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Probamos nuevos análisis en función de las sugerencias de Miquel, donde indica que no le queda claro por qué usamos distintas metodologías para la parte psicométrica y las relaciones entre variables. En los comentarios él se pregunta por qué no usar ESEM (modelo de ecuaciones estructurales exploratorio) para integrar los análisis. Tiene sentido probar las relaciones en un SEM, aunque no sé por qué lo haríamos de forma exploratoria, cuando todos los instrumentos están validados con una estructura particular y que funciona (el único que podría pensarse es el de id. social, pero el único cambio es el idioma). Sería como hacer un factorial confirmatorio o uno exploratorio (con modelos bastante más complejos).
Otro inconveniente que surge al buscar evaluar las moderaciones con SEM es que la inclusión de la interacción de variables reduce ampliamente los grados de libertad de los modelos. En las regresiones tomamos las medias de cada variable y las multiplicamos, generando una única variable nueva. En los modelos SEM, las interacciones se incluyen multiplicando ítem a ítem, haciendo que sean muchas más variables. Por ejemplo, la interacción entre sexo y liderazgo del coach en una regresión lineal implicaría sumar una única variable (sexo*Liderazgo), mientras que en SEM se sumarían 16 variables (sexo*LT01, sexo*LT02, …).
Hasta ahí, no hay tanto problema con los grados de libertad. El problema es que esas nuevas variables (las de la interacción) no son independientes de las variables originales (los ítems), entonces hay que incluir en el modelo esa falta de independencia, incluyendo la estimación de las covarianzas de error. Ahí es donde se disparan los parámetros a estimar y, por lo tanto, el modelo termina con grados de libertad negativos. Es decir, con la estimación de tantos parámetros el modelo no está identificado (no se puede ajustar).
Dada esta problemática, propongo tres soluciones:
- SEM-MG
- SEM y Reg. Lineal
- SEM con moderación
SEM-MG
Una posibilidad es ajustar modelos de ecuaciones estructurales multigrupos (SEM-MG). La aproximación es la misma que la utilizada para estimar la invarianza de un instrumento, solo que en vez de incluir solo el instrumento de identidad social, se incluiría el modelo completo (con las relaciones con liderazgo). A diferencia del estudio de invarianza, en este caso buscaríamos que haya diferencias significativas entre modelos (que no sea lo mismo en varones que en mujeres, por ejemplo).
A partir de este análisis se podrían obtener estimaciones para cada parámetro en en caso de varones/mujeres o early/late adolescents y presentar las figuras en cada caso.
La desventaja de este abordaje es que no se podrían incluir ambas interacciones (sexo y edad) en simultaneo. PENSAR SI PODRÍA CREARSE UNA NUEVA VARIABLE SEXO*EDAD, CON CUATRO GRUPOS (varon-joven, varon-tardio, mujer-joven, mujer-tardia)
Coach
Sexo
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTc =~ tcq02 + tcq05 + tcq12 + tcq16 + tcq01 + tcq09 + tcq11 + tcq14 + tcq03 + tcq07 + tcq10 + tcq13 + tcq04 + tcq06 + tcq08 + tcq15
# REGRESIONES
SIQS ~ LTc + edad
'
# Configural model
sem.config <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config 590 2347.6
## sem.metric 613 2729.4 381.81 0.218088 23 < 2e-16 ***
## sem.scalar 633 2763.3 33.90 0.046039 20 0.02678 *
## sem.strict 658 2925.0 161.62 0.129077 25 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config 2347.627† 590 NA .095† .975† .972† .057†
## sem.metric 2729.438 613 NA .103 .969 .968 .063
## sem.scalar 2763.342 633 NA .101 .969 .968 .064
## sem.strict 2924.960 658 NA .103 .967 .968 .067
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config 23 0.007 -0.005 -0.004 0.006
## sem.scalar - sem.metric 20 -0.001 0.000 0.001 0.001
## sem.strict - sem.scalar 25 0.001 -0.002 -0.001 0.003El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para el entrenador.
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
semPaths(# Argumentos globales
sem.config, what="diagram", whatLabels="std",layout="tree3", rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Varones; 2=Mujeres.
# [Grupo 0] SIQS = 0 + .358 * LTc
# [Grupo 1] SIQS = 0 + .567 * LTc
graf <- data.frame(sexo=rep(0:1,2),
"LTc (std)"=c(rep(min(scale(datos$tcq_LT),na.rm = T),2),rep(max(scale(datos$tcq_LT),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$sexo[i]==0,
.358*graf$`LTc (std)`[i],
.567*graf$`LTc (std)`[i])
}
graf %>%
ggplot(aes(x=`LTc (std)`, y=`siqs (std)`, color=as.factor(sexo), group=as.factor(sexo)))+
geom_line()+
scale_color_manual(name="Sex",
values = 2:3,
labels = c("females",
"males"))
Edad
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTc =~ tcq02 + tcq05 + tcq12 + tcq16 + tcq01 + tcq09 + tcq11 + tcq14 + tcq03 + tcq07 + tcq10 + tcq13 + tcq04 + tcq06 + tcq08 + tcq15
# REGRESIONES
SIQS ~ LTc + sexo
'
# Configural model
sem.config <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config 590 1428.5
## sem.metric 613 1794.4 365.98 0.213222 23 < 2.2e-16 ***
## sem.scalar 633 1837.4 42.93 0.059124 20 0.002087 **
## sem.strict 658 2031.1 193.75 0.143454 25 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config 1428.454† 590 NA .066† .987† .986† .055†
## sem.metric 1794.430 613 NA .077 .982 .981 .061
## sem.scalar 1837.362 633 NA .076 .982 .981 .061
## sem.strict 2031.110 658 NA .080 .979 .980 .064
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config 23 0.011 -0.005 -0.005 0.005
## sem.scalar - sem.metric 20 0.000 0.000 0.000 0.001
## sem.strict - sem.scalar 25 0.004 -0.003 -0.002 0.003El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para el entrenador.
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
semPaths(# Argumentos globales
sem.config, what="diagram", whatLabels="std",layout="tree3", rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Late; 2=Early.
# [Grupo 1] SIQS = 0 + .481 * LTc
# [Grupo 2] SIQS = 0 + .381 * LTc
graf <- data.frame(edad=rep(1:2,2),
"LTc (std)"=c(rep(min(scale(datos$tcq_LT),na.rm = T),2),rep(max(scale(datos$tcq_LT),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$edad[i]==1,
.481*graf$`LTc (std)`[i],
.381*graf$`LTc (std)`[i])
}
graf %>%
ggplot(aes(x=`LTc (std)`, y=`siqs (std)`, color=as.factor(edad), group=as.factor(edad)))+
geom_line()+
scale_color_manual(name="Age stage",
values = 2:3,
labels = c("early",
"late"))
Madre
Sexo
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTm =~ tpqm02 + tpqm05 + tpqm12 + tpqm16 + tpqm01 + tpqm09 + tpqm11 + tpqm14 + tpqm03 + tpqm07 + tpqm10 + tpqm13 + tpqm04 + tpqm06 + tpqm08 + tpqm15
# REGRESIONES
SIQS ~ LTm + edad
'
# Configural model
sem.config <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config 590 705.61
## sem.metric 613 1110.81 405.19 0.225082 23 <2e-16 ***
## sem.scalar 633 1131.03 20.22 0.005758 20 0.4444
## sem.strict 658 1294.55 163.53 0.129976 25 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config 705.614† 590 NA .024† 0.997† 0.996† .049†
## sem.metric 1110.808 613 NA .050 .986 .985 .066
## sem.scalar 1131.026 633 NA .049 .986 .986 .066
## sem.strict 1294.554 658 NA .054 .982 .982 .072
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config 23 0.025 -0.011 -0.011 0.017
## sem.scalar - sem.metric 20 -0.001 0.000 0.000 0.001
## sem.strict - sem.scalar 25 0.005 -0.004 -0.003 0.006El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para la madre
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
semPaths(# Argumentos globales
sem.config, what="diagram", whatLabels="std",layout="tree3", rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Varones; 2=Mujeres.
# [Grupo 0] SIQS = 0 + .4 * LTc
# [Grupo 1] SIQS = 0 + .29 * LTc
graf <- data.frame(sexo=rep(0:1,2),
"LTm (std)"=c(rep(min(scale(datos$tpqm_LT),na.rm = T),2),rep(max(scale(datos$tpqm_LT),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$sexo[i]==0,
.4*graf$`LTm (std)`[i],
.29*graf$`LTm (std)`[i])
}
graf %>%
ggplot(aes(x=`LTm (std)`, y=`siqs (std)`, color=as.factor(sexo), group=as.factor(sexo)))+
geom_line()+
scale_color_manual(name="Sex",
values = 2:3,
labels = c("females",
"males"))
Edad
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTm =~ tpqm02 + tpqm05 + tpqm12 + tpqm16 + tpqm01 + tpqm09 + tpqm11 + tpqm14 + tpqm03 + tpqm07 + tpqm10 + tpqm13 + tpqm04 + tpqm06 + tpqm08 + tpqm15
# REGRESIONES
SIQS ~ LTm + sexo
'
# Configural model
sem.config <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config 590 551.16
## sem.metric 613 849.89 298.730 0.191179 23 < 2.2e-16 ***
## sem.scalar 633 865.98 16.089 0.000000 20 0.7111
## sem.strict 658 939.58 73.607 0.076991 25 1.106e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config 551.158† 590 NA .000† 1.000† 1.001† .049†
## sem.metric 849.888 613 NA .034 .993 .992 .062
## sem.scalar 865.977 633 NA .034 .993 .993 .063
## sem.strict 939.584 658 NA .036 .991 .991 .066
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config 23 0.034 -0.007 -0.009 0.013
## sem.scalar - sem.metric 20 -0.001 0.000 0.000 0.001
## sem.strict - sem.scalar 25 0.003 -0.001 -0.001 0.003El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para la madre.
