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sp.model <-'RIGHTS =~ KEY16 + LOCK15
SOCIAL =~ SHOPPING15 + ENTERTAIN15 + EATOUT15 + ERRANDS15
CHOICE =~ SCHEDULE15 + FREETIME15 + CHOOSBUY15'
baseline <- measEq.syntax(configural.model = sp.model,
data = nci.proxy2,
ordered = c("KEY16", "LOCK15", "SHOPPING15", "ENTERTAIN15", "EATOUT15", "ERRANDS15", "SCHEDULE15", "FREETIME15", "CHOOSBUY15"),
parameterization = "delta",
ID.fac = "std.lv",
ID.cat = "Wu.Estabrook.2016",
group = "proxy_cat",
group.equal = "configural")lavaan WARNING: group variable 㤼㸱proxy_cat㤼㸲 contains missing valuessummary(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)*KEY16 + c(lambda.1_1.g1, lambda.1_1.g2, lambda.1_1.g3)*KEY16
RIGHTS =~ c(NA, NA, NA)*LOCK15 + c(lambda.2_1.g1, lambda.2_1.g2, lambda.2_1.g3)*LOCK15
SOCIAL =~ c(NA, NA, NA)*SHOPPING15 + c(lambda.3_2.g1, lambda.3_2.g2, lambda.3_2.g3)*SHOPPING15
SOCIAL =~ c(NA, NA, NA)*ENTERTAIN15 + c(lambda.4_2.g1, lambda.4_2.g2, lambda.4_2.g3)*ENTERTAIN15
SOCIAL =~ c(NA, NA, NA)*EATOUT15 + c(lambda.5_2.g1, lambda.5_2.g2, lambda.5_2.g3)*EATOUT15
SOCIAL =~ c(NA, NA, NA)*ERRANDS15 + c(lambda.6_2.g1, lambda.6_2.g2, lambda.6_2.g3)*ERRANDS15
CHOICE =~ c(NA, NA, NA)*SCHEDULE15 + c(lambda.7_3.g1, lambda.7_3.g2, lambda.7_3.g3)*SCHEDULE15
CHOICE =~ c(NA, NA, NA)*FREETIME15 + c(lambda.8_3.g1, lambda.8_3.g2, lambda.8_3.g3)*FREETIME15
CHOICE =~ c(NA, NA, NA)*CHOOSBUY15 + c(lambda.9_3.g1, lambda.9_3.g2, lambda.9_3.g3)*CHOOSBUY15
## THRESHOLDS:
KEY16 | c(NA, NA, NA)*t1 + c(KEY16.thr1.g1, KEY16.thr1.g2, KEY16.thr1.g3)*t1
KEY16 | c(NA, NA, NA)*t2 + c(KEY16.thr2.g1, KEY16.thr2.g2, KEY16.thr2.g3)*t2
LOCK15 | c(NA, NA, NA)*t1 + c(LOCK15.thr1.g1, LOCK15.thr1.g2, LOCK15.thr1.g3)*t1
LOCK15 | c(NA, NA, NA)*t2 + c(LOCK15.thr2.g1, LOCK15.thr2.g2, LOCK15.thr2.g3)*t2
SHOPPING15 | c(NA, NA, NA)*t1 + c(SHOPPING15.thr1.g1, SHOPPING15.thr1.g2, SHOPPING15.thr1.g3)*t1
SHOPPING15 | c(NA, NA, NA)*t2 + c(SHOPPING15.thr2.g1, SHOPPING15.thr2.g2, SHOPPING15.thr2.g3)*t2
SHOPPING15 | c(NA, NA, NA)*t3 + c(SHOPPING15.thr3.g1, SHOPPING15.thr3.g2, SHOPPING15.thr3.g3)*t3
ENTERTAIN15 | c(NA, NA, NA)*t1 + c(ENTERTAIN15.thr1.g1, ENTERTAIN15.thr1.g2, ENTERTAIN15.thr1.g3)*t1
ENTERTAIN15 | c(NA, NA, NA)*t2 + c(ENTERTAIN15.thr2.g1, ENTERTAIN15.thr2.g2, ENTERTAIN15.thr2.g3)*t2
ENTERTAIN15 | c(NA, NA, NA)*t3 + c(ENTERTAIN15.thr3.g1, ENTERTAIN15.thr3.g2, ENTERTAIN15.thr3.g3)*t3
EATOUT15 | c(NA, NA, NA)*t1 + c(EATOUT15.thr1.g1, EATOUT15.thr1.g2, EATOUT15.thr1.g3)*t1
EATOUT15 | c(NA, NA, NA)*t2 + c(EATOUT15.thr2.g1, EATOUT15.thr2.g2, EATOUT15.thr2.g3)*t2
EATOUT15 | c(NA, NA, NA)*t3 + c(EATOUT15.thr3.g1, EATOUT15.thr3.g2, EATOUT15.thr3.g3)*t3
ERRANDS15 | c(NA, NA, NA)*t1 + c(ERRANDS15.thr1.g1, ERRANDS15.thr1.g2, ERRANDS15.thr1.g3)*t1
ERRANDS15 | c(NA, NA, NA)*t2 + c(ERRANDS15.thr2.g1, ERRANDS15.thr2.g2, ERRANDS15.thr2.g3)*t2
ERRANDS15 | c(NA, NA, NA)*t3 + c(ERRANDS15.thr3.g1, ERRANDS15.thr3.g2, ERRANDS15.thr3.g3)*t3
SCHEDULE15 | c(NA, NA, NA)*t1 + c(SCHEDULE15.thr1.g1, SCHEDULE15.thr1.g2, SCHEDULE15.thr1.g3)*t1
SCHEDULE15 | c(NA, NA, NA)*t2 + c(SCHEDULE15.thr2.g1, SCHEDULE15.thr2.g2, SCHEDULE15.thr2.g3)*t2
FREETIME15 | c(NA, NA, NA)*t1 + c(FREETIME15.thr1.g1, FREETIME15.thr1.g2, FREETIME15.thr1.g3)*t1
FREETIME15 | c(NA, NA, NA)*t2 + c(FREETIME15.thr2.g1, FREETIME15.thr2.g2, FREETIME15.thr2.g3)*t2
CHOOSBUY15 | c(NA, NA, NA)*t1 + c(CHOOSBUY15.thr1.g1, CHOOSBUY15.thr1.g2, CHOOSBUY15.thr1.g3)*t1
