксация дисперсии латентной переменной. На диаграмме вместо коэффициентов отражены t-значения, то есть отношение коэффициента к его стандартной ошибке. Сверьте получившиеся статистики согласия и коэффициенты с изложенными в статье. Если какие-то значения совсем не совпадают, попробуйте понять, почему.
2.4 Построим вторую факторную модель
Постройте вторую факторную модель - измерительную модель для независимых переменных, призванных, согласно теории, объяснять политическое участие в Испании (Figure 3).
load("data/Spain.Rdata")
# Формула первой измерительной модели
cfa1.formula <- "institutionalized =~ NA*contact+ NA*workorg;
non.institutionalized =~ NA*petition + NA*demonstr + NA*boycott;
institutionalized ~~ 1*institutionalized;
non.institutionalized ~~ 1*non.institutionalized;"
# Формула второй измерительной модели
cfa2.formula <- "associationalism =~ NA*voluntary + NA*participate; associationalism~~1*associationalism;
closeness =~ 1*close.party; close.party ~~ 0*close.party;
social.trust =~ NA*trust + NA*fairness; social.trust ~~ 1*social.trust;
information =~ NA*tv + NA*radio; information ~~ 1*information;
pol.interest =~ 1*interest; interest ~~ 0*interest;
sociability =~ 1*soc.activity; soc.activity ~~ 0*soc.activity;"
# Собственно КФА
CFA1<-cfa(cfa1.formula, Spain, estimator = "ML", missing="listwise")
CFA2<-cfa(cfa2.formula, Spain, estimator = "ML", missing="listwise")
# Диаграммы моделей
semPaths(CFA1, intercepts=F, nCharNodes = 10, whatLabels="est", layout="tree2", rotation=2)
Обратите внимание, что в модели присутствует несколько факторов, измеренных одним индикатором. Чтобы модель была идентифицирована необходимо зафиксировать остаток индикатора ~~0. На практике это означает, что данные индикаторы подменяются латентными переменными, это было сделано из-за того, что в программе LISREL, в которой строилась эта модель, нельзя использовать наблюдаемые переменные как таковые. Итак, мы воспроизвели две измерительные модели для латентных переменных, интересующих нас (политическое участие) и для латентных переменных, призванных объяснить политическое участие (участие в ассоциациях, межличностное доверие и потребление политической информации). Теперь можно переходить к тестированию причинно-следственных связей между латентными переменными.
2.5 Постройте структурную модель
Постройте структурную модель, изображенную на Рисунке 4 в статье. Используйте эстиматор MLR, т.к. ML в данном случае скорее всего не сработает. Сравните характеристики качества модели и величины коэффициентов На рис.4 в статье приведены стандартизованные коэффициенты. Совпадают ли они? Каковы могут быть причины несовпадений? Обратите внимание на размер выборки после LISTWISE-удаления пропущенных данных. Все ли ключевые параметры значимы? Проверьте вывод авторов «In our study we find that [institutionalized individual participation] holds an inverse relation with social trust (-0.11)… and is related positively with associationalism (0.53) and closeness to a political party (0.08)».
- Возможны ли другие причинно-следственные отношения между переменными?
- Отвечают ли выстроенные отношения условиям каузальности?
- Что следует изменить в модели?
# Здесь берем старые формулы КФА и добавляем к ним регрессионные связи между латентными переменными.
structural.formula <- paste(cfa1.formula,
cfa2.formula,
"institutionalized ~ associationalism + closeness + social.trust +
information + pol.interest + sociability + non.institutionalized;
non.institutionalized ~ associationalism + closeness + social.trust +
information + pol.interest + sociability + institutionalized")
structural.model <- sem(structural.formula,
data=Spain,
estimator = "MLR",
missing="listwise")
semPaths(structural.model,
intercepts=F,
nCharNodes = 10,
whatLabels="std",
layout="tree2",
rotation=4,
residuals=F,
shapeMan="rectangle",
sizeMan=10,
sizeMan2=1.5)
2.6 Улучшим модель
Теперь перестроим эту модель заново, с учетом того, что многие переменные не имеют нормального распределения (какие это переменные?), а многие факторы не являются факторами.
- Постройте КФА с пятью факторами, используемыми в статье, без фальшивых одноиндикаторных факторов и с использованием подходящего для категориальных переменных эстиматора WLS и учетом дихотомичности некоторых переменных. Модель, вероятно, не сойдется до тех пор пока не вы не зафиксируете остаток индикатора tv равным 0,05 (это вынужденная мера).
- Постройте структурную модель с одними латентными переменными. Исключите взаименое влияние двух типов политического участия, т.к. они не соответствуют условиям каузальности и теперь такая нерекурсивная модель не идентифицирована. Посмотрите на статистики согласия и на путевую диаграмму.
- Наконец, включите наблюдаемые переменные
close.party,interestиsoc.activityв качестве наблюдаемых. - Посмотрите на статистики согласия итоговой модели, на модификационные индексы, на параметры.
Подтверждаются ли выводы авторов статьи? Как еще можно улучшить модель с теоретической и практической точки зрения?