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
semPaths(# Argumentos globales
sem.config, what="diagram", whatLabels="std",layout="tree3", rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Late; 2=Early.
sem.config %>% summary(standardized=T)## lavaan 0.6.16 ended normally after 76 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 164
##
## Number of observations per group:
## 2 302
## 1 354
##
## Model Test User Model:
##
## Test statistic 551.158
## Degrees of freedom 590
## P-value (Unknown) NA
## Test statistic for each group:
## 2 322.487
## 1 228.671
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.906 0.844
## siqs02 0.852 0.043 19.866 0.000 0.772 0.664
## siqs03 1.126 0.052 21.487 0.000 1.021 0.863
## cc =~
## siqs04 1.000 1.054 0.769
## siqs05 1.002 0.059 16.922 0.000 1.056 0.760
## siqs06 0.717 0.044 16.278 0.000 0.756 0.639
## ia =~
## siqs07 1.000 0.850 0.852
## siqs08 1.011 0.051 19.819 0.000 0.860 0.892
## siqs09 0.969 0.050 19.561 0.000 0.824 0.744
## SIQS =~
## it 1.000 0.893 0.893
## cc 0.640 0.040 15.875 0.000 0.491 0.491
## ia 0.925 0.056 16.440 0.000 0.880 0.880
## LTm =~
## tpqm02 1.000 0.705 0.674
## tpqm05 0.825 0.038 21.793 0.000 0.582 0.695
## tpqm12 1.143 0.045 25.243 0.000 0.806 0.720
## tpqm16 0.854 0.038 22.193 0.000 0.602 0.789
## tpqm01 0.826 0.038 21.805 0.000 0.582 0.692
## tpqm09 1.001 0.042 23.937 0.000 0.706 0.758
## tpqm11 1.125 0.045 25.094 0.000 0.793 0.752
## tpqm14 1.075 0.044 24.656 0.000 0.758 0.852
## tpqm03 0.755 0.036 20.735 0.000 0.532 0.523
## tpqm07 1.001 0.042 23.940 0.000 0.706 0.662
## tpqm10 1.031 0.043 24.244 0.000 0.727 0.844
## tpqm13 1.208 0.047 25.735 0.000 0.851 0.741
## tpqm04 0.936 0.040 23.221 0.000 0.660 0.761
## tpqm06 1.111 0.044 24.979 0.000 0.783 0.820
## tpqm08 0.851 0.038 22.148 0.000 0.600 0.704
## tpqm15 1.218 0.047 25.808 0.000 0.859 0.787
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LTm 0.422 0.023 18.553 0.000 0.368 0.368
## sexo 0.286 0.112 2.562 0.010 0.354 0.177
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 4.739 0.082 57.485 0.000 4.739 4.414
## .siqs02 4.847 0.076 63.378 0.000 4.847 4.169
## .siqs03 4.568 0.088 51.977 0.000 4.568 3.862
## .siqs04 4.208 0.069 61.042 0.000 4.208 3.070
## .siqs05 4.165 0.069 60.380 0.000 4.165 2.996
## .siqs06 4.771 0.064 74.869 0.000 4.771 4.034
## .siqs07 5.124 0.079 64.551 0.000 5.124 5.134
## .siqs08 5.109 0.080 64.034 0.000 5.109 5.299
## .siqs09 4.853 0.078 62.049 0.000 4.853 4.384
## .tpqm02 5.411 0.058 93.883 0.000 5.411 5.175
## .tpqm05 5.672 0.058 98.422 0.000 5.672 6.782
## .tpqm12 5.328 0.058 92.446 0.000 5.328 4.763
## .tpqm16 5.666 0.058 98.307 0.000 5.666 7.426
## .tpqm01 5.546 0.058 96.238 0.000 5.546 6.596
## .tpqm09 5.626 0.058 97.617 0.000 5.626 6.045
## .tpqm11 5.371 0.058 93.193 0.000 5.371 5.095
## .tpqm14 5.606 0.058 97.273 0.000 5.606 6.303
## .tpqm03 5.268 0.058 91.412 0.000 5.268 5.181
## .tpqm07 5.278 0.058 91.584 0.000 5.278 4.947
## .tpqm10 5.586 0.058 96.928 0.000 5.586 6.486
## .tpqm13 5.301 0.058 91.987 0.000 5.301 4.613
## .tpqm04 5.566 0.058 96.583 0.000 5.566 6.421
## .tpqm06 5.560 0.058 96.468 0.000 5.560 5.823
## .tpqm08 5.566 0.058 96.583 0.000 5.566 6.538
## .tpqm15 5.325 0.058 92.389 0.000 5.325 4.881
## sexo 0.507 0.058 8.791 0.000 0.507 1.012
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## LTm 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.332 0.080 4.122 0.000 0.332 0.288
## .siqs02 0.756 0.073 10.346 0.000 0.756 0.559
## .siqs03 0.358 0.089 4.013 0.000 0.358 0.256
## .siqs04 0.768 0.101 7.607 0.000 0.768 0.409
## .siqs05 0.818 0.101 8.081 0.000 0.818 0.423
## .siqs06 0.828 0.077 10.808 0.000 0.828 0.592
## .siqs07 0.273 0.079 3.457 0.001 0.273 0.274
## .siqs08 0.191 0.080 2.395 0.017 0.191 0.205
## .siqs09 0.547 0.077 7.078 0.000 0.547 0.446
## .tpqm02 0.596 0.064 9.248 0.000 0.596 0.545
## .tpqm05 0.361 0.062 5.805 0.000 0.361 0.517
## .tpqm12 0.602 0.067 9.022 0.000 0.602 0.481
## .tpqm16 0.220 0.063 3.512 0.000 0.220 0.378
## .tpqm01 0.368 0.062 5.916 0.000 0.368 0.521
## .tpqm09 0.368 0.064 5.711 0.000 0.368 0.425
## .tpqm11 0.482 0.066 7.265 0.000 0.482 0.434
## .tpqm14 0.217 0.066 3.309 0.001 0.217 0.274
## .tpqm03 0.751 0.061 12.220 0.000 0.751 0.726
## .tpqm07 0.640 0.064 9.925 0.000 0.640 0.562
## .tpqm10 0.213 0.065 3.283 0.001 0.213 0.287
## .tpqm13 0.596 0.068 8.791 0.000 0.596 0.451
## .tpqm04 0.316 0.064 4.970 0.000 0.316 0.421
## .tpqm06 0.298 0.066 4.504 0.000 0.298 0.327
## .tpqm08 0.365 0.063 5.841 0.000 0.365 0.504
## .tpqm15 0.453 0.068 6.661 0.000 0.453 0.381
## .it 0.167 0.050 3.354 0.001 0.203 0.203
## .cc 0.843 0.071 11.869 0.000 0.759 0.759
## .ia 0.163 0.047 3.495 0.000 0.225 0.225
## .SIQS 0.545 0.046 11.905 0.000 0.833 0.833
## LTm 0.497 0.029 17.182 0.000 1.000 1.000
## sexo 0.251 0.058 4.352 0.000 0.251 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.979 0.871
## siqs02 0.957 0.036 26.874 0.000 0.936 0.708
## siqs03 1.176 0.042 28.178 0.000 1.151 0.932
## cc =~
## siqs04 1.000 0.854 0.696
## siqs05 1.112 0.049 22.708 0.000 0.949 0.725
## siqs06 1.073 0.048 22.513 0.000 0.916 0.778
## ia =~
## siqs07 1.000 0.813 0.877
## siqs08 1.075 0.044 24.249 0.000 0.874 0.890
## siqs09 0.920 0.040 22.902 0.000 0.748 0.683
## SIQS =~
## it 1.000 0.856 0.856
## cc 0.737 0.036 20.702 