#Reading in the data
library(haven)
## Warning: package 'haven' was built under R version 4.3.3
csek12 <- as.data.frame(read_sav("RegRep_K12 Sex Ed_De-Id Data_Clean_11-24-23.sav"))
#Making variables nominal
csek12$Vote <- factor(csek12$Vote, levels = c(1:3))
csek12$Gender_3cat <- factor(csek12$Gender_3cat, levels = c(1:3))
csek12$Gender_5cat <- factor(csek12$Gender_5cat, levels = c(1:5))
csek12$Race_cat <- factor(csek12$Race_cat, levels = c(1:7))
csek12$SO <- factor(csek12$SO, levels = c(1:6))
csek12$Geo <- factor(csek12$Geo, levels = c(1:4))
csek12$Parent <- factor(csek12$Parent, levels = c(1:3))
#setting "I don't know" responses to NA
csek12$PolAff <- ifelse(csek12$Politic == 8, NA, csek12$Politic)
#setting "I would abstain (not vote)" responses to NA
csek12$Vote1 <- ifelse(csek12$Vote == 3, NA, csek12$Vote)
csek12$Vote1 <- factor(csek12$Vote1, levels = c(1:2))
csek12$Vote1 <- as.character(csek12$Vote1)
#validity analyses
library(lavaan)
## Warning: package 'lavaan' was built under R version 4.3.3
## This is lavaan 0.6-18
## lavaan is FREE software! Please report any bugs.
##do attitudes toward comprehensive sex education relate to perceptions of importance of CSE in middle and high school?
csek12one.val <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
Q4_4 ~~ Q4_5
Q4_4 ~~ Q4_6
Q4_5 ~~ Q4_6
Q4_7 ~~ Q4_8
Q2_8 ~~ Q2_9
CSE ~~ Important_Middle
CSE ~~ Important_High
'
csek12one.fit.val <- cfa(csek12one.val, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR")
summary(csek12one.fit.val)
## lavaan 0.6-18 ended normally after 164 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 138
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2720.260 1629.211
## Degrees of freedom 764 764
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.670
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## CSE =~
## Q2_1 0.775 0.053 14.751 0.000
## Q2_2 0.701 0.058 12.187 0.000
## Q2_3 0.714 0.056 12.850 0.000
## Q2_4 0.774 0.052 14.959 0.000
## Q2_5 0.788 0.054 14.537 0.000
## Q2_6 0.754 0.057 13.173 0.000
## Q2_7 0.704 0.061 11.590 0.000
## Q2_8 0.696 0.057 12.228 0.000
## Q2_9 0.605 0.062 9.755 0.000
## Q3_1 0.809 0.051 16.000 0.000
## Q3_2 0.798 0.052 15.268 0.000
## Q3_3 0.707 0.065 10.901 0.000
## Q3_4 0.834 0.052 15.928 0.000
## Q3_5 0.751 0.060 12.453 0.000
## Q3_6 0.708 0.061 11.590 0.000
## Q3_7 0.843 0.050 16.720 0.000
## Q3_8 0.835 0.053 15.848 0.000
## Q3_9 0.867 0.045 19.393 0.000
## Q3_10 0.753 0.054 14.074 0.000
## Q4_1 0.803 0.055 14.665 0.000
## Q4_2 0.817 0.052 15.679 0.000
## Q4_3 0.802 0.055 14.509 0.000
## Q4_4 0.797 0.058 13.682 0.000
## Q4_5 0.731 0.060 12.113 0.000
## Q4_6 0.819 0.056 14.706 0.000
## Q4_7 0.876 0.048 18.097 0.000
## Q4_8 0.737 0.064 11.478 0.000
## Q4_9 0.543 0.065 8.410 0.000
## Q4_10 0.787 0.056 14.076 0.000
## Q5_1 0.784 0.054 14.568 0.000
## Q5_2 0.798 0.056 14.178 0.000
## Q5_4 0.752 0.060 12.552 0.000
## Q5_5 0.693 0.062 11.125 0.000
## Q5_6 0.687 0.061 11.265 0.000
## Q5_7 0.819 0.058 14.065 0.000
## Q5_8 0.613 0.069 8.932 0.000
## Q5_9 0.678 0.060 11.246 0.000
## Q5_10 0.636 0.065 9.852 0.000
## Q5_11 0.784 0.058 13.626 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .Q2_3 ~~
## .Q2_4 0.069 0.035 1.993 0.046
## .Q2_5 0.032 0.023 1.411 0.158
## .Q5_5 0.015 0.021 0.708 0.479
## .Q5_6 0.004 0.015 0.232 0.816
## .Q2_4 ~~
## .Q2_5 0.140 0.033 4.267 0.000
## .Q5_5 0.019 0.023 0.835 0.404
## .Q5_6 -0.034 0.015 -2.287 0.022
## .Q2_5 ~~
## .Q5_5 0.067 0.030 2.266 0.023
## .Q5_6 -0.054 0.017 -3.121 0.002
## .Q5_5 ~~
## .Q5_6 0.003 0.020 0.155 0.877
## .Q4_4 ~~
## .Q4_5 0.060 0.022 2.775 0.006
## .Q4_6 0.039 0.017 2.270 0.023
## .Q4_5 ~~
## .Q4_6 0.065 0.021 3.119 0.002
## .Q4_7 ~~
## .Q4_8 0.019 0.025 0.771 0.441
## .Q2_8 ~~
## .Q2_9 0.142 0.032 4.420 0.000
## CSE ~~
## Important_Mddl -0.533 0.085 -6.255 0.000
## Important_High -0.448 0.104 -4.289 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Q2_1 4.275 0.061 70.405 0.000
## .Q2_2 4.208 0.064 65.628 0.000
## .Q2_3 4.440 0.055 81.208 0.000
## .Q2_4 4.282 0.062 69.578 0.000
## .Q2_5 4.289 0.062 69.393 0.000
## .Q2_6 4.369 0.061 71.522 0.000
## .Q2_7 4.456 0.055 80.830 0.000
## .Q2_8 4.349 0.056 77.492 0.000
## .Q2_9 4.453 0.052 86.045 0.000
## .Q3_1 4.369 0.059 73.547 0.000
## .Q3_2 4.403 0.058 75.537 0.000
## .Q3_3 4.483 0.053 84.090 0.000
## .Q3_4 4.386 0.060 73.221 0.000
## .Q3_5 4.460 0.056 79.138 0.000
## .Q3_6 4.490 0.056 80.328 0.000
## .Q3_7 4.228 0.068 61.889 0.000
## .Q3_8 4.074 0.077 52.615 0.000
## .Q3_9 3.815 0.083 45.705 0.000
## .Q3_10 4.248 0.061 69.311 0.000
## .Q4_1 4.419 0.058 76.735 0.000
## .Q4_2 4.322 0.061 70.787 0.000
## .Q4_3 4.463 0.056 79.187 0.000
## .Q4_4 4.463 0.058 77.292 0.000
## .Q4_5 4.497 0.056 80.009 0.000
## .Q4_6 4.406 0.059 74.112 0.000
## .Q4_7 4.228 0.067 62.650 0.000
## .Q4_8 4.540 0.057 80.283 0.000
## .Q4_9 4.205 0.063 66.926 0.000
