This website contains analytical procedures and their outcomes used in writing the article “Dark triad and estimated probability of sexual coercion”. Previous studies have shown that people can estimate Dark triad traits of a person based on various information sources (thin slices), while another line of studies revealed that Dark triad traits are related to sexual harrassment and rape. The aim of this study was to observe if these two lines of studies converge, that is if people actually use information on the Dark triad traits of another person to estimate the probability of that person to attempt to rape them. More detailed information on the methodological approach can be found in the mentioned article, while the main function of this file is to provide detailed information regarding the analytical approach.
The following analyses were conducted on 1107 participant (803 of which were women).
Dirty dozen questionnaire was used to measure participants’ perception of DT of target profile. The questionnaire measures narcissism, Machiavellianism and psychopathy with 12 items (four per each factor), each on a scale from 1 to 7 with higher values indicating higher expression of the trait.
Probability of rape attempt was also measured on a seven-point scale ranging from extremely low (1) to extremely high (7).
lapply(c("readxl", "lavaan", "lavaan.survey", "semTools", "Hmisc", "psych"), library, character.only = T)
rr <- read_excel("Renato i Renata PAID.xlsx")rr$Profile_gender <- as.factor(rr$Profile_gender) #turning string variable into factor
rr$mach <- rr$m1 + rr$m2 + rr$m3 + rr$m4
rr$nar <- rr$n1 + rr$n2 + rr$n3 + rr$n4
rr$psi <- rr$p1 + rr$p2 + rr$p3 + rr$p4
rr1 <- rr[complete.cases(rr), ] #removing cases with missing data on estimated rape probability
rr1$grp <- ifelse(rr1$Profile_gender == "Renato", 1, 2)
rrxx <- subset(rr1, grp == 1) #subset with female participants
rrxy <- subset(rr1, grp == 2) #subset with male participantsdescribeBy(rrxx$rape, group = rrxx$Triad, digits = 2)##
## Descriptive statistics by group
## group: npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 171 1.92 1.12 2 1.73 1.48 1 7 6 1.61 3.26 0.09
## ------------------------------------------------------------------------------------------
## group: npM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 170 2.48 1.4 2 2.3 1.48 1 7 6 0.96 0.35 0.11
## ------------------------------------------------------------------------------------------
## group: nPm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 136 2.42 1.36 2 2.26 1.48 1 7 6 0.89 0.2 0.12
## ------------------------------------------------------------------------------------------
## group: Npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 167 1.92 1.18 2 1.7 1.48 1 7 6 1.82 3.95 0.09
## ------------------------------------------------------------------------------------------
## group: NPM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 159 3.3 1.61 3 3.23 1.48 1 7 6 0.3 -0.87 0.13describeBy(rrxy$rape, group = rrxy$Triad, digits = 2)##
## Descriptive statistics by group
## group: npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 59 1.73 1.2 1 1.49 0 1 7 6 2.16 5.23 0.16
## ------------------------------------------------------------------------------------------
## group: npM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 61 2.21 1.61 2 1.92 1.48 1 7 6 1.32 0.91 0.21
## ------------------------------------------------------------------------------------------
## group: nPm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 2.28 1.47 2 2.06 1.48 1 6 5 0.95 -0.26 0.19
## ------------------------------------------------------------------------------------------
## group: Npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 63 2.02 1.53 1 1.71 0 1 7 6 1.6 1.85 0.19
## ------------------------------------------------------------------------------------------
## group: NPM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 61 2.82 1.89 2 2.57 1.48 1 7 6 0.82 -0.52 0.24describeBy(rrxx$mach, group = rrxx$Triad, digits = 2)##
## Descriptive statistics by group
## group: npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 171 7.29 4.45 5 6.44 1.48 4 28 24 1.96 4.72 0.34
## ------------------------------------------------------------------------------------------
## group: npM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 170 21.24 5.71 22.5 21.9 5.19 4 28 24 -0.97 0.42 0.44
## ------------------------------------------------------------------------------------------
## group: nPm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 136 9.68 5.22 8 9.06 4.45 4 25 21 0.88 -0.11 0.45
## ------------------------------------------------------------------------------------------
## group: Npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 167 9.52 5.55 8 8.8 5.93 4 26 22 0.87 -0.26 0.43
## ------------------------------------------------------------------------------------------
