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library(lavaan)## Warning: package 'lavaan' was built under R version 4.0.5## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.library(semTools)## Warning: package 'semTools' was built under R version 4.0.5## ## ################################################################################# This is semTools 0.5-4## All users of R (or SEM) are invited to submit functions or ideas for functions.## ###############################################################################all.results<-matrix(NA, nrow = 3, ncol = 6)
setwd("~/GRA/NCI mental health/New Folder")
load("nci.mentalhealth2.Rdata")
```r
sp.model <-'rights =~ KEY16 + LOCK15
social =~ SHOPPING15 + ENTERTAIN15 + EATOUT15 + ERRANDS15
choice =~ SCHEDULE15 + FREETIME15 + CHOOSBUY15'
baseline <- measEq.syntax(configural.model = sp.model,
data = nci.mentalhealth.2,
ordered = c("KEY16", "LOCK15", "SHOPPING15", "ENTERTAIN15", "EATOUT15", "ERRANDS15", "SCHEDULE15", "FREETIME15", "CHOOSBUY15"),
parameterization = "delta",
ID.fac = "std.lv",
ID.cat = "Wu.Estabrook.2016",
group = "mh_cat",
group.equal = "configural")
summary(baseline)## This lavaan model syntax specifies a CFA with 9 manifest indicators (9 of which are ordinal) of 3 common factor(s).
##
## To identify the location and scale of each common factor, the factor means and variances were fixed to 0 and 1, respectively, unless equality constraints on measurement parameters allow them to be freed.
##
## The location and scale of each latent item-response underlying 9 ordinal indicators were identified using the "delta" parameterization, and the identification constraints recommended by Wu & Estabrook (2016). For details, read:
##
## https://doi.org/10.1007/s11336-016-9506-0
##
## Pattern matrix indicating num(eric), ord(ered), and lat(ent) indicators per factor:
##
## rights social choice
## KEY16 ord
## LOCK15 ord
## SHOPPING15 ord
## ENTERTAIN15 ord
## EATOUT15 ord
## ERRANDS15 ord
## SCHEDULE15 ord
## FREETIME15 ord
## CHOOSBUY15 ord
##
## The following types of parameter were constrained to equality across groups:
##
## configuralcat(as.character(baseline))## ## LOADINGS:
##
## rights =~ c(NA, NA, NA, NA)*KEY16 + c(lambda.1_1.g1, lambda.1_1.g2, lambda.1_1.g3, lambda.1_1.g4)*KEY16
## rights =~ c(NA, NA, NA, NA)*LOCK15 + c(lambda.2_1.g1, lambda.2_1.g2, lambda.2_1.g3, lambda.2_1.g4)*LOCK15
## social =~ c(NA, NA, NA, NA)*SHOPPING15 + c(lambda.3_2.g1, lambda.3_2.g2, lambda.3_2.g3, lambda.3_2.g4)*SHOPPING15
## social =~ c(NA, NA, NA, NA)*ENTERTAIN15 + c(lambda.4_2.g1, lambda.4_2.g2, lambda.4_2.g3, lambda.4_2.g4)*ENTERTAIN15
## social =~ c(NA, NA, NA, NA)*EATOUT15 + c(lambda.5_2.g1, lambda.5_2.g2, lambda.5_2.g3, lambda.5_2.g4)*EATOUT15
## social =~ c(NA, NA, NA, NA)*ERRANDS15 + c(lambda.6_2.g1, lambda.6_2.g2, lambda.6_2.g3, lambda.6_2.g4)*ERRANDS15
## choice =~ c(NA, NA, NA, NA)*SCHEDULE15 + c(lambda.7_3.g1, lambda.7_3.g2, lambda.7_3.g3, lambda.7_3.g4)*SCHEDULE15
## choice =~ c(NA, NA, NA, NA)*FREETIME15 + c(lambda.8_3.g1, lambda.8_3.g2, lambda.8_3.g3, lambda.8_3.g4)*FREETIME15
## choice =~ c(NA, NA, NA, NA)*CHOOSBUY15 + c(lambda.9_3.g1, lambda.9_3.g2, lambda.9_3.g3, lambda.9_3.g4)*CHOOSBUY15
##
## ## THRESHOLDS:
##
## KEY16 | c(NA, NA, NA, NA)*t1 + c(KEY16.thr1.g1, KEY16.thr1.g2, KEY16.thr1.g3, KEY16.thr1.g4)*t1
## KEY16 | c(NA, NA, NA, NA)*t2 + c(KEY16.thr2.g1, KEY16.thr2.g2, KEY16.thr2.g3, KEY16.thr2.g4)*t2
## LOCK15 | c(NA, NA, NA, NA)*t1 + c(LOCK15.thr1.g1, LOCK15.thr1.g2, LOCK15.thr1.g3, LOCK15.thr1.g4)*t1
## LOCK15 | c(NA, NA, NA, NA)*t2 + c(LOCK15.thr2.g1, LOCK15.thr2.g2, LOCK15.thr2.g3, LOCK15.thr2.g4)*t2
## SHOPPING15 | c(NA, NA, NA, NA)*t1 + c(SHOPPING15.thr1.g1, SHOPPING15.thr1.g2, SHOPPING15.thr1.g3, SHOPPING15.thr1.g4)*t1
## SHOPPING15 | c(NA, NA, NA, NA)*t2 + c(SHOPPING15.thr2.g1, SHOPPING15.thr2.g2, SHOPPING15.thr2.g3, SHOPPING15.thr2.g4)*t2
## SHOPPING15 | c(NA, NA, NA, NA)*t3 + c(SHOPPING15.thr3.g1, SHOPPING15.thr3.g2, SHOPPING15.thr3.g3, SHOPPING15.thr3.g4)*t3
## ENTERTAIN15 | c(NA, NA, NA, NA)*t1 + c(ENTERTAIN15.thr1.g1, ENTERTAIN15.thr1.g2, ENTERTAIN15.thr1.g3, ENTERTAIN15.thr1.g4)*t1
## ENTERTAIN15 | c(NA, NA, NA, NA)*t2 + c(ENTERTAIN15.thr2.g1, ENTERTAIN15.thr2.g2, ENTERTAIN15.thr2.g3, ENTERTAIN15.thr2.g4)*t2
## ENTERTAIN15 | c(NA, NA, NA, NA)*t3 + c(ENTERTAIN15.thr3.g1, ENTERTAIN15.thr3.g2, ENTERTAIN15.thr3.g3, ENTERTAIN15.thr3.g4)*t3
## EATOUT15 | c(NA, NA, NA, NA)*t1 + c(EATOUT15.thr1.g1, EATOUT15.thr1.g2, EATOUT15.thr1.g3, EATOUT15.thr1.g4)*t1
## EATOUT15 | c(NA, NA, NA, NA)*t2 + c(EATOUT15.thr2.g1, EATOUT15.thr2.g2, EATOUT15.thr2.g3, EATOUT15.thr2.g4)*t2
## EATOUT15 | c(NA, NA, NA, NA)*t3 + c(EATOUT15.thr3.g1, EATOUT15.thr3.g2, EATOUT15.thr3.g3, EATOUT15.thr3.g4)*t3
## ERRANDS15 | c(NA, NA, NA, NA)*t1 + c(ERRANDS15.thr1.g1, ERRANDS15.thr1.g2, ERRANDS15.thr1.g3, ERRANDS15.thr1.g4)*t1
## ERRANDS15 | c(NA, NA, NA, NA)*t2 + c(ERRANDS15.thr2.g1, ERRANDS15.thr2.g2, ERRANDS15.thr2.g3, ERRANDS15.thr2.g4)*t2
## ERRANDS15 | c(NA, NA, NA, NA)*t3 + c(ERRANDS15.thr3.g1, ERRANDS15.thr3.g2, ERRANDS15.thr3.g3, ERRANDS15.thr3.g4)*t3
## SCHEDULE15 | c(NA, NA, NA, NA)*t1 + c(SCHEDULE15.thr1.g1, SCHEDULE15.thr1.g2, SCHEDULE15.thr1.g3, SCHEDULE15.thr1.g4)*t1
## SCHEDULE15 | c(NA, NA, NA, NA)*t2 + c(SCHEDULE15.thr2.g1, SCHEDULE15.thr2.g2, SCHEDULE15.thr2.g3, SCHEDULE15.thr2.g4)*t2
## FREETIME15 | c(NA, NA, NA, NA)*t1 + c(FREETIME15.thr1.g1, FREETIME15.thr1.g2, FREETIME15.thr1.g3, FREETIME15.thr1.g4)*t1
## FREETIME15 | c(NA, NA, NA, NA)*t2 + c(FREETIME15.thr2.g1, FREETIME15.thr2.g2, FREETIME15.thr2.g3, FREETIME15.thr2.g4)*t2
## CHOOSBUY15 | c(NA, NA, NA, NA)*t1 + c(CHOOSBUY15.thr1.g1, CHOOSBUY15.thr1.g2, CHOOSBUY15.thr1.g3, CHOOSBUY15.thr1.g4)*t1
## CHOOSBUY15 | c(NA, NA, NA, NA)*t2 + c(CHOOSBUY15.thr2.g1, CHOOSBUY15.thr2.g2, CHOOSBUY15.thr2.g3, CHOOSBUY15.thr2.g4)*t2
##
## ## INTERCEPTS:
##
## KEY16 ~ c(0, 0, 0, 0)*1 + c(nu.1.g1, nu.1.g2, nu.1.g3, nu.1.g4)*1
## LOCK15 ~ c(0, 0, 0, 0)*1 + c(nu.2.g1, nu.2.g2, nu.2.g3, nu.2.g4)*1
## SHOPPING15 ~ c(0, 0, 0, 0)*1 + c(nu.3.g1, nu.3.g2, nu.3.g3, nu.3.g4)*1
## ENTERTAIN15 ~ c(0, 0, 0, 0)*1 + c(nu.4.g1, nu.4.g2, nu.4.g3, nu.4.g4)*1
## EATOUT15 ~ c(0, 0, 0, 0)*1 + c(nu.5.g1, nu.5.g2, nu.5.g3, nu.5.g4)*1
## ERRANDS15 ~ c(0, 0, 0, 0)*1 + c(nu.6.g1, nu.6.g2, nu.6.g3, nu.6.g4)*1
## SCHEDULE15 ~ c(0, 0, 0, 0)*1 + c(nu.7.g1, nu.7.g2, nu.7.g3, nu.7.g4)*1
## FREETIME15 ~ c(0, 0, 0, 0)*1 + c(nu.8.g1, nu.8.g2, nu.8.g3, nu.8.g4)*1
## CHOOSBUY15 ~ c(0, 0, 0, 0)*1 + c(nu.9.g1, nu.9.g2, nu.9.g3, nu.9.g4)*1
##
## ## SCALING FACTORS:
##
## KEY16 ~*~ c(1, 1, 1, 1)*KEY16
## LOCK15 ~*~ c(1, 1, 1, 1)*LOCK15
## SHOPPING15 ~*~ c(1, 1, 1, 1)*SHOPPING15
## ENTERTAIN15 ~*~ c(1, 1, 1, 1)*ENTERTAIN15
## EATOUT15 ~*~ c(1, 1, 1, 1)*EATOUT15
## ERRANDS15 ~*~ c(1, 1, 1, 1)*ERRANDS15
## SCHEDULE15 ~*~ c(1, 1, 1, 1)*SCHEDULE15
## FREETIME15 ~*~ c(1, 1, 1, 1)*FREETIME15
## CHOOSBUY15 ~*~ c(1, 1, 1, 1)*CHOOSBUY15
##
##
## ## LATENT MEANS/INTERCEPTS:
##
## rights ~ c(0, 0, 0, 0)*1 + c(alpha.1.g1, alpha.1.g2, alpha.1.g3, alpha.1.g4)*1
## social ~ c(0, 0, 0, 0)*1 + c(alpha.2.g1, alpha.2.g2, alpha.2.g3, alpha.2.g4)*1
## choice ~ c(0, 0, 0, 0)*1 + c(alpha.3.g1, alpha.3.g2, alpha.3.g3, alpha.3.g4)*1
##
## ## COMMON-FACTOR VARIANCES:
##
## rights ~~ c(1, 1, 1, 1)*rights + c(psi.1_1.g1, psi.1_1.g2, psi.1_1.g3, psi.1_1.g4)*rights
## social ~~ c(1, 1, 1, 1)*social + c(psi.2_2.g1, psi.2_2.g2, psi.2_2.g3, psi.2_2.g4)*social
## choice ~~ c(1, 1, 1, 1)*choice + c(psi.3_3.g1, psi.3_3.g2, psi.3_3.g3, psi.3_3.g4)*choice
##
## ## COMMON-FACTOR COVARIANCES:
##
## rights ~~ c(NA, NA, NA, NA)*social + c(psi.2_1.g1, psi.2_1.g2, psi.2_1.g3, psi.2_1.g4)*social
## rights ~~ c(NA, NA, NA, NA)*choice + c(psi.3_1.g1, psi.3_1.g2, psi.3_1.g3, psi.3_1.g4)*choice
## social ~~ c(NA, NA, NA, NA)*choice + c(psi.3_2.g1, psi.3_2.g2, psi.3_2.g3, psi.3_2.g4)*choicemodel.baseline <- as.character(baseline)
fit.baseline <- cfa(model.baseline, data = nci.mentalhealth.2, group =
"mh_cat",
ordered = c("KEY16", "LOCK15", "SHOPPING15", "ENTERTAIN15", "EATOUT15", "ERRANDS15", "SCHEDULE15", "FREETIME15", "CHOOSBUY15"))
summary(fit.baseline, fit.measures=TRUE)## lavaan 0.6-8 ended normally after 62 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 136