## .Q4_10 4.406 0.058 75.823 0.000
## .Q5_1 4.322 0.060 72.333 0.000
## .Q5_2 4.369 0.062 70.883 0.000
## .Q5_4 4.503 0.054 82.830 0.000
## .Q5_5 4.419 0.056 78.347 0.000
## .Q5_6 4.510 0.052 86.312 0.000
## .Q5_7 4.423 0.060 73.826 0.000
## .Q5_8 4.362 0.061 71.255 0.000
## .Q5_9 4.557 0.051 89.635 0.000
## .Q5_10 4.379 0.056 78.816 0.000
## .Q5_11 4.477 0.057 78.561 0.000
## Important_Mddl 1.859 0.066 28.195 0.000
## Important_High 1.302 0.049 26.722 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Q2_1 0.301 0.046 6.555 0.000
## .Q2_2 0.573 0.078 7.381 0.000
## .Q2_3 0.214 0.038 5.658 0.000
## .Q2_4 0.334 0.052 6.446 0.000
## .Q2_5 0.314 0.042 7.478 0.000
## .Q2_6 0.358 0.079 4.502 0.000
## .Q2_7 0.247 0.041 5.994 0.000
## .Q2_8 0.296 0.041 7.284 0.000
## .Q2_9 0.312 0.040 7.899 0.000
## .Q3_1 0.183 0.055 3.328 0.001
## .Q3_2 0.166 0.031 5.274 0.000
## .Q3_3 0.183 0.051 3.592 0.000
## .Q3_4 0.145 0.029 4.993 0.000
## .Q3_5 0.197 0.043 4.579 0.000
## .Q3_6 0.264 0.045 5.861 0.000
## .Q3_7 0.447 0.078 5.738 0.000
## .Q3_8 0.861 0.122 7.046 0.000
## .Q3_9 1.079 0.118 9.126 0.000
## .Q3_10 0.366 0.068 5.377 0.000
## .Q4_1 0.133 0.034 3.944 0.000
## .Q4_2 0.224 0.041 5.400 0.000
## .Q4_3 0.093 0.016 5.925 0.000
## .Q4_4 0.149 0.026 5.698 0.000
## .Q4_5 0.233 0.036 6.428 0.000
## .Q4_6 0.163 0.025 6.481 0.000
## .Q4_7 0.338 0.056 6.002 0.000
## .Q4_8 0.232 0.035 6.530 0.000
## .Q4_9 0.785 0.100 7.839 0.000
## .Q4_10 0.183 0.039 4.632 0.000
## .Q5_1 0.248 0.040 6.174 0.000
## .Q5_2 0.287 0.055 5.166 0.000
## .Q5_4 0.131 0.024 5.527 0.000
## .Q5_5 0.311 0.059 5.307 0.000
## .Q5_6 0.187 0.031 6.029 0.000
## .Q5_7 0.180 0.028 6.393 0.000
## .Q5_8 0.618 0.106 5.853 0.000
## .Q5_9 0.159 0.020 7.759 0.000
## .Q5_10 0.383 0.054 7.154 0.000
## .Q5_11 0.151 0.035 4.313 0.000
## Important_Mddl 1.296 0.129 10.061 0.000
## Important_High 0.707 0.151 4.700 0.000
## CSE 1.000
##do attitudes toward comprehensive sex education relate to sex positivity and sex negativity?
csek12one.val1 <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
Q4_4 ~~ Q4_5
Q4_4 ~~ Q4_6
Q4_5 ~~ Q4_6
Q4_7 ~~ Q4_8
Q2_8 ~~ Q2_9
CSE ~~ Important_Middle
CSE ~~ Important_High
CSE ~~ Sex_Pos
CSE ~~ Sex_Neg
'
csek12one.fit.val1 <- cfa(csek12one.val1, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR")
summary(csek12one.fit.val1, standardized = TRUE)
## lavaan 0.6-18 ended normally after 200 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 144
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2949.510 1825.891
## Degrees of freedom 845 845
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.615
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.768 0.053 14.535 0.000 0.768 0.814
## Q2_2 0.694 0.058 11.909 0.000 0.694 0.676
## Q2_3 0.707 0.056 12.588 0.000 0.707 0.837
## Q2_4 0.766 0.052 14.623 0.000 0.766 0.798
## Q2_5 0.781 0.055 14.262 0.000 0.781 0.812
## Q2_6 0.747 0.058 12.771 0.000 0.747 0.780
## Q2_7 0.698 0.061 11.485 0.000 0.698 0.814
## Q2_8 0.689 0.059 11.734 0.000 0.689 0.785
## Q2_9 0.599 0.063 9.528 0.000 0.599 0.731
## Q3_1 0.801 0.052 15.314 0.000 0.801 0.882
## Q3_2 0.791 0.054 14.708 0.000 0.791 0.889
## Q3_3 0.701 0.065 10.861 0.000 0.701 0.854
## Q3_4 0.827 0.054 15.436 0.000 0.827 0.908
## Q3_5 0.744 0.061 12.139 0.000 0.744 0.859
## Q3_6 0.701 0.063 11.193 0.000 0.701 0.807
## Q3_7 0.835 0.051 16.285 0.000 0.835 0.781
## Q3_8 0.827 0.053 15.510 0.000 0.827 0.665
## Q3_9 0.859 0.045 19.214 0.000 0.859 0.637
## Q3_10 0.746 0.056 13.444 0.000 0.746 0.777
## Q4_1 0.795 0.056 14.254 0.000 0.795 0.909
## Q4_2 0.810 0.053 15.220 0.000 0.810 0.863
## Q4_3 0.794 0.056 14.143 0.000 0.794 0.933
## Q4_4 0.790 0.059 13.446 0.000 0.790 0.898
## Q4_5 0.724 0.061 11.919 0.000 0.724 0.832
## Q4_6 0.811 0.057 14.343 0.000 0.811 0.895
## Q4_7 0.868 0.049 17.605 0.000 0.868 0.831
## Q4_8 0.730 0.066 11.119 0.000 0.730 0.835
## Q4_9 0.538 0.065 8.258 0.000 0.538 0.519
## Q4_10 0.780 0.057 13.706 0.000 0.780 0.877
## Q5_1 0.776 0.055 14.235 0.000 0.776 0.842
## Q5_2 0.791 0.057 13.845 0.000 0.791 0.828
## Q5_4 0.745 0.061 12.246 0.000 0.745 0.900
## Q5_5 0.686 0.063 10.867 0.000 0.686 0.776
## Q5_6 0.681 0.062 10.982 0.000 0.681 0.844
## Q5_7 0.811 0.059 13.698 0.000 0.811 0.886
## Q5_8 0.607 0.070 8.726 0.000 0.607 0.611
## Q5_9 0.672 0.061 10.973 0.000 0.672 0.860
## Q5_10 0.630 0.066 9.577 0.000 0.630 0.714
## Q5_11 0.777 0.059 13.165 0.000 0.777 0.894
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_3 ~~