## group: NPM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 159 23.6 4.72 25 24.42 2.97 5 28 23 -1.69 3.07 0.37describeBy(rrxy$mach, group = rrxy$Triad, digits = 2)##
## Descriptive statistics by group
## group: npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 59 8.9 5.88 7 8 4.45 4 26 22 1.17 0.39 0.77
## ------------------------------------------------------------------------------------------
## group: npM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 61 20.34 5.96 22 20.98 4.45 5 28 23 -0.91 -0.11 0.76
## ------------------------------------------------------------------------------------------
## group: nPm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 11.38 5.34 10 11.1 5.93 4 22 18 0.45 -1.02 0.69
## ------------------------------------------------------------------------------------------
## group: Npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 63 11.25 6.68 10 10.43 5.93 4 28 24 0.93 -0.03 0.84
## ------------------------------------------------------------------------------------------
## group: NPM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 61 22.39 5.71 24 23.35 4.45 4 28 24 -1.4 1.56 0.73describeBy(rrxx$nar, group = rrxx$Triad, digits = 2)##
## Descriptive statistics by group
## group: npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 171 9.88 4.95 9 9.4 5.93 4 28 24 0.75 0.07 0.38
## ------------------------------------------------------------------------------------------
## group: npM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 170 15.01 5.91 14 14.99 5.93 4 28 24 0.09 -0.75 0.45
## ------------------------------------------------------------------------------------------
## group: nPm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 136 11.76 5.55 10 11.21 4.45 4 28 24 0.91 0.5 0.48
## ------------------------------------------------------------------------------------------
## group: Npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 167 19.71 5.5 20 20.16 4.45 4 28 24 -0.73 0.14 0.43
## ------------------------------------------------------------------------------------------
## group: NPM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 159 23.81 4.62 25 24.7 2.97 8 28 20 -1.8 3.1 0.37describeBy(rrxy$nar, group = rrxy$Triad, digits = 2)##
## Descriptive statistics by group
## group: npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 59 10.54 5.83 8 9.88 5.93 4 27 23 0.95 0.24 0.76
## ------------------------------------------------------------------------------------------
## group: npM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 61 15.61 4.24 15 15.53 4.45 8 25 17 0.17 -1.04 0.54
## ------------------------------------------------------------------------------------------
## group: nPm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 13.53 6.12 12 13.21 5.93 4 28 24 0.47 -0.67 0.79
## ------------------------------------------------------------------------------------------
## group: Npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 63 20.33 5.66 21 20.82 5.93 4 28 24 -0.77 0.25 0.71
## ------------------------------------------------------------------------------------------
## group: NPM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 61 23.49 4.99 25 24.37 4.45 4 28 24 -1.79 3.68 0.64describeBy(rrxx$psi, group = rrxx$Triad, digits = 2)##
## Descriptive statistics by group
## group: npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 171 12.48 4.19 12 12.39 2.97 4 27 23 0.32 0.29 0.32
## ------------------------------------------------------------------------------------------
## group: npM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 170 15.54 5 16 15.65 4.45 4 26 22 -0.2 -0.32 0.38
## ------------------------------------------------------------------------------------------
## group: nPm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 136 17.35 4.81 18 17.48 5.19 4 28 24 -0.21 -0.4 0.41
## ------------------------------------------------------------------------------------------
## group: Npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 167 12.73 4.68 12 12.53 4.45 4 26 22 0.37 -0.27 0.36
## ------------------------------------------------------------------------------------------
## group: NPM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 159 19.9 5.01 20 20.16 4.45 4 28 24 -0.56 0.17 0.4describeBy(rrxy$psi, group = rrxy$Triad, digits = 2)##
## Descriptive statistics by group
## group: npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 59 12.17 4.67 13 12.14 5.93 4 23 19 0.07 -0.87 0.61
## ------------------------------------------------------------------------------------------
## group: npM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 61 15.7 4.5 16 15.73 5.93 6 24 18 -0.04 -1.03 0.58
## ------------------------------------------------------------------------------------------
## group: nPm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 60 16.67 4.34 16 16.75 4.45 6 25 19 -0.14 -0.3 0.56
## ------------------------------------------------------------------------------------------
## group: Npm
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 63 13.63 4.84 13 13.35 4.45 4 28 24 0.58 -0.03 0.61