##
## Number of observations per group: Used Total
## 0 429 477
## 3 513 595
## 2 294 324
## 1 183 208
##
## Model Test User Model:
## Standard Robust
## Test Statistic 99.325 147.136
## Degrees of freedom 96 96
## P-value (Chi-square) 0.388 0.001
## Scaling correction factor 0.743
## Shift parameter for each group:
## 0 4.079
## 3 4.877
## 2 2.795
## 1 1.740
## simple second-order correction
## Test statistic for each group:
## 0 28.958 43.042
## 3 16.956 27.692
## 2 30.173 43.394
## 1 23.238 33.008
##
## Model Test Baseline Model:
##
## Test statistic 5813.724 4134.297
## Degrees of freedom 144 144
## P-value 0.000 0.000
## Scaling correction factor 1.421
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999 0.987
## Tucker-Lewis Index (TLI) 0.999 0.981
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.010 0.039
## 90 Percent confidence interval - lower 0.000 0.026
## 90 Percent confidence interval - upper 0.030 0.051
## P-value RMSEA <= 0.05 1.000 0.935
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.045 0.045
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## rights =~
## KEY16 (l.1_) 0.516 0.081 6.338 0.000
## LOCK15 (l.2_) 0.789 0.105 7.546 0.000
## social =~
## SHOPPIN (l.3_) 0.748 0.034 22.119 0.000
## ENTERTA (l.4_) 0.736 0.032 22.742 0.000
## EATOUT1 (l.5_) 0.816 0.030 27.571 0.000
## ERRANDS (l.6_) 0.634 0.040 15.907 0.000
## choice =~
## SCHEDUL (l.7_) 0.844 0.031 26.915 0.000
## FREETIM (l.8_) 0.879 0.035 25.447 0.000
## CHOOSBU (l.9_) 0.772 0.034 22.800 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## rights ~~
## social (p.2_) 0.489 0.081 6.028 0.000
## choice (p.3_1) 0.413 0.076 5.467 0.000
## social ~~
## choice (p.3_2) 0.394 0.056 7.029 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .KEY16 (n.1.) 0.000
## .LOCK15 (n.2.) 0.000
## .SHOPPIN (n.3.) 0.000
## .ENTERTA (n.4.) 0.000
## .EATOUT1 (n.5.) 0.000
## .ERRANDS (n.6.) 0.000
## .SCHEDUL (n.7.) 0.000
## .FREETIM (n.8.) 0.000
## .CHOOSBU (n.9.) 0.000
## rights (a.1.) 0.000
## social (a.2.) 0.000
## choice (a.3.) 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## (KEY16.1) -0.003 0.061 -0.048 0.962
## (KEY16.2) 0.015 0.061 0.241 0.810
## (LOCK15.1) -0.120 0.061 -1.976 0.048
## (LOCK15.2) -0.067 0.061 -1.109 0.268
## (SHOPPING15.1) -0.935 0.071 -13.120 0.000
## (SHOPPING15.2) -0.306 0.062 -4.958 0.000
## (SHOPPING15.3) 0.324 0.062 5.246 0.000
## (ENTERTAIN15.1) -0.584 0.065 -9.049 0.000
## (ENTERTAIN15.2) 0.132 0.061 2.169 0.030
## (ENTERTAIN15.3) 0.729 0.067 10.905 0.000
## (EATOUT15.1) -0.840 0.069 -12.166 0.000
## (EATOUT15.2) -0.150 0.061 -2.458 0.014
## (EATOUT15.3) 0.405 0.062 6.490 0.000
## (ERRANDS15.1) -0.832 0.069 -12.077 0.000
## (ERRANDS15.2) 0.251 0.061 4.094 0.000
## (ERRANDS15.3) 0.908 0.071 12.864 0.000
## (SCHEDULE15.1) -0.783 0.068 -11.541 0.000
## (SCHEDULE15.2) 0.197 0.061 3.228 0.001
## (FREETIME15.1) -1.254 0.082 -15.382 0.000
## (FREETIME15.2) -0.269 0.061 -4.382 0.000
## (CHOOSBUY15.1) -1.020 0.074 -13.866 0.000
## (CHOOSBUY15.2) 0.138 0.061 2.265 0.023
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## rights (p.1_) 1.000