## .Q2_4 0.069 0.035 1.992 0.046 0.069 0.258
## .Q2_5 0.032 0.023 1.408 0.159 0.032 0.125
## .Q5_5 0.015 0.021 0.707 0.480 0.015 0.057
## .Q5_6 0.004 0.015 0.231 0.817 0.004 0.018
## .Q2_4 ~~
## .Q2_5 0.139 0.033 4.266 0.000 0.139 0.431
## .Q5_5 0.019 0.023 0.833 0.405 0.019 0.060
## .Q5_6 -0.034 0.015 -2.287 0.022 -0.034 -0.137
## .Q2_5 ~~
## .Q5_5 0.067 0.030 2.263 0.024 0.067 0.214
## .Q5_6 -0.054 0.017 -3.123 0.002 -0.054 -0.224
## .Q5_5 ~~
## .Q5_6 0.003 0.020 0.156 0.876 0.003 0.013
## .Q4_4 ~~
## .Q4_5 0.060 0.022 2.779 0.005 0.060 0.323
## .Q4_6 0.039 0.017 2.272 0.023 0.039 0.250
## .Q4_5 ~~
## .Q4_6 0.065 0.021 3.119 0.002 0.065 0.331
## .Q4_7 ~~
## .Q4_8 0.019 0.025 0.774 0.439 0.019 0.069
## .Q2_8 ~~
## .Q2_9 0.142 0.032 4.422 0.000 0.142 0.468
## CSE ~~
## Important_Mddl -0.501 0.090 -5.574 0.000 -0.501 -0.440
## Important_High -0.462 0.107 -4.301 0.000 -0.462 -0.549
## Sex_Pos 0.533 0.468 1.139 0.255 0.533 0.064
## Sex_Neg -0.200 0.386 -0.518 0.604 -0.200 -0.044
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.531
## .Q2_2 4.208 0.064 65.628 0.000 4.208 4.097
## .Q2_3 4.440 0.055 81.208 0.000 4.440 5.254
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.461
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.464
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.567
## .Q2_7 4.456 0.055 80.829 0.000 4.456 5.202
## .Q2_8 4.349 0.056 77.492 0.000 4.349 4.953
## .Q2_9 4.453 0.052 86.045 0.000 4.453 5.435
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.810
## .Q3_2 4.403 0.058 75.537 0.000 4.403 4.949
## .Q3_3 4.483 0.053 84.091 0.000 4.483 5.462
## .Q3_4 4.386 0.060 73.222 0.000 4.386 4.820
## .Q3_5 4.460 0.056 79.139 0.000 4.460 5.147
## .Q3_6 4.490 0.056 80.328 0.000 4.490 5.167
## .Q3_7 4.228 0.068 61.890 0.000 4.228 3.953
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.278
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.831
## .Q3_10 4.248 0.061 69.311 0.000 4.248 4.422
## .Q4_1 4.419 0.058 76.735 0.000 4.419 5.052
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.609
## .Q4_3 4.463 0.056 79.187 0.000 4.463 5.246
## .Q4_4 4.463 0.058 77.292 0.000 4.463 5.076
## .Q4_5 4.497 0.056 80.009 0.000 4.497 5.171
## .Q4_6 4.406 0.059 74.112 0.000 4.406 4.863
## .Q4_7 4.228 0.067 62.650 0.000 4.228 4.048
## .Q4_8 4.540 0.057 80.284 0.000 4.540 5.192
## .Q4_9 4.205 0.063 66.927 0.000 4.205 4.057
## .Q4_10 4.406 0.058 75.822 0.000 4.406 4.953
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.685
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.577
## .Q5_4 4.503 0.054 82.830 0.000 4.503 5.441
## .Q5_5 4.419 0.056 78.347 0.000 4.419 4.998
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.594
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.833
## .Q5_8 4.362 0.061 71.255 0.000 4.362 4.392
## .Q5_9 4.557 0.051 89.636 0.000 4.557 5.831
## .Q5_10 4.379 0.056 78.816 0.000 4.379 4.959
## .Q5_11 4.477 0.057 78.561 0.000 4.477 5.154
## Important_Mddl 1.859 0.066 28.195 0.000 1.859 1.633
## Important_High 1.302 0.049 26.723 0.000 1.302 1.548
## Sex_Pos 38.876 0.482 80.598 0.000 38.876 4.669
## Sex_Neg 11.218 0.263 42.646 0.000 11.218 2.470
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.300 0.046 6.556 0.000 0.300 0.337
## .Q2_2 0.573 0.078 7.381 0.000 0.573 0.543
## .Q2_3 0.214 0.038 5.659 0.000 0.214 0.300
## .Q2_4 0.334 0.052 6.447 0.000 0.334 0.362
## .Q2_5 0.314 0.042 7.477 0.000 0.314 0.340
## .Q2_6 0.358 0.079 4.502 0.000 0.358 0.391
## .Q2_7 0.247 0.041 5.997 0.000 0.247 0.337
## .Q2_8 0.296 0.041 7.289 0.000 0.296 0.384
## .Q2_9 0.312 0.040 7.900 0.000 0.312 0.465
## .Q3_1 0.183 0.055 3.327 0.001 0.183 0.222
## .Q3_2 0.166 0.031 5.274 0.000 0.166 0.210
## .Q3_3 0.183 0.051 3.593 0.000 0.183 0.271
## .Q3_4 0.145 0.029 4.994 0.000 0.145 0.175
## .Q3_5 0.197 0.043 4.579 0.000 0.197 0.262
## .Q3_6 0.264 0.045 5.862 0.000 0.264 0.349
## .Q3_7 0.446 0.078 5.735 0.000 0.446 0.390
## .Q3_8 0.861 0.122 7.046 0.000 0.861 0.557
## .Q3_9 1.079 0.118 9.122 0.000 1.079 0.594
## .Q3_10 0.366 0.068 5.377 0.000 0.366 0.397
## .Q4_1 0.133 0.034 3.940 0.000 0.133 0.174
## .Q4_2 0.224 0.041 5.401 0.000 0.224 0.255
## .Q4_3 0.093 0.016 5.926 0.000 0.093 0.129
## .Q4_4 0.149 0.026 5.705 0.000 0.149 0.193
## .Q4_5 0.233 0.036 6.433 0.000 0.233 0.308
## .Q4_6 0.163 0.025 6.490 0.000 0.163 0.199
## .Q4_7 0.338 0.056 6.000 0.000 0.338 0.310
## .Q4_8 0.232 0.036 6.526 0.000 0.232 0.303
## .Q4_9 0.785 0.100 7.837 0.000 0.785 0.731
## .Q4_10 0.183 0.039 4.630 0.000 0.183 0.231
## .Q5_1 0.248 0.040 6.171 0.000 0.248 0.292
## .Q5_2 0.286 0.055 5.168 0.000 0.286 0.314
## .Q5_4 0.131 0.024 5.531 0.000 0.131 0.191
## .Q5_5 0.311 0.059 5.307 0.000 0.311 0.398
## .Q5_6 0.187 0.031 6.034 0.000 0.187 0.287
## .Q5_7 0.180 0.028 6.399 0.000 0.180 0.215
## .Q5_8 0.618 0.106 5.853 0.000 0.618 0.626
## .Q5_9 0.159 0.020 7.766 0.000 0.159 0.260
## .Q5_10 0.383 0.053 7.156 0.000 0.383 0.491
## .Q5_11 0.151 0.035 4.313 0.000 0.151 0.200
## Important_Mddl 1.296 0.129 10.061 0.000 1.296 1.000
## Important_High 0.707 0.151 4.700 0.000 0.707 1.000
## Sex_Pos 69.330 7.624 9.094 0.000 69.330 1.000
## Sex_Neg 20.620 5.179 3.982 0.000 20.620 1.000
## CSE 1.000 1.000 1.000
##do attitudes toward comprehensive sex education relate to political affiliation?