## ------------------------------------------------------------------------------------------
## group: NPM
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 61 19.25 5.33 19 19.33 5.93 4 28 24 -0.25 -0.17 0.68rcorr(as.matrix(rrxx[,c("nar", "mach", "psi", "rape")]))## nar mach psi rape
## nar 1.00 0.55 0.42 0.29
## mach 0.55 1.00 0.52 0.41
## psi 0.42 0.52 1.00 0.35
## rape 0.29 0.41 0.35 1.00
##
## n= 803
##
##
## P
## nar mach psi rape
## nar 0 0 0
## mach 0 0 0
## psi 0 0 0
## rape 0 0 0rcorr(as.matrix(rrxy[,c("nar", "mach", "psi", "rape")]))## nar mach psi rape
## nar 1.00 0.55 0.43 0.20
## mach 0.55 1.00 0.56 0.32
## psi 0.43 0.56 1.00 0.25
## rape 0.20 0.32 0.25 1.00
##
## n= 304
##
##
## P
## nar mach psi rape
## nar 0e+00 0e+00 4e-04
## mach 0e+00 0e+00 0e+00
## psi 0e+00 0e+00 0e+00
## rape 4e-04 0e+00 0e+00svy.df <- svydesign(ids =~ 1, strata =~ Triad, nest = T, data = rr1) #correction due to unequal (but similar) number of participants per Triad combination## Warning in svydesign.default(ids = ~1, strata = ~Triad, nest = T, data = rr1): No weights or probabilities supplied,
## assuming equal probabilitymodel <- '
psih =~ p1 + p2 + p3 + p4
nar =~ n1+ n2 + n3 +n4
mach =~ m1+ m2 + m3 + m4'
rrcfa <- cfa(model, data = rr1, estimator = "MLM")
prepcfa <- lavaan.survey(rrcfa, svy.df, estimator = "MLM")
fitmeasures(prepcfa)## npar fmin chisq df
## 39.000 0.517 1144.726 51.000
## pvalue chisq.scaled df.scaled pvalue.scaled
## 0.000 942.137 51.000 0.000
## chisq.scaling.factor baseline.chisq baseline.df baseline.pvalue
## 1.215 10635.457 66.000 0.000
## baseline.chisq.scaled baseline.df.scaled baseline.pvalue.scaled baseline.chisq.scaling.factor
## 12296.373 66.000 0.000 0.865
## cfi tli nnfi rfi
## 0.897 0.866 0.866 0.861
## nfi pnfi ifi rni
## 0.892 0.690 0.897 0.897
## cfi.scaled tli.scaled cfi.robust tli.robust
## 0.927 0.906 0.898 0.868
## nnfi.scaled nnfi.robust rfi.scaled nfi.scaled
## 0.906 0.868 0.901 0.923
## ifi.scaled rni.scaled rni.robust logl
## 0.927 0.927 0.898 -23539.834
## unrestricted.logl aic bic ntotal
## -22967.470 47157.667 47353.034 1107.000
## bic2 rmsea rmsea.ci.lower rmsea.ci.upper
## 47229.160 0.139 0.132 0.146
## rmsea.pvalue rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.000 0.126 0.119 0.132
## rmsea.pvalue.scaled rmsea.robust rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.000 0.138 0.131 0.146
## rmsea.pvalue.robust rmr rmr_nomean srmr
## NA 0.479 0.515 0.114
## srmr_bentler srmr_bentler_nomean crmr crmr_nomean
## 0.114 0.122 0.122 0.133
## srmr_mplus srmr_mplus_nomean cn_05 cn_01
## 0.114 0.122 67.406 75.836
## gfi agfi pgfi mfi
## 0.938 0.890 0.531 0.610
## ecvi
## 1.105modindices(prepcfa)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 46 psih =~ n1 12.587 -0.198 -0.140 -0.064 -0.064
## 47 psih =~ n2 77.320 -0.500 -0.354 -0.161 -0.161
## 48 psih =~ n3 128.976 0.858 0.608 0.296 0.296
## 49 psih =~ n4 207.823 1.171 0.829 0.432 0.432
## 50 psih =~ m1 0.262 -0.036 -0.025 -0.011 -0.011
## 51 psih =~ m2 1.499 0.080 0.057 0.027 0.027
## 52 psih =~ m3 44.905 -0.535 -0.378 -0.156 -0.156
## 53 psih =~ m4 29.807 0.362 0.256 0.119 0.119
## 54 nar =~ p1 2.819 -0.052 -0.107 -0.056 -0.056
## 55 nar =~ p2 12.195 -0.098 -0.201 -0.103 -0.103
## 56 nar =~ p3 4.725 -0.053 -0.108 -0.064 -0.064
## 57 nar =~ p4 37.795 0.151 0.309 0.181 0.181
## 58 nar =~ m1 0.341 0.010 0.021 0.009 0.009
## 59 nar =~ m2 8.831 -0.049 -0.100 -0.047 -0.047
## 60 nar =~ m3 0.012 -0.002 -0.005 -0.002 -0.002
## 61 nar =~ m4 6.257 0.042 0.086 0.040 0.040
## 62 mach =~ p1 4.891 -0.086 -0.178 -0.093 -0.093
## 63 mach =~ p2 46.815 0.256 0.528 0.270 0.270
## 64 mach =~ p3 36.818 -0.196 -0.404 -0.239 -0.239
## 65 mach =~ p4 0.072 0.009 0.018 0.011 0.011
## 66 mach =~ n1 39.564 -0.111 -0.230 -0.105 -0.105
## 67 mach =~ n2 113.717 -0.192 -0.396 -0.180 -0.180
## 68 mach =~ n3 269.483 0.392 0.807 0.393 0.393
## 69 mach =~ n4 345.064 0.476 0.981 0.511 0.511
## 70 p1 ~~ p2 11.748 0.291 0.291 0.124 0.124
## 71 p1 ~~ p3 0.043 -0.015 -0.015 -0.008 -0.008
## 72 p1 ~~ p4 1.044 -0.075 -0.075 -0.039 -0.039
## 73 p1 ~~ n1 0.039 -0.010 -0.010 -0.008 -0.008
## 74 p1 ~~ n2 0.451 0.036 0.036 0.026 0.026
## 75 p1 ~~ n3 1.000 -0.076 -0.076 -0.032 -0.032
## 76 p1 ~~ n4 2.069 -0.117 -0.117 -0.045 -0.045
## 77 p1 ~~ m1 1.953 -0.076 -0.076 -0.049 -0.049
## 78 p1 ~~ m2 1.986 -0.072 -0.072 -0.050 -0.050
## 79 p1 ~~ m3 0.331 -0.036 -0.036 -0.020 -0.020
## 80 p1 ~~ m4 5.127 0.117 0.117 0.080 0.080
## 81 p2 ~~ p3 0.672 0.066 0.066 0.043 0.043
## 82 p2 ~~ p4 51.813 -0.609 -0.609 -0.418 -0.418
## 83 p2 ~~ n1 6.154 -0.108 -0.108 -0.113 -0.113
## 84 p2 ~~ n2 7.609 -0.124 -0.124 -0.118 -0.118
## 85 p2 ~~ n3 5.101 0.142 0.142 0.079 0.079
## 86 p2 ~~ n4 17.498 0.284 0.284 0.144 0.144
## 87 p2 ~~ m1 1.062 0.047 0.047 0.040 0.040
## 88 p2 ~~ m2 1.894 0.058 0.058 0.054 0.054
## 89 p2 ~~ m3 0.058 -0.013 -0.013 -0.009 -0.009
## 90 p2 ~~ m4 3.405 0.080 0.080 0.072 0.072
## 91 p3 ~~ p4 27.300 0.381 0.381 0.304 0.304
## 92 p3 ~~ n1 2.374 0.058 0.058 0.070 0.070
## 93 p3 ~~ n2 2.787 -0.065 -0.065 -0.071 -0.071
## 94 p3 ~~ n3 0.113 -0.018 -0.018 -0.012 -0.012
## 95 p3 ~~ n4 0.182 -0.025 -0.025 -0.015 -0.015
## 96 p3 ~~ m1 2.000 -0.055 -0.055 -0.055 -0.055
## 97 p3 ~~ m2 0.127 -0.013 -0.013 -0.014 -0.014
## 98 p3 ~~ m3 29.958 -0.246 -0.246 -0.207 -0.207
## 99 p3 ~~ m4 15.646 0.147 0.147 0.155 0.155
## 100 p4 ~~ n1 1.819 0.050 0.050 0.063 0.063
## 101 p4 ~~ n2 0.909 0.037 0.037 0.042 0.042
## 102 p4 ~~ n3 0.093 0.016 0.016 0.011 0.011
## 103 p4 ~~ n4 1.291 0.066 0.066 0.040 0.040
## 104 p4 ~~ m1 0.037 0.008 0.008 0.008 0.008
## 105 p4 ~~ m2 2.851 0.061 0.061 0.068 0.068
## 106 p4 ~~ m3 2.654 -0.073 -0.073 -0.063 -0.063
## 107 p4 ~~ m4 1.227 -0.041 -0.041 -0.045 -0.045
## 108 n1 ~~ n2 830.928 3.376 3.376 5.905 5.905
## 109 n1 ~~ n3 101.644 -0.637 -0.637 -0.651 -0.651
## 110 n1 ~~ n4 53.166 -0.389 -0.389 -0.363 -0.363
## 111 n1 ~~ m1 0.010 0.003 0.003 0.005 0.005
## 112 n1 ~~ m2 5.522 -0.065 -0.065 -0.110 -0.110
## 113 n1 ~~ m3 0.711 -0.029 -0.029 -0.038 -0.038
## 114 n1 ~~ m4 0.018 -0.004 -0.004 -0.006 -0.006
## 115 n2 ~~ n3 29.662 -0.344 -0.344 -0.321 -0.321
## 116 n2 ~~ n4 74.204 -0.465 -0.465 -0.395 -0.395
## 117 n2 ~~ m1 0.347 -0.018 -0.018 -0.026 -0.026
## 118 n2 ~~ m2 2.578 -0.046 -0.046 -0.070 -0.070
## 119 n2 ~~ m3 0.018 -0.005 -0.005 -0.006 -0.006
## 120 n2 ~~ m4 4.367 -0.060 -0.060 -0.092 -0.092
## 121 n3 ~~ n4 275.510 1.075 1.075 0.534 0.534
## 122 n3 ~~ m1 4.273 0.089 0.089 0.074 0.074
## 123 n3 ~~ m2 1.884 0.055 0.055 0.049 0.049
## 124 n3 ~~ m3 6.336 0.124 0.124 0.087 0.087
## 125 n3 ~~ m4 5.496 0.095 0.095 0.084 0.084
## 126 n4 ~~ m1 0.421 -0.030 -0.030 -0.023 -0.023
## 127 n4 ~~ m2 16.222 0.173 0.173 0.142 0.142
## 128 n4 ~~ m3 0.402 0.034 0.034 0.022 0.022
## 129 n4 ~~ m4 20.838 0.200 0.200 0.161 0.161
## 130 m1 ~~ m2 14.478 0.145 0.145 0.200 0.200
## 131 m1 ~~ m3 0.254 -0.022 -0.022 -0.024 -0.024
## 132 m1 ~~ m4 10.381 -0.124 -0.124 -0.169 -0.169
## 133 m2 ~~ m3 0.349 -0.024 -0.024 -0.028 -0.028
## 134 m2 ~~ m4 9.649 -0.113 -0.113 -0.166 -0.166
## 135 m3 ~~ m4 13.319 0.152 0.152 0.174 0.174modx <- '
psih =~ p1 + p2 + p3 + p4
nar =~ n1 + n2 + n3 + n4
mach =~ m1 + m2 + m3 + m4
n1~~n2'
rrcfax <- cfa(modx, data = rr1, estimator = "MLM")
finalcfa <- lavaan.survey(rrcfax, svy.df, estimator = "MLM")
fitmeasures(finalcfa)## npar fmin chisq df
## 40.000 0.208 459.991 50.000
## pvalue chisq.scaled df.scaled pvalue.scaled
## 0.000 366.348 50.000 0.000
## chisq.scaling.factor baseline.chisq baseline.df baseline.pvalue
## 1.256 10635.457 66.000 0.000
## baseline.chisq.scaled baseline.df.scaled baseline.pvalue.scaled baseline.chisq.scaling.factor
## 12296.373 66.000 0.000 0.865
## cfi tli nnfi rfi
## 0.961 0.949 0.949 0.943
## nfi pnfi ifi rni
## 0.957 0.725 0.961 0.961
## cfi.scaled tli.scaled cfi.robust tli.robust
## 0.974 0.966 0.962 0.950
## nnfi.scaled nnfi.robust rfi.scaled nfi.scaled
## 0.966 0.950 0.961 0.970
## ifi.scaled rni.scaled rni.robust logl
## 0.974 0.974 0.962 -23197.466
## unrestricted.logl aic bic ntotal
## -22967.470 46474.932 46675.308 1107.000
## bic2 rmsea rmsea.ci.lower rmsea.ci.upper
## 46548.258 0.086 0.079 0.093
## rmsea.pvalue rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.000 0.076 0.069 0.082
## rmsea.pvalue.scaled rmsea.robust rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.000 0.085 0.077 0.093
## rmsea.pvalue.robust rmr rmr_nomean srmr
## NA 0.250 0.268 0.057
## srmr_bentler srmr_bentler_nomean crmr crmr_nomean
## 0.057 0.061 0.061 0.066
## srmr_mplus srmr_mplus_nomean cn_05 cn_01
## 0.057 0.061 163.455 184.270
## gfi agfi pgfi mfi
## 0.974 0.954 0.541 0.831
## ecvi
## 0.488conf <- sem(modx, data = rr1, estimator = "MLM", group = "grp")
weak <- sem(modx, data = rr1, estimator = "MLM", group = "grp", group.equal = "loadings")
strong <- sem(modx, data = rr1, estimator = "MLM", group = "grp", group.equal = c("loadings", "intercepts"))
regressions <- sem(modx, data = rr1, estimator = "MLM", group = "grp", group.equal = c("loadings", "intercepts", "regressions"))biglavaan <- lavaan.survey(conf, svy.df, estimator = "MLM")
biglavaan2 <- lavaan.survey(weak, svy.df, estimator = "MLM")
biglavaan3 <- lavaan.survey(strong, svy.df, estimator = "MLM")
biglavaan4 <- lavaan.survey(regressions, svy.df, estimator = "MLM")
fitMeasures(biglavaan, c("chisq", "df", "pvalue", "cfi.robust", "rmsea.robust", "srmr"))## chisq df pvalue cfi.robust rmsea.robust srmr