0.000 0.723 0.723
## ia 0.887 0.041 21.391 0.000 0.914 0.914
## LTm =~
## tpqm02 1.000 0.607 0.639
## tpqm05 1.249 0.050 25.151 0.000 0.758 0.816
## tpqm12 1.447 0.055 26.321 0.000 0.878 0.789
## tpqm16 0.667 0.036 18.370 0.000 0.405 0.662
## tpqm01 1.012 0.044 23.145 0.000 0.614 0.643
## tpqm09 1.398 0.054 26.066 0.000 0.848 0.843
## tpqm11 1.287 0.051 25.407 0.000 0.781 0.760
## tpqm14 1.115 0.046 24.113 0.000 0.676 0.835
## tpqm03 0.824 0.039 20.879 0.000 0.500 0.502
## tpqm07 1.005 0.044 23.073 0.000 0.610 0.589
## tpqm10 1.081 0.045 23.811 0.000 0.656 0.819
## tpqm13 1.530 0.057 26.707 0.000 0.928 0.800
## tpqm04 0.890 0.041 21.755 0.000 0.540 0.685
## tpqm06 1.222 0.049 24.961 0.000 0.741 0.826
## tpqm08 1.259 0.050 25.222 0.000 0.764 0.797
## tpqm15 1.326 0.052 25.654 0.000 0.805 0.733
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LTm 0.563 0.026 21.582 0.000 0.407 0.407
## sexo 0.404 0.120 3.371 0.001 0.482 0.238
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 4.817 0.076 63.612 0.000 4.817 4.286
## .siqs02 4.680 0.074 63.171 0.000 4.680 3.537
## .siqs03 4.657 0.083 56.330 0.000 4.657 3.771
## .siqs04 4.524 0.066 68.100 0.000 4.524 3.689
## .siqs05 4.491 0.069 64.927 0.000 4.491 3.431
## .siqs06 4.939 0.068 72.434 0.000 4.939 4.195
## .siqs07 5.259 0.072 73.483 0.000 5.259 5.672
## .siqs08 5.191 0.074 70.147 0.000 5.191 5.285
## .siqs09 4.907 0.069 71.030 0.000 4.907 4.486
## .tpqm02 5.492 0.053 103.165 0.000 5.492 5.782
## .tpqm05 5.585 0.053 104.916 0.000 5.585 6.018
## .tpqm12 5.373 0.053 100.936 0.000 5.373 4.830
## .tpqm16 5.729 0.053 107.623 0.000 5.729 9.370
## .tpqm01 5.446 0.053 102.316 0.000 5.446 5.707
## .tpqm09 5.551 0.053 104.279 0.000 5.551 5.515
## .tpqm11 5.373 0.053 100.936 0.000 5.373 5.228
## .tpqm14 5.619 0.053 105.553 0.000 5.619 6.938
## .tpqm03 5.325 0.053 100.034 0.000 5.325 5.350
## .tpqm07 5.331 0.053 100.140 0.000 5.331 5.149
## .tpqm10 5.633 0.053 105.818 0.000 5.633 7.036
## .tpqm13 5.356 0.053 100.618 0.000 5.356 4.616
## .tpqm04 5.621 0.053 105.606 0.000 5.621 7.132
## .tpqm06 5.582 0.053 104.863 0.000 5.582 6.222
## .tpqm08 5.508 0.053 103.483 0.000 5.508 5.746
## .tpqm15 5.359 0.053 100.671 0.000 5.359 4.881
## sexo 0.412 0.053 7.748 0.000 0.412 0.837
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## LTm 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.306 0.073 4.197 0.000 0.306 0.242
## .siqs02 0.874 0.071 12.346 0.000 0.874 0.499
## .siqs03 0.201 0.083 2.418 0.016 0.201 0.132
## .siqs04 0.775 0.071 10.888 0.000 0.775 0.516
## .siqs05 0.812 0.077 10.573 0.000 0.812 0.474
## .siqs06 0.548 0.075 7.332 0.000 0.548 0.395
## .siqs07 0.199 0.069 2.862 0.004 0.199 0.231
## .siqs08 0.200 0.073 2.744 0.006 0.200 0.208
## .siqs09 0.638 0.066 9.634 0.000 0.638 0.533
## .tpqm02 0.534 0.058 9.254 0.000 0.534 0.592
## .tpqm05 0.287 0.060 4.758 0.000 0.287 0.334
## .tpqm12 0.467 0.063 7.416 0.000 0.467 0.377
## .tpqm16 0.210 0.055 3.806 0.000 0.210 0.562
## .tpqm01 0.534 0.058 9.225 0.000 0.534 0.586
## .tpqm09 0.294 0.062 4.717 0.000 0.294 0.290
## .tpqm11 0.446 0.061 7.340 0.000 0.446 0.423
## .tpqm14 0.198 0.059 3.372 0.001 0.198 0.303
## .tpqm03 0.741 0.056 13.162 0.000 0.741 0.748
## .tpqm07 0.700 0.058 12.112 0.000 0.700 0.653
## .tpqm10 0.211 0.059 3.607 0.000 0.211 0.329
## .tpqm13 0.485 0.064 7.556 0.000 0.485 0.360
## .tpqm04 0.330 0.057 5.806 0.000 0.330 0.531
## .tpqm06 0.255 0.060 4.252 0.000 0.255 0.317
## .tpqm08 0.336 0.060 5.552 0.000 0.336 0.365
## .tpqm15 0.558 0.061 9.098 0.000 0.558 0.463
## .it 0.256 0.041 6.216 0.000 0.267 0.267
## .cc 0.348 0.037 9.464 0.000 0.477 0.477
## .ia 0.108 0.039 2.780 0.005 0.164 0.164
## .SIQS 0.546 0.039 14.169 0.000 0.778 0.778
## LTm 0.368 0.022 16.448 0.000 1.000 1.000
## sexo 0.243 0.053 4.565 0.000 0.243 1.000# [Grupo 1] SIQS = 0 + .407 * LTc
# [Grupo 2] SIQS = 0 + .368 * LTc
graf <- data.frame(edad=rep(1:2,2),
"LTm (std)"=c(rep(min(scale(datos$tpqm_LT),na.rm = T),2),rep(max(scale(datos$tpqm_LT),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$edad[i]==1,
.481*graf$`LTm (std)`[i],
.381*graf$`LTm (std)`[i])
}
graf %>%
ggplot(aes(x=`LTm (std)`, y=`siqs (std)`, color=as.factor(edad), group=as.factor(edad)))+
geom_line()+
scale_color_manual(name="Age stage",
values = 2:3,
labels = c("early",
"late"))
Padre
Sexo
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTp =~ tpqp02 + tpqp05 + tpqp12 + tpqp16 + tpqp01 + tpqp09 + tpqp11 + tpqp14 + tpqp03 + tpqp07 + tpqp10 + tpqp13 + tpqp04 + tpqp06 + tpqp08 + tpqp15
# REGRESIONES
SIQS ~ LTp + edad
'
# Configural model
sem.config <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config 590 925.72
## sem.metric 613 1210.66 284.94 0.186336 23 <2e-16 ***
## sem.scalar 633 1234.98 24.32 0.025661 20 0.2287
## sem.strict 658 1383.73 148.75 0.122849 25 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config 925.724† 590 NA .042† .994† .994† .045†
## sem.metric 1210.659 613 NA .055 .990 .989 .053
## sem.scalar 1234.978 633 NA .054 .990 .990 .054
## sem.strict 1383.731 658 NA .058 .988 .988 .058
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config 23 0.013 -0.004 -0.004 0.008
## sem.scalar - sem.metric 20 -0.001 0.000 0.000 0.001
## sem.strict - sem.scalar 25 0.004 -0.002 -0.002 0.005El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para el padre.