csek12one.val2 <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
Q4_4 ~~ Q4_5
Q4_4 ~~ Q4_6
Q4_5 ~~ Q4_6
Q4_7 ~~ Q4_8
Q2_8 ~~ Q2_9
CSE ~~ Important_Middle
CSE ~~ Important_High
CSE ~~ Sex_Pos
CSE ~~ Sex_Neg
CSE ~~ PolAff
'
csek12one.fit.val2 <- cfa(csek12one.val2, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR")
summary(csek12one.fit.val2, standardized = TRUE)
## lavaan 0.6-18 ended normally after 206 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 147
##
## Number of observations 298
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3221.503 2021.611
## Degrees of freedom 887 887
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.594
## Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.741 0.050 14.675 0.000 0.741 0.804
## Q2_2 0.669 0.057 11.693 0.000 0.669 0.662
## Q2_3 0.681 0.055 12.325 0.000 0.681 0.827
## Q2_4 0.739 0.051 14.607 0.000 0.739 0.788
## Q2_5 0.752 0.053 14.233 0.000 0.752 0.802
## Q2_6 0.720 0.056 12.749 0.000 0.720 0.769
## Q2_7 0.672 0.060 11.123 0.000 0.672 0.804
## Q2_8 0.664 0.058 11.368 0.000 0.664 0.774
## Q2_9 0.577 0.063 9.186 0.000 0.577 0.719
## Q3_1 0.772 0.051 15.253 0.000 0.772 0.875
## Q3_2 0.762 0.053 14.495 0.000 0.762 0.882
## Q3_3 0.675 0.064 10.558 0.000 0.675 0.845
## Q3_4 0.797 0.051 15.534 0.000 0.797 0.902
## Q3_5 0.717 0.059 12.219 0.000 0.717 0.850
## Q3_6 0.676 0.062 10.961 0.000 0.676 0.796
## Q3_7 0.806 0.049 16.479 0.000 0.806 0.770
## Q3_8 0.798 0.049 16.228 0.000 0.798 0.653
## Q3_9 0.829 0.040 20.562 0.000 0.829 0.624
## Q3_10 0.719 0.056 12.752 0.000 0.719 0.765
## Q4_1 0.766 0.053 14.395 0.000 0.766 0.903
## Q4_2 0.780 0.051 15.267 0.000 0.780 0.855
## Q4_3 0.765 0.055 13.933 0.000 0.765 0.929
## Q4_4 0.761 0.057 13.382 0.000 0.761 0.892
## Q4_5 0.697 0.059 11.855 0.000 0.697 0.822
## Q4_6 0.782 0.055 14.275 0.000 0.782 0.888
## Q4_7 0.837 0.048 17.461 0.000 0.837 0.822
## Q4_8 0.704 0.063 11.140 0.000 0.704 0.825
## Q4_9 0.518 0.064 8.110 0.000 0.518 0.504
## Q4_10 0.752 0.055 13.556 0.000 0.752 0.869
## Q5_1 0.748 0.053 14.076 0.000 0.748 0.832
## Q5_2 0.762 0.056 13.671 0.000 0.762 0.818
## Q5_4 0.717 0.059 12.099 0.000 0.717 0.893
## Q5_5 0.661 0.062 10.721 0.000 0.661 0.764
## Q5_6 0.656 0.061 10.770 0.000 0.656 0.835
## Q5_7 0.781 0.058 13.546 0.000 0.781 0.879
## Q5_8 0.585 0.068 8.556 0.000 0.585 0.597
## Q5_9 0.648 0.061 10.686 0.000 0.648 0.851
## Q5_10 0.607 0.066 9.200 0.000 0.607 0.700
## Q5_11 0.748 0.058 12.865 0.000 0.748 0.887
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_3 ~~
## .Q2_4 0.069 0.035 1.983 0.047 0.069 0.257
## .Q2_5 0.032 0.023 1.399 0.162 0.032 0.124
## .Q5_5 0.015 0.021 0.712 0.477 0.015 0.058
## .Q5_6 0.004 0.015 0.236 0.813 0.004 0.018
## .Q2_4 ~~
## .Q2_5 0.139 0.033 4.251 0.000 0.139 0.430
## .Q5_5 0.019 0.023 0.822 0.411 0.019 0.059
## .Q5_6 -0.035 0.015 -2.306 0.021 -0.035 -0.139
## .Q2_5 ~~
## .Q5_5 0.067 0.030 2.260 0.024 0.067 0.214
## .Q5_6 -0.054 0.017 -3.138 0.002 -0.054 -0.225
## .Q5_5 ~~
## .Q5_6 0.003 0.020 0.155 0.876 0.003 0.013
## .Q4_4 ~~
## .Q4_5 0.060 0.022 2.784 0.005 0.060 0.324
## .Q4_6 0.039 0.017 2.279 0.023 0.039 0.250
## .Q4_5 ~~
## .Q4_6 0.065 0.021 3.128 0.002 0.065 0.332
## .Q4_7 ~~
## .Q4_8 0.019 0.025 0.759 0.448 0.019 0.067
## .Q2_8 ~~
## .Q2_9 0.142 0.032 4.427 0.000 0.142 0.468
## CSE ~~
## Important_Mddl -0.396 0.105 -3.791 0.000 -0.396 -0.348
## Important_High -0.478 0.110 -4.358 0.000 -0.478 -0.569
## Sex_Pos 0.417 0.486 0.859 0.391 0.417 0.050
## Sex_Neg -0.269 0.392 -0.687 0.492 -0.269 -0.059
## PolAff 0.388 0.135 2.862 0.004 0.388 0.197
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.642
## .Q2_2 4.208 0.064 65.628 0.000 4.208 4.165
## .Q2_3 4.440 0.055 81.208 0.000 4.440 5.390
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.567
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.573
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.670
## .Q2_7 4.456 0.055 80.830 0.000 4.456 5.329
## .Q2_8 4.349 0.056 77.492 0.000 4.349 5.066
## .Q2_9 4.453 0.052 86.045 0.000 4.453 5.541
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.950