## social (p.2_) 1.000
## choice (p.3_) 1.000
## .KEY16 0.734
## .LOCK15 0.377
## .SHOPPIN 0.440
## .ENTERTA 0.458
## .EATOUT1 0.334
## .ERRANDS 0.598
## .SCHEDUL 0.288
## .FREETIM 0.227
## .CHOOSBU 0.404
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## KEY16 1.000
## LOCK15 1.000
## SHOPPING15 1.000
## ENTERTAIN15 1.000
## EATOUT15 1.000
## ERRANDS15 1.000
## SCHEDULE15 1.000
## FREETIME15 1.000
## CHOOSBUY15 1.000
##
##
## Group 2 [3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## rights =~
## KEY16 (l.1_) 0.319 0.093 3.434 0.001
## LOCK15 (l.2_) 0.866 0.218 3.977 0.000
## social =~
## SHOPPIN (l.3_) 0.782 0.040 19.513 0.000
## ENTERTA (l.4_) 0.533 0.045 11.946 0.000
## EATOUT1 (l.5_) 0.688 0.042 16.268 0.000
## ERRANDS (l.6_) 0.544 0.045 12.205 0.000
## choice =~
## SCHEDUL (l.7_) 0.725 0.048 14.971 0.000
## FREETIM (l.8_) 0.735 0.054 13.500 0.000
## CHOOSBU (l.9_) 0.647 0.050 12.999 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## rights ~~
## social (p.2_) 0.292 0.096 3.045 0.002
## choice (p.3_1) 0.394 0.111 3.565 0.000
## social ~~
## choice (p.3_2) 0.381 0.060 6.315 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .KEY16 (n.1.) 0.000
## .LOCK15 (n.2.) 0.000
## .SHOPPIN (n.3.) 0.000
## .ENTERTA (n.4.) 0.000
## .EATOUT1 (n.5.) 0.000
## .ERRANDS (n.6.) 0.000
## .SCHEDUL (n.7.) 0.000
## .FREETIM (n.8.) 0.000
## .CHOOSBU (n.9.) 0.000
## rights (a.1.) 0.000
## social (a.2.) 0.000
## choice (a.3.) 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## (KEY16.1) 0.061 0.055 1.102 0.270
## (KEY16.2) 0.076 0.055 1.367 0.172
## (LOCK15.1) -0.436 0.057 -7.601 0.000
## (LOCK15.2) -0.393 0.057 -6.903 0.000
## (SHOPPING15.1) -1.657 0.094 -17.591 0.000
## (SHOPPING15.2) -0.701 0.061 -11.554 0.000
## (SHOPPING15.3) 0.037 0.055 0.661 0.508
## (ENTERTAIN15.1) -0.918 0.065 -14.167 0.000
## (ENTERTAIN15.2) -0.042 0.055 -0.749 0.454
## (ENTERTAIN15.3) 0.558 0.059 9.508 0.000
## (EATOUT15.1) -1.535 0.087 -17.630 0.000
## (EATOUT15.2) -0.535 0.058 -9.163 0.000
## (EATOUT15.3) 0.120 0.056 2.160 0.031
## (ERRANDS15.1) -1.210 0.073 -16.578 0.000
## (ERRANDS15.2) -0.032 0.055 -0.573 0.567
## (ERRANDS15.3) 0.658 0.060 10.962 0.000
## (SCHEDULE15.1) -1.124 0.070 -16.005 0.000
## (SCHEDULE15.2) 0.214 0.056 3.833 0.000
## (FREETIME15.1) -1.620 0.092 -17.627 0.000
## (FREETIME15.2) -0.490 0.058 -8.471 0.000
## (CHOOSBUY15.1) -1.519 0.086 -17.618 0.000
## (CHOOSBUY15.2) -0.066 0.055 -1.190 0.234
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## rights (p.1_) 1.000