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
sem.config %>%
summary(standardized=T)## lavaan 0.6.16 ended normally after 112 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 164
##
## Number of observations per group:
## 1 299
## 0 357
##
## Model Test User Model:
##
## Test statistic 925.724
## Degrees of freedom 590
## P-value (Unknown) NA
## Test statistic for each group:
## 1 340.039
## 0 585.685
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.740 0.890
## siqs02 0.863 0.073 11.885 0.000 0.639 0.622
## siqs03 1.155 0.092 12.601 0.000 0.855 0.884
## cc =~
## siqs04 1.000 1.094 0.805
## siqs05 0.915 0.056 16.206 0.000 1.001 0.724
## siqs06 0.655 0.042 15.425 0.000 0.716 0.612
## ia =~
## siqs07 1.000 0.532 0.743
## siqs08 1.233 0.112 11.028 0.000 0.656 0.821
## siqs09 1.372 0.122 11.287 0.000 0.730 0.716
## SIQS =~
## it 1.000 0.688 0.688
## cc 1.562 0.141 11.037 0.000 0.726 0.726
## ia 0.871 0.089 9.778 0.000 0.833 0.833
## LTp =~
## tpqp02 1.000 0.527 0.582
## tpqp05 1.283 0.063 20.434 0.000 0.676 0.784
## tpqp12 1.780 0.080 22.330 0.000 0.938 0.745
## tpqp16 1.452 0.068 21.244 0.000 0.765 0.832
## tpqp01 1.078 0.056 19.106 0.000 0.568 0.658
## tpqp09 1.447 0.068 21.227 0.000 0.763 0.741
## tpqp11 1.527 0.071 21.545 0.000 0.805 0.725
## tpqp14 1.395 0.066 20.997 0.000 0.735 0.833
## tpqp03 1.127 0.058 19.468 0.000 0.594 0.541
## tpqp07 1.398 0.067 21.008 0.000 0.737 0.658
## tpqp10 1.359 0.065 20.824 0.000 0.716 0.829
## tpqp13 1.648 0.075 21.958 0.000 0.869 0.815
## tpqp04 1.307 0.064 20.560 0.000 0.689 0.745
## tpqp06 1.310 0.064 20.581 0.000 0.691 0.776
## tpqp08 1.492 0.070 21.408 0.000 0.786 0.773
## tpqp15 1.561 0.072 21.667 0.000 0.823 0.745
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LTp 0.169 0.017 10.089 0.000 0.175 0.175
## edad -0.081 0.009 -9.151 0.000 -0.159 -0.249
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 6.444 0.149 43.319 0.000 6.444 7.748
## .siqs02 6.205 0.135 45.874 0.000 6.205 6.041
## .siqs03 6.491 0.164 39.613 0.000 6.491 6.715
## .siqs04 6.465 0.202 32.013 0.000 6.465 4.756
## .siqs05 6.362 0.189 33.748 0.000 6.362 4.604
## .siqs06 6.288 0.146 43.030 0.000 6.288 5.374
## .siqs07 6.596 0.139 47.459 0.000 6.596 9.213
## .siqs08 6.797 0.158 43.062 0.000 6.797 8.511
## .siqs09 6.627 0.169 39.162 0.000 6.627 6.499
## .tpqp02 5.478 0.058 94.583 0.000 5.478 6.047
## .tpqp05 5.605 0.058 96.778 0.000 5.605 6.503
## .tpqp12 5.161 0.058 89.098 0.000 5.161 4.099
## .tpqp16 5.559 0.058 95.969 0.000 5.559 6.048
## .tpqp01 5.548 0.058 95.796 0.000 5.548 6.428
## .tpqp09 5.548 0.058 95.796 0.000 5.548 5.388
## .tpqp11 5.288 0.058 91.292 0.000 5.288 4.763
## .tpqp14 5.625 0.058 97.124 0.000 5.625 6.375
## .tpqp03 5.301 0.058 91.523 0.000 5.301 4.830
## .tpqp07 5.237 0.058 90.426 0.000 5.237 4.674
## .tpqp10 5.622 0.058 97.066 0.000 5.622 6.509
## .tpqp13 5.415 0.058 93.486 0.000 5.415 5.080
## .tpqp04 5.488 0.058 94.757 0.000 5.488 5.936
## .tpqp06 5.542 0.058 95.681 0.000 5.542 6.224
## .tpqp08 5.482 0.058 94.641 0.000 5.482 5.386
## .tpqp15 5.361 0.058 92.562 0.000 5.361 4.858
## edad 15.525 0.058 268.044 0.000 15.525 9.902
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## LTp 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.144 0.084 1.717 0.086 0.144 0.208
## .siqs02 0.647 0.076 8.495 0.000 0.647 0.613
## .siqs03 0.203 0.096 2.115 0.034 0.203 0.218
## .siqs04 0.651 0.108 6.001 0.000 0.651 0.352
## .siqs05 0.908 0.098 9.298 0.000 0.908 0.475
## .siqs06 0.856 0.075 11.349 0.000 0.856 0.625
## .siqs07 0.229 0.071 3.253 0.001 0.229 0.448
## .siqs08 0.208 0.079 2.620 0.009 0.208 0.326
## .siqs09 0.507 0.087 5.855 0.000 0.507 0.488
## .tpqp02 0.543 0.062 8.808 0.000 0.543 0.662
## .tpqp05 0.286 0.064 4.456 0.000 0.286 0.385
## .tpqp12 0.705 0.070 10.031 0.000 0.705 0.445
## .tpqp16 0.259 0.066 3.933 0.000 0.259 0.307
## .tpqp01 0.422 0.062 6.784 0.000 0.422 0.567
## .tpqp09 0.478 0.066 7.261 0.000 0.478 0.451
## .tpqp11 0.584 0.067 8.744 0.000 0.584 0.474
## .tpqp14 0.238 0.065 3.641 0.000 0.238 0.305
## .tpqp03 0.851 0.063 13.582 0.000 0.851 0.707
## .tpqp07 0.713 0.065 10.909 0.000 0.713 0.568
## .tpqp10 0.233 0.065 3.592 0.000 0.233 0.313
## .tpqp13 0.382 0.068 5.580 0.000 0.382 0.336
## .tpqp04 0.380 0.064 5.911 0.000 0.380 0.445
## .tpqp06 0.316 0.064 4.901 0.000 0.316 0.398
## .tpqp08 0.417 0.066 6.286 0.000 0.417 0.403
## .tpqp15 0.541 0.067 8.048 0.000 0.541 0.444
## .it 0.289 0.048 5.989 0.000 0.527 0.527
## .cc 0.566 0.081 6.995 0.000 0.472 0.472
## .ia 0.087 0.030 2.874 0.004 0.306 0.306
## .SIQS 0.235 0.031 7.593 0.000 0.908 0.908
## LTp 0.278 0.021 13.168 0.000 1.000 1.000
## edad 2.458 0.058 42.442 0.000 2.458 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.063 0.853
## siqs02 0.900 0.029 30.568 0.000 0.957 0.690
## siqs03 1.142 0.035 32.491 0.000 1.214 0.898
## cc =~
## siqs04 1.000 0.862 0.684
## siqs05 1.133 0.045 25.086 0.000 0.977 0.733
## siqs06 1.151 0.046 25.177 0.000 0.992 0.829
## ia =~
## siqs07 1.000 1.004 0.905
## siqs08 0.992 0.033 30.013 0.000 0.996 0.918
## siqs09 0.826 0.029 28.046 0.000 0.830 0.719
## SIQS =~
## it 1.000 0.852 0.852
## cc 0.683 0.029 23.560 0.000 0.718 0.718
## ia 0.986 0.038 25.833 0.000 0.889 0.889
## LTp =~
## tpqp02 1.000 0.749 0.685
## tpqp05 1.334 0.032 41.907 0.000 1.000 0.807
## tpqp12 1.541 0.035 43.665 0.000 1.155 0.816
## tpqp16 1.121 0.028 39.360 0.000 0.840 0.754
## tpqp01 1.181 0.029 40.174 0.000 0.885 0.723
## tpqp09 1.413 0.033 42.649 0.000 1.059 0.828
## tpqp11 1.371 0.032 42.260 0.000 1.027 0.788
## tpqp14 1.177 0.029 40.116 0.000 0.882 0.853
## tpqp03 0.782 0.024 32.949 0.000 0.586 0.539
## tpqp07 1.222 0.030 40.681 0.000 0.916 0.732
## tpqp10 1.243 0.030 40.929 0.000 0.932 0.862
## tpqp13 1.499 0.035 43.350 0.000 1.123 0.808
## tpqp04 1.293 0.031 41.476 0.000 0.969 0.795
## tpqp06 1.265 0.031 41.177 0.000 0.948 0.844
## tpqp08 1.308 0.031 41.637 0.000 0.980 0.835
## tpqp15 1.380 0.033 42.350 0.000 1.034 0.800
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LTp 0.397 0.014 28.606 0.000 0.329 0.329
## edad -0.051 0.007 -7.069 0.000 -0.056 -0.093
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 5.488 0.120 45.637 0.000 5.488 4.403
## .siqs02 5.398 0.111 48.683 0.000 5.398 3.891
## .siqs03 5.462 0.134 40.807 0.000 5.462 4.041
## .siqs04 4.992 0.091 54.568 0.000 4.992 3.963
## .siqs05 4.952 0.099 49.800 0.000 4.952 3.715
## .siqs06 5.527 0.101 54.985 0.000 5.527 4.617
## .siqs07 5.951 0.119 50.017 0.000 5.951 5.360
## .siqs08 5.922 0.118 50.088 0.000 5.922 5.453
## .siqs09 5.540 0.103 53.707 0.000 5.540 4.798
## .tpqp02 5.311 0.053 100.194 0.000 5.311 4.852
## .tpqp05 5.224 0.053 98.556 0.000 5.224 4.218
## .tpqp12 4.748 0.053 89.572 0.000 4.748 3.354
## .tpqp16 5.406 0.053 101.991 0.000 5.406 4.851
## .tpqp01 5.176 0.053 97.657 0.000 5.176 4.227
## .tpqp09 5.210 0.053 98.291 0.000 5.210 4.070
## .tpqp11 5.098 0.053 96.178 0.000 5.098 3.909
## .tpqp14 5.451 0.053 102.836 0.000 5.451 5.273
## .tpqp03 5.210 0.053 98.291 0.000 5.210 4.790
## .tpqp07 5.073 0.053 95.702 0.000 5.073 4.052
## .tpqp10 5.401 0.053 101.885 0.000 5.401 4.998
## .tpqp13 4.955 0.053 93.483 0.000 4.955 3.566
## .tpqp04 5.140 0.053 96.970 0.000 5.140 4.217
## .tpqp06 5.370 0.053 101.304 0.000 5.370 4.780
## .tpqp08 5.283 0.053 99.665 0.000 5.283 4.501
## .tpqp15 4.992 0.053 94.170 0.000 4.992 3.861
## edad 15.059 0.053 284.094 0.000 15.059 9.022
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## LTp 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.423 0.073 5.758 0.000 0.423 0.272
## .siqs02 1.009 0.069 14.662 0.000 1.009 0.524
## .siqs03 0.353 0.082 4.301 0.000 0.353 0.194
## .siqs04 0.843 0.069 12.209 0.000 0.843 0.531
## .siqs05 0.822 0.075 10.943 0.000 0.822 0.463
## .siqs06 0.448 0.076 5.894 0.000 0.448 0.313
## .siqs07 0.224 0.074 3.028 0.002 0.224 0.182
## .siqs08 0.187 0.074 2.537 0.011 0.187 0.158
## .siqs09 0.644 0.066 9.796 0.000 0.644 0.483
## .tpqp02 0.636 0.057 11.164 0.000 0.636 0.531
## .tpqp05 0.534 0.060 8.861 0.000 0.534 0.348
## .tpqp12 0.670 0.063 10.663 0.000 0.670 0.334
## .tpqp16 0.536 0.058 9.236 0.000 0.536 0.432
## .tpqp01 0.716 0.059 12.207 0.000 0.716 0.477
## .tpqp09 0.516 0.061 8.439 0.000 0.516 0.315
## .tpqp11 0.646 0.061 10.644 0.000 0.646 0.380
## .tpqp14 0.291 0.059 4.960 0.000 0.291 0.272
## .tpqp03 0.840 0.055 15.153 0.000 0.840 0.710
## .tpqp07 0.729 0.059 12.338 0.000 0.729 0.465
## .tpqp10 0.300 0.059 5.053 0.000 0.300 0.256
## .tpqp13 0.669 0.062 10.749 0.000 0.669 0.347
## .tpqp04 0.548 0.060 9.158 0.000 0.548 0.369
## .tpqp06 0.363 0.060 6.097 0.000 0.363 0.288
## .tpqp08 0.417 0.060 6.955 0.000 0.417 0.303
## .tpqp15 0.601 0.061 9.890 0.000 0.601 0.360
## .it 0.310 0.043 7.278 0.000 0.274 0.274
## .cc 0.361 0.034 10.589 0.000 0.485 0.485
## .ia 0.212 0.046 4.641 0.000 0.210 0.210
## .SIQS 0.725 0.038 19.255 0.000 0.883 0.883
## LTp 0.562 0.021 26.746 0.000 1.000 1.000
## edad 2.786 0.053 52.557 0.000 2.786 1.000semPaths(# Argumentos globales
sem.config, what="diagram", whatLabels="std",layout="tree3", rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Varones; 2=Mujeres.