## .Q3_2 4.403 0.058 75.537 0.000 4.403 5.094
## .Q3_3 4.483 0.053 84.091 0.000 4.483 5.609
## .Q3_4 4.386 0.060 73.221 0.000 4.386 4.969
## .Q3_5 4.460 0.056 79.139 0.000 4.460 5.288
## .Q3_6 4.490 0.056 80.328 0.000 4.490 5.291
## .Q3_7 4.228 0.068 61.889 0.000 4.228 4.042
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.331
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.874
## .Q3_10 4.248 0.061 69.311 0.000 4.248 4.521
## .Q4_1 4.419 0.058 76.735 0.000 4.419 5.208
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.736
## .Q4_3 4.463 0.056 79.187 0.000 4.463 5.416
## .Q4_4 4.463 0.058 77.292 0.000 4.463 5.228
## .Q4_5 4.497 0.056 80.009 0.000 4.497 5.303
## .Q4_6 4.406 0.059 74.112 0.000 4.406 5.008
## .Q4_7 4.228 0.067 62.650 0.000 4.228 4.152
## .Q4_8 4.540 0.057 80.283 0.000 4.540 5.326
## .Q4_9 4.205 0.063 66.927 0.000 4.205 4.096
## .Q4_10 4.406 0.058 75.823 0.000 4.406 5.095
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.808
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.693
## .Q5_4 4.503 0.054 82.831 0.000 4.503 5.605
## .Q5_5 4.419 0.056 78.347 0.000 4.419 5.109
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.742
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.974
## .Q5_8 4.362 0.061 71.255 0.000 4.362 4.451
## .Q5_9 4.557 0.051 89.635 0.000 4.557 5.991
## .Q5_10 4.379 0.056 78.816 0.000 4.379 5.052
## .Q5_11 4.477 0.057 78.561 0.000 4.477 5.307
## Important_Mddl 1.859 0.066 28.195 0.000 1.859 1.633
## Important_High 1.302 0.049 26.723 0.000 1.302 1.548
## Sex_Pos 38.876 0.482 80.598 0.000 38.876 4.669
## Sex_Neg 11.218 0.263 42.646 0.000 11.218 2.470
## PolAff 4.946 0.115 43.130 0.000 4.946 2.517
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.300 0.046 6.554 0.000 0.300 0.353
## .Q2_2 0.573 0.078 7.383 0.000 0.573 0.561
## .Q2_3 0.214 0.038 5.658 0.000 0.214 0.316
## .Q2_4 0.333 0.052 6.438 0.000 0.333 0.379
## .Q2_5 0.313 0.042 7.479 0.000 0.313 0.356
## .Q2_6 0.358 0.079 4.503 0.000 0.358 0.409
## .Q2_7 0.247 0.041 6.009 0.000 0.247 0.354
## .Q2_8 0.296 0.041 7.289 0.000 0.296 0.402
## .Q2_9 0.312 0.040 7.904 0.000 0.312 0.484
## .Q3_1 0.183 0.055 3.325 0.001 0.183 0.235
## .Q3_2 0.166 0.031 5.274 0.000 0.166 0.222
## .Q3_3 0.183 0.051 3.599 0.000 0.183 0.287
## .Q3_4 0.145 0.029 4.991 0.000 0.145 0.186
## .Q3_5 0.197 0.043 4.583 0.000 0.197 0.277
## .Q3_6 0.264 0.045 5.862 0.000 0.264 0.366
## .Q3_7 0.445 0.078 5.736 0.000 0.445 0.407
## .Q3_8 0.859 0.122 7.038 0.000 0.859 0.574
## .Q3_9 1.076 0.118 9.096 0.000 1.076 0.610
## .Q3_10 0.366 0.068 5.376 0.000 0.366 0.415
## .Q4_1 0.133 0.034 3.944 0.000 0.133 0.185
## .Q4_2 0.224 0.042 5.403 0.000 0.224 0.269
## .Q4_3 0.094 0.016 5.933 0.000 0.094 0.138
## .Q4_4 0.149 0.026 5.705 0.000 0.149 0.205
## .Q4_5 0.233 0.036 6.437 0.000 0.233 0.324
## .Q4_6 0.163 0.025 6.508 0.000 0.163 0.211
## .Q4_7 0.337 0.056 6.008 0.000 0.337 0.325
## .Q4_8 0.232 0.035 6.540 0.000 0.232 0.319
## .Q4_9 0.786 0.100 7.844 0.000 0.786 0.746
## .Q4_10 0.183 0.040 4.629 0.000 0.183 0.245
## .Q5_1 0.248 0.040 6.169 0.000 0.248 0.307
## .Q5_2 0.287 0.055 5.172 0.000 0.287 0.331
## .Q5_4 0.131 0.024 5.532 0.000 0.131 0.203
## .Q5_5 0.311 0.059 5.310 0.000 0.311 0.416
## .Q5_6 0.187 0.031 6.034 0.000 0.187 0.302
## .Q5_7 0.180 0.028 6.401 0.000 0.180 0.228
## .Q5_8 0.618 0.106 5.856 0.000 0.618 0.644
## .Q5_9 0.159 0.021 7.759 0.000 0.159 0.275
## .Q5_10 0.383 0.053 7.162 0.000 0.383 0.510
## .Q5_11 0.151 0.035 4.318 0.000 0.151 0.213
## Important_Mddl 1.296 0.129 10.061 0.000 1.296 1.000
## Important_High 0.707 0.151 4.700 0.000 0.707 1.000
## Sex_Pos 69.331 7.624 9.094 0.000 69.331 1.000
## Sex_Neg 20.620 5.178 3.982 0.000 20.620 1.000
## PolAff 3.861 0.235 16.419 0.000 3.861 1.000
## CSE 1.000 1.000 1.000
##do attitudes toward comprehensive sex education predict voting intentions? Do voting intentions predict sex positivity and negativity?