#### 3. Better and more correct way to model. Does it change the results? ####
#Correct common CFA with ordinal indicators and without fake factors
my.cfa.formula <- "institutionalized =~ NA*contact + NA*workorg;
non.institutionalized =~ NA*petition + NA*demonstr + NA*boycott;
institutionalized ~~ 1*institutionalized;
non.institutionalized ~~ 1*non.institutionalized;
associationalism =~ NA*voluntary + NA*participate;
associationalism~~1*associationalism;
social.trust =~ NA*trust + NA*fairness; social.trust ~~ 1*social.trust;
information =~ NA*tv + NA*radio; information ~~ 1*information;
tv ~~ .05*tv;"
myCFA<-cfa(my.cfa.formula, Spain, estimator = "WLS", missing="listwise", ordered=c("contact", "workorg", "petition", "demonstr", "boycott", "voluntary", "participate"))
semPaths(myCFA, intercepts=F, nCharNodes = 10, whatLabels="std", layout="tree2", rotation=4, residuals=T, shapeMan="rectangle", sizeMan=10, sizeMan2=1.5)
> lavaan 0.6-9 ended normally after 42 iterations
>
> Estimator WLS
> Optimization method NLMINB
> Number of model parameters 35
>
> Used Total
> Number of observations 1062 1729
>
> Model Test User Model:
>
> Test statistic 33.017
> Degrees of freedom 35
> P-value (Chi-square) 0.564
>
> Model Test Baseline Model:
>
> Test statistic 1727.080
> Degrees of freedom 55
> P-value 0.000
>
> User Model versus Baseline Model:
>
> Comparative Fit Index (CFI) 1.000
> Tucker-Lewis Index (TLI) 1.002
>
> Root Mean Square Error of Approximation:
>
> RMSEA 0.000
> 90 Percent confidence interval - lower 0.000
> 90 Percent confidence interval - upper 0.020
> P-value RMSEA <= 0.05 1.000
>
> Standardized Root Mean Square Residual:
>
> SRMR 0.035
>
> Parameter Estimates:
>
> Standard errors Standard
> Information Expected
> Information saturated (h1) model Unstructured
>
> Latent Variables:
> Estimate Std.Err z-value P(>|z|)
> institutionalized =~
> contact 0.657 0.043 15.267 0.000
> workorg 0.847 0.038 22.056 0.000
> non.institutionalized =~
> petition 0.890 0.026 33.783 0.000
> demonstr 0.884 0.026 33.669 0.000
> boycott 0.676 0.043 15.735 0.000
> associationalism =~
> voluntary 0.863 0.050 17.297 0.000
> participate 0.845 0.046 18.556 0.000
> social.trust =~
> trust 1.726 0.199 8.684 0.000
> fairness 1.535 0.173 8.865 0.000
> information =~
> tv 1.349 0.025 54.788 0.000
> radio 0.577 0.049 11.739 0.000
>
> Covariances:
> Estimate Std.Err z-value P(>|z|)
> institutionalized ~~
> non.instttnlzd 0.864 0.043 20.114 0.000
> associationlsm 0.784 0.057 13.798 0.000
> social.trust 0.034 0.056 0.614 0.539
> information -0.065 0.054 -1.193 0.233
> non.institutionalized ~~
> associationlsm 0.520 0.049 10.518 0.000
> social.trust 0.216 0.050 4.364 0.000
> information -0.042 0.042 -0.996 0.319
> associationalism ~~
> social.trust 0.050 0.052 0.962 0.336
> information -0.016 0.048 -0.336 0.737
> social.trust ~~
> information 0.045 0.035 1.283 0.199
>
> Intercepts:
> Estimate Std.Err z-value P(>|z|)
> .contact 0.000
> .workorg 0.000
> .petition 0.000
> .demonstr 0.000
> .boycott 0.000
> .voluntary 0.000
> .participate 0.000
> .trust 6.016 0.068 88.021 0.000
> .fairness 6.333 0.067 93.928 0.000
> .tv 3.080 0.052 58.922 0.000
> .radio 2.864 0.087 33.025 0.000
> institutionlzd 0.000
> non.instttnlzd 0.000
> associationlsm 0.000
> social.trust 0.000
> information 0.000
>
> Thresholds:
> Estimate Std.Err z-value P(>|z|)
> contact|t1 1.129 0.048 23.379 0.000
> workorg|t1 0.918 0.044 20.816 0.000
> petition|t1 0.621 0.041 15.283 0.000
> demonstr|t1 0.885 0.044 20.180 0.000
> boycott|t1 1.335 0.052 25.540 0.000
> voluntary|t1 1.470 0.058 25.529 0.000
> participate|t1 0.587 0.041 14.435 0.000
>
> Variances:
> Estimate Std.Err z-value P(>|z|)
> institutionlzd 1.000
> non.instttnlzd 1.000
> associationlsm 1.000
> social.trust 1.000
> information 1.000
> .tv 0.050
> .contact 0.568
> .workorg 0.282
> .petition 0.208
> .demonstr 0.219
> .boycott 0.543
> .voluntary 0.255
> .participate 0.286
> .trust 1.862 0.632 2.947 0.003
> .fairness 2.228 0.516 4.319 0.000
> .radio 3.207 0.157 20.438 0.000
>
> Scales y*:
> Estimate Std.Err z-value P(>|z|)
> contact 1.000
> workorg 1.000
> petition 1.000
> demonstr 1.000
> boycott 1.000
> voluntary 1.000
> participate 1.000> lhs op rhs mi epc sepc.lv sepc.all sepc.nox
> 138 petition ~~ radio 4.764 -0.146 -0.146 -0.179 -0.179
> 116 contact ~~ voluntary 3.711 -0.138 -0.138 -0.362 -0.362
> 89 associationalism =~ boycott 3.477 -0.173 -0.173 -0.173 -0.173