## social (p.2_) 1.000
## choice (p.3_) 1.000
## .KEY16 0.898
## .LOCK15 0.251
## .SHOPPIN 0.389
## .ENTERTA 0.715
## .EATOUT1 0.527
## .ERRANDS 0.704
## .SCHEDUL 0.475
## .FREETIM 0.460
## .CHOOSBU 0.581
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## KEY16 1.000
## LOCK15 1.000
## SHOPPING15 1.000
## ENTERTAIN15 1.000
## EATOUT15 1.000
## ERRANDS15 1.000
## SCHEDULE15 1.000
## FREETIME15 1.000
## CHOOSBUY15 1.000
##
##
## Group 3 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## rights =~
## KEY16 (l.1_) 0.543 0.104 5.218 0.000
## LOCK15 (l.2_) 0.635 0.118 5.403 0.000
## social =~
## SHOPPIN (l.3_) 0.787 0.044 18.040 0.000
## ENTERTA (l.4_) 0.591 0.052 11.395 0.000
## EATOUT1 (l.5_) 0.798 0.039 20.397 0.000
## ERRANDS (l.6_) 0.640 0.055 11.708 0.000
## choice =~
## SCHEDUL (l.7_) 0.653 0.055 11.900 0.000
## FREETIM (l.8_) 0.823 0.058 14.292 0.000
## CHOOSBU (l.9_) 0.728 0.051 14.304 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## rights ~~
## social (p.2_) 0.503 0.105 4.788 0.000
## choice (p.3_1) 0.478 0.114 4.177 0.000
## social ~~
## choice (p.3_2) 0.357 0.075 4.757 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .KEY16 (n.1.) 0.000
## .LOCK15 (n.2.) 0.000
## .SHOPPIN (n.3.) 0.000
## .ENTERTA (n.4.) 0.000
## .EATOUT1 (n.5.) 0.000
## .ERRANDS (n.6.) 0.000
## .SCHEDUL (n.7.) 0.000
## .FREETIM (n.8.) 0.000
## .CHOOSBU (n.9.) 0.000
## rights (a.1.) 0.000
## social (a.2.) 0.000
## choice (a.3.) 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## (KEY16.1) 0.224 0.074 3.026 0.002
## (KEY16.2) 0.241 0.074 3.258 0.001
## (LOCK15.1) -0.197 0.074 -2.677 0.007
## (LOCK15.2) -0.111 0.073 -1.514 0.130
## (SHOPPING15.1) -1.417 0.107 -13.207 0.000
## (SHOPPING15.2) -0.586 0.078 -7.518 0.000
## (SHOPPING15.3) 0.128 0.073 1.747 0.081
## (ENTERTAIN15.1) -0.981 0.088 -11.205 0.000
## (ENTERTAIN15.2) -0.051 0.073 -0.699 0.485
## (ENTERTAIN15.3) 0.576 0.078 7.405 0.000
## (EATOUT15.1) -1.350 0.103 -13.054 0.000
## (EATOUT15.2) -0.366 0.075 -4.881 0.000
## (EATOUT15.3) 0.206 0.074 2.794 0.005
## (ERRANDS15.1) -1.163 0.094 -12.324 0.000
## (ERRANDS15.2) 0.171 0.074 2.328 0.020
## (ERRANDS15.3) 0.701 0.080 8.754 0.000
## (SCHEDULE15.1) -0.701 0.080 -8.754 0.000
## (SCHEDULE15.2) 0.546 0.077 7.064 0.000
## (FREETIME15.1) -1.270 0.099 -12.794 0.000
## (FREETIME15.2) -0.145 0.073 -1.979 0.048
## (CHOOSBUY15.1) -0.851 0.084 -10.171 0.000
## (CHOOSBUY15.2) 0.303 0.074 4.071 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## rights (p.1_) 1.000
## social (p.2_) 1.000
## choice (p.3_) 1.000
## .KEY16 0.706
## .LOCK15 0.597
## .SHOPPIN 0.380
## .ENTERTA 0.651
## .EATOUT1 0.363
## .ERRANDS 0.590
## .SCHEDUL 0.573
## .FREETIM 0.322
## .CHOOSBU 0.470
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## KEY16 1.000