## INTERCEPTS:
KEY16 ~ c(0, 0, 0)*1 + c(nu.1.g1, nu.1.g2, nu.1.g3)*1
LOCK15 ~ c(0, 0, 0)*1 + c(nu.2.g1, nu.2.g2, nu.2.g3)*1
SHOPPING15 ~ c(0, 0, 0)*1 + c(nu.3.g1, nu.3.g2, nu.3.g3)*1
ENTERTAIN15 ~ c(0, 0, 0)*1 + c(nu.4.g1, nu.4.g2, nu.4.g3)*1
EATOUT15 ~ c(0, 0, 0)*1 + c(nu.5.g1, nu.5.g2, nu.5.g3)*1
ERRANDS15 ~ c(0, 0, 0)*1 + c(nu.6.g1, nu.6.g2, nu.6.g3)*1
SCHEDULE15 ~ c(0, 0, 0)*1 + c(nu.7.g1, nu.7.g2, nu.7.g3)*1
FREETIME15 ~ c(0, 0, 0)*1 + c(nu.8.g1, nu.8.g2, nu.8.g3)*1
CHOOSBUY15 ~ c(0, 0, 0)*1 + c(nu.9.g1, nu.9.g2, nu.9.g3)*1
## SCALING FACTORS:
KEY16 ~*~ c(1, 1, 1)*KEY16
LOCK15 ~*~ c(1, 1, 1)*LOCK15
SHOPPING15 ~*~ c(1, 1, 1)*SHOPPING15
ENTERTAIN15 ~*~ c(1, 1, 1)*ENTERTAIN15
EATOUT15 ~*~ c(1, 1, 1)*EATOUT15
ERRANDS15 ~*~ c(1, 1, 1)*ERRANDS15
SCHEDULE15 ~*~ c(1, 1, 1)*SCHEDULE15
FREETIME15 ~*~ c(1, 1, 1)*FREETIME15
CHOOSBUY15 ~*~ c(1, 1, 1)*CHOOSBUY15
## LATENT MEANS/INTERCEPTS:
RIGHTS ~ c(0, 0, 0)*1 + c(alpha.1.g1, alpha.1.g2, alpha.1.g3)*1
SOCIAL ~ c(0, 0, 0)*1 + c(alpha.2.g1, alpha.2.g2, alpha.2.g3)*1
CHOICE ~ c(0, 0, 0)*1 + c(alpha.3.g1, alpha.3.g2, alpha.3.g3)*1
## COMMON-FACTOR VARIANCES:
RIGHTS ~~ c(1, 1, 1)*RIGHTS + c(psi.1_1.g1, psi.1_1.g2, psi.1_1.g3)*RIGHTS
SOCIAL ~~ c(1, 1, 1)*SOCIAL + c(psi.2_2.g1, psi.2_2.g2, psi.2_2.g3)*SOCIAL
CHOICE ~~ c(1, 1, 1)*CHOICE + c(psi.3_3.g1, psi.3_3.g2, psi.3_3.g3)*CHOICE
## COMMON-FACTOR COVARIANCES:
RIGHTS ~~ c(NA, NA, NA)*SOCIAL + c(psi.2_1.g1, psi.2_1.g2, psi.2_1.g3)*SOCIAL
RIGHTS ~~ c(NA, NA, NA)*CHOICE + c(psi.3_1.g1, psi.3_1.g2, psi.3_1.g3)*CHOICE
SOCIAL ~~ c(NA, NA, NA)*CHOICE + c(psi.3_2.g1, psi.3_2.g2, psi.3_2.g3)*CHOICEmodel.baseline <- as.character(baseline)
fit.baseline <- cfa(model.baseline, data = nci.proxy2, group =
"proxy_cat",
ordered = c("KEY16", "LOCK15", "SHOPPING15", "ENTERTAIN15", "EATOUT15", "ERRANDS15", "SCHEDULE15", "FREETIME15", "CHOOSBUY15"))lavaan WARNING: group variable 㤼㸱proxy_cat㤼㸲 contains missing values
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.lavaan WARNING: could not invert information matrix needed for robust test statistic
lavaan WARNING: some estimated ov variances are negativesummary(fit.baseline, fit.measures=TRUE)lavaan 0.6-8 ended normally after 776 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 99
Number of observations per group: Used Total
2 691 762
3 470 520
1 257 317
Model Test User Model:number of rows of result is not a multiple of vector length (arg 3) Standard Robust
Test Statistic 138.999 138.999
Degrees of freedom 72 72
P-value (Chi-square) 0.000 0.000
Scaling correction factor NA
Shift parameter for each group:
2
3 Robust
1 138.999
72
Test statistic for each group:
2 93.916 93.916
3 18.833 18.833
1 26.250 26.250
Model Test Baseline Model:
Test statistic 4660.874 3350.295
Degrees of freedom 108 108
P-value 0.000 0.000
Scaling correction factor 1.404
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.985 0.979
Tucker-Lewis Index (TLI) 0.978 0.969
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.044 0.044
90 Percent confidence interval - lower 0.033 0.033
90 Percent confidence interval - upper 0.055 0.055
P-value RMSEA <= 0.05 0.788 0.788
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.069 0.069
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Group 1 [2]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIGHTS =~
KEY16 (l.1_) 0.459 NA
LOCK15 (l.2_) 0.847 NA
SOCIAL =~