> 147 boycott ~~ participate 3.435 -0.099 -0.099 -0.251 -0.251
> 133 petition ~~ voluntary 3.104 0.088 0.088 0.384 0.384
> 128 workorg ~~ fairness 2.976 -0.144 -0.144 -0.182 -0.182
> 137 petition ~~ tv 2.532 0.092 0.092 0.905 0.905
> 102 social.trust =~ radio 2.478 -0.103 -0.103 -0.055 -0.055
> 101 social.trust =~ tv 2.478 0.240 0.240 0.176 0.176
> 165 fairness ~~ radio 2.242 -0.145 -0.145 -0.054 -0.054
> 70 institutionalized =~ boycott 2.159 -0.242 -0.242 -0.242 -0.242
> 164 fairness ~~ tv 2.104 0.111 0.111 0.332 0.332
> 127 workorg ~~ trust 2.085 0.124 0.124 0.171 0.171
> 155 voluntary ~~ tv 2.012 -0.124 -0.124 -1.101 -1.101
> 136 petition ~~ fairness 1.890 -0.094 -0.094 -0.138 -0.138
> 143 demonstr ~~ fairness 1.885 0.096 0.096 0.137 0.137
> 87 associationalism =~ petition 1.857 0.114 0.114 0.114 0.114
> 131 petition ~~ demonstr 1.744 -0.105 -0.105 -0.492 -0.492
> 154 voluntary ~~ fairness 1.705 0.137 0.137 0.181 0.181
> 108 information =~ voluntary 1.551 -0.081 -0.081 -0.081 -0.081
> 109 information =~ participate 1.551 0.079 0.079 0.079 0.079
> 72 institutionalized =~ participate 1.453 -0.336 -0.336 -0.336 -0.336
> 71 institutionalized =~ voluntary 1.453 0.343 0.343 0.343 0.343
> 84 non.institutionalized =~ radio 1.403 -0.083 -0.083 -0.044 -0.044
> 83 non.institutionalized =~ tv 1.403 0.194 0.194 0.142 0.142
> 78 non.institutionalized =~ workorg 1.351 -0.251 -0.251 -0.251 -0.251
> 77 non.institutionalized =~ contact 1.350 0.195 0.195 0.195 0.195
> 153 voluntary ~~ trust 1.349 -0.112 -0.112 -0.162 -0.162
> 81 non.institutionalized =~ trust 1.259 0.153 0.153 0.069 0.069
> 82 non.institutionalized =~ fairness 1.259 -0.136 -0.136 -0.063 -0.063
> 85 associationalism =~ contact 1.228 -0.149 -0.149 -0.149 -0.149
> 86 associationalism =~ workorg 1.228 0.192 0.192 0.192 0.192
> 68 institutionalized =~ petition 1.220 0.167 0.167 0.167 0.167
> 110 information =~ trust 1.182 -0.076 -0.076 -0.034 -0.034
> 111 information =~ fairness 1.182 0.067 0.067 0.031 0.031
> 140 demonstr ~~ voluntary 1.155 -0.057 -0.057 -0.240 -0.240
> 80 non.institutionalized =~ participate 1.141 -0.153 -0.153 -0.153 -0.153
> 79 non.institutionalized =~ voluntary 1.141 0.156 0.156 0.156 0.156
> 159 participate ~~ tv 1.130 0.085 0.085 0.710 0.710
> 162 trust ~~ tv 1.085 -0.091 -0.091 -0.298 -0.298
> 132 petition ~~ boycott 0.937 0.050 0.050 0.149 0.149
> 151 boycott ~~ radio 0.918 0.087 0.087 0.066 0.066
> 130 workorg ~~ radio 0.779 0.063 0.063 0.066 0.066
> 135 petition ~~ trust 0.704 0.061 0.061 0.098 0.098
> 105 information =~ petition 0.704 0.039 0.039 0.039 0.039
> 124 workorg ~~ boycott 0.624 -0.041 -0.041 -0.105 -0.105
> 150 boycott ~~ tv 0.612 -0.056 -0.056 -0.340 -0.340
> 158 participate ~~ fairness 0.529 0.050 0.050 0.062 0.062
> 149 boycott ~~ fairness 0.456 -0.060 -0.060 -0.054 -0.054
> 142 demonstr ~~ trust 0.427 -0.048 -0.048 -0.075 -0.075
> 115 contact ~~ boycott 0.391 0.040 0.040 0.071 0.071
> 129 workorg ~~ tv 0.386 -0.052 -0.052 -0.434 -0.434
> 157 participate ~~ trust 0.344 -0.041 -0.041 -0.056 -0.056
> 114 contact ~~ demonstr 0.303 0.027 0.027 0.078 0.078
> 73 institutionalized =~ trust 0.292 0.057 0.057 0.026 0.026
> 74 institutionalized =~ fairness 0.292 -0.051 -0.051 -0.024 -0.024
> 97 social.trust =~ demonstr 0.291 0.024 0.024 0.024 0.024
> 76 institutionalized =~ radio 0.285 -0.037 -0.037 -0.020 -0.020
> 75 institutionalized =~ tv 0.285 0.087 0.087 0.063 0.063
> 125 workorg ~~ voluntary 0.267 0.036 0.036 0.133 0.133
> 141 demonstr ~~ participate 0.267 0.021 0.021 0.083 0.083
> 117 contact ~~ participate 0.265 0.031 0.031 0.077 0.077
> 106 information =~ demonstr 0.265 -0.023 -0.023 -0.023 -0.023
> 144 demonstr ~~ tv 0.242 -0.028 -0.028 -0.266 -0.266
> 107 information =~ boycott 0.232 -0.026 -0.026 -0.026 -0.026
> 166 tv ~~ radio 0.232 0.596 0.596 1.488 1.488
> 17 tv ~~ tv 0.232 -1.393 -0.050 -0.027 -0.027
> 156 voluntary ~~ radio 0.223 0.047 0.047 0.052 0.052
> 96 social.trust =~ petition 0.200 -0.021 -0.021 -0.021 -0.021
> 121 contact ~~ radio 0.199 -0.038 -0.038 -0.028 -0.028
> 126 workorg ~~ participate 0.184 0.026 0.026 0.092 0.092
> 122 workorg ~~ petition 0.167 -0.017 -0.017 -0.071 -0.071
> 90 associationalism =~ trust 0.152 -0.037 -0.037 -0.017 -0.017
> 91 associationalism =~ fairness 0.152 0.033 0.033 0.015 0.015
> 123 workorg ~~ demonstr 0.139 0.016 0.016 0.066 0.066
> 146 boycott ~~ voluntary 0.133 0.026 0.026 0.070 0.070
> 95 social.trust =~ workorg 0.126 -0.027 -0.027 -0.027 -0.027