# [Grupo 0] SIQS = 0 + .329 * LTc
# [Grupo 1] SIQS = 0 + .175 * LTc
graf <- data.frame(sexo=rep(0:1,2),
"LTp (std)"=c(rep(min(scale(datos$tpqp_LT),na.rm = T),2),rep(max(scale(datos$tpqp_LT),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$sexo[i]==0,
.329*graf$`LTp (std)`[i],
.175*graf$`LTp (std)`[i])
}
graf %>%
ggplot(aes(x=`LTp (std)`, y=`siqs (std)`, color=as.factor(sexo), group=as.factor(sexo)))+
geom_line()+
scale_color_manual(name="Sex",
values = 2:3,
labels = c("females",
"males"))
Edad
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTp =~ tpqp02 + tpqp05 + tpqp12 + tpqp16 + tpqp01 + tpqp09 + tpqp11 + tpqp14 + tpqp03 + tpqp07 + tpqp10 + tpqp13 + tpqp04 + tpqp06 + tpqp08 + tpqp15
# REGRESIONES
SIQS ~ LTp + sexo
'
# Configural model
sem.config <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config 590 799.73
## sem.metric 613 1135.56 335.83 0.203637 23 < 2.2e-16 ***
## sem.scalar 633 1163.09 27.53 0.033877 20 0.121
## sem.strict 658 1278.27 115.18 0.104871 25 1.552e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config 799.727† 590 NA .033† 0.996† 0.996† .050†
## sem.metric 1135.561 613 NA .051 .991 .990 .057
## sem.scalar 1163.089 633 NA .051 .991 .991 .058
## sem.strict 1278.272 658 NA .054 .989 .989 .060
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config 23 0.018 -0.005 -0.006 0.007
## sem.scalar - sem.metric 20 0.000 0.000 0.000 0.001
## sem.strict - sem.scalar 25 0.003 -0.002 -0.001 0.002El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para el padre
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
sem.config %>%
summary(standardized=T)## lavaan 0.6.16 ended normally after 87 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 164
##
## Number of observations per group:
## 2 302
## 1 354
##
## Model Test User Model:
##
## Test statistic 799.727
## Degrees of freedom 590
## P-value (Unknown) NA
## Test statistic for each group:
## 2 465.820
## 1 333.907
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.884 0.823
## siqs02 0.904 0.048 18.930 0.000 0.799 0.687
## siqs03 1.150 0.057 20.045 0.000 1.017 0.859
## cc =~
## siqs04 1.000 1.066 0.778
## siqs05 0.988 0.059 16.608 0.000 1.053 0.758
## siqs06 0.700 0.044 15.998 0.000 0.747 0.631
## ia =~
## siqs07 1.000 0.871 0.873
## siqs08 0.987 0.053 18.521 0.000 0.860 0.892
## siqs09 0.923 0.051 18.165 0.000 0.804 0.726
## SIQS =~
## it 1.000 0.905 0.905
## cc 0.678 0.046 14.680 0.000 0.509 0.509
## ia 0.923 0.063 14.570 0.000 0.848 0.848
## LTp =~
## tpqp02 1.000 0.715 0.676
## tpqp05 1.148 0.036 31.597 0.000 0.820 0.777
## tpqp12 1.594 0.046 34.958 0.000 1.139 0.798
## tpqp16 1.187 0.037 32.002 0.000 0.848 0.784
## tpqp01 1.118 0.036 31.278 0.000 0.799 0.751
## tpqp09 1.253 0.038 32.622 0.000 0.895 0.789
## tpqp11 1.196 0.037 32.093 0.000 0.855 0.726
## tpqp14 1.174 0.037 31.867 0.000 0.839 0.852
## tpqp03 0.843 0.031 27.375 0.000 0.602 0.560
## tpqp07 1.336 0.040 33.316 0.000 0.954 0.746
## tpqp10 1.165 0.037 31.785 0.000 0.833 0.858
## tpqp13 1.446 0.042 34.101 0.000 1.033 0.804
## tpqp04 1.204 0.037 32.168 0.000 0.860 0.760
## tpqp06 1.243 0.038 32.540 0.000 0.889 0.850
## tpqp08 1.253 0.038 32.629 0.000 0.896 0.841
## tpqp15 1.361 0.041 33.512 0.000 0.973 0.796
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LTp 0.244 0.014 16.987 0.000 0.218 0.218
## sexo 0.281 0.111 2.547 0.011 0.352 0.176
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 4.741 0.082 57.845 0.000 4.741 4.416
## .siqs02 4.841 0.078 61.991 0.000 4.841 4.165
## .siqs03 4.568 0.088 51.696 0.000 4.568 3.861
## .siqs04 4.205 0.070 60.128 0.000 4.205 3.067
## .siqs05 4.163 0.070 59.755 0.000 4.163 2.994
## .siqs06 4.770 0.064 74.545 0.000 4.770 4.033
## .siqs07 5.127 0.079 64.999 0.000 5.127 5.136
## .siqs08 5.115 0.078 65.232 0.000 5.115 5.305
## .siqs09 4.862 0.076 63.866 0.000 4.862 4.392
## .tpqp02 5.308 0.058 92.102 0.000 5.308 5.021
## .tpqp05 5.464 0.058 94.802 0.000 5.464 5.178
## .tpqp12 4.851 0.058 84.173 0.000 4.851 3.401
## .tpqp16 5.440 0.058 94.400 0.000 5.440 5.028
## .tpqp01 5.371 0.058 93.193 0.000 5.371 5.050
## .tpqp09 5.411 0.058 93.883 0.000 5.411 4.771
## .tpqp11 5.192 0.058 90.091 0.000 5.192 4.412
## .tpqp14 5.523 0.058 95.836 0.000 5.523 5.614
## .tpqp03 5.275 0.058 91.527 0.000 5.275 4.904
## .tpqp07 5.053 0.058 87.677 0.000 5.053 3.948
## .tpqp10 5.520 0.058 95.779 0.000 5.520 5.689
## .tpqp13 5.132 0.058 89.056 0.000 5.132 3.995
## .tpqp04 5.285 0.058 91.699 0.000 5.285 4.670
## .tpqp06 5.447 0.058 94.515 0.000 5.447 5.213
## .tpqp08 5.424 0.058 94.113 0.000 5.424 5.090
## .tpqp15 5.126 0.058 88.941 0.000 5.126 4.196
## sexo 0.507 0.058 8.791 0.000 0.507 1.012
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## LTp 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.371 0.081 4.608 0.000 0.371 0.322