SHOPPIN (l.3_) 0.815 NA
ENTERTA (l.4_) 0.674 NA
EATOUT1 (l.5_) 0.806 NA
ERRANDS (l.6_) 0.605 NA
CHOICE =~
SCHEDUL (l.7_) 0.708 NA
FREETIM (l.8_) 0.684 NA
CHOOSBU (l.9_) 0.869 NA
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.435 NA
CHOICE (p.3_1) 0.425 NA
SOCIAL ~~
CHOICE (p.3_2) 0.473 NA
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) 0.184 NA
(KEY16.2) 0.192 NA
(LOCK15.1) -0.067 NA
(LOCK15.2) -0.016 NA
(SHOPPING15.1) -1.139 NA
(SHOPPING15.2) -0.369 NA
(SHOPPING15.3) 0.304 NA
(ENTERTAIN15.1) -0.696 NA
(ENTERTAIN15.2) 0.074 NA
(ENTERTAIN15.3) 0.715 NA
(EATOUT15.1) -0.912 NA
(EATOUT15.2) -0.173 NA
(EATOUT15.3) 0.396 NA
(ERRANDS15.1) -0.874 NA
(ERRANDS15.2) 0.210 NA
(ERRANDS15.3) 0.853 NA
(SCHEDULE15.1) -0.633 NA
(SCHEDULE15.2) 0.758 NA
(FREETIME15.1) -1.167 NA
(FREETIME15.2) 0.107 NA
(CHOO) -0.802 NA
Variances:
Estimate Std.Err z-value P(>|z|)
RIGHTS (p.1_) 1.000
SOCIAL (p.2_) 1.000
CHOICE (p.3_) 1.000
.KEY16 0.789
.LOCK15 0.283
.SHOPPIN 0.335
.ENTERTA 0.545
.EATOUT1 0.350
.ERRANDS 0.634
.SCHEDUL 0.499
.FREETIM 0.533
.CHOOSBU 0.245
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_) 0.421 NA
LOCK15 (l.2_) 0.540 NA
SOCIAL =~
SHOPPIN (l.3_) 0.704 NA
ENTERTA (l.4_) 0.494 NA
EATOUT1 (l.5_) 0.678 NA
ERRANDS (l.6_) 0.561 NA
CHOICE =~
SCHEDUL (l.7_) 0.634 NA
FREETIM (l.8_) 0.810 NA
CHOOSBU (l.9_) 0.755 NA
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.309 NA
CHOICE (p.3_1) 0.462 NA
SOCIAL ~~
CHOICE (p.3_2) 0.212 NA
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) -0.048 NA
(KEY16.2) -0.011 NA
(LOCK15.1) -0.512 NA
(LOCK15.2) -0.435 NA
(SHOPPING15.1) -1.523 NA
(SHOPPING15.2) -0.804 NA
(SHOPPING15.3) -0.059 NA
(ENTERTAIN15.1) -0.970 NA
(ENTERTAIN15.2) -0.075 NA
(ENTERTAIN15.3) 0.494 NA
(EATOUT15.1) -1.655 NA
(EATOUT15.2) -0.685 NA
(EATOUT15.3) 0.000 NA
(ERRANDS15.1) -1.385 NA
(ERRANDS15.2) -0.064 NA
(ERRANDS15.3) 0.599 NA
(SCHEDULE15.1) -1.306 NA
(SCHEDULE15.2) -0.080 NA
(FREETIME15.1) -1.853 NA
(FREETIME15.2) -0.804 NA
(CHOO) -1.746 NA
Variances:
Estimate Std.Err z-value P(>|z|)
RIGHTS (p.1_) 1.000
SOCIAL (p.2_) 1.000
CHOICE (p.3_) 1.000
.KEY16 0.823
.LOCK15 0.709
.SHOPPIN 0.504
.ENTERTA 0.756
.EATOUT1 0.541
.ERRANDS 0.686
.SCHEDUL 0.598
.FREETIM 0.345
.CHOOSBU 0.430
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 [1]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIGHTS =~
KEY16 (l.1_) 0.497 NA
LOCK15 (l.2_) 0.642 NA
SOCIAL =~
SHOPPIN (l.3_) 0.802 NA
ENTERTA (l.4_) 0.697 NA
EATOUT1 (l.5_) 0.788 NA
ERRANDS (l.6_) 0.600 NA
CHOICE =~
SCHEDUL (l.7_) 151.396 NA
FREETIM (l.8_) 0.003 NA
CHOOSBU (l.9_) -0.000 NA
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.501 NA
CHOICE (p.3_1) 0.003 NA
SOCIAL ~~
CHOICE (p.3_2) 0.000 NA
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) -0.093 NA
(KEY16.2) -0.083 NA
(LOCK15.1) -0.449 NA
(LOCK15.2) -0.395 NA
(SHOPPING15.1) -1.153 NA
(SHOPPING15.2) -0.395 NA
(SHOPPING15.3) 0.333 NA
(ENTERTAIN15.1) -0.779 NA
(ENTERTAIN15.2) 0.083 NA
(ENTERTAIN15.3) 0.740 NA
(EATOUT15.1) -1.344 NA
(EATOUT15.2) -0.282 NA
(EATOUT15.3) 0.364 NA
(ERRANDS15.1) -1.046 NA
(ERRANDS15.2) 0.181 NA
(ERRANDS15.3) 0.890 NA
(SCHEDULE15.1) -1.344 NA
(SCHEDULE15.2) -0.312 NA
(FREETIME15.1) -1.812 NA
(FREETIME15.2) -1.134 NA
(CHOO) -2.065 NA
Variances:
Estimate Std.Err z-value P(>|z|)
RIGHTS (p.1_) 1.000
SOCIAL (p.2_) 1.000
CHOICE (p.3_) 1.000
.KEY16 0.753
.LOCK15 0.588
.SHOPPIN 0.357
.ENTERTA 0.514