> 94 social.trust =~ contact 0.126 0.021 0.021 0.021 0.021
> 120 contact ~~ tv 0.125 0.024 0.024 0.145 0.145
> 148 boycott ~~ trust 0.108 0.029 0.029 0.029 0.029
> 119 contact ~~ fairness 0.082 0.025 0.025 0.022 0.022
> 163 trust ~~ radio 0.055 0.025 0.025 0.010 0.010
> 134 petition ~~ participate 0.047 -0.008 -0.008 -0.035 -0.035
> 104 information =~ workorg 0.038 -0.013 -0.013 -0.013 -0.013
> 103 information =~ contact 0.038 0.010 0.010 0.010 0.010
> 160 participate ~~ radio 0.033 0.012 0.012 0.013 0.013
> 98 social.trust =~ boycott 0.030 -0.010 -0.010 -0.010 -0.010
> 139 demonstr ~~ boycott 0.023 0.008 0.008 0.022 0.022
> 113 contact ~~ petition 0.009 -0.004 -0.004 -0.013 -0.013
> 88 associationalism =~ demonstr 0.008 -0.007 -0.007 -0.007 -0.007
> 69 institutionalized =~ demonstr 0.006 -0.011 -0.011 -0.011 -0.011
> 118 contact ~~ trust 0.006 -0.006 -0.006 -0.006 -0.006
> 100 social.trust =~ participate 0.003 -0.004 -0.004 -0.004 -0.004
> 99 social.trust =~ voluntary 0.003 0.004 0.004 0.004 0.004
> 93 associationalism =~ radio 0.002 -0.003 -0.003 -0.002 -0.002
> 92 associationalism =~ tv 0.002 0.008 0.008 0.006 0.006
> 145 demonstr ~~ radio 0.000 0.000 0.000 0.000 0.000#Correct structural model using latent variables only
structural.formula <- paste(my.cfa.formula,
" institutionalized ~ associationalism + social.trust + information +a*non.institutionalized;
non.institutionalized ~ associationalism + social.trust + information +a*institutionalized;
")
my.structural.model <-sem(structural.formula, Spain, estimator = "WLS", missing="listwise", ordered=c("contact", "workorg", "petition", "demonstr", "boycott", "voluntary", "participate"))
semPaths(my.structural.model, intercepts=F, nCharNodes = 10, whatLabels="std", layout="tree2", rotation=4, residuals=F, shapeMan="rectangle", sizeMan=10, sizeMan2=1.5)
> lavaan 0.6-9 ended normally after 75 iterations
>
> Estimator WLS
> Optimization method NLMINB
> Number of model parameters 36
> Number of equality constraints 1
>
> Used Total
> Number of observations 1062 1729
>
> Model Test User Model:
>
> Test statistic 33.017
> Degrees of freedom 35
> P-value (Chi-square) 0.564
>
> Model Test Baseline Model:
>
> Test statistic 1727.080
> Degrees of freedom 55
> P-value 0.000
>
> User Model versus Baseline Model:
>
> Comparative Fit Index (CFI) 1.000
> Tucker-Lewis Index (TLI) 1.002
>
> Root Mean Square Error of Approximation:
>
> RMSEA 0.000
> 90 Percent confidence interval - lower 0.000
> 90 Percent confidence interval - upper 0.020
> P-value RMSEA <= 0.05 1.000
>
> Standardized Root Mean Square Residual:
>
> SRMR 0.035
>
> Parameter Estimates:
>
> Standard errors Standard
> Information Expected
> Information saturated (h1) model Unstructured
>
> Latent Variables:
> Estimate Std.Err z-value P(>|z|)
> institutionalized =~
> contact 0.222 0.101 2.199 0.028
> workorg 0.286 0.132 2.164 0.030
> non.institutionalized =~
> petition 0.404 0.144 2.815 0.005
> demonstr 0.401 0.143 2.815 0.005
> boycott 0.307 0.111 2.762 0.006
> associationalism =~
> voluntary 0.863 0.050 17.297 0.000
> participate 0.845 0.046 18.556 0.000
> social.trust =~
> trust 1.726 0.199 8.684 0.000
> fairness 1.535 0.173 8.865 0.000
> information =~
> tv 1.349 0.025 54.788 0.000
> radio 0.577 0.049 11.739 0.000
>
> Regressions:
> Estimate Std.Err z-value P(>|z|)
> institutionalized ~
> assoctnlsm 1.641 0.741 2.214 0.027
> socil.trst -0.263 0.225 -1.169 0.242
> informatin -0.098 0.151 -0.649 0.516
> nn.nstttnl (a) 0.602 0.153 3.947 0.000
> non.institutionalized ~
> assoctnlsm -0.272 0.645 -0.422 0.673
> socil.trst 0.429 0.194 2.209 0.027
> informatin -0.001 0.104 -0.008 0.993
> instttnlzd (a) 0.602 0.153 3.947 0.000
>
> Covariances:
> Estimate Std.Err z-value P(>|z|)
> associationalism ~~
> social.trust 0.050 0.052 0.962 0.336
> information -0.016 0.048 -0.336 0.737
> social.trust ~~
> information 0.045 0.035 1.283 0.199
>
> Intercepts:
> Estimate Std.Err z-value P(>|z|)
> .contact 0.000
> .workorg 0.000
> .petition 0.000
> .demonstr 0.000
> .boycott 0.000
> .voluntary 0.000
> .participate 0.000
> .trust 6.016 0.068 88.021 0.000
> .fairness 6.333 0.067 93.928 0.000
> .tv 3.080 0.052 58.922 0.000
> .radio 2.864 0.087 33.025 0.000
> .institutionlzd 0.000
> .non.instttnlzd 0.000
> associationlsm 0.000
> social.trust 0.000
> information 0.000
>
> Thresholds:
> Estimate Std.Err z-value P(>|z|)
> contact|t1 1.129 0.048 23.379 0.000
> workorg|t1 0.918 0.044 20.816 0.000
> petition|t1 0.621 0.041 15.283 0.000
