data <- read.csv("C:/Users/LENOVO/OneDrive/Documents/tugas kelompok bu guru/tugas kelompok buguru/WVS_Cross-National_Wave_7_csv_v6_0.csv")
str(data)
## 'data.frame': 97220 obs. of 613 variables:
## $ version : chr "6-0-0 (2024-04-30)" "6-0-0 (2024-04-30)" "6-0-0 (2024-04-30)" "6-0-0 (2024-04-30)" ...
## $ doi : chr "doi.org/10.14281/18241.24" "doi.org/10.14281/18241.24" "doi.org/10.14281/18241.24" "doi.org/10.14281/18241.24" ...
## $ A_WAVE : int 7 7 7 7 7 7 7 7 7 7 ...
## $ A_YEAR : int 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 ...
## $ A_STUDY : int 2 2 2 2 2 2 2 2 2 2 ...
## $ B_COUNTRY : int 20 20 20 20 20 20 20 20 20 20 ...
## $ B_COUNTRY_ALPHA : chr "AND" "AND" "AND" "AND" ...
## $ C_COW_NUM : int 232 232 232 232 232 232 232 232 232 232 ...
## $ C_COW_ALPHA : chr "AND" "AND" "AND" "AND" ...
## $ D_INTERVIEW : int 20070001 20070002 20070003 20070004 20070005 20070006 20070007 20070008 20070009 20070010 ...
## $ S007 : int 20720001 20720002 20720003 20720004 20720005 20720006 20720007 20720008 20720009 20720010 ...
## $ J_INTDATE : int 20180704 20180714 20180704 20180702 20180708 20180724 20180721 20180703 20180703 20180703 ...
## $ FW_START : int 201807 201807 201807 201807 201807 201807 201807 201807 201807 201807 ...
## $ FW_END : int 201809 201809 201809 201809 201809 201809 201809 201809 201809 201809 ...
## $ K_TIME_START : num 18.2 9.35 10.15 17.05 10.2 ...
## $ K_TIME_END : num 19.5 11 10.4 18.2 11.5 ...
## $ K_DURATION : int 88 85 30 75 89 45 44 60 74 41 ...
## $ Q_MODE : int 2 2 2 2 2 2 2 2 2 2 ...
## $ N_REGION_ISO : int 20007 20003 20003 20003 20003 20006 20006 20008 20008 20008 ...
## $ N_REGION_WVS : int 20005 20002 20002 20002 20002 20006 20006 20007 20007 20007 ...
## $ N_REGION_NUTS2 : int -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 ...
## $ N_REG_NUTS1 : int -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 ...
## $ N_TOWN : int 20005 20002 20002 20002 20002 20006 20006 20007 20007 20007 ...
## $ G_TOWNSIZE : int 5 3 3 3 3 3 3 4 4 4 ...
## $ G_TOWNSIZE2 : int 3 2 2 2 2 2 2 2 2 2 ...
## $ H_SETTLEMENT : int 1 2 2 2 2 2 2 2 2 2 ...
## $ H_URBRURAL : int 1 1 1 1 1 1 1 1 1 1 ...
## $ I_PSU : int -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 ...
## $ O1_LONGITUDE : num 1.52 1.53 1.58 1.58 1.58 1.52 1.49 1.54 1.54 1.73 ...
## $ O2_LATITUDE : num 42.5 42.5 42.5 42.5 42.5 ...
## $ L_INTERVIEWER_NUMBER : int -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 ...
## $ S_INTLANGUAGE : int 810 810 810 810 810 1270 1270 810 810 810 ...
## $ LNGE_ISO : chr "ca" "ca" "ca" "ca" ...
## $ E_RESPINT : int 1 1 1 2 2 1 1 1 1 1 ...
## $ F_INTPRIVACY : int 1 1 1 1 1 1 1 2 2 2 ...
## $ E1_LITERACY : int -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 ...
## $ W_WEIGHT : num 1 1 1 1 1 1 1 1 1 1 ...
## $ S018 : num 0.996 0.996 0.996 0.996 0.996 ...
## $ PWGHT : num 0.00684 0.00684 0.00684 0.00684 0.00684 ...
## $ S025 : int 202018 202018 202018 202018 202018 202018 202018 202018 202018 202018 ...
## $ Q1 : int 1 1 1 1 1 1 1 1 2 1 ...
## $ Q2 : int 1 1 2 1 1 3 2 1 2 1 ...
## $ Q3 : int 1 1 2 1 1 1 1 1 1 2 ...
## $ Q4 : int 3 4 2 4 3 4 4 1 1 4 ...
## $ Q5 : int 1 1 3 2 1 1 1 1 2 2 ...
## $ Q6 : int 4 4 3 4 3 3 2 3 1 2 ...
## $ Q7 : int 1 1 2 1 1 1 1 1 1 1 ...
## $ Q8 : int 1 2 1 2 2 2 2 2 2 1 ...
## $ Q9 : int 2 1 2 2 1 2 2 1 2 2 ...
## $ Q10 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Q11 : int 1 2 1 2 2 2 2 2 2 1 ...
## $ Q12 : int 2 1 1 1 1 1 1 2 1 1 ...
## $ Q13 : int 2 2 2 2 2 1 1 1 2 2 ...
## $ Q14 : int 2 2 1 1 1 2 2 2 1 2 ...
## $ Q15 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Q16 : int 2 2 2 2 2 1 1 2 1 2 ...
## $ Q17 : int 1 1 2 1 2 2 2 1 2 2 ...
## $ Q18 : int 1 1 1 1 1 1 2 2 1 1 ...
## $ Q19 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Q20 : int 1 2 2 2 2 2 2 2 1 2 ...
## $ Q21 : int 2 2 2 2 2 2 2 1 2 2 ...
## $ Q22 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Q23 : int 2 2 2 2 2 2 2 1 2 2 ...
## $ Q24 : int 1 2 1 2 1 1 2 1 2 1 ...
## $ Q25 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Q26 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Q27 : int 3 3 3 3 3 1 1 2 2 1 ...