## .siqs02 0.713 0.076 9.442 0.000 0.713 0.528
## .siqs03 0.366 0.091 4.028 0.000 0.366 0.262
## .siqs04 0.742 0.103 7.192 0.000 0.742 0.395
## .siqs05 0.823 0.102 8.091 0.000 0.823 0.426
## .siqs06 0.842 0.076 11.019 0.000 0.842 0.602
## .siqs07 0.238 0.082 2.891 0.004 0.238 0.239
## .siqs08 0.190 0.081 2.338 0.019 0.190 0.205
## .siqs09 0.579 0.078 7.459 0.000 0.579 0.473
## .tpqp02 0.607 0.062 9.722 0.000 0.607 0.543
## .tpqp05 0.441 0.064 6.891 0.000 0.441 0.396
## .tpqp12 0.737 0.070 10.488 0.000 0.737 0.362
## .tpqp16 0.452 0.064 7.006 0.000 0.452 0.386
## .tpqp01 0.492 0.064 7.737 0.000 0.492 0.435
## .tpqp09 0.485 0.065 7.430 0.000 0.485 0.377
## .tpqp11 0.654 0.065 10.138 0.000 0.654 0.473
## .tpqp14 0.265 0.064 4.117 0.000 0.265 0.273
## .tpqp03 0.794 0.061 13.018 0.000 0.794 0.687
## .tpqp07 0.728 0.066 10.966 0.000 0.728 0.444
## .tpqp10 0.248 0.064 3.861 0.000 0.248 0.263
## .tpqp13 0.583 0.068 8.582 0.000 0.583 0.353
## .tpqp04 0.541 0.065 8.366 0.000 0.541 0.422
## .tpqp06 0.302 0.065 4.641 0.000 0.302 0.277
## .tpqp08 0.333 0.065 5.105 0.000 0.333 0.293
## .tpqp15 0.546 0.067 8.189 0.000 0.546 0.366
## .it 0.141 0.051 2.739 0.006 0.180 0.180
## .cc 0.842 0.073 11.543 0.000 0.741 0.741
## .ia 0.213 0.051 4.173 0.000 0.281 0.281
## .SIQS 0.590 0.053 11.088 0.000 0.922 0.922
## LTp 0.511 0.024 21.305 0.000 1.000 1.000
## sexo 0.251 0.058 4.352 0.000 0.251 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.012 0.901
## siqs02 0.917 0.034 26.862 0.000 0.928 0.701
## siqs03 1.112 0.039 28.159 0.000 1.125 0.911
## cc =~
## siqs04 1.000 0.818 0.667
## siqs05 1.154 0.048 24.099 0.000 0.944 0.721
## siqs06 1.162 0.048 24.140 0.000 0.951 0.808
## ia =~
## siqs07 1.000 0.798 0.861
## siqs08 1.102 0.044 24.812 0.000 0.879 0.895
## siqs09 0.950 0.040 23.521 0.000 0.758 0.693
## SIQS =~
## it 1.000 0.791 0.791
## cc 0.811 0.037 21.751 0.000 0.794 0.794
## ia 0.914 0.041 22.362 0.000 0.917 0.917
## LTp =~
## tpqp02 1.000 0.617 0.632
## tpqp05 1.536 0.046 33.130 0.000 0.947 0.835
## tpqp12 1.673 0.049 33.842 0.000 1.032 0.793
## tpqp16 1.243 0.040 31.019 0.000 0.767 0.776
## tpqp01 1.233 0.040 30.929 0.000 0.761 0.683
## tpqp09 1.618 0.048 33.576 0.000 0.998 0.815
## tpqp11 1.618 0.048 33.576 0.000 0.998 0.791
## tpqp14 1.303 0.041 31.527 0.000 0.803 0.836
## tpqp03 0.931 0.034 27.378 0.000 0.574 0.518
## tpqp07 1.202 0.039 30.639 0.000 0.741 0.665
## tpqp10 1.396 0.043 32.239 0.000 0.861 0.849
## tpqp13 1.692 0.050 33.929 0.000 1.043 0.826
## tpqp04 1.431 0.044 32.479 0.000 0.882 0.811
## tpqp06 1.310 0.041 31.584 0.000 0.807 0.798
## tpqp08 1.473 0.045 32.757 0.000 0.908 0.794
## tpqp15 1.529 0.046 33.092 0.000 0.943 0.769
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LTp 0.490 0.020 24.324 0.000 0.377 0.377
## sexo 0.385 0.115 3.357 0.001 0.480 0.237
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 4.824 0.074 65.141 0.000 4.824 4.293
## .siqs02 4.694 0.071 65.954 0.000 4.694 3.548
## .siqs03 4.677 0.078 59.851 0.000 4.677 3.787
## .siqs04 4.518 0.068 66.740 0.000 4.518 3.684
## .siqs05 4.479 0.072 62.355 0.000 4.479 3.422
## .siqs06 4.921 0.072 68.300 0.000 4.921 4.180
## .siqs07 5.262 0.071 74.033 0.000 5.262 5.675
## .siqs08 5.191 0.074 69.851 0.000 5.191 5.285
## .siqs09 4.905 0.070 70.523 0.000 4.905 4.484
## .tpqp02 5.455 0.053 102.475 0.000 5.455 5.593
## .tpqp05 5.342 0.053 100.352 0.000 5.342 4.712
## .tpqp12 5.008 0.053 94.090 0.000 5.008 3.851
## .tpqp16 5.506 0.053 103.430 0.000 5.506 5.573
## .tpqp01 5.325 0.053 100.034 0.000 5.325 4.782
## .tpqp09 5.325 0.053 100.034 0.000 5.325 4.347
## .tpqp11 5.178 0.053 97.274 0.000 5.178 4.104
## .tpqp14 5.537 0.053 104.014 0.000 5.537 5.761
## .tpqp03 5.232 0.053 98.283 0.000 5.232 4.724
## .tpqp07 5.229 0.053 98.229 0.000 5.229 4.692
## .tpqp10 5.486 0.053 103.059 0.000 5.486 5.414
## .tpqp13 5.192 0.053 97.540 0.000 5.192 4.111
## .tpqp04 5.311 0.053 99.768 0.000 5.311 4.881
## .tpqp06 5.449 0.053 102.369 0.000 5.449 5.384
## .tpqp08 5.331 0.053 100.140 0.000 5.331 4.658
## .tpqp15 5.189 0.053 97.487 0.000 5.189 4.234
## sexo 0.412 0.053 7.748 0.000 0.412 0.837
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## LTp 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.239 0.075 3.193 0.001 0.239 0.189
## .siqs02 0.889 0.071 12.594 0.000 0.889 0.508
## .siqs03 0.259 0.082 3.175 0.001 0.259 0.170
## .siqs04 0.834 0.068 12.251 0.000 0.834 0.555
## .siqs05 0.821 0.075 10.996 0.000 0.821 0.479
## .siqs06 0.482 0.075 6.411 0.000 0.482 0.347
## .siqs07 0.223 0.068 3.265 0.001 0.223 0.259
## .siqs08 0.192 0.073 2.631 0.009 0.192 0.199
## .siqs09 0.622 0.066 9.379 0.000 0.622 0.520
## .tpqp02 0.571 0.056 10.113 0.000 0.571 0.600
## .tpqp05 0.389 0.061 6.366 0.000 0.389 0.302
## .tpqp12 0.627 0.063 10.016 0.000 0.627 0.371
## .tpqp16 0.388 0.058 6.661 0.000 0.388 0.398
## .tpqp01 0.661 0.058 11.365 0.000 0.661 0.533
## .tpqp09 0.505 0.062 8.145 0.000 0.505 0.336
## .tpqp11 0.596 0.062 9.614 0.000 0.596 0.374
## .tpqp14 0.279 0.059 4.738 0.000 0.279 0.302
## .tpqp03 0.897 0.056 16.011 0.000 0.897 0.731
## .tpqp07 0.692 0.058 11.950 0.000 0.692 0.557
## .tpqp10 0.286 0.060 4.794 0.000 0.286 0.278
## .tpqp13 0.506 0.063 8.058 0.000 0.506 0.317
## .tpqp04 0.405 0.060 6.761 0.000 0.405 0.343
## .tpqp06 0.372 0.059 6.327 0.000 0.372 0.363
## .tpqp08 0.484 0.060 8.021 0.000 0.484 0.370
## .tpqp15 0.614 0.061 10.064 0.000 0.614 0.408
## .it 0.383 0.042 9.078 0.000 0.374 0.374
## .cc 0.248 0.033 7.584 0.000 0.370 0.370
## .ia 0.101 0.037 2.741 0.006 0.159 0.159
## .SIQS 0.514 0.035 14.787 0.000 0.802 0.802
## LTp 0.380 0.019 20.193 0.000 1.000 1.000
## sexo 0.243 0.053 4.565 0.000 0.243 1.000semPaths(# Argumentos globales
sem.config, what="diagram", whatLabels="std",layout="tree3", rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Late; 2=Early.
# [Grupo 1] SIQS = 0 + .377 * LTc
# [Grupo 2] SIQS = 0 + .218 * LTc
graf <- data.frame(edad=rep(1:2,2),
"LTp (std)"=c(rep(min(scale(datos$tpqp_LT),na.rm = T),2),rep(max(scale(datos$tpqp_LT),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$edad[i]==1,
.377*graf$`LTp (std)`[i],
.218*graf$`LTp (std)`[i])
}
graf %>%
ggplot(aes(x=`LTp (std)`, y=`siqs (std)`, color=as.factor(edad), group=as.factor(edad)))+
geom_line()+
scale_color_manual(name="Age stage",
values = 2:3,
labels = c("early",
"late"))
Madre y Padre
Probé también el modelo de contexto familiar (i.e., cada LT por separado aporta a un LT “del hogar” y eso como predictor de la identidad social).