## 518.901 100.000 0.000 0.963 0.084 0.059fitMeasures(biglavaan2, c("chisq", "df", "pvalue", "cfi.robust", "rmsea.robust", "srmr"))## chisq df pvalue cfi.robust rmsea.robust srmr
## 527.883 109.000 0.000 0.963 0.081 0.061fitMeasures(biglavaan3, c("chisq", "df", "pvalue", "cfi.robust", "rmsea.robust", "srmr"))## chisq df pvalue cfi.robust rmsea.robust srmr
## 542.837 118.000 0.000 0.962 0.078 0.061fitMeasures(biglavaan4, c("chisq", "df", "pvalue", "cfi.robust", "rmsea.robust", "srmr"))## chisq df pvalue cfi.robust rmsea.robust srmr
## 542.837 118.000 0.000 0.962 0.078 0.061modelf <- '
psih =~ p1 + p2 + p3 + p4
nar =~ n1 + n2 + n3 + n4
mach =~ m1 + m2 + m3 + m4
rape ~ psih + nar + mach
n1~~n2'
rrsem <- sem(modelf, data = rr1, estimator = "MLM")
finalavaan <- lavaan.survey(rrsem, svy.df, estimator = "MLM")
fitmeasures(finalavaan) #adjusted## npar fmin chisq df
## 45.000 0.231 510.950 59.000
## pvalue chisq.scaled df.scaled pvalue.scaled
## 0.000 415.193 59.000 0.000
## chisq.scaling.factor baseline.chisq baseline.df baseline.pvalue
## 1.231 10902.413 78.000 0.000
## baseline.chisq.scaled baseline.df.scaled baseline.pvalue.scaled baseline.chisq.scaling.factor
## 12122.267 78.000 0.000 0.899
## cfi tli nnfi rfi
## 0.958 0.945 0.945 0.938
## nfi pnfi ifi rni
## 0.953 0.721 0.958 0.958
## cfi.scaled tli.scaled cfi.robust tli.robust
## 0.970 0.961 0.960 0.947
## nnfi.scaled nnfi.robust rfi.scaled nfi.scaled
## 0.961 0.947 0.955 0.966
## ifi.scaled rni.scaled rni.robust logl
## 0.970 0.970 0.960 -25092.470
## unrestricted.logl aic bic ntotal
## -24836.994 50274.939 50500.363 1107.000
## bic2 rmsea rmsea.ci.lower rmsea.ci.upper
## 50357.431 0.083 0.077 0.090
## rmsea.pvalue rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.000 0.074 0.068 0.080
## rmsea.pvalue.scaled rmsea.robust rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.000 0.082 0.075 0.089
## rmsea.pvalue.robust rmr rmr_nomean srmr
## NA 0.235 0.252 0.054
## srmr_bentler srmr_bentler_nomean crmr crmr_nomean
## 0.054 0.058 0.058 0.062
## srmr_mplus srmr_mplus_nomean cn_05 cn_01
## 0.054 0.058 169.840 189.849
## gfi agfi pgfi mfi
## 0.973 0.953 0.552 0.815
## ecvi
## 0.543fitmeasures(rrsem) #not adjusted## npar fmin chisq df
## 32.000 0.231 510.950 59.000
## pvalue chisq.scaled df.scaled pvalue.scaled
## 0.000 414.273 59.000 0.000
## chisq.scaling.factor baseline.chisq baseline.df baseline.pvalue
## 1.233 10902.413 78.000 0.000
## baseline.chisq.scaled baseline.df.scaled baseline.pvalue.scaled baseline.chisq.scaling.factor
## 10981.775 78.000 0.000 0.993
## cfi tli nnfi rfi
## 0.958 0.945 0.945 0.938
## nfi pnfi ifi rni
## 0.953 0.721 0.958 0.958
## cfi.scaled tli.scaled cfi.robust tli.robust
## 0.967 0.957 0.960 0.946
## nnfi.scaled nnfi.robust rfi.scaled nfi.scaled
## 0.957 0.946 0.950 0.962
## ifi.scaled rni.scaled rni.robust logl
## 0.967 0.967 0.960 -25092.470
## unrestricted.logl aic bic ntotal
## -24836.994 50248.939 50409.240 1107.000
## bic2 rmsea rmsea.ci.lower rmsea.ci.upper
## 50307.600 0.083 0.077 0.090
## rmsea.pvalue rmsea.scaled rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.000 0.074 0.068 0.080
## rmsea.pvalue.scaled rmsea.robust rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.000 0.082 0.075 0.089
## rmsea.pvalue.robust rmr rmr_nomean srmr
## NA 0.252 0.252 0.058
## srmr_bentler srmr_bentler_nomean crmr crmr_nomean
## 0.058 0.058 0.062 0.062
## srmr_mplus srmr_mplus_nomean cn_05 cn_01
## 0.058 0.058 169.840 189.849
## gfi agfi pgfi mfi
## 0.933 0.897 0.605 0.815
## ecvi
## 0.519parameterestimates(finalavaan, standardized = T) #adjusted## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all std.nox
## 1 psih =~ p1 1.000 0.000 NA NA 1.000 1.000 0.695 0.366 0.366
## 2 psih =~ p2 2.069 0.184 11.241 0.000 1.708 2.429 1.438 0.734 0.734
## 3 psih =~ p3 1.800 0.163 11.062 0.000 1.481 2.119 1.251 0.742 0.742
## 4 psih =~ p4 1.885 0.172 10.982 0.000 1.549 2.222 1.310 0.766 0.766
## 5 nar =~ n1 1.000 0.000 NA NA 1.000 1.000 1.550 0.712 0.712
## 6 nar =~ n2 0.993 0.021 48.091 0.000 0.953 1.034 1.539 0.699 0.699
## 7 nar =~ n3 1.193 0.039 30.673 0.000 1.117 1.270 1.849 0.900 0.900
## 8 nar =~ n4 1.012 0.036 28.385 0.000 0.943 1.082 1.569 0.817 0.817
## 9 mach =~ m1 1.000 0.000 NA NA 1.000 1.000 2.058 0.918 0.918
## 10 mach =~ m2 0.948 0.016 60.447 0.000 0.917 0.979 1.951 0.922 0.922
## 11 mach =~ m3 1.066 0.016 64.882 0.000 1.034 1.099 2.194 0.902 0.902
## 12 mach =~ m4 0.962 0.016 61.251 0.000 0.931 0.993 1.980 0.923 0.923
## 13 rape ~ psih 0.536 0.114 4.686 0.000 0.312 0.760 0.373 0.252 0.252
## 14 rape ~ nar -0.043 0.048 -0.914 0.361 -0.137 0.050 -0.067 -0.046 -0.046
## 15 rape ~ mach 0.191 0.038 5.057 0.000 0.117 0.265 0.393 0.266 0.266
## 16 n1 ~~ n2 1.911 0.125 15.322 0.000 1.666 2.155 1.911 0.793 0.793
## 17 p1 ~~ p1 3.132 0.109 28.756 0.000 2.918 3.345 3.132 0.866 0.866
## 18 p2 ~~ p2 1.764 0.118 14.983 0.000 1.534 1.995 1.764 0.461 0.461
## 19 p3 ~~ p3 1.280 0.091 14.005 0.000 1.101 1.459 1.280 0.450 0.450
## 20 p4 ~~ p4 1.210 0.092 13.174 0.000 