.EATOUT1 0.378
.ERRANDS 0.641
.SCHEDUL -22919.803
.FREETIM 1.000
.CHOOSBU 1.000
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 all.results[1,]<- round(data.matrix(fitmeasures(fit.baseline,
fit.measures = c("chisq.scaled", "df.scaled", "pvalue.scaled", "rmsea.scaled", "cfi.scaled", "tli.scaled"))), digits=3)prop4 <- measEq.syntax (configural.model = sp.model,
data = nci.proxy2,
ordered = c("KEY16", "LOCK15", "SHOPPING15", "ENTERTAIN15", "EATOUT15", "ERRANDS15", "SCHEDULE15", "FREETIME15", "CHOOSBUY15"),
parameterization = "delta",
ID.fac = "std.lv",
ID.cat = "Wu.Estabrook.2016",
group = "proxy_cat",
group.equal = c("thresholds"))lavaan WARNING: group variable 㤼㸱proxy_cat㤼㸲 contains missing valuesmodel.prop4 <- as.character(prop4)
fit.prop4 <- cfa(model.prop4, data = nci.proxy2, group =
"proxy_cat", ordered = c("KEY16", "LOCK15", "SHOPPING15", "ENTERTAIN15", "EATOUT15", "ERRANDS15", "SCHEDULE15", "FREETIME15", "CHOOSBUY15"))lavaan WARNING: group variable 㤼㸱proxy_cat㤼㸲 contains missing values
lavaan WARNING: some estimated ov variances are negativesummary(fit.prop4)lavaan 0.6-8 ended normally after 481 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 133
Number of equality constraints 42
Number of observations per group: Used Total
2 691 762
3 470 520
1 257 317
Model Test User Model:
Standard Robust
Test Statistic 141.474 180.015
Degrees of freedom 80 80
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.876
Shift parameter for each group:
2 9.038
3 6.147
1 3.361
simple second-order correction
Test statistic for each group:
2 94.852 117.296
3 19.968 28.937
1 26.654 33.782
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Group 1 [2]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIGHTS =~
KEY16 (l.1_) 0.459 0.068 6.726 0.000
LOCK15 (l.2_) 0.847 0.104 8.130 0.000
SOCIAL =~
SHOPPIN (l.3_) 0.815 0.024 34.328 0.000
ENTERTA (l.4_) 0.674 0.028 23.925 0.000
EATOUT1 (l.5_) 0.806 0.023 35.817 0.000
ERRANDS (l.6_) 0.605 0.033 18.455 0.000
CHOICE =~
SCHEDUL (l.7_) 0.708 0.036 19.555 0.000
FREETIM (l.8_) 0.684 0.039 17.535 0.000
CHOOSBU (l.9_) 0.869 0.045 19.239 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.435 0.065 6.684 0.000
CHOICE (p.3_1) 0.425 0.069 6.129 0.000
SOCIAL ~~
CHOICE (p.3_2) 0.473 0.046 10.283 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) 0.185 0.048 3.856 0.000
(KEY16.2) 0.191 0.048 3.968 0.000
(LOCK15.1) -0.067 0.048 -1.406 0.160
(LOCK15.2) -0.016 0.048 -0.342 0.732
(SHOPPING15.1) -1.128 0.059 -18.973 0.000
(SHOPPING15.2) -0.384 0.046 -8.394 0.000
(SHOPPING15.3) 0.312 0.048 6.433 0.000
(ENTERTAIN15.1) -0.708 0.052 -13.677 0.000
(ENTERTAIN15.2) 0.098 0.044 2.197 0.028
(ENTERTAIN15.3) 0.699 0.052 13.543 0.000
(EATOUT15.1) -0.922 0.055 -16.728 0.000
(EATOUT15.2) -0.156 0.045 -3.484 0.000
(EATOUT15.3) 0.386 0.049 7.892 0.000
(ERRANDS15.1) -0.882 0.054 -16.237 0.000
(ERRANDS15.2) 0.226 0.045 5.062 0.000
(ERRANDS15.3) 0.840 0.053 15.760 0.000
(SCHEDULE15.1) -0.633 0.051 -12.321 0.000
(SCHEDULE15.2) 0.758 0.053 14.281 0.000
(FREETIME15.1) -1.167 0.062 -18.934 0.000
(FREETIME15.2) 0.107 0.048 2.242 0.025
(CHOO) -0.802 0.054 -14.921 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 0.789
.LOCK15 0.283
.SHOPPIN 0.335
.ENTERTA 0.545
.EATOUT1 0.350
.ERRANDS 0.634
.SCHEDUL 0.499
.FREETIM 0.533
.CHOOSBU 0.245
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_) 0.061 0.064 0.949 0.343