## $ Q28 : int 3 3 3 3 3 1 1 3 3 1 ...
## $ Q29 : int 3 3 3 4 3 2 4 4 4 3 ...
## $ Q30 : int 3 3 3 4 3 4 4 4 4 3 ...
## $ Q31 : int 3 3 3 4 3 4 2 4 4 3 ...
## $ Q32 : int 3 3 -2 4 2 4 1 4 3 3 ...
## $ Q33 : int 4 4 4 4 4 5 5 5 5 4 ...
## $ Q33_3 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Q34 : int 2 2 4 2 4 5 5 1 1 4 ...
## $ Q34_3 : int 1 1 2 1 2 2 2 1 1 2 ...
## $ Q35 : int 3 4 2 4 4 5 5 1 4 5 ...
## $ Q35_3 : int 3 2 1 2 2 2 2 1 2 2 ...
## $ Q36 : int 3 2 4 3 3 1 1 3 3 1 ...
## $ Q37 : int 4 4 4 4 3 1 5 5 5 3 ...
## $ Q38 : int 3 2 3 4 3 1 3 5 3 3 ...
## $ Q39 : int 2 2 2 2 3 1 3 -2 3 1 ...
## $ Q40 : int 3 2 4 4 3 1 1 1 2 4 ...
## $ Q41 : int 3 2 4 4 4 1 5 3 5 5 ...
## $ Q42 : int 2 2 2 1 1 2 2 2 2 2 ...
## $ Q43 : int 2 2 1 1 1 1 1 2 1 1 ...
## $ Q44 : int 3 1 2 1 1 3 2 1 2 1 ...
## $ Q45 : int 1 1 1 2 2 1 1 2 2 2 ...
## $ Q46 : int 1 1 2 2 2 1 2 2 1 1 ...
## $ Q47 : int 3 1 1 2 2 1 1 2 2 1 ...
## $ Q48 : int 10 9 9 9 8 10 10 8 8 10 ...
## $ Q49 : int 10 9 9 8 7 10 5 8 8 10 ...
## $ Q50 : int 5 9 8 6 7 6 4 9 6 10 ...
## $ Q51 : int 4 4 4 4 4 4 2 4 4 4 ...
## $ Q52 : int 4 4 4 4 4 4 3 4 4 4 ...
## $ Q53 : int 4 4 4 4 4 4 4 4 4 4 ...
## $ Q54 : int 4 4 3 4 4 4 2 4 3 4 ...
## $ Q55 : int 4 4 4 4 4 4 2 4 4 4 ...
## $ Q56 : int 1 1 2 3 -2 3 1 -2 3 2 ...
## [list output truncated]
## Seleksi variabel (lebih banyak tapi aman)
data_sem <- data[, c(
"Q46","Q47","Q48", # WellBeing
"Q57","Q58","Q59", # Trust
"Q164","Q165","Q166", # Religion
"Q108","Q109" # Freedom
)]
# Rename
colnames(data_sem) <- c(
"Happy","LifeSat","Health",
"Trust1","Trust2","Trust3",
"Rel1","Rel2","Rel3",
"Freedom1","Freedom2"
)
head(data_sem)
## Happy LifeSat Health Trust1 Trust2 Trust3 Rel1 Rel2 Rel3 Freedom1 Freedom2
## 1 1 3 10 2 1 2 7 1 1 2 10
## 2 1 1 9 2 1 2 1 2 2 2 2
## 3 2 1 9 2 1 3 8 1 1 5 2
## 4 2 2 9 2 1 2 1 2 2 5 4
## 5 2 2 8 2 1 2 4 2 2 7 2
## 6 1 1 10 2 2 2 3 1 1 1 10
data_sem[data_sem < 0] <- NA
data_sem <- na.omit(data_sem)
sum(is.na(data_sem))
## [1] 0
par(mfrow = c(1, 4))
for (col in colnames(data_sem)) {
hist(data_sem[[col]],
main = col,
col = "lightblue",
border = "black")
}
par(mfrow = c(1,1))
data_z <- as.data.frame(scale(data_sem))
summary(data_z)
## Happy LifeSat Health Trust1
## Min. :-1.2023 Min. :-1.3478 Min. :-2.7901 Min. :-1.7489
## 1st Qu.:-1.2023 1st Qu.:-0.1979 1st Qu.:-0.5605 1st Qu.: 0.5718
## Median : 0.2239 Median :-0.1979 Median : 0.3313 Median : 0.5718
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2239 3rd Qu.: 0.9521 3rd Qu.: 0.7773 3rd Qu.: 0.5718
## Max. : 3.0761 Max. : 3.2520 Max. : 1.2232 Max. : 0.5718
## Trust2 Trust3 Rel1 Rel2
## Min. :-0.4901 Min. :-1.502 Min. :-1.9675 Min. :-0.5007
## 1st Qu.:-0.4901 1st Qu.:-0.243 1st Qu.:-0.7324 1st Qu.:-0.5007
## Median :-0.4901 Median :-0.243 Median : 0.5027 Median :-0.5007
## Mean : 0.0000 Mean : 0.000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.4901 3rd Qu.: 1.016 3rd Qu.: 0.8115 3rd Qu.:-0.5007
## Max. : 4.7711 Max. : 2.274 Max. : 0.8115 Max. : 1.9970
## Rel3 Freedom1 Freedom2
## Min. :-0.7482 Min. :-1.35475 Min. :-1.11935
## 1st Qu.:-0.7482 1st Qu.:-1.01997 1st Qu.:-1.11935
## Median :-0.7482 Median :-0.01562 Median :-0.01004
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 1.3364 3rd Qu.: 0.65394 3rd Qu.: 0.72950