Sexo
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTp =~ tpqp02 + tpqp05 + tpqp12 + tpqp16 + tpqp01 + tpqp09 + tpqp11 + tpqp14 + tpqp03 + tpqp07 + tpqp10 + tpqp13 + tpqp04 + tpqp06 + tpqp08 + tpqp15
LTm =~ tpqm02 + tpqm05 + tpqm12 + tpqm16 + tpqm01 + tpqm09 + tpqm11 + tpqm14 + tpqm03 + tpqm07 + tpqm10 + tpqm13 + tpqm04 + tpqm06 + tpqm08 + tpqm15
LT =~ LTm + LTp
# REGRESIONES
SIQS ~ LT + edad
'
# Configural model
sem.config <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config 1626 2623.5
## sem.metric 1665 3329.9 706.38 0.228411 39 <2e-16 ***
## sem.scalar 1699 3371.0 41.09 0.025219 34 0.1877
## sem.strict 1740 3650.7 279.72 0.133235 41 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config 2623.548† 1626 NA .043† .991† .991† .057†
## sem.metric 3329.927 1665 NA .055 .985 .985 .067
## sem.scalar 3371.019 1699 NA .055 .985 .985 .068
## sem.strict 3650.741 1740 NA .058 .983 .983 .072
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config 39 0.012 -0.006 -0.006 0.010
## sem.scalar - sem.metric 34 0.000 0.000 0.000 0.000
## sem.strict - sem.scalar 41 0.003 -0.002 -0.002 0.004El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para la parentalidad transformacional
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
sem.config %>%
summary(standardized=T)## lavaan 0.6.16 ended normally after 138 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 264
##
## Number of observations per group:
## 1 299
## 0 357
##
## Model Test User Model:
##
## Test statistic 2623.548
## Degrees of freedom 1626
## P-value (Unknown) NA
## Test statistic for each group:
## 1 928.986
## 0 1694.562
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.725 0.871
## siqs02 0.948 0.072 13.111 0.000 0.687 0.669
## siqs03 1.140 0.083 13.684 0.000 0.826 0.854
## cc =~
## siqs04 1.000 1.076 0.792
## siqs05 0.934 0.055 16.915 0.000 1.005 0.727
## siqs06 0.677 0.042 16.024 0.000 0.729 0.623
## ia =~
## siqs07 1.000 0.531 0.741
## siqs08 1.249 0.109 11.472 0.000 0.663 0.830
## siqs09 1.364 0.117 11.706 0.000 0.724 0.710
## SIQS =~
## it 1.000 0.745 0.745
## cc 1.384 0.112 12.318 0.000 0.694 0.694
## ia 0.800 0.076 10.463 0.000 0.813 0.813
## LTp =~
## tpqp02 1.000 0.530 0.585
## tpqp05 1.273 0.058 22.127 0.000 0.675 0.782
## tpqp12 1.782 0.073 24.293 0.000 0.944 0.750
## tpqp16 1.385 0.061 22.754 0.000 0.734 0.798
## tpqp01 1.071 0.052 20.670 0.000 0.567 0.657
## tpqp09 1.464 0.063 23.138 0.000 0.776 0.753
## tpqp11 1.495 0.064 23.276 0.000 0.792 0.713
## tpqp14 1.359 0.060 22.620 0.000 0.720 0.816
## tpqp03 1.215 0.056 21.754 0.000 0.644 0.586
## tpqp07 1.463 0.063 23.135 0.000 0.775 0.692
## tpqp10 1.322 0.059 22.414 0.000 0.700 0.811
## tpqp13 1.647 0.069 23.867 0.000 0.872 0.818
## tpqp04 1.287 0.058 22.207 0.000 0.682 0.737
## tpqp06 1.259 0.057 22.040 0.000 0.667 0.749
## tpqp08 1.436 0.062 23.007 0.000 0.761 0.747
## tpqp15 1.589 0.067 23.657 0.000 0.842 0.763
## LTm =~
## tpqm02 1.000 0.448 0.529
## tpqm05 0.692 0.058 11.847 0.000 0.310 0.479
## tpqm12 1.453 0.086 16.805 0.000 0.651 0.702
## tpqm16 0.808 0.062 13.036 0.000 0.362 0.640
## tpqm01 1.020 0.069 14.714 0.000 0.457 0.567
## tpqm09 1.031 0.070 14.791 0.000 0.462 0.678
## tpqm11 1.203 0.076 15.774 0.000 0.539 0.661
## tpqm14 1.352 0.082 16.435 0.000 0.606 0.829
## tpqm03 1.267 0.079 16.076 0.000 0.568 0.576
## tpqm07 1.517 0.089 17.012 0.000 0.680 0.632
## tpqm10 1.088 0.072 15.145 0.000 0.488 0.710
## tpqm13 1.463 0.087 16.840 0.000 0.656 0.753
## tpqm04 0.913 0.065 13.936 0.000 0.409 0.627
## tpqm06 1.308 0.080 16.254 0.000 0.586 0.752
## tpqm08 1.381 0.083 16.549 0.000 0.619 0.711
## tpqm15 1.470 0.087 16.862 0.000 0.659 0.719
## LT =~
## LTm 1.000 0.938 0.938
## LTp 0.723 0.073 9.934 0.000 0.574 0.574
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.400 0.040 10.095 0.000 0.312 0.312
## edad -0.084 0.009 -9.396 0.000 -0.156 -0.245
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 6.499 0.151 43.063 0.000 6.499 7.815
## .siqs02 6.365 0.146 43.704 0.000 6.365 6.196
## .siqs03 6.535 0.165 39.565 0.000 6.535 6.761
## .siqs04 6.320 0.189 33.474 0.000 6.320 4.649
## .siqs05 6.264 0.179 34.979 0.000 6.264 4.533
## .siqs06 6.234 0.141 44.350 0.000 6.234 5.328
## .siqs07 6.553 0.133 49.310 0.000 6.553 9.153
## .siqs08 6.760 0.152 44.444 0.000 6.760 8.465
## .siqs09 6.560 0.161 40.697 0.000 6.560 6.433
## .tpqp02 5.478 0.058 94.583 0.000 5.478 6.047
## .tpqp05 5.605 0.058 96.778 0.000 5.605 6.503
## .tpqp12 5.161 0.058 89.098 0.000 5.161 4.099
## .tpqp16 5.559 0.058 95.969 0.000 5.559 6.048
## .tpqp01 5.548 0.058 95.796 0.000 5.548 6.428
## .tpqp09 5.548 0.058 95.796 0.000 5.548 5.388
## .tpqp11 5.288 0.058 91.292 0.000 5.288 4.763
## .tpqp14 5.625 0.058 97.124 0.000 5.625 6.375
## .tpqp03 5.301 0.058 91.523 0.000 5.301 4.830
## .tpqp07 5.237 0.058 90.426 0.000 5.237 4.674
## .tpqp10 5.622 0.058 97.066 0.000 5.622 6.509
## .tpqp13 5.415 0.058 93.486 0.000 5.415 5.080
## .tpqp04 5.488 0.058 94.757 0.000 5.488 5.936
## .tpqp06 5.542 0.058 95.681 0.000 5.542 6.224
## .tpqp08 5.482 0.058 94.641 0.000 5.482 5.386
## .tpqp15 5.361 0.058 92.562 0.000 5.361 4.858
## .tpqm02 5.555 0.058 95.912 0.000 5.555 6.558
## .tpqm05 5.756 0.058 99.376 0.000 5.756 8.881
## .tpqm12 5.505 0.058 95.045 0.000 5.505 5.931
## .tpqm16 5.739 0.058 99.087 0.000 5.739 10.130
## .tpqm01 5.599 0.058 96.662 0.000 5.599 6.942
## .tpqm09 5.746 0.058 99.203 0.000 5.746 8.423
## .tpqm11 5.535 0.058 95.565 0.000 5.535 6.784
## .tpqm14 5.679 0.058 98.048 0.000 5.679 7.770
## .tpqm03 5.351 0.058 92.389 0.000 5.351 5.423
## .tpqm07 5.304 0.058 91.581 0.000 5.304 4.928
## .tpqm10 5.682 0.058 98.106 0.000 5.682 8.266
## .tpqm13 5.548 0.058 95.796 0.000 5.548 6.371
## .tpqm04 5.662 0.058 97.759 0.000 5.662 8.677
## .tpqm06 5.662 0.058 97.759 0.000 5.662 7.268
## .tpqm08 5.589 0.058 96.489 0.000 5.589 6.412
## .tpqm15 5.492 0.058 94.814 0.000 5.492 5.987
## edad 15.525 0.058 268.044 0.000 15.525 9.902
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.167 0.080 2.076 0.038 0.167 0.241