LOCK15 (l.2_) 0.353 0.163 2.173 0.030
SOCIAL =~
SHOPPIN (l.3_) 0.687 0.073 9.432 0.000
ENTERTA (l.4_) 0.475 0.060 7.968 0.000
EATOUT1 (l.5_) 0.535 0.059 9.147 0.000
ERRANDS (l.6_) 0.486 0.054 9.038 0.000
CHOICE =~
SCHEDUL (l.7_) 0.719 0.097 7.387 0.000
FREETIM (l.8_) 0.983 0.150 6.557 0.000
CHOOSBU (l.9_) 0.755 0.115 6.577 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.309 0.116 2.675 0.007
CHOICE (p.3_1) 0.462 0.135 3.431 0.001
SOCIAL ~~
CHOICE (p.3_2) 0.212 0.085 2.505 0.012
Intercepts:
Estimate Std.Err z-value P(>|z|)
.KEY16 (n.1.) 0.192 0.049 3.930 0.000
.LOCK15 (n.2.) 0.268 0.132 2.034 0.042
.SHOPPIN (n.3.) 0.378 0.077 4.933 0.000
.ENTERTA (n.4.) 0.203 0.064 3.176 0.001
.EATOUT1 (n.5.) 0.386 0.067 5.775 0.000
.ERRANDS (n.6.) 0.304 0.061 5.002 0.000
.SCHEDUL (n.7.) 0.849 0.088 9.649 0.000
.FREETIM (n.8.) 1.084 0.164 6.600 0.000
.CHOOSBU (n.9.) 0.944 0.118 8.023 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) 0.185 0.048 3.856 0.000
(KEY16.2) 0.191 0.048 3.968 0.000
(LOCK15.1) -0.067 0.048 -1.406 0.160
(LOCK15.2) -0.016 0.048 -0.342 0.732
(SHOPPING15.1) -1.128 0.059 -18.973 0.000
(SHOPPING15.2) -0.384 0.046 -8.394 0.000
(SHOPPING15.3) 0.312 0.048 6.433 0.000
(ENTERTAIN15.1) -0.708 0.052 -13.677 0.000
(ENTERTAIN15.2) 0.098 0.044 2.197 0.028
(ENTERTAIN15.3) 0.699 0.052 13.543 0.000
(EATOUT15.1) -0.922 0.055 -16.728 0.000
(EATOUT15.2) -0.156 0.045 -3.484 0.000
(EATOUT15.3) 0.386 0.049 7.892 0.000
(ERRANDS15.1) -0.882 0.054 -16.237 0.000
(ERRANDS15.2) 0.226 0.045 5.062 0.000
(ERRANDS15.3) 0.840 0.053 15.760 0.000
(SCHEDULE15.1) -0.633 0.051 -12.321 0.000
(SCHEDULE15.2) 0.758 0.053 14.281 0.000
(FREETIME15.1) -1.167 0.062 -18.934 0.000
(FREETIME15.2) 0.107 0.048 2.242 0.025
(CHOO) -0.802 0.054 -14.921 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 0.017
.LOCK15 0.304
.SHOPPIN 0.479
.ENTERTA 0.697
.EATOUT1 0.338
.ERRANDS 0.516
.SCHEDUL 0.769
.FREETIM 0.508
.CHOOSBU 0.430
Scales y*:
Estimate Std.Err z-value P(>|z|)
KEY16 6.893 7.068 0.975 0.329
LOCK15 1.527 0.582 2.626 0.009
SHOPPING15 1.026 0.078 13.197 0.000
ENTERTAIN15 1.041 0.071 14.645 0.000
EATOUT15 1.266 0.096 13.211 0.000
ERRANDS15 1.153 0.071 16.174 0.000
SCHEDULE15 0.882 0.070 12.640 0.000
FREETIME15 0.823 0.095 8.664 0.000
CHOOSBUY15 1.000
Group 3 [1]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIGHTS =~
KEY16 (l.1_) 0.260 0.353 0.735 0.462
LOCK15 (l.2_) 0.612 0.349 1.754 0.079
SOCIAL =~
SHOPPIN (l.3_) 0.776 0.084 9.188 0.000
ENTERTA (l.4_) 0.646 0.074 8.774 0.000
EATOUT1 (l.5_) 0.606 0.065 9.303 0.000
ERRANDS (l.6_) 0.534 0.067 7.987 0.000
CHOICE =~
SCHEDUL (l.7_) 12.302 385.325 0.032 0.975
FREETIM (l.8_) 0.109 3.404 0.032 0.975
CHOOSBU (l.9_) -0.004 0.113 -0.033 0.974
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.501 0.126 3.971 0.000
CHOICE (p.3_1) 0.042 1.335 0.032 0.975
SOCIAL ~~
CHOICE (p.3_2) 0.008 0.242 0.032 0.975
Intercepts:
Estimate Std.Err z-value P(>|z|)
.KEY16 (n.1.) 0.234 0.088 2.656 0.008
.LOCK15 (n.2.) 0.361 0.225 1.606 0.108
.SHOPPIN (n.3.) -0.008 0.080 -0.094 0.925
.ENTERTA (n.4.) 0.016 0.074 0.218 0.827
.EATOUT1 (n.5.) 0.089 0.069 1.298 0.194
.ERRANDS (n.6.) 0.055 0.072 0.762 0.446
.SCHEDUL (n.7.) 1.179 0.144 8.162 0.000
.FREETIM (n.8.) 2.240 0.505 4.437 0.000
.CHOOSBU (n.9.) 1.263 0.190 6.644 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) 0.185 0.048 3.856 0.000
(KEY16.2) 0.191 0.048 3.968 0.000
(LOCK15.1) -0.067 0.048 -1.406 0.160