1.030 1.391 1.210 0.413 0.413
## 21 n1 ~~ n1 2.339 0.131 17.806 0.000 2.081 2.596 2.339 0.493 0.493
## 22 n2 ~~ n2 2.486 0.135 18.364 0.000 2.220 2.751 2.486 0.512 0.512
## 23 n3 ~~ n3 0.802 0.086 9.305 0.000 0.633 0.971 0.802 0.190 0.190
## 24 n4 ~~ n4 1.227 0.081 15.120 0.000 1.068 1.386 1.227 0.333 0.333
## 25 m1 ~~ m1 0.789 0.067 11.840 0.000 0.659 0.920 0.789 0.157 0.157
## 26 m2 ~~ m2 0.671 0.064 10.529 0.000 0.546 0.796 0.671 0.150 0.150
## 27 m3 ~~ m3 1.099 0.089 12.408 0.000 0.926 1.273 1.099 0.186 0.186
## 28 m4 ~~ m4 0.684 0.069 9.903 0.000 0.548 0.819 0.684 0.149 0.149
## 29 rape ~~ rape 1.764 0.089 19.725 0.000 1.588 1.939 1.764 0.808 0.808
## 30 psih ~~ psih 0.483 0.086 5.642 0.000 0.315 0.651 1.000 1.000 1.000
## 31 nar ~~ nar 2.402 0.149 16.125 0.000 2.110 2.694 1.000 1.000 1.000
## 32 mach ~~ mach 4.235 0.127 33.358 0.000 3.986 4.484 1.000 1.000 1.000
## 33 psih ~~ nar 0.633 0.068 9.355 0.000 0.501 0.766 0.588 0.588 0.588
## 34 psih ~~ mach 0.922 0.091 10.193 0.000 0.745 1.100 0.645 0.645 0.645
## 35 nar ~~ mach 2.224 0.097 22.853 0.000 2.033 2.414 0.697 0.697 0.697
## 36 p1 ~1 4.384 0.056 78.245 0.000 4.274 4.494 4.384 2.306 2.306
## 37 p2 ~1 3.918 0.052 75.833 0.000 3.817 4.019 3.918 2.001 2.001
## 38 p3 ~1 3.472 0.046 75.378 0.000 3.382 3.563 3.472 2.059 2.059
## 39 p4 ~1 3.704 0.047 78.931 0.000 3.612 3.796 3.704 2.165 2.165
## 40 n1 ~1 4.121 0.048 85.488 0.000 4.027 4.216 4.121 1.893 1.893
## 41 n2 ~1 4.233 0.047 89.206 0.000 4.140 4.326 4.233 1.921 1.921
## 42 n3 ~1 4.223 0.051 83.332 0.000 4.124 4.322 4.223 2.055 2.055
## 43 n4 ~1 3.698 0.049 75.855 0.000 3.603 3.794 3.698 1.926 1.926
## 44 m1 ~1 3.763 0.048 78.977 0.000 3.670 3.857 3.763 1.679 1.679
## 45 m2 ~1 3.358 0.045 73.830 0.000 3.269 3.447 3.358 1.587 1.587
## 46 m3 ~1 3.753 0.048 78.920 0.000 3.660 3.847 3.753 1.543 1.543
## 47 m4 ~1 3.614 0.046 78.994 0.000 3.525 3.704 3.614 1.684 1.684
## 48 rape ~1 2.348 0.042 55.545 0.000 2.265 2.431 2.348 1.589 1.589
## 49 psih ~1 0.000 0.000 NA NA 0.000 0.000 0.000 0.000 0.000
## 50 nar ~1 0.000 0.000 NA NA 0.000 0.000 0.000 0.000 0.000
## 51 mach ~1 0.000 0.000 NA NA 0.000 0.000 0.000 0.000 0.000parameterestimates(rrsem, standardized = T) #not adjusted## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all std.nox
## 1 psih =~ p1 1.000 0.000 NA NA 1.000 1.000 0.695 0.366 0.366
## 2 psih =~ p2 2.069 0.184 11.242 0.00 1.708 2.429 1.438 0.734 0.734
## 3 psih =~ p3 1.800 0.163 11.063 0.00 1.481 2.119 1.251 0.742 0.742
## 4 psih =~ p4 1.885 0.172 10.968 0.00 1.548 2.222 1.310 0.766 0.766
## 5 nar =~ n1 1.000 0.000 NA NA 1.000 1.000 1.550 0.712 0.712
## 6 nar =~ n2 0.993 0.021 48.158 0.00 0.953 1.034 1.539 0.699 0.699
## 7 nar =~ n3 1.193 0.040 29.554 0.00 1.114 1.273 1.849 0.900 0.900
## 8 nar =~ n4 1.012 0.036 27.950 0.00 0.941 1.083 1.569 0.817 0.817
## 9 mach =~ m1 1.000 0.000 NA NA 1.000 1.000 2.058 0.918 0.918
## 10 mach =~ m2 0.948 0.016 60.526 0.00 0.918 0.979 1.951 0.922 0.922
## 11 mach =~ m3 1.066 0.016 64.775 0.00 1.034 1.099 2.194 0.902 0.902
## 12 mach =~ m4 0.962 0.016 61.333 0.00 0.931 0.993 1.980 0.923 0.923
## 13 rape ~ psih 0.536 0.114 4.693 0.00 0.312 0.760 0.373 0.252 0.252
## 14 rape ~ nar -0.043 0.047 -0.916 0.36 -0.136 0.050 -0.067 -0.046 -0.046
## 15 rape ~ mach 0.191 0.038 5.057 0.00 0.117 0.265 0.393 0.266 0.266
## 16 n1 ~~ n2 1.911 0.133 14.333 0.00 1.650 2.172 1.911 0.793 0.793
## 17 p1 ~~ p1 3.132 0.109 28.730 0.00 2.918 3.346 3.132 0.866 0.866
## 18 p2 ~~ p2 1.764 0.118 14.957 0.00 1.533 1.996 1.764 0.461 0.461
## 19 p3 ~~ p3 1.280 0.091 14.004 0.00 1.101 1.459 1.280 0.450 0.450
## 20 p4 ~~ p4 1.210 0.092 13.169 0.00 1.030 1.391 1.210 0.413 0.413
## 21 n1 ~~ n1 2.339 0.139 16.826 0.00 2.066 2.611 2.339 0.493 0.493
## 22 n2 ~~ n2 2.486 0.146 17.038 0.00 2.200 2.772 2.486 0.512 0.512
## 23 n3 ~~ n3 0.802 0.086 9.325 0.00 0.633 0.970 0.802 0.190 0.190
## 24 n4 ~~ n4 1.227 0.082 14.944 0.00 1.066 1.387 1.227 0.333 0.333
## 25 m1 ~~ m1 0.789 0.067 11.819 0.00 0.659 0.920 0.789 0.157 0.157
## 26 m2 ~~ m2 0.671 0.065 10.396 0.00 0.545 0.798 0.671 0.150 0.150
## 27 m3 ~~ m3 1.099 0.090 12.284 0.00 0.924 1.275 1.099 0.186 0.186
## 28 m4 ~~ m4 0.684 0.069 9.873 0.00 0.548 0.820 0.684 0.149 0.149
## 29 rape ~~ rape 1.764 0.090 19.506 0.00 1.586 1.941 1.764 0.808 0.808
## 30 psih ~~ psih 0.483 0.086 5.637 0.00 0.315 0.651 1.000 1.000 1.000
## 31 nar ~~ nar 2.402 0.160 15.005 0.00 2.088 2.715 1.000 1.000 1.000
## 32 mach ~~ mach 4.235 0.132 32.176 0.00 3.977 4.493 1.000 1.000 1.000
## 33 psih ~~ nar 0.633 0.070 9.062 0.00 0.496 0.770 0.588 0.588 0.588
## 34 psih ~~ mach 0.922 0.092 10.017 0.00 0.742 1.103 0.645 0.645 0.645
## 35 nar ~~ mach 2.224 0.114 19.515 0.00 2.000 2.447 0.697 0.697 0.697In order to check if exclusion of those who could not discriminate between the Dark Triad traits (or did not put enough effort into it) would change the results, an additional model was computed.