## LOCK15 1.000
## SHOPPING15 1.000
## ENTERTAIN15 1.000
## EATOUT15 1.000
## ERRANDS15 1.000
## SCHEDULE15 1.000
## FREETIME15 1.000
## CHOOSBUY15 1.000
##
##
## Group 4 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## rights =~
## KEY16 (l.1_) 0.566 0.111 5.105 0.000
## LOCK15 (l.2_) 0.644 0.123 5.233 0.000
## social =~
## SHOPPIN (l.3_) 0.844 0.039 21.496 0.000
## ENTERTA (l.4_) 0.606 0.058 10.481 0.000
## EATOUT1 (l.5_) 0.822 0.044 18.689 0.000
## ERRANDS (l.6_) 0.586 0.059 10.013 0.000
## choice =~
## SCHEDUL (l.7_) 0.699 0.078 8.937 0.000
## FREETIM (l.8_) 0.698 0.091 7.633 0.000
## CHOOSBU (l.9_) 0.679 0.080 8.442 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## rights ~~
## social (p.2_) 0.539 0.133 4.064 0.000
## choice (p.3_1) 0.477 0.156 3.059 0.002
## social ~~
## choice (p.3_2) 0.125 0.108 1.157 0.247
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .KEY16 (n.1.) 0.000
## .LOCK15 (n.2.) 0.000
## .SHOPPIN (n.3.) 0.000
## .ENTERTA (n.4.) 0.000
## .EATOUT1 (n.5.) 0.000
## .ERRANDS (n.6.) 0.000
## .SCHEDUL (n.7.) 0.000
## .FREETIM (n.8.) 0.000
## .CHOOSBU (n.9.) 0.000
## rights (a.1.) 0.000
## social (a.2.) 0.000
## choice (a.3.) 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## (KEY16.1) -0.075 0.093 -0.812 0.417
## (KEY16.2) -0.048 0.093 -0.517 0.605
## (LOCK15.1) -0.357 0.095 -3.757 0.000
## (LOCK15.2) -0.285 0.094 -3.023 0.003
## (SHOPPING15.1) -1.047 0.114 -9.197 0.000
## (SHOPPING15.2) -0.357 0.095 -3.757 0.000
## (SHOPPING15.3) 0.401 0.096 4.196 0.000
## (ENTERTAIN15.1) -0.723 0.102 -7.074 0.000
## (ENTERTAIN15.2) 0.103 0.093 1.107 0.268
## (ENTERTAIN15.3) 0.815 0.105 7.767 0.000
## (EATOUT15.1) -1.047 0.114 -9.197 0.000
## (EATOUT15.2) -0.313 0.094 -3.316 0.001
## (EATOUT15.3) 0.386 0.095 4.049 0.000
## (ERRANDS15.1) -1.024 0.113 -9.075 0.000
## (ERRANDS15.2) 0.117 0.093 1.255 0.210
## (ERRANDS15.3) 0.914 0.108 8.438 0.000
## (SCHEDULE15.1) -1.230 0.123 -9.970 0.000
## (SCHEDULE15.2) 0.089 0.093 0.960 0.337
## (FREETIME15.1) -1.841 0.180 -10.228 0.000
## (FREETIME15.2) -0.636 0.100 -6.367 0.000
## (CHOOSBUY15.1) -1.468 0.140 -10.484 0.000
## (CHOOSBUY15.2) -0.103 0.093 -1.107 0.268
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## rights (p.1_) 1.000
## social (p.2_) 1.000
## choice (p.3_) 1.000
## .KEY16 0.679
## .LOCK15 0.585
## .SHOPPIN 0.288
## .ENTERTA 0.633
## .EATOUT1 0.324
## .ERRANDS 0.657
## .SCHEDUL 0.512
## .FREETIM 0.513
## .CHOOSBU 0.539
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## KEY16 1.000
## LOCK15 1.000
## SHOPPING15 1.000
## ENTERTAIN15 1.000
## EATOUT15 1.000
## ERRANDS15 1.000
## SCHEDULE15 1.000
## FREETIME15 1.000
## CHOOSBUY15 1.000