## Max. : 1.3364 Max. : 1.65829 Max. : 2.20859
summary(data_sem)
## Happy LifeSat Health Trust1
## Min. :1.000 Min. :1.000 Min. : 1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.: 6.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median : 8.000 Median :2.000
## Mean :1.843 Mean :2.172 Mean : 7.257 Mean :1.754
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.: 9.000 3rd Qu.:2.000
## Max. :4.000 Max. :5.000 Max. :10.000 Max. :2.000
## Trust2 Trust3 Rel1 Rel2 Rel3
## Min. :1.000 Min. :1.000 Min. : 1.000 Min. :1.0 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.: 5.000 1st Qu.:1.0 1st Qu.:1.000
## Median :1.000 Median :2.000 Median : 9.000 Median :1.0 Median :1.000
## Mean :1.279 Mean :2.193 Mean : 7.372 Mean :1.2 Mean :1.359
## 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:10.000 3rd Qu.:1.0 3rd Qu.:2.000
## Max. :4.000 Max. :4.000 Max. :10.000 Max. :2.0 Max. :2.000
## Freedom1 Freedom2
## Min. : 1.000 Min. : 1.000
## 1st Qu.: 2.000 1st Qu.: 1.000
## Median : 5.000 Median : 4.000
## Mean : 5.047 Mean : 4.027
## 3rd Qu.: 7.000 3rd Qu.: 6.000
## Max. :10.000 Max. :10.000
describe(data_sem)
## vars n mean sd median trimmed mad min max range skew kurtosis
## Happy 1 83948 1.84 0.70 2 1.78 0.00 1 4 3 0.58 0.35
## LifeSat 2 83948 2.17 0.87 2 2.13 1.48 1 5 4 0.49 0.12
## Health 3 83948 7.26 2.24 8 7.48 2.97 1 10 9 -0.68 0.02
## Trust1 4 83948 1.75 0.43 2 1.82 0.00 1 2 1 -1.18 -0.61
## Trust2 5 83948 1.28 0.57 1 1.16 0.00 1 4 3 2.22 5.10
## Trust3 6 83948 2.19 0.79 2 2.15 0.00 1 4 3 0.48 -0.04
## Rel1 7 83948 7.37 3.24 9 7.84 1.48 1 10 9 -0.90 -0.67
## Rel2 8 83948 1.20 0.40 1 1.13 0.00 1 2 1 1.50 0.24
## Rel3 9 83948 1.36 0.48 1 1.32 0.00 1 2 1 0.59 -1.65
## Freedom1 10 83948 5.05 2.99 5 4.93 4.45 1 10 9 0.15 -1.15
## Freedom2 11 83948 4.03 2.70 4 3.73 2.97 1 10 9 0.63 -0.53
## se
## Happy 0.00
## LifeSat 0.00
## Health 0.01
## Trust1 0.00
## Trust2 0.00
## Trust3 0.00
## Rel1 0.01
## Rel2 0.00
## Rel3 0.00
## Freedom1 0.01
## Freedom2 0.01
# karena menggunakan semua kardia maka harus dibutuhkan 60 Gb sehingga terlalu berat solusinya adalah dengan mengambil smple saja
data_z_sample <- data_z[sample(nrow(data_z), 500), ]
mardia(data_z_sample)
## Call: mardia(x = data_z_sample)
##
## Mardia tests of multivariate skew and kurtosis
## Use describe(x) the to get univariate tests
## n.obs = 500 num.vars = 11
## b1p = 15.04 skew = 1253.6 with probability <= 4.5e-121
## small sample skew = 1262.38 with probability <= 1.5e-122
## b2p = 153.96 kurtosis = 7.25 with probability <= 4.2e-13
model_vif <- lm(Happy ~ LifeSat + Trust1 + Trust2 + Trust3 + Rel1 + Rel2 + Rel3 + Freedom1 + Freedom2,