rrxxier <- subset(rrxx, Triad == "npm" & nar < 16 & mach < 16 & psi < 16|Triad == "npm" & nar > 16 & mach > 16 & psi > 16|Triad == "Npm" & nar > 16 & mach < nar & psi < nar|Triad == "npM" & mach > 16 & mach > nar & mach > psi|Triad == "nPm" & psi > 16 & psi > mach & psi > nar)
rrxyier <- subset(rrxy, Triad == "npm" & nar < 16 & mach < 16 & psi < 16|Triad == "npm" & nar > 16 & mach > 16 & psi > 16|Triad == "Npm" & nar > 16 & mach < nar & psi < nar|Triad == "npM" & mach > 16 & mach > nar & mach > psi|Triad == "nPm" & psi > 16 & psi > mach & psi > nar)
checkdata <- rbind(rrxxier,rrxyier)
modelf <- '
psih =~ p1 + p2 + p3 + p4
nar =~ n1 + n2 + n3 + n4
mach =~ m1 + m2 + m3 + m4
rape ~ psih + nar + mach
n1~~n2'
svy.df2 <- svydesign(ids =~ 1, strata =~ Triad, nest = T, data = checkdata)## Warning in svydesign.default(ids = ~1, strata = ~Triad, nest = T, data = checkdata): No weights or probabilities
## supplied, assuming equal probabilityrrsemx <- sem(modelf, data = checkdata, estimator = "MLM")
finalavaanx <- lavaan.survey(rrsemx, svy.df2, estimator = "MLM")
parameterestimates(finalavaanx, standardized = T) #adjusted## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all std.nox
## 1 psih =~ p1 1.000 0.000 NA NA 1.000 1.000 0.703 0.374 0.374
## 2 psih =~ p2 2.041 0.261 7.811 0.000 1.529 2.553 1.434 0.746 0.746
## 3 psih =~ p3 1.592 0.207 7.690 0.000 1.186 1.998 1.119 0.692 0.692
## 4 psih =~ p4 1.601 0.213 7.509 0.000 1.183 2.019 1.125 0.680 0.680
## 5 nar =~ n1 1.000 0.000 NA NA 1.000 1.000 1.286 0.593 0.593
## 6 nar =~ n2 1.009 0.034 29.555 0.000 0.942 1.076 1.297 0.589 0.589
## 7 nar =~ n3 1.550 0.108 14.326 0.000 1.338 1.762 1.992 0.987 0.987
## 8 nar =~ n4 0.908 0.065 13.927 0.000 0.780 1.036 1.167 0.638 0.638
## 9 mach =~ m1 1.000 0.000 NA NA 1.000 1.000 2.068 0.936 0.936
## 10 mach =~ m2 0.914 0.022 41.271 0.000 0.870 0.957 1.890 0.927 0.927
## 11 mach =~ m3 1.065 0.023 46.543 0.000 1.020 1.109 2.202 0.908 0.908
## 12 mach =~ m4 0.914 0.022 40.783 0.000 0.870 0.958 1.890 0.909 0.909
## 13 rape ~ psih 0.390 0.119 3.264 0.001 0.156 0.624 0.274 0.200 0.200
## 14 rape ~ nar -0.072 0.056 -1.278 0.201 -0.182 0.038 -0.092 -0.067 -0.067
## 15 rape ~ mach 0.166 0.042 3.992 0.000 0.084 0.247 0.343 0.251 0.251
## 16 n1 ~~ n2 2.650 0.193 13.710 0.000 2.271 3.029 2.650 0.852 0.852
## 17 p1 ~~ p1 3.030 0.148 20.441 0.000 2.740 3.321 3.030 0.860 0.860
## 18 p2 ~~ p2 1.636 0.175 9.356 0.000 1.293 1.979 1.636 0.443 0.443
## 19 p3 ~~ p3 1.358 0.138 9.823 0.000 1.087 1.629 1.358 0.520 0.520
## 20 p4 ~~ p4 1.472 0.133 11.082 0.000 1.212 1.732 1.472 0.538 0.538
## 21 n1 ~~ n1 3.049 0.195 15.628 0.000 2.667 3.432 3.049 0.649 0.649
## 22 n2 ~~ n2 3.173 0.204 15.581 0.000 2.774 3.573 3.173 0.653 0.653
## 23 n3 ~~ n3 0.108 0.193 0.560 0.575 -0.270 0.487 0.108 0.027 0.027
## 24 n4 ~~ n4 1.986 0.132 15.105 0.000 1.729 2.244 1.986 0.593 0.593
## 25 m1 ~~ m1 0.607 0.079 7.698 0.000 0.452 0.761 0.607 0.124 0.124
## 26 m2 ~~ m2 0.582 0.082 7.053 0.000 0.420 0.743 0.582 0.140 0.140
## 27 m3 ~~ m3 1.037 0.127 8.139 0.000 0.787 1.287 1.037 0.176 0.176
## 28 m4 ~~ m4 0.746 0.108 6.880 0.000 0.534 0.959 0.746 0.173 0.173
## 29 rape ~~ rape 1.624 0.136 11.947 0.000 1.358 1.891 1.624 0.866 0.866
## 30 psih ~~ psih 0.494 0.121 4.084 0.000 0.257 0.731 1.000 1.000 1.000
## 31 nar ~~ nar 1.653 0.201 8.228 0.000 1.259 2.046 1.000 1.000 1.000
## 32 mach ~~ mach 4.277 0.159 26.940 0.000 3.966 4.589 1.000 1.000 1.000
## 33 psih ~~ nar 0.282 0.056 5.008 0.000 0.172 0.393 0.313 0.313 0.313
## 34 psih ~~ mach 0.727 0.107 6.822 0.000 0.518 0.936 0.501 0.501 0.501
## 35 nar ~~ mach 1.230 0.115 10.689 0.000 1.004 1.455 0.462 0.462 0.462
## 36 p1 ~1 4.344 0.074 58.897 0.000 4.199 