csek12one.val3 <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
Q4_4 ~~ Q4_5
Q4_4 ~~ Q4_6
Q4_5 ~~ Q4_6
Q4_7 ~~ Q4_8
Q2_8 ~~ Q2_9
CSE ~~ Important_Middle
CSE ~~ Important_High
CSE ~~ Sex_Pos
CSE ~~ Sex_Neg
CSE ~~ PolAff
#regressions
Sex_Pos ~ Vote1
Sex_Neg ~ Vote1
'
csek12one.fit.val3 <- cfa(csek12one.val3, data = csek12, std.lv = TRUE, estimator = "MLR")
summary(csek12one.fit.val3, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-18 ended normally after 161 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 106
##
## Used Total
## Number of observations 273 298
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3364.557 2083.222
## Degrees of freedom 928 928
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.615
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17666.465 10333.399
## Degrees of freedom 990 990
## P-value 0.000 0.000
## Scaling correction factor 1.710
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.854 0.876
## Tucker-Lewis Index (TLI) 0.844 0.868
##
## Robust Comparative Fit Index (CFI) 0.883
## Robust Tucker-Lewis Index (TLI) 0.875
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -11092.593 -11092.593
## Scaling correction factor 3.006
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.758
## for the MLR correction
##
## Akaike (AIC) 22397.185 22397.185
## Bayesian (BIC) 22779.789 22779.789
## Sample-size adjusted Bayesian (SABIC) 22443.689 22443.689
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.098 0.068
## 90 Percent confidence interval - lower 0.095 0.064
## 90 Percent confidence interval - upper 0.102 0.071
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 0.000
##
## Robust RMSEA 0.086
## 90 Percent confidence interval - lower 0.081
## 90 Percent confidence interval - upper 0.091
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.974
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.265 0.265
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.726 0.054 13.471 0.000 0.726 0.804
## Q2_2 0.668 0.060 11.060 0.000 0.668 0.659
## Q2_3 0.673 0.057 11.905 0.000 0.673 0.825
## Q2_4 0.747 0.052 14.287 0.000 0.747 0.810
## Q2_5 0.751 0.056 13.533 0.000 0.751 0.819
## Q2_6 0.710 0.060 11.839 0.000 0.710 0.794
## Q2_7 0.670 0.064 10.481 0.000 0.670 0.806
## Q2_8 0.652 0.061 10.659 0.000 0.652 0.769
## Q2_9 0.560 0.066 8.515 0.000 0.560 0.711
## Q3_1 0.774 0.052 14.993 0.000 0.774 0.878
## Q3_2 0.756 0.055 13.809 0.000 0.756 0.879
## Q3_3 0.651 0.068 9.579 0.000 0.651 0.848
## Q3_4 0.791 0.054 14.705 0.000 0.791 0.900
## Q3_5 0.702 0.062 11.396 0.000 0.702 0.841
## Q3_6 0.661 0.064 10.298 0.000 0.661 0.802
## Q3_7 0.815 0.050 16.384 0.000 0.815 0.791
## Q3_8 0.821 0.050 16.317 0.000 0.821 0.680
## Q3_9 0.848 0.042 20.433 0.000 0.848 0.645
## Q3_10 0.715 0.059 12.206 0.000 0.715 0.791
## Q4_1 0.749 0.056 13.412 0.000 0.749 0.912
## Q4_2 0.777 0.053 14.735 0.000 0.777 0.849
## Q4_3 0.756 0.057 13.317 0.000 0.756 0.928
## Q4_4 0.747 0.060 12.490 0.000 0.747 0.893
## Q4_5 0.683 0.061 11.139 0.000 0.683 0.824
## Q4_6 0.773 0.057 13.586 0.000 0.773 0.889
## Q4_7 0.825 0.051 16.104 0.000 0.825 0.837
## Q4_8 0.689 0.066 10.389 0.000 0.689 0.824
## Q4_9 0.504 0.066 7.592 0.000 0.504 0.489
## Q4_10 0.733 0.059 12.355 0.000 0.733 0.877
## Q5_1 0.722 0.056 12.838 0.000 0.722 0.852
## Q5_2 0.754 0.059 12.873 0.000 0.754 0.810
## Q5_4 0.700 0.062 11.241 0.000 0.700 0.891
## Q5_5 0.658 0.064 10.220 0.000 0.658 0.766
## Q5_6 0.649 0.064 10.197 0.000 0.649 0.839
## Q5_7 0.769 0.061 12.669 0.000 0.769 0.889
## Q5_8 0.578 0.070 8.268 0.000 0.578 0.588
## Q5_9 0.639 0.063 10.173 0.000 0.639 0.858
## Q5_10 0.582 0.069 8.396 0.000 0.582 0.681
## Q5_11 0.733 0.062 11.909 0.000 0.733 0.895
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Sex_Pos ~
## Vote1 2.997 1.817 1.650 0.099 2.997 0.128
## Sex_Neg ~
## Vote1 0.177 0.861 0.206 0.837 0.177 0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_3 ~~
## .Q2_4 0.067 0.037 1.803 0.071 0.067 0.268
## .Q2_5 0.028 0.025 1.153 0.249 0.028 0.117
## .Q5_5 0.017 0.022 0.772 0.440 0.017 0.066
## .Q5_6 0.016 0.015 1.053 0.293 0.016 0.084
## .Q2_4 ~~
## .Q2_5 0.118 0.028 4.221 0.000 0.118 0.416
## .Q5_5 0.017 0.023 0.729 0.466 0.017 0.056
## .Q5_6 -0.035 0.015 -2.386 0.017 -0.035 -0.153
## .Q2_5 ~~
## .Q5_5 0.052 0.030 1.731 0.083 0.052 0.180
## .Q5_6 -0.049 0.018 -2.708 0.007 -0.049 -0.223
## .Q5_5 ~~
## .Q5_6 0.006 0.020 0.275 0.783 0.006 0.024
## .Q4_4 ~~
## .Q4_5 0.054 0.022 2.467 0.014 0.054 0.307
## .Q4_6 0.030 0.018 1.656 0.098 0.030 0.198
## .Q4_5 ~~
## .Q4_6 0.053 0.021 2.532 0.011 0.053 0.283
## .Q4_7 ~~
## .Q4_8 0.015 0.025 0.616 0.538 0.015 0.059
## .Q2_8 ~~
## .Q2_9 0.139 0.034 4.101 0.000 0.139 0.466
## CSE ~~
## Important_Mddl -0.384 0.112 -3.431 0.001 -0.384 -0.344
## Important_High -0.481 0.114 -4.206 0.000 -0.481 -0.578
## .Sex_Pos 0.476 0.485 0.981 0.327 0.476 0.061
## .Sex_Neg -0.330 0.383 -0.862 0.388 -0.330 -0.074
## PolAff 0.430 0.143 3.010 0.003 0.430 0.223
## .Sex_Pos ~~
## .Sex_Neg -16.332 4.884 -3.344 0.001 -16.332 -0.470
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.288 0.048 5.942 0.000 0.288 0.353