(LOCK15.2) -0.016 0.048 -0.342 0.732
(SHOPPING15.1) -1.128 0.059 -18.973 0.000
(SHOPPING15.2) -0.384 0.046 -8.394 0.000
(SHOPPING15.3) 0.312 0.048 6.433 0.000
(ENTERTAIN15.1) -0.708 0.052 -13.677 0.000
(ENTERTAIN15.2) 0.098 0.044 2.197 0.028
(ENTERTAIN15.3) 0.699 0.052 13.543 0.000
(EATOUT15.1) -0.922 0.055 -16.728 0.000
(EATOUT15.2) -0.156 0.045 -3.484 0.000
(EATOUT15.3) 0.386 0.049 7.892 0.000
(ERRANDS15.1) -0.882 0.054 -16.237 0.000
(ERRANDS15.2) 0.226 0.045 5.062 0.000
(ERRANDS15.3) 0.840 0.053 15.760 0.000
(SCHEDULE15.1) -0.633 0.051 -12.321 0.000
(SCHEDULE15.2) 0.758 0.053 14.281 0.000
(FREETIME15.1) -1.167 0.062 -18.934 0.000
(FREETIME15.2) 0.107 0.048 2.242 0.025
(CHOO) -0.802 0.054 -14.921 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 0.205
.LOCK15 0.536
.SHOPPIN 0.335
.ENTERTA 0.441
.EATOUT1 0.224
.ERRANDS 0.508
.SCHEDUL -149.534
.FREETIM 3.525
.CHOOSBU 1.000
Scales y*:
Estimate Std.Err z-value P(>|z|)
KEY16 1.914 2.559 0.748 0.455
LOCK15 1.048 0.542 1.936 0.053
SHOPPING15 1.033 0.088 11.729 0.000
ENTERTAIN15 1.079 0.087 12.340 0.000
EATOUT15 1.301 0.109 11.931 0.000
ERRANDS15 1.122 0.082 13.749 0.000
SCHEDULE15 0.742 0.083 8.892 0.000
FREETIME15 0.532 0.107 4.990 0.000
CHOOSBUY15 1.000 lavTestLRT(fit.baseline,fit.prop4)Error in A %*% P.inv : requires numeric/complex matrix/vector argumentsall.results[2,]<- round(data.matrix(fitmeasures(fit.prop4,
fit.measures = c("chisq.scaled", "df.scaled", "pvalue.scaled", "rmsea.scaled", "cfi.scaled", "tli.scaled"))), digits=3)prop7 <- measEq.syntax(configural.model = sp.model,
data = nci.proxy2,
ordered = c("KEY16", "LOCK15", "SHOPPING15", "ENTERTAIN15", "EATOUT15", "ERRANDS15", "SCHEDULE15", "FREETIME15", "CHOOSBUY15"),
parameterization = "delta",
ID.fac = "std.lv",
ID.cat = "Wu.Estabrook.2016",
group = "proxy_cat",
group.equal = c("thresholds", "loadings"))lavaan WARNING: group variable 㤼㸱proxy_cat㤼㸲 contains missing valuesmodel.prop7 <- as.character(prop7)
fit.prop7<- cfa(model.prop7, data = nci.proxy2, group =
"proxy_cat", ordered = c("KEY16", "LOCK15", "SHOPPING15", "ENTERTAIN15", "EATOUT15", "ERRANDS15", "SCHEDULE15", "FREETIME15", "CHOOSBUY15"))lavaan WARNING: group variable 㤼㸱proxy_cat㤼㸲 contains missing valuessummary(fit.prop7)lavaan 0.6-8 ended normally after 219 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 139
Number of equality constraints 60
Number of observations per group: Used Total
2 691 762
3 470 520
1 257 317
Model Test User Model:
Standard Robust
Test Statistic 162.989 210.279
Degrees of freedom 92 92
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.858
Shift parameter for each group:
2 9.920
3 6.747
1 3.689
simple second-order correction
Test statistic for each group:
2 98.003 124.117
3 24.435 35.220
1 40.551 50.941
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Group 1 [2]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIGHTS =~
KEY16 (l.1_) 0.459 0.068 6.736 0.000
LOCK15 (l.2_) 0.846 0.104 8.133 0.000
SOCIAL =~
SHOPPIN (l.3_) 0.827 0.022 37.312 0.000
ENTERTA (l.4_) 0.676 0.026 25.891 0.000
EATOUT1 (l.5_) 0.791 0.021 37.546 0.000
ERRANDS (l.6_) 0.609 0.029 20.925 0.000
CHOICE =~
SCHEDUL (l.7_) 0.720 0.034 21.255 0.000
FREETIM (l.8_) 0.709 0.037 19.166 0.000
CHOOSBU (l.9_) 0.827 0.042 19.770 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.435 0.065 6.682 0.000
CHOICE (p.3_1) 0.422 0.069 6.093 0.000
SOCIAL ~~