4.488 4.344 2.314 2.314
## 37 p2 ~1 3.603 0.064 56.390 0.000 3.478 3.728 3.603 1.875 1.875
## 38 p3 ~1 3.153 0.059 53.709 0.000 3.038 3.269 3.153 1.952 1.952
## 39 p4 ~1 3.383 0.061 55.503 0.000 3.263 3.502 3.383 2.045 2.045
## 40 n1 ~1 3.721 0.054 69.026 0.000 3.616 3.827 3.721 1.716 1.716
## 41 n2 ~1 3.825 0.054 71.491 0.000 3.721 3.930 3.825 1.736 1.736
## 42 n3 ~1 3.871 0.065 59.207 0.000 3.743 3.999 3.871 1.917 1.917
## 43 n4 ~1 3.328 0.065 51.147 0.000 3.201 3.456 3.328 1.819 1.819
## 44 m1 ~1 3.268 0.056 58.165 0.000 3.158 3.378 3.268 1.479 1.479
## 45 m2 ~1 2.887 0.051 56.126 0.000 2.786 2.988 2.887 1.417 1.417
## 46 m3 ~1 3.219 0.055 58.229 0.000 3.110 3.327 3.219 1.327 1.327
## 47 m4 ~1 3.125 0.055 56.396 0.000 3.017 3.234 3.125 1.504 1.504
## 48 rape ~1 2.152 0.056 38.390 0.000 2.042 2.262 2.152 1.571 1.571
## 49 psih ~1 0.000 0.000 NA NA 0.000 0.000 0.000 0.000 0.000
## 50 nar ~1 0.000 0.000 NA NA 0.000 0.000 0.000 0.000 0.000
## 51 mach ~1 0.000 0.000 NA NA 0.000 0.000 0.000 0.000 0.000parameterestimates(rrsemx, standardized = T) #not adjusted## lhs op rhs est se z pvalue ci.lower ci.upper std.lv std.all std.nox
## 1 psih =~ p1 1.000 0.000 NA NA 1.000 1.000 0.703 0.374 0.374
## 2 psih =~ p2 2.041 0.262 7.798 0.000 1.528 2.554 1.434 0.746 0.746
## 3 psih =~ p3 1.592 0.208 7.672 0.000 1.185 1.999 1.119 0.692 0.692
## 4 psih =~ p4 1.601 0.215 7.439 0.000 1.179 2.023 1.125 0.680 0.680
## 5 nar =~ n1 1.000 0.000 NA NA 1.000 1.000 1.286 0.593 0.593
## 6 nar =~ n2 1.009 0.034 29.549 0.000 0.942 1.076 1.297 0.589 0.589
## 7 nar =~ n3 1.550 0.117 13.295 0.000 1.321 1.778 1.992 0.987 0.987
## 8 nar =~ n4 0.908 0.066 13.670 0.000 0.778 1.038 1.167 0.638 0.638
## 9 mach =~ m1 1.000 0.000 NA NA 1.000 1.000 2.068 0.936 0.936
## 10 mach =~ m2 0.914 0.022 41.297 0.000 0.870 0.957 1.890 0.927 0.927
## 11 mach =~ m3 1.065 0.023 46.306 0.000 1.020 1.110 2.202 0.908 0.908
## 12 mach =~ m4 0.914 0.022 40.882 0.000 0.870 0.958 1.890 0.909 0.909
## 13 rape ~ psih 0.390 0.119 3.277 0.001 0.157 0.623 0.274 0.200 0.200
## 14 rape ~ nar -0.072 0.056 -1.282 0.200 -0.181 0.038 -0.092 -0.067 -0.067
## 15 rape ~ mach 0.166 0.041 4.003 0.000 0.085 0.247 0.343 0.251 0.251
## 16 n1 ~~ n2 2.650 0.211 12.572 0.000 2.237 3.064 2.650 0.852 0.852
## 17 p1 ~~ p1 3.030 0.149 20.368 0.000 2.738 3.322 3.030 0.860 0.860
## 18 p2 ~~ p2 1.636 0.175 9.348 0.000 1.293 1.979 1.636 0.443 0.443
## 19 p3 ~~ p3 1.358 0.138 9.855 0.000 1.088 1.628 1.358 0.520 0.520
## 20 p4 ~~ p4 1.472 0.134 10.982 0.000 1.209 1.735 1.472 0.538 0.538
## 21 n1 ~~ n1 3.049 0.209 14.611 0.000 2.640 3.458 3.049 0.649 0.649
## 22 n2 ~~ n2 3.173 0.223 14.246 0.000 2.737 3.610 3.173 0.653 0.653
## 23 n3 ~~ n3 0.108 0.199 0.544 0.587 -0.282 0.498 0.108 0.027 0.027
## 24 n4 ~~ n4 1.986 0.139 14.278 0.000 1.714 2.259 1.986 0.593 0.593
## 25 m1 ~~ m1 0.607 0.079 7.689 0.000 0.452 0.761 0.607 0.124 0.124
## 26 m2 ~~ m2 0.582 0.084 6.951 0.000 0.418 0.746 0.582 0.140 0.140
## 27 m3 ~~ m3 1.037 0.129 8.050 0.000 0.785 1.290 1.037 0.176 0.176
## 28 m4 ~~ m4 0.746 0.110 6.800 0.000 0.531 0.962 0.746 0.173 0.173
## 29 rape ~~ rape 1.624 0.137 11.878 0.000 1.356 1.892 1.624 0.866 0.866
## 30 psih ~~ psih 0.494 0.122 4.038 0.000 0.254 0.733 1.000 1.000 1.000
## 31 nar ~~ nar 1.653 0.215 7.670 0.000 1.230 2.075 1.000 1.000 1.000
## 32 mach ~~ mach 4.277 0.194 22.088 0.000 3.898 4.657 1.000 1.000 1.000
## 33 psih ~~ nar 0.282 0.059 4.765 0.000 0.166 0.399 0.313 0.313 0.313
## 34 psih ~~ mach 0.727 0.108 6.737 0.000 0.516 0.939 0.501 0.501 0.501
## 35 nar ~~ mach 1.230 0.125 9.862 0.000 0.985 1.474 0.462 0.462 0.462The model reveals only a negligible change in parameters, pointing out the robustness of outcomes.