data = data_z)
vif(model_vif)
## LifeSat Trust1 Trust2 Trust3 Rel1 Rel2 Rel3 Freedom1
## 1.026105 1.133169 1.125227 1.167758 2.191392 2.276172 1.352579 1.022354
## Freedom2
## 1.024749
KMO(cor(data_z))
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(data_z))
## Overall MSA = 0.65
## MSA for each item =
## Happy LifeSat Health Trust1 Trust2 Trust3 Rel1 Rel2
## 0.62 0.63 0.66 0.75 0.60 0.58 0.64 0.63
## Rel3 Freedom1 Freedom2
## 0.82 0.57 0.56
model_cfa <- '
WellBeing =~ Happy + LifeSat + Health
Trust =~ Trust1 + Trust2 + Trust3
Religion =~ Rel1 + Rel2 + Rel3
Freedom =~ Freedom1 + Freedom2
'
fit_cfa <- cfa(model_cfa, data = data_z, std.lv = TRUE)
## Warning: lavaan->lav_model_vcov():
## Could not compute standard errors! The information matrix could not be
## inverted. This may be a symptom that the model is not identified.
## Warning: lavaan->lav_object_post_check():
## some estimated ov variances are negative
summary(fit_cfa, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-21 ended normally after 7585 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Number of observations 83948
##
## Model Test User Model:
##
## Test statistic 9156.102
## Degrees of freedom 38
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 133105.281
## Degrees of freedom 55
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.931
## Tucker-Lewis Index (TLI) 0.901
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1248307.482
## Loglikelihood unrestricted model (H1) -1243729.432
##
## Akaike (AIC) 2496670.965
## Bayesian (BIC) 2496932.428
## Sample-size adjusted Bayesian (SABIC) 2496843.443
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053
## 90 Percent confidence interval - lower 0.053
## 90 Percent confidence interval - upper 0.054
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## WellBeing =~
## Happy 0.658 NA 0.658 0.658
## LifeSat 0.552 NA 0.552 0.552
## Health -0.374 NA -0.374 -0.374
## Trust =~
## Trust1 0.343 NA 0.343 0.343
## Trust2 0.448 NA 0.448 0.448
## Trust3 0.652 NA 0.652 0.652
## Religion =~
## Rel1 0.814 NA 0.814 0.814
## Rel2 -0.889 NA -0.889 -0.889
## Rel3 -0.550 NA -0.550 -0.550
## Freedom =~
## Freedom1 27.547 NA 27.547 27.533
## Freedom2 0.005 NA 0.005 0.005
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## WellBeing ~~
## Trust 0.278 NA 0.278 0.278
## Religion -0.080 NA -0.080 -0.080
## Freedom -0.005 NA -0.005 -0.005
## Trust ~~
## Religion 0.160 NA 0.160 0.160
## Freedom -0.000 NA -0.000 -0.000
## Religion ~~
## Freedom -0.001 NA -0.001 -0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Happy 0.567 NA 0.567 0.567
## .LifeSat 0.695 NA 0.695 0.695
## .Health 0.860 NA 0.860 0.860
## .Trust1 0.882 NA 0.882 0.882
## .Trust2 0.799 NA 0.799 0.799
## .Trust3 0.575 NA 0.575 0.575
## .Rel1 0.337 NA 0.337 0.337
## .Rel2 0.210 NA 0.210 0.210
## .Rel3 0.698 NA 0.698 0.698
## .Freedom1 -757.817 NA -757.817 -757.058
## .Freedom2 1.000 NA 1.000 1.000
## WellBeing 1.000 1.000 1.000
## Trust 1.000 1.000 1.000
## Religion 1.000 1.000 1.000
## Freedom 1.000 1.000 1.000
semPaths(