CHOICE (p.3_2) 0.467 0.046 10.134 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) 0.181 0.048 3.779 0.000
(KEY16.2) 0.194 0.048 4.039 0.000
(LOCK15.1) -0.066 0.048 -1.392 0.164
(LOCK15.2) -0.017 0.048 -0.357 0.721
(SHOPPING15.1) -1.106 0.058 -18.964 0.000
(SHOPPING15.2) -0.382 0.046 -8.353 0.000
(SHOPPING15.3) 0.295 0.047 6.262 0.000
(ENTERTAIN15.1) -0.705 0.051 -13.779 0.000
(ENTERTAIN15.2) 0.097 0.044 2.180 0.029
(ENTERTAIN15.3) 0.697 0.049 14.082 0.000
(EATOUT15.1) -0.952 0.054 -17.488 0.000
(EATOUT15.2) -0.158 0.045 -3.510 0.000
(EATOUT15.3) 0.412 0.047 8.700 0.000
(ERRANDS15.1) -0.875 0.053 -16.420 0.000
(ERRANDS15.2) 0.226 0.045 5.072 0.000
(ERRANDS15.3) 0.834 0.052 16.145 0.000
(SCHEDULE15.1) -0.625 0.051 -12.232 0.000
(SCHEDULE15.2) 0.749 0.052 14.345 0.000
(FREETIME15.1) -1.129 0.060 -18.698 0.000
(FREETIME15.2) 0.084 0.047 1.784 0.074
(CHOO) -0.802 0.054 -14.921 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 0.789
.LOCK15 0.284
.SHOPPIN 0.317
.ENTERTA 0.543
.EATOUT1 0.374
.ERRANDS 0.629
.SCHEDUL 0.481
.FREETIM 0.498
.CHOOSBU 0.316
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_) 0.459 0.068 6.736 0.000
LOCK15 (l.2_) 0.846 0.104 8.133 0.000
SOCIAL =~
SHOPPIN (l.3_) 0.827 0.022 37.312 0.000
ENTERTA (l.4_) 0.676 0.026 25.891 0.000
EATOUT1 (l.5_) 0.791 0.021 37.546 0.000
ERRANDS (l.6_) 0.609 0.029 20.925 0.000
CHOICE =~
SCHEDUL (l.7_) 0.720 0.034 21.255 0.000
FREETIM (l.8_) 0.709 0.037 19.166 0.000
CHOOSBU (l.9_) 0.827 0.042 19.770 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.091 0.050 1.823 0.068
CHOICE (p.3_1) 0.195 0.095 2.061 0.039
SOCIAL ~~
CHOICE (p.3_2) 0.169 0.070 2.402 0.016
Intercepts:
Estimate Std.Err z-value P(>|z|)
.KEY16 (n.1.) 0.200 0.054 3.693 0.000
.LOCK15 (n.2.) 0.250 0.120 2.073 0.038
.SHOPPIN (n.3.) 0.344 0.069 4.977 0.000
.ENTERTA (n.4.) 0.205 0.064 3.212 0.001
.EATOUT1 (n.5.) 0.421 0.065 6.451 0.000
.ERRANDS (n.6.) 0.301 0.060 5.058 0.000
.SCHEDUL (n.7.) 0.845 0.084 10.005 0.000
.FREETIM (n.8.) 0.882 0.118 7.502 0.000
.CHOOSBU (n.9.) 0.944 0.118 8.023 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) 0.181 0.048 3.779 0.000
(KEY16.2) 0.194 0.048 4.039 0.000
(LOCK15.1) -0.066 0.048 -1.392 0.164
(LOCK15.2) -0.017 0.048 -0.357 0.721
(SHOPPING15.1) -1.106 0.058 -18.964 0.000
(SHOPPING15.2) -0.382 0.046 -8.353 0.000
(SHOPPING15.3) 0.295 0.047 6.262 0.000
(ENTERTAIN15.1) -0.705 0.051 -13.779 0.000
(ENTERTAIN15.2) 0.097 0.044 2.180 0.029
(ENTERTAIN15.3) 0.697 0.049 14.082 0.000
(EATOUT15.1) -0.952 0.054 -17.488 0.000
(EATOUT15.2) -0.158 0.045 -3.510 0.000
(EATOUT15.3) 0.412 0.047 8.700 0.000
(ERRANDS15.1) -0.875 0.053 -16.420 0.000
(ERRANDS15.2) 0.226 0.045 5.072 0.000
(ERRANDS15.3) 0.834 0.052 16.145 0.000
(SCHEDULE15.1) -0.625 0.051 -12.232 0.000
(SCHEDULE15.2) 0.749 0.052 14.345 0.000
(FREETIME15.1) -1.129 0.060 -18.698 0.000
(FREETIME15.2) 0.084 0.047 1.784 0.074
(CHOO) -0.802 0.054 -14.921 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
RIGHTS (p.1_) 0.151 0.134 1.129 0.259
SOCIAL (p.2_) 0.568 0.089 6.382 0.000
CHOICE (p.3_) 1.075 0.194 5.547 0.000
.KEY16 0.145
.LOCK15 0.271
.SHOPPIN 0.463
.ENTERTA 0.702
.EATOUT1 0.360
.ERRANDS 0.511
.SCHEDUL 0.725
.FREETIM 0.526
.CHOOSBU 0.265
Scales y*:
Estimate Std.Err z-value P(>|z|)
KEY16 2.381 1.307 1.822 0.068
LOCK15 1.625 0.589 2.758 0.006
SHOPPING15 1.084 0.072 14.996 0.000
ENTERTAIN15 1.020 0.062 16.546 0.000
EATOUT15 1.183 0.080 14.722 0.000