## .Q2_2 0.580 0.084 6.926 0.000 0.580 0.565
## .Q2_3 0.212 0.041 5.217 0.000 0.212 0.319
## .Q2_4 0.293 0.048 6.116 0.000 0.293 0.344
## .Q2_5 0.276 0.041 6.789 0.000 0.276 0.329
## .Q2_6 0.295 0.069 4.256 0.000 0.295 0.369
## .Q2_7 0.241 0.044 5.491 0.000 0.241 0.350
## .Q2_8 0.293 0.043 6.869 0.000 0.293 0.408
## .Q2_9 0.306 0.041 7.468 0.000 0.306 0.494
## .Q3_1 0.179 0.059 3.022 0.003 0.179 0.230
## .Q3_2 0.168 0.034 4.963 0.000 0.168 0.228
## .Q3_3 0.166 0.052 3.187 0.001 0.166 0.282
## .Q3_4 0.146 0.032 4.640 0.000 0.146 0.190
## .Q3_5 0.205 0.046 4.419 0.000 0.205 0.293
## .Q3_6 0.243 0.047 5.205 0.000 0.243 0.357
## .Q3_7 0.398 0.081 4.905 0.000 0.398 0.375
## .Q3_8 0.782 0.123 6.332 0.000 0.782 0.537
## .Q3_9 1.009 0.122 8.280 0.000 1.009 0.584
## .Q3_10 0.306 0.054 5.700 0.000 0.306 0.375
## .Q4_1 0.113 0.024 4.644 0.000 0.113 0.168
## .Q4_2 0.233 0.044 5.236 0.000 0.233 0.278
## .Q4_3 0.092 0.016 5.683 0.000 0.092 0.139
## .Q4_4 0.141 0.027 5.314 0.000 0.141 0.202
## .Q4_5 0.221 0.036 6.130 0.000 0.221 0.321
## .Q4_6 0.159 0.026 6.047 0.000 0.159 0.210
## .Q4_7 0.292 0.058 5.035 0.000 0.292 0.300
## .Q4_8 0.225 0.038 5.965 0.000 0.225 0.321
## .Q4_9 0.810 0.107 7.582 0.000 0.810 0.761
## .Q4_10 0.161 0.033 4.819 0.000 0.161 0.230
## .Q5_1 0.197 0.033 5.928 0.000 0.197 0.275
## .Q5_2 0.298 0.060 4.982 0.000 0.298 0.344
## .Q5_4 0.128 0.024 5.251 0.000 0.128 0.206
## .Q5_5 0.305 0.063 4.830 0.000 0.305 0.413
## .Q5_6 0.177 0.033 5.356 0.000 0.177 0.295
## .Q5_7 0.157 0.026 5.952 0.000 0.157 0.210
## .Q5_8 0.631 0.113 5.579 0.000 0.631 0.654
## .Q5_9 0.147 0.021 6.837 0.000 0.147 0.264
## .Q5_10 0.392 0.056 6.951 0.000 0.392 0.537
## .Q5_11 0.134 0.024 5.456 0.000 0.134 0.199
## .Sex_Pos 61.246 6.698 9.144 0.000 61.246 0.983
## .Sex_Neg 19.746 5.637 3.503 0.000 19.746 1.000
## Important_Mddl 1.242 0.140 8.850 0.000 1.242 1.000
## Important_High 0.692 0.159 4.349 0.000 0.692 1.000
## PolAff 3.717 0.257 14.453 0.000 3.717 1.000
## CSE 1.000 1.000 1.000
psych::describeBy(csek12$Sex_Pos, group = csek12$Vote1)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 36.33 9.35 40 37.23 9.64 15 48 33 -0.78 -0.29 1.56
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 243 39.85 7.59 40 40.76 8.9 8 48 40 -1.08 1.55 0.49
psych::describeBy(csek12$Sex_Neg, group = csek12$Vote1)
##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 36 11.22 4.15 10 10.6 2.97 8 24 16 1.26 0.72 0.69
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 243 11.01 4.46 10 10.12 2.97 8 48 40 3.88 22.48 0.29
csek12one.val3.1 <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
Q4_4 ~~ Q4_5
Q4_4 ~~ Q4_6
Q4_5 ~~ Q4_6
Q4_7 ~~ Q4_8
Q2_8 ~~ Q2_9
CSE ~~ Important_Middle
CSE ~~ Important_High
CSE ~~ Sex_Pos
CSE ~~ Sex_Neg
CSE ~~ PolAff
#regressions
Vote1 ~ CSE
'
csek12one.fit.val3.1 <- cfa(csek12one.val3.1, data = csek12, std.lv = TRUE, estimator = "DWLS")
## Warning: lavaan->lav_options_est_dwls():
## estimator "DWLS" is not recommended for continuous data. Did you forget to
## set the ordered= argument?
## Warning: lavaan->lav_samplestats_from_data():
## number of observations (273) too small to compute Gamma
summary(csek12one.fit.val3.1, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-18 ended normally after 65 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 105
##
## Used Total
## Number of observations 273 298
##
## Model Test User Model:
##
## Test statistic 295.351
## Degrees of freedom 930
## P-value (Chi-square) 1.000
##
## Model Test Baseline Model:
##
## Test statistic 31100.762
## Degrees of freedom 990
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.022
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 1.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.051
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.892 0.022 40.450 0.000 0.892 0.866