> demonstr|t1 0.885 0.044 20.180 0.000
> boycott|t1 1.335 0.052 25.540 0.000
> voluntary|t1 1.470 0.058 25.529 0.000
> participate|t1 0.587 0.041 14.435 0.000
>
> Variances:
> Estimate Std.Err z-value P(>|z|)
> .institutionlzd 1.000
> .non.instttnlzd 1.000
> associationlsm 1.000
> social.trust 1.000
> information 1.000
> .tv 0.050
> .contact 0.568
> .workorg 0.282
> .petition 0.208
> .demonstr 0.219
> .boycott 0.543
> .voluntary 0.255
> .participate 0.286
> .trust 1.862 0.632 2.947 0.003
> .fairness 2.228 0.516 4.319 0.000
> .radio 3.207 0.157 20.438 0.000
>
> Scales y*:
> Estimate Std.Err z-value P(>|z|)
> contact 1.000
> workorg 1.000
> petition 1.000
> demonstr 1.000
> boycott 1.000
> voluntary 1.000
> participate 1.000> lhs op rhs mi epc sepc.lv sepc.all sepc.nox
> 140 petition ~~ radio 4.764 -0.146 -0.146 -0.179 -0.179
> 118 contact ~~ voluntary 3.711 -0.138 -0.138 -0.362 -0.362
> 91 associationalism =~ boycott 3.477 -0.173 -0.173 -0.173 -0.173
> 149 boycott ~~ participate 3.435 -0.099 -0.099 -0.251 -0.251
> 135 petition ~~ voluntary 3.104 0.088 0.088 0.384 0.384
> 130 workorg ~~ fairness 2.976 -0.144 -0.144 -0.182 -0.182
> 139 petition ~~ tv 2.532 0.092 0.092 0.905 0.905
> 103 social.trust =~ tv 2.478 0.240 0.240 0.176 0.176
> 104 social.trust =~ radio 2.478 -0.103 -0.103 -0.055 -0.055
> 167 fairness ~~ radio 2.242 -0.145 -0.145 -0.054 -0.054
> 72 institutionalized =~ boycott 2.159 -0.082 -0.242 -0.242 -0.242
> 166 fairness ~~ tv 2.104 0.111 0.111 0.332 0.332
> 129 workorg ~~ trust 2.085 0.124 0.124 0.171 0.171
> 157 voluntary ~~ tv 2.012 -0.124 -0.124 -1.101 -1.101
> 138 petition ~~ fairness 1.890 -0.094 -0.094 -0.138 -0.138
> 145 demonstr ~~ fairness 1.885 0.096 0.096 0.137 0.137
> 89 associationalism =~ petition 1.858 0.114 0.114 0.114 0.114
> 133 petition ~~ demonstr 1.744 -0.105 -0.105 -0.492 -0.492
> 156 voluntary ~~ fairness 1.705 0.137 0.137 0.181 0.181
> 110 information =~ voluntary 1.551 -0.081 -0.081 -0.081 -0.081
> 111 information =~ participate 1.551 0.079 0.079 0.079 0.079
> 73 institutionalized =~ voluntary 1.453 0.116 0.343 0.343 0.343
> 74 institutionalized =~ participate 1.453 -0.113 -0.336 -0.336 -0.336
> 86 non.institutionalized =~ radio 1.403 -0.038 -0.083 -0.044 -0.044
> 85 non.institutionalized =~ tv 1.403 0.088 0.194 0.142 0.142
> 79 non.institutionalized =~ contact 1.350 0.088 0.195 0.195 0.195
> 80 non.institutionalized =~ workorg 1.350 -0.114 -0.251 -0.251 -0.251
> 155 voluntary ~~ trust 1.349 -0.112 -0.112 -0.162 -0.162
> 84 non.institutionalized =~ fairness 1.259 -0.062 -0.136 -0.063 -0.063
> 83 non.institutionalized =~ trust 1.259 0.069 0.153 0.069 0.069
> 88 associationalism =~ workorg 1.228 0.192 0.192 0.192 0.192
> 87 associationalism =~ contact 1.228 -0.149 -0.149 -0.149 -0.149
> 70 institutionalized =~ petition 1.220 0.056 0.167 0.167 0.167
> 113 information =~ fairness 1.182 0.067 0.067 0.031 0.031
> 112 information =~ trust 1.182 -0.076 -0.076 -0.034 -0.034
> 142 demonstr ~~ voluntary 1.155 -0.057 -0.057 -0.240 -0.240
> 81 non.institutionalized =~ voluntary 1.141 0.071 0.156 0.156 0.156
> 82 non.institutionalized =~ participate 1.141 -0.069 -0.152 -0.152 -0.152
> 161 participate ~~ tv 1.130 0.085 0.085 0.710 0.710
> 164 trust ~~ tv 1.085 -0.091 -0.091 -0.298 -0.298
> 134 petition ~~ boycott 0.937 0.050 0.050 0.149 0.149
> 153 boycott ~~ radio 0.918 0.087 0.087 0.066 0.066
> 132 workorg ~~ radio 0.779 0.063 0.063 0.066 0.066
> 137 petition ~~ trust 0.704 0.061 0.061 0.098 0.098
> 107 information =~ petition 0.704 0.039 0.039 0.039 0.039
> 126 workorg ~~ boycott 0.624 -0.041 -0.041 -0.105 -0.105
> 152 boycott ~~ tv 0.612 -0.056 -0.056 -0.340 -0.340
> 160 participate ~~ fairness 0.529 0.050 0.050 0.062 0.062
> 151 boycott ~~ fairness 0.456 -0.060 -0.060 -0.054 -0.054
> 144 demonstr ~~ trust 0.427 -0.048 -0.048 -0.075 -0.075
> 117 contact ~~ boycott 0.391 0.040 0.040 0.071 0.071
> 131 workorg ~~ tv 0.386 -0.051 -0.051 -0.434 -0.434
> 159 participate ~~ trust 0.344 -0.041 -0.041 -0.056 -0.056
> 116 contact ~~ demonstr 0.303 0.027 0.027 0.078 0.078
> 76 institutionalized =~ fairness 0.292 -0.017 -0.051 -0.024 -0.024
> 75 institutionalized =~ trust 0.292 0.019 0.057 0.026 0.026
> 99 social.trust =~ demonstr 0.291 0.024 0.024 0.024 0.024
> 78 institutionalized =~ radio 0.285 -0.013 -0.037 -0.020 -0.020
> 77 institutionalized =~ tv 0.285 0.029 0.087 0.063 0.063
> 127 workorg ~~ voluntary 0.267 0.036 0.036 0.133 0.133
> 143 demonstr ~~ participate 0.267 0.021 0.021 0.083 0.083