ERRANDS15 1.178 0.066 17.904 0.000
SCHEDULE15 0.883 0.064 13.748 0.000
FREETIME15 0.969 0.088 11.062 0.000
CHOOSBUY15 1.000
Group 3 [1]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIGHTS =~
KEY16 (l.1_) 0.459 0.068 6.736 0.000
LOCK15 (l.2_) 0.846 0.104 8.133 0.000
SOCIAL =~
SHOPPIN (l.3_) 0.827 0.022 37.312 0.000
ENTERTA (l.4_) 0.676 0.026 25.891 0.000
EATOUT1 (l.5_) 0.791 0.021 37.546 0.000
ERRANDS (l.6_) 0.609 0.029 20.925 0.000
CHOICE =~
SCHEDUL (l.7_) 0.720 0.034 21.255 0.000
FREETIM (l.8_) 0.709 0.037 19.166 0.000
CHOOSBU (l.9_) 0.827 0.042 19.770 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
RIGHTS ~~
SOCIAL (p.2_) 0.333 0.191 1.741 0.082
CHOICE (p.3_1) 0.389 0.251 1.548 0.122
SOCIAL ~~
CHOICE (p.3_2) 0.085 0.099 0.859 0.390
Intercepts:
Estimate Std.Err z-value P(>|z|)
.KEY16 (n.1.) 0.249 0.080 3.129 0.002
.LOCK15 (n.2.) 0.377 0.224 1.680 0.093
.SHOPPIN (n.3.) -0.021 0.075 -0.276 0.782
.ENTERTA (n.4.) 0.017 0.072 0.240 0.810
.EATOUT1 (n.5.) 0.113 0.071 1.584 0.113
.ERRANDS (n.6.) 0.055 0.072 0.759 0.448
.SCHEDUL (n.7.) 1.091 0.128 8.506 0.000
.FREETIM (n.8.) 1.714 0.316 5.433 0.000
.CHOOSBU (n.9.) 1.263 0.190 6.644 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) 0.181 0.048 3.779 0.000
(KEY16.2) 0.194 0.048 4.039 0.000
(LOCK15.1) -0.066 0.048 -1.392 0.164
(LOCK15.2) -0.017 0.048 -0.357 0.721
(SHOPPING15.1) -1.106 0.058 -18.964 0.000
(SHOPPING15.2) -0.382 0.046 -8.353 0.000
(SHOPPING15.3) 0.295 0.047 6.262 0.000
(ENTERTAIN15.1) -0.705 0.051 -13.779 0.000
(ENTERTAIN15.2) 0.097 0.044 2.180 0.029
(ENTERTAIN15.3) 0.697 0.049 14.082 0.000
(EATOUT15.1) -0.952 0.054 -17.488 0.000
(EATOUT15.2) -0.158 0.045 -3.510 0.000
(EATOUT15.3) 0.412 0.047 8.700 0.000
(ERRANDS15.1) -0.875 0.053 -16.420 0.000
(ERRANDS15.2) 0.226 0.045 5.072 0.000
(ERRANDS15.3) 0.834 0.052 16.145 0.000
(SCHEDULE15.1) -0.625 0.051 -12.232 0.000
(SCHEDULE15.2) 0.749 0.052 14.345 0.000
(FREETIME15.1) -1.129 0.060 -18.698 0.000
(FREETIME15.2) 0.084 0.047 1.784 0.074
(CHOO) -0.802 0.054 -14.921 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
RIGHTS (p.1_) 0.569 0.624 0.911 0.362
SOCIAL (p.2_) 0.777 0.130 5.990 0.000
CHOICE (p.3_) 1.316 0.387 3.401 0.001
.KEY16 0.368
.LOCK15 0.577
.SHOPPIN 0.323
.ENTERTA 0.424
.EATOUT1 0.242
.ERRANDS 0.505
.SCHEDUL 0.807
.FREETIM 1.567
.CHOOSBU 0.100
Scales y*:
Estimate Std.Err z-value P(>|z|)
KEY16 1.432 0.881 1.625 0.104
LOCK15 1.008 0.500 2.017 0.044
SHOPPING15 1.082 0.079 13.762 0.000
ENTERTAIN15 1.133 0.074 15.374 0.000
EATOUT15 1.172 0.087 13.508 0.000
ERRANDS15 1.123 0.071 15.737 0.000
SCHEDULE15 0.819 0.081 10.103 0.000
FREETIME15 0.670 0.102 6.594 0.000
CHOOSBUY15 1.000 all.results[3,]<- round(data.matrix(fitmeasures(fit.prop7,
fit.measures = c("chisq.scaled", "df.scaled", "pvalue.scaled", "rmsea.scaled", "cfi.scaled", "tli.scaled"))), digits=3)lavTestLRT(fit.baseline, fit.prop4)Error in A %*% P.inv : requires numeric/complex matrix/vector argumentslavTestLRT(fit.prop4, fit.prop7)Scaled Chi-Squared Difference Test (method = “satorra.2000”)
lavaan NOTE:
The “Chisq” column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
fit.prop4 80 141.47
fit.prop7 92 162.99 21.422 12 0.04453 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1all.results [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 138.999 72 0 0.044 0.979 0.969
[2,] 180.015 80 0 0.051 0.969 0.958
[3,] 210.279 92 0 0.052 0.964 0.957