## Q2_2 0.801 0.022 37.013 0.000 0.801 0.721
## Q2_3 0.811 0.022 36.375 0.000 0.811 0.867
## Q2_4 0.922 0.024 38.548 0.000 0.922 0.875
## Q2_5 0.916 0.025 37.242 0.000 0.916 0.872
## Q2_6 0.858 0.023 36.641 0.000 0.858 0.844
## Q2_7 0.803 0.022 36.830 0.000 0.803 0.847
## Q2_8 0.792 0.022 35.894 0.000 0.792 0.828
## Q2_9 0.667 0.020 33.414 0.000 0.667 0.763
## Q3_1 0.936 0.023 40.318 0.000 0.936 0.911
## Q3_2 0.915 0.023 39.269 0.000 0.915 0.913
## Q3_3 0.782 0.022 36.327 0.000 0.782 0.881
## Q3_4 0.955 0.024 39.780 0.000 0.955 0.928
## Q3_5 0.849 0.023 36.368 0.000 0.849 0.881
## Q3_6 0.787 0.023 34.874 0.000 0.787 0.838
## Q3_7 1.000 0.024 41.961 0.000 1.000 0.854
## Q3_8 1.014 0.024 41.793 0.000 1.014 0.762
## Q3_9 1.052 0.023 46.191 0.000 1.052 0.732
## Q3_10 0.869 0.022 39.293 0.000 0.869 0.847
## Q4_1 0.905 0.024 38.420 0.000 0.905 0.937
## Q4_2 0.934 0.023 39.805 0.000 0.934 0.884
## Q4_3 0.907 0.023 39.053 0.000 0.907 0.942
## Q4_4 0.900 0.024 36.767 0.000 0.900 0.920
## Q4_5 0.820 0.023 35.005 0.000 0.820 0.862
## Q4_6 0.934 0.024 38.254 0.000 0.934 0.919
## Q4_7 1.003 0.024 41.444 0.000 1.003 0.883
## Q4_8 0.823 0.025 33.470 0.000 0.823 0.858
## Q4_9 0.611 0.020 29.950 0.000 0.611 0.561
## Q4_10 0.883 0.023 38.189 0.000 0.883 0.907
## Q5_1 0.879 0.023 38.709 0.000 0.879 0.898
## Q5_2 0.903 0.024 38.056 0.000 0.903 0.849
## Q5_4 0.835 0.023 36.074 0.000 0.835 0.908
## Q5_5 0.791 0.023 33.847 0.000 0.791 0.816
## Q5_6 0.784 0.023 34.232 0.000 0.784 0.881
## Q5_7 0.923 0.024 37.932 0.000 0.923 0.913
## Q5_8 0.686 0.022 30.910 0.000 0.686 0.648
## Q5_9 0.765 0.022 35.371 0.000 0.765 0.887
## Q5_10 0.701 0.021 33.432 0.000 0.701 0.744
## Q5_11 0.879 0.024 37.170 0.000 0.879 0.917
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Vote1 ~
## CSE 0.260 0.007 37.083 0.000 0.260 0.766
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_3 ~~
## .Q2_4 0.054 0.125 0.434 0.664 0.054 0.228
## .Q2_5 0.024 0.123 0.200 0.842 0.024 0.102
## .Q5_5 0.023 0.120 0.191 0.848 0.023 0.088
## .Q5_6 0.019 0.115 0.163 0.870 0.019 0.095
## .Q2_4 ~~
## .Q2_5 0.095 0.135 0.702 0.483 0.095 0.362
## .Q5_5 0.007 0.125 0.052 0.958 0.007 0.023
## .Q5_6 -0.049 0.121 -0.403 0.687 -0.049 -0.227
## .Q2_5 ~~
## .Q5_5 0.051 0.128 0.396 0.692 0.051 0.177
## .Q5_6 -0.055 0.122 -0.450 0.653 -0.055 -0.254
## .Q5_5 ~~
## .Q5_6 0.010 0.120 0.083 0.934 0.010 0.042
## .Q4_4 ~~
## .Q4_5 0.063 0.142 0.442 0.658 0.063 0.339
## .Q4_6 0.033 0.146 0.226 0.821 0.033 0.215
## .Q4_5 ~~
## .Q4_6 0.060 0.139 0.431 0.666 0.060 0.310
## .Q4_7 ~~
## .Q4_8 0.022 0.148 0.148 0.883 0.022 0.083
## .Q2_8 ~~
## .Q2_9 0.145 0.113 1.287 0.198 0.145 0.478
## CSE ~~
## Important_Mddl -0.802 0.022 -35.936 0.000 -0.802 -0.718
## Important_High -0.612 0.023 -26.373 0.000 -0.612 -0.735
## Sex_Pos 1.512 0.082 18.442 0.000 1.512 0.191
## Sex_Neg -0.446 0.053 -8.339 0.000 -0.446 -0.100
## PolAff 1.050 0.029 36.427 0.000 1.050 0.544
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.265 0.140 1.890 0.059 0.265 0.250
## .Q2_2 0.594 0.138 4.291 0.000 0.594 0.481
## .Q2_3 0.217 0.130 1.668 0.095 0.217 0.248
## .Q2_4 0.260 0.140 1.858 0.063 0.260 0.234
## .Q2_5 0.263 0.144 1.823 0.068 0.263 0.239
## .Q2_6 0.297 0.151 1.965 0.049 0.297 0.288
## .Q2_7 0.253 0.141 1.793 0.073 0.253 0.282
## .Q2_8 0.288 0.126 2.290 0.022 0.288 0.314
## .Q2_9 0.320 0.115 2.785 0.005 0.320 0.418
## .Q3_1 0.179 0.147 1.221 0.222 0.179 0.170
## .Q3_2 0.167 0.144 1.157 0.247 0.167 0.166
## .Q3_3 0.176 0.128 1.369 0.171 0.176 0.223
## .Q3_4 0.148 0.154 0.961 0.336 0.148 0.140
## .Q3_5 0.207 0.145 1.426 0.154 0.207 0.223
## .Q3_6 0.262 0.145 1.813 0.070 0.262 0.297
## .Q3_7 0.370 0.159 2.332 0.020 0.370 0.270
## .Q3_8 0.742 0.178 4.165 0.000 0.742 0.419
## .Q3_9 0.958 0.157 6.083 0.000 0.958 0.464
## .Q3_10 0.299 0.135 2.218 0.027 0.299 0.283
## .Q4_1 0.113 0.145 0.780 0.435 0.113 0.121
## .Q4_2 0.244 0.144 1.699 0.089 0.244 0.219
## .Q4_3 0.104 0.143 0.730 0.465 0.104 0.112
## .Q4_4 0.147 0.155 0.949 0.343 0.147 0.154
## .Q4_5 0.232 0.146 1.596 0.111 0.232 0.257
## .Q4_6 0.160 0.151 1.059 0.290 0.160 0.155
## .Q4_7 0.283 0.157 1.799 0.072 0.283 0.220
## .Q4_8 0.243 0.161 1.509 0.131 0.243 0.264
## .Q4_9 0.812 0.127 6.388 0.000 0.812 0.685
## .Q4_10 0.168 0.144 1.163 0.245 0.168 0.177
## .Q5_1 0.186 0.133 1.403 0.161 0.186 0.194
## .Q5_2 0.315 0.154 2.048 0.041 0.315 0.279
## .Q5_4 0.148 0.143 1.036 0.300 0.148 0.175
## .Q5_5 0.314 0.140 2.239 0.025 0.314 0.334
## .Q5_6 0.178 0.135 1.323 0.186 0.178 0.225
## .Q5_7 0.170 0.160 1.067 0.286 0.170 0.167
## .Q5_8 0.652 0.145 4.483 0.000 0.652 0.581
## .Q5_9 0.159 0.129 1.237 0.216 0.159 0.214
## .Q5_10 0.397 0.118 3.361 0.001 0.397 0.447
## .Q5_11 0.147 0.151 0.968 0.333 0.147 0.159
## .Vote1 0.048 0.016 3.058 0.002 0.048 0.413
## Important_Mddl 1.247 0.141 8.866 0.000 1.247 1.000
## Important_High 0.694 0.159 4.357 0.000 0.694 1.000
## Sex_Pos 62.853 6.933 9.065 0.000 62.853 1.000
## Sex_Neg 19.808 5.646 3.508 0.000 19.808 1.000
## PolAff 3.731 0.258 14.480 0.000 3.731 1.000
## CSE 1.000 1.000 1.000