> 119 contact ~~ participate 0.265 0.031 0.031 0.077 0.077
> 108 information =~ demonstr 0.265 -0.023 -0.023 -0.023 -0.023
> 146 demonstr ~~ tv 0.242 -0.028 -0.028 -0.266 -0.266
> 109 information =~ boycott 0.232 -0.026 -0.026 -0.026 -0.026
> 17 tv ~~ tv 0.232 -1.393 -0.050 -0.027 -0.027
> 168 tv ~~ radio 0.232 0.596 0.596 1.488 1.488
> 158 voluntary ~~ radio 0.223 0.047 0.047 0.052 0.052
> 98 social.trust =~ petition 0.200 -0.021 -0.021 -0.021 -0.021
> 123 contact ~~ radio 0.199 -0.038 -0.038 -0.028 -0.028
> 128 workorg ~~ participate 0.184 0.026 0.026 0.092 0.092
> 124 workorg ~~ petition 0.167 -0.017 -0.017 -0.071 -0.071
> 92 associationalism =~ trust 0.152 -0.037 -0.037 -0.017 -0.017
> 93 associationalism =~ fairness 0.152 0.033 0.033 0.015 0.015
> 125 workorg ~~ demonstr 0.139 0.016 0.016 0.066 0.066
> 148 boycott ~~ voluntary 0.133 0.026 0.026 0.070 0.070
> 97 social.trust =~ workorg 0.126 -0.027 -0.027 -0.027 -0.027
> 96 social.trust =~ contact 0.126 0.021 0.021 0.021 0.021
> 122 contact ~~ tv 0.125 0.024 0.024 0.145 0.145
> 150 boycott ~~ trust 0.108 0.029 0.029 0.029 0.029
> 121 contact ~~ fairness 0.082 0.025 0.025 0.022 0.022
> 165 trust ~~ radio 0.055 0.025 0.025 0.010 0.010
> 136 petition ~~ participate 0.047 -0.008 -0.008 -0.035 -0.035
> 105 information =~ contact 0.038 0.010 0.010 0.010 0.010
> 106 information =~ workorg 0.038 -0.013 -0.013 -0.013 -0.013
> 162 participate ~~ radio 0.033 0.012 0.012 0.013 0.013
> 100 social.trust =~ boycott 0.030 -0.010 -0.010 -0.010 -0.010
> 141 demonstr ~~ boycott 0.023 0.008 0.008 0.022 0.022
> 115 contact ~~ petition 0.009 -0.004 -0.004 -0.013 -0.013
> 90 associationalism =~ demonstr 0.008 -0.007 -0.007 -0.007 -0.007
> 71 institutionalized =~ demonstr 0.006 -0.004 -0.011 -0.011 -0.011
> 120 contact ~~ trust 0.006 -0.006 -0.006 -0.006 -0.006
> 102 social.trust =~ participate 0.003 -0.004 -0.004 -0.004 -0.004
> 101 social.trust =~ voluntary 0.003 0.004 0.004 0.004 0.004
> 95 associationalism =~ radio 0.002 -0.003 -0.003 -0.002 -0.002
> 94 associationalism =~ tv 0.002 0.008 0.008 0.006 0.006
> 147 demonstr ~~ radio 0.000 0.000 0.000 0.000 0.000#Correct structural model using both latent variables and manifest variables
structural.formula2 <- paste(my.cfa.formula,
" institutionalized ~ associationalism + social.trust + information +
close.party + interest + soc.activity + a*non.institutionalized;
non.institutionalized ~ associationalism + social.trust + information +
close.party + interest + soc.activity + a*institutionalized;
associationalism ~ interest
")
my.structural.model2 <-sem(structural.formula2, Spain, estimator = "WLS", missing="listwise",
ordered=c("contact", "workorg", "petition", "demonstr", "boycott", "voluntary", "participate"))
semPaths(my.structural.model2,
intercepts=F,
nCharNodes = 10,
whatLabels="std",
layout="tree2",
rotation=4,
residuals=F,
shapeMan="rectangle",
sizeMan=15,
sizeMan2=2)
> lavaan 0.6-9 did NOT end normally after 2615 iterations
> ** WARNING ** Estimates below are most likely unreliable
>
> Estimator WLS
> Optimization method NLMINB
> Number of model parameters 41
> Number of equality constraints 1
>
> Used Total
> Number of observations 983 1729
>
> Model Test User Model:
>
> Test statistic NA
> Degrees of freedom NA
>
> Parameter Estimates:
>
> Standard errors Standard
> Information Expected
> Information saturated (h1) model Unstructured
>
> Latent Variables:
> Estimate Std.Err z-value P(>|z|)
> institutionalized =~
> contact 0.009 NA
> workorg 0.012 NA
> non.institutionalized =~
> petition 0.029 NA
> demonstr 0.055 NA
> boycott 0.022 NA
> associationalism =~
> voluntary 0.835 NA
> participate 0.801 NA
> social.trust =~
> trust 0.918 NA
> fairness 2.203 NA
> information =~
> tv 1.323 NA
> radio 0.554 NA
>
> Regressions:
> Estimate Std.Err z-value P(>|z|)
> institutionalized ~
> assoctnlsm 48.754 NA
> socil.trst 0.496 NA
> informatin -5.519 NA
> close.prty 8.934 NA
> interest 5.235 NA
> soc.actvty 13.446 NA
> nn.nstttnl (a) 0.936 NA
> non.institutionalized ~
> assoctnlsm -44.677 NA
> socil.trst 1.513 NA
> informatin 4.948 NA
> close.prty -7.921 NA
> interest -4.190 NA
> soc.actvty -12.104 NA
> instttnlzd (a) 0.936 NA
> associationalism ~
> interest 0.407 NA
>
> Covariances:
> Estimate Std.Err z-value P(>|z|)
> social.trust ~~
> information 0.025 NA
>
> Intercepts:
> Estimate Std.Err z-value P(>|z|)
> .contact 0.000
> .workorg 0.000
> .petition 0.000
> .demonstr 0.000