fit_cfa,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 6,
sizeLat = 7
)
data.frame(
CFI = fitMeasures(fit_cfa, "cfi"),
RMSEA = fitMeasures(fit_cfa, "rmsea"),
SRMR = fitMeasures(fit_cfa, "srmr"),
TLI = fitMeasures(fit_cfa, "tli")
)
## CFI RMSEA SRMR TLI
## cfi 0.9314688 0.05346324 0.04594048 0.90081
hitung_CR <- function(fit) {
std <- standardizedSolution(fit)
lambda <- std$est[std$op == "=~"]
theta <- 1 - lambda^2
sum(lambda)^2 / (sum(lambda)^2 + sum(theta))
}
hitung_CR(fit_cfa)
## Warning: lavaan->lav_model_vcov():
## Could not compute standard errors! The information matrix could not be
## inverted. This may be a symptom that the model is not identified.
## [1] 8.374382
model_sem <- '
WellBeing =~ Happy + LifeSat + Health
Trust =~ Trust1 + Trust2 + Trust3
Religion =~ Rel1 + Rel2 + Rel3
Freedom =~ Freedom1 + Freedom2
WellBeing ~ Trust + Religion + Freedom
'
fit_sem <- sem(model_sem, data = data_z, std.lv = TRUE)
## Warning: lavaan->lav_lavaan_step11_estoptim():
## the optimizer warns that a solution has NOT been found!
summary(fit_sem, fit.measures = TRUE, standardized = TRUE)
## Warning: lavaan->lav_object_summary():
## fit measures not available if model did not converge
## lavaan 0.6-21 did NOT end normally after 8876 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Number of observations 83948
##
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## WellBeing =~
## Happy 0.627 NA 0.658 0.658
## LifeSat 0.526 NA 0.552 0.552
## Health -0.356 NA -0.374 -0.374
## Trust =~
## Trust1 0.343 NA 0.343 0.343
## Trust2 0.448 NA 0.448 0.448
## Trust3 0.652 NA 0.652 0.652
## Religion =~
## Rel1 -0.814 NA -0.814 -0.814
## Rel2 0.889 NA 0.889 0.889
## Rel3 0.550 NA 0.550 0.550
## Freedom =~
## Freedom1 29.792 NA 29.792 29.777
## Freedom2 0.004 NA 0.004 0.004
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## WellBeing ~
## Trust 0.313 NA 0.298 0.298
## Religion 0.134 NA 0.128 0.128
## Freedom -0.005 NA -0.004 -0.004
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Trust ~~
## Religion -0.160 NA -0.160 -0.160
## Freedom -0.000 NA -0.000 -0.000
## Religion ~~
## Freedom 0.001 NA 0.001 0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Happy 0.567 NA 0.567 0.567
## .LifeSat 0.695 NA 0.695 0.695
## .Health 0.860 NA 0.860 0.860
## .Trust1 0.882 NA 0.882 0.882
## .Trust2 0.799 NA 0.799 0.799
## .Trust3 0.575 NA 0.575 0.575
## .Rel1 0.337 NA 0.337 0.337
## .Rel2 0.210 NA 0.210 0.210
## .Rel3 0.698 NA 0.698 0.698
## .Freedom1 -886.573 NA -886.573 -885.691
## .Freedom2 1.000 NA 1.000 1.000
## .WellBeing 1.000 0.907 0.907
## Trust 1.000 1.000 1.000
## Religion 1.000 1.000 1.000
## Freedom 1.000 1.000 1.000
semPaths(
fit_sem,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 7,
sizeLat = 7
)