> .boycott 0.000
> .voluntary 0.000
> .participate 0.000
> .trust 5.920 NA
> .fairness 6.211 NA
> .tv 3.080 NA
> .radio 2.977 NA
> .institutionlzd 0.000
> .non.instttnlzd 0.000
> .associationlsm 0.000
> social.trust 0.000
> information 0.000
>
> Thresholds:
> Estimate Std.Err z-value P(>|z|)
> contact|t1 2.394 NA
> workorg|t1 2.662 NA
> petition|t1 1.580 NA
> demonstr|t1 2.896 NA
> boycott|t1 2.116 NA
> voluntary|t1 2.199 NA
> participate|t1 1.241 NA
>
> Variances:
> Estimate Std.Err z-value P(>|z|)
> .institutionlzd 1.000
> .non.instttnlzd 1.000
> .associationlsm 1.000
> social.trust 1.000
> information 1.000
> .tv 0.050
> .contact 0.682
> .workorg 0.475
> .petition 0.628
> .demonstr -0.349
> .boycott 0.788
> .voluntary 0.302
> .participate 0.358
> .trust 3.200 NA
> .fairness -1.047 NA
> .radio 3.130 NA
>
> Scales y*:
> Estimate Std.Err z-value P(>|z|)
> contact 1.000
> workorg 1.000
> petition 1.000
> demonstr 1.000
> boycott 1.000
> voluntary 1.000
> participate 1.000> lhs op rhs mi epc sepc.lv sepc.all sepc.nox
> 159 demonstr ~~ fairness 288.711 3.433 3.433 5.677 5.677
> 113 social.trust =~ demonstr 160.659 1.672 1.672 1.484 1.484
> 152 petition ~~ fairness 137.424 -1.037 -1.037 -1.278 -1.278
> 85 institutionalized =~ demonstr 115.357 -0.018 -1.258 -1.116 -1.116
> 103 associationalism =~ petition 109.266 0.506 0.535 0.516 0.516
> 104 associationalism =~ demonstr 108.009 -0.862 -0.912 -0.809 -0.809
> 84 institutionalized =~ petition 103.078 0.010 0.698 0.673 0.673
> 112 social.trust =~ petition 92.763 -0.644 -0.644 -0.621 -0.621
> 183 institutionalized ~~ non.institutionalized 69.795 -420.296 -420.296 -420.296 -420.296
> 158 demonstr ~~ trust 50.235 -0.896 -0.896 -0.848 -0.848
> 151 petition ~~ trust 45.872 0.467 0.467 0.329 0.329
> 7 non.institutionalized ~~ non.institutionalized 37.789 -150.843 -0.002 -0.002 -0.002
> 138 workorg ~~ petition 27.016 0.201 0.201 0.367 0.367
> 177 trust ~~ fairness 26.987 -118.345 -118.345 -64.636 -64.636
> 148 petition ~~ boycott 25.744 0.233 0.233 0.331 0.331
> 155 demonstr ~~ boycott 17.102 -0.247 -0.247 -0.472 -0.472
> 197 associationalism ~ soc.activity 16.648 0.195 0.185 0.171 0.185
> 192 associationalism ~ institutionalized 15.968 0.007 0.457 0.457 0.457
> 149 petition ~~ voluntary 12.100 0.202 0.202 0.463 0.463
> 114 social.trust =~ boycott 11.593 -0.289 -0.289 -0.283 -0.283
> 154 petition ~~ radio 11.444 -0.218 -0.218 -0.155 -0.155
> 168 voluntary ~~ participate 11.344 0.372 0.372 1.130 1.130
> 210 information ~ interest 10.777 0.112 0.112 0.094 0.112
> 132 contact ~~ voluntary 10.452 -0.265 -0.265 -0.584 -0.584
> 156 demonstr ~~ voluntary 9.377 -0.205 -0.205 -0.631 -0.631
> 86 institutionalized =~ boycott 8.930 0.004 0.254 0.248 0.248
> 118 social.trust =~ radio 7.929 -0.136 -0.136 -0.073 -0.073
> 174 participate ~~ fairness 7.329 0.189 0.189 0.308 0.308
> 117 social.trust =~ tv 7.207 0.308 0.308 0.230 0.230
> 202 social.trust ~ close.party 6.971 0.154 0.154 0.077 0.154
> 88 institutionalized =~ participate 6.914 0.004 0.281 0.271 0.271
> 153 petition ~~ tv 6.668 0.119 0.119 0.672 0.672
> 193 associationalism ~ non.institutionalized 6.562 0.007 0.142 0.142 0.142
> 107 associationalism =~ fairness 6.316 0.173 0.183 0.094 0.094
> 181 fairness ~~ radio 5.903 -0.243 -0.243 -0.134 -0.134
> 198 social.trust ~ institutionalized 5.812 0.001 0.090 0.090 0.090
> 146 workorg ~~ radio 5.747 0.173 0.173 0.142 0.142
> 90 institutionalized =~ fairness 5.644 0.002 0.161 0.082 0.082
> 211 information ~ soc.activity 5.580 -0.073 -0.073 -0.068 -0.073
> 165 boycott ~~ fairness 5.392 -0.234 -0.234 -0.258 -0.258
> 209 information ~ close.party 5.369 0.147 0.147 0.073 0.147
> 144 workorg ~~ fairness 5.359 -0.235 -0.235 -0.333 -0.333
> 199 social.trust ~ non.institutionalized 5.337 0.006 0.135 0.135 0.135
> 131 contact ~~ boycott 5.332 0.147 0.147 0.201 0.201
> 87 institutionalized =~ voluntary 5.330 0.005 0.330 0.317 0.317
> 106 associationalism =~ trust 4.123 -0.139 -0.147 -0.073 -0.073
> 200 social.trust ~ associationalism 3.980 0.075 0.079 0.079 0.079
> 140 workorg ~~ boycott 3.941 -0.110 -0.110 -0.179 -0.179
> 98 non.institutionalized =~ fairness 3.871 0.009 0.211 0.108 0.108
> 164 boycott ~~ trust 3.843 0.161 0.161 0.101 0.101
> 170 voluntary ~~ fairness 3.681 0.196 0.196 0.348 0.348
> 105 associationalism =~ boycott 3.672 0.123 0.130 0.127 0.127
> 116 social.trust =~ participate 3.450 0.064 0.064 0.062 0.062
> 109 associationalism =~ radio 3.395 0.129 0.136 0.074 0.074
> 157 demonstr ~~ participate 3.387 -0.094 -0.094 -0.265 -0.265
> 171 voluntary ~~ tv 3.265 -0.146 -0.146 -1.184 -1.184
> 99 non.institutionalized =~ tv 3.137 0.005 0.125 0.094 0.094
> 96 non.institutionalized =~ participate 2.889 0.003 0.077 0.074 0.074
> 129 contact ~~ petition 2.882 0.078 0.078 0.119 0.119
> 190 associationalism ~~ social.trust 2.717 0.070 0.070 0.070 0.070
> 167 boycott ~~ radio 2.690 0.143 0.143 0.091 0.091
> 194 associationalism ~ social.trust 2.600 0.068 0.065 0.065 0.065
> 124 information =~ voluntary 2.503 -0.096 -0.096 -0.092 -0.092
> 182 tv ~~ radio 2.383 1.518 1.518 3.836 3.836
> 191 associationalism ~~ information 2.222 -0.070 -0.070 -0.070 -0.070
> 169 voluntary ~~ trust 2.212 -0.145 -0.145 -0.148 -0.148
> 195 associationalism ~ information 2.083 -0.068 -0.064 -0.064 -0.064
> 111 social.trust =~ workorg 2.053 -0.084 -0.084 -0.077 -0.077
> 128 contact ~~ workorg 2.029 0.084 0.084 0.148 0.148
> 6 institutionalized ~~ institutionalized 1.986 29.724 0.000 0.000 0.000
> 100 non.institutionalized =~ radio 1.717 -0.003 -0.069 -0.037 -0.037
> 162 boycott ~~ voluntary 1.653 0.100 0.100 0.205 0.205
> 206 information ~ non.institutionalized 1.520 0.003 0.078 0.078 0.078
> 141 workorg ~~ voluntary 1.490 -0.085 -0.085 -0.224 -0.224
> 147 petition ~~ demonstr 1.449 -0.102 -0.102 -0.218 -0.218
> 130 contact ~~ demonstr 1.389 -0.067 -0.067 -0.137 -0.137
> 203 social.trust ~ interest 1.338 0.038 0.038 0.032 0.038
> 95 non.institutionalized =~ voluntary 1.312 0.003 0.077 0.074 0.074
> 134 contact ~~ trust 1.253 -0.093 -0.093 -0.063 -0.063
> 160 demonstr ~~ tv 1.088 -0.079 -0.079 -0.596 -0.596
> 179 trust ~~ radio 1.080 0.110 0.110 0.035 0.035
> 150 petition ~~ participate 0.990 0.041 0.041 0.086 0.086
> 139 workorg ~~ demonstr 0.951 -0.051 -0.051 -0.125 -0.125
> 89 institutionalized =~ trust 0.893 -0.001 -0.065 -0.032 -0.032
> 143 workorg ~~ trust 0.885 -0.073 -0.073 -0.060 -0.060
> 204 social.trust ~ soc.activity 0.881 0.026 0.026 0.024 0.026
> 173 participate ~~ trust 0.826 -0.062 -0.062 -0.058 -0.058
> 121 information =~ petition 0.811 0.033 0.033 0.032 0.032
> 92 institutionalized =~ radio 0.797 0.001 0.057 0.031 0.031
> 166 boycott ~~ tv 0.735 -0.060 -0.060 -0.300 -0.300
> 180 fairness ~~ tv 0.602 0.106 0.106 0.465 0.465
> 145 workorg ~~ tv 0.542 -0.048 -0.048 -0.312 -0.312
> 122 information =~ demonstr 0.488 -0.045 -0.045 -0.040 -0.040
> 142 workorg ~~ participate 0.480 -0.044 -0.044 -0.106 -0.106
> 17 tv ~~ tv 0.421 -1.109 -0.050 -0.028 -0.028
> 175 participate ~~ tv 0.356 -0.032 -0.032 -0.242 -0.242
> 126 information =~ trust 0.351 0.032 0.032 0.016 0.016
> 127 information =~ fairness 0.339 -0.077 -0.077 -0.039 -0.039
> 115 social.trust =~ voluntary 0.329 0.030 0.030 0.029 0.029
> 94 non.institutionalized =~ workorg 0.300 -0.002 -0.052 -0.048 -0.048
> 93 non.institutionalized =~ contact 0.293 0.002 0.040 0.038 0.038
> 101 associationalism =~ contact 0.221 -0.060 -0.063 -0.060 -0.060
> 125 information =~ participate 0.212 -0.019 -0.019 -0.018 -0.018
> 163 boycott ~~ participate 0.211 0.025 0.025 0.047 0.047
> 102 associationalism =~ workorg 0.167 0.058 0.061 0.056 0.056
> 136 contact ~~ tv 0.138 0.024 0.024 0.129 0.129
> 205 information ~ institutionalized 0.117 0.000 0.014 0.014 0.014
> 133 contact ~~ participate 0.105 0.021 0.021 0.042 0.042
> 207 information ~ associationalism 0.096 0.013 0.013 0.013 0.013
> 172 voluntary ~~ radio 0.089 -0.029 -0.029 -0.030 -0.030
> 108 associationalism =~ tv 0.080 -0.015 -0.016 -0.012 -0.012
> 196 associationalism ~ close.party 0.078 0.025 0.024 0.012 0.024
> 123 information =~ boycott 0.071 -0.014 -0.014 -0.014 -0.014
> 178 trust ~~ tv 0.044 0.015 0.015 0.037 0.037
> 161 demonstr ~~ radio 0.034 -0.013 -0.013 -0.013 -0.013
> 119 information =~ contact 0.030 0.009 0.009 0.008 0.008
> 120 information =~ workorg 0.015 -0.006 -0.006 -0.006 -0.006
> 135 contact ~~ fairness 0.014 -0.012 -0.012 -0.014 -0.014
> 137 contact ~~ radio 0.013 -0.010 -0.010 -0.007 -0.007
> 188 non.institutionalized ~~ social.trust 0.010 -0.070 -0.070 -0.070 -0.070
> 176 participate ~~ radio 0.010 -0.007 -0.007 -0.007 -0.007
> 65 workorg ~1 0.008 15.098 15.098 13.813 13.813
> 189 non.institutionalized ~~ information 0.007 -0.599 -0.599 -0.599 -0.599
> 110 social.trust =~ contact 0.002 0.002 0.002 0.002 0.002
> 97 non.institutionalized =~ trust 0.002 0.000 -0.005 -0.002 -0.002
> [ reached 'max' / getOption("max.print") -- omitted 9 rows ]