## .siqs02 0.583 0.078 7.523 0.000 0.583 0.553
## .siqs03 0.253 0.089 2.824 0.005 0.253 0.270
## .siqs04 0.689 0.104 6.626 0.000 0.689 0.373
## .siqs05 0.899 0.096 9.338 0.000 0.899 0.471
## .siqs06 0.837 0.075 11.098 0.000 0.837 0.612
## .siqs07 0.231 0.070 3.301 0.001 0.231 0.450
## .siqs08 0.198 0.079 2.511 0.012 0.198 0.311
## .siqs09 0.515 0.085 6.065 0.000 0.515 0.496
## .tpqp02 0.540 0.061 8.832 0.000 0.540 0.658
## .tpqp05 0.288 0.063 4.555 0.000 0.288 0.388
## .tpqp12 0.694 0.069 10.115 0.000 0.694 0.438
## .tpqp16 0.306 0.064 4.770 0.000 0.306 0.363
## .tpqp01 0.423 0.062 6.866 0.000 0.423 0.568
## .tpqp09 0.459 0.065 7.059 0.000 0.459 0.433
## .tpqp11 0.605 0.065 9.262 0.000 0.605 0.491
## .tpqp14 0.260 0.064 4.064 0.000 0.260 0.334
## .tpqp03 0.790 0.063 12.592 0.000 0.790 0.656
## .tpqp07 0.655 0.065 10.068 0.000 0.655 0.521
## .tpqp10 0.256 0.064 4.014 0.000 0.256 0.343
## .tpqp13 0.375 0.067 5.602 0.000 0.375 0.330
## .tpqp04 0.390 0.063 6.160 0.000 0.390 0.457
## .tpqp06 0.348 0.063 5.509 0.000 0.348 0.439
## .tpqp08 0.457 0.065 7.059 0.000 0.457 0.441
## .tpqp15 0.510 0.066 7.682 0.000 0.510 0.418
## .tpqm02 0.517 0.061 8.456 0.000 0.517 0.720
## .tpqm05 0.324 0.059 5.450 0.000 0.324 0.771
## .tpqm12 0.437 0.065 6.751 0.000 0.437 0.507
## .tpqm16 0.190 0.060 3.163 0.002 0.190 0.591
## .tpqm01 0.441 0.061 7.211 0.000 0.441 0.679
## .tpqm09 0.252 0.061 4.104 0.000 0.252 0.541
## .tpqm11 0.375 0.063 5.992 0.000 0.375 0.563
## .tpqm14 0.167 0.064 2.614 0.009 0.167 0.312
## .tpqm03 0.651 0.063 10.323 0.000 0.651 0.669
## .tpqm07 0.696 0.065 10.646 0.000 0.696 0.601
## .tpqm10 0.235 0.062 3.803 0.000 0.235 0.496
## .tpqm13 0.328 0.065 5.059 0.000 0.328 0.433
## .tpqm04 0.258 0.061 4.266 0.000 0.258 0.607
## .tpqm06 0.263 0.063 4.152 0.000 0.263 0.434
## .tpqm08 0.376 0.064 5.871 0.000 0.376 0.495
## .tpqm15 0.407 0.065 6.268 0.000 0.407 0.484
## .it 0.234 0.044 5.352 0.000 0.446 0.446
## .cc 0.601 0.073 8.221 0.000 0.519 0.519
## .ia 0.095 0.029 3.281 0.001 0.338 0.338
## .SIQS 0.245 0.030 8.254 0.000 0.843 0.843
## .LTp 0.188 0.015 12.214 0.000 0.671 0.671
## .LTm 0.024 0.015 1.578 0.114 0.121 0.121
## LT 0.177 0.022 7.912 0.000 1.000 1.000
## edad 2.458 0.058 42.442 0.000 2.458 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.059 0.850
## siqs02 0.901 0.026 34.259 0.000 0.954 0.688
## siqs03 1.153 0.031 36.950 0.000 1.221 0.903
## cc =~
## siqs04 1.000 0.885 0.702
## siqs05 1.085 0.041 26.525 0.000 0.960 0.720
## siqs06 1.115 0.042 26.716 0.000 0.986 0.824
## ia =~
## siqs07 1.000 1.003 0.903
## siqs08 0.976 0.028 34.269 0.000 0.979 0.901
## siqs09 0.846 0.026 32.313 0.000 0.849 0.735
## SIQS =~
## it 1.000 0.867 0.867
## cc 0.648 0.024 26.727 0.000 0.672 0.672
## ia 1.003 0.032 30.920 0.000 0.918 0.918
## LTp =~
## tpqp02 1.000 0.768 0.702
## tpqp05 1.273 0.026 49.032 0.000 0.978 0.789
## tpqp12 1.537 0.030 51.956 0.000 1.181 0.834
## tpqp16 1.053 0.023 45.484 0.000 0.809 0.726
## tpqp01 1.110 0.024 46.524 0.000 0.852 0.696
## tpqp09 1.396 0.028 50.542 0.000 1.072 0.838
## tpqp11 1.375 0.027 50.308 0.000 1.057 0.810
## tpqp14 1.175 0.025 47.607 0.000 0.903 0.873
## tpqp03 0.771 0.020 38.672 0.000 0.592 0.545
## tpqp07 1.176 0.025 47.618 0.000 0.903 0.721
## tpqp10 1.185 0.025 47.770 0.000 0.911 0.843
## tpqp13 1.449 0.028 51.105 0.000 1.113 0.801
## tpqp04 1.200 0.025 47.992 0.000 0.922 0.756
## tpqp06 1.228 0.025 48.414 0.000 0.944 0.840
## tpqp08 1.279 0.026 49.111 0.000 0.982 0.837
## tpqp15 1.407 0.028 50.663 0.000 1.081 0.836
## LTm =~
## tpqm02 1.000 0.740 0.674
## tpqm05 1.081 0.026 42.247 0.000 0.800 0.774
## tpqm12 1.377 0.030 46.199 0.000 1.020 0.825
## tpqm16 0.768 0.022 35.427 0.000 0.569 0.738
## tpqm01 0.865 0.023 37.910 0.000 0.640 0.659
## tpqm09 1.243 0.028 44.636 0.000 0.920 0.804
## tpqm11 1.298 0.029 45.310 0.000 0.961 0.815
## tpqm14 1.096 0.026 42.485 0.000 0.811 0.872
## tpqm03 0.715 0.021 33.889 0.000 0.529 0.519
## tpqm07 0.925 0.024 39.269 0.000 0.685 0.666
## tpqm10 1.102 0.026 42.596 0.000 0.816 0.880
## tpqm13 1.394 0.030 46.367 0.000 1.032 0.781
## tpqm04 0.909 0.023 38.928 0.000 0.673 0.714
## tpqm06 1.159 0.027 43.476 0.000 0.858 0.838
## tpqm08 1.009 0.025 40.971 0.000 0.747 0.794
## tpqm15 1.329 0.029 45.677 0.000 0.984 0.814
## LT =~
## LTm 1.000 0.908 0.908
## LTp 0.848 0.028 30.202 0.000 0.742 0.742
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.601 0.019 31.207 0.000 0.440 0.440
## edad -0.050 0.007 -6.909 0.000 -0.054 -0.090
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 5.474 0.121 45.380 0.000 5.474 4.392
## .siqs02 5.386 0.111 48.423 0.000 5.386 3.882
## .siqs03 5.454 0.135 40.265 0.000 5.454 4.035
## .siqs04 4.955 0.089 55.977 0.000 4.955 3.934
## .siqs05 4.887 0.093 52.389 0.000 4.887 3.667
## .siqs06 5.468 0.095 57.576 0.000 5.468 4.568
## .siqs07 5.950 0.121 49.174 0.000 5.950 5.359
## .siqs08 5.909 0.119 49.784 0.000 5.909 5.441
## .siqs09 5.554 0.106 52.162 0.000 5.554 4.810
## .tpqp02 5.311 0.053 100.194 0.000 5.311 4.852
## .tpqp05 5.224 0.053 98.556 0.000 5.224 4.218
## .tpqp12 4.748 0.053 89.572 0.000 4.748 3.354
## .tpqp16 5.406 0.053 101.991 0.000 5.406 4.851
## .tpqp01 5.176 0.053 97.657 0.000 5.176 4.227
## .tpqp09 5.210 0.053 98.291 0.000 5.210 4.070
## .tpqp11 5.098 0.053 96.178 0.000 5.098 3.909
## .tpqp14 5.451 0.053 102.836 0.000 5.451 5.273
## .tpqp03 5.210 0.053 98.291 0.000 5.210 4.790
## .tpqp07 5.073 0.053 95.702 0.000 5.073 4.052
## .tpqp10 5.401 0.053 101.885 0.000 5.401 4.998
## .tpqp13 4.955 0.053 93.483 0.000 4.955 3.566
## .tpqp04 5.140 0.053 96.970 0.000 5.140 4.217
## .tpqp06 5.370 0.053 101.304 0.000 5.370 4.780
## .tpqp08 5.283 0.053 99.665 0.000 5.283 4.501
## .tpqp15 4.992 0.053 94.170 0.000 4.992 3.861
## .tpqm02 5.370 0.053 101.304 0.000 5.370 4.890
## .tpqm05 5.515 0.053 104.052 0.000 5.515 5.330
## .tpqm12 5.224 0.053 98.556 0.000 5.224 4.226
## .tpqm16 5.667 0.053 106.905 0.000 5.667 7.355
## .tpqm01 5.403 0.053 101.938 0.000 5.403 5.563
## .tpqm09 5.451 0.053 102.836 0.000 5.451 4.762
## .tpqm11 5.235 0.053 98.767 0.000 5.235 4.443
## .tpqm14 5.557 0.053 104.844 0.000 5.557 5.974
## .tpqm03 5.255 0.053 99.137 0.000 5.255 5.156
## .tpqm07 5.308 0.053 100.141 0.000 5.308 5.164
## .tpqm10 5.552 0.053 104.739 0.000 5.552 5.985
## .tpqm13 5.148 0.053 97.129 0.000 5.148 3.898
## .tpqm04 5.541 0.053 104.527 0.000 5.541 5.875
## .tpqm06 5.496 0.053 103.682 0.000 5.496 5.367
## .tpqm08 5.490 0.053 103.576 0.000 5.490 5.835
## .tpqm15 5.218 0.053 98.450 0.000 5.218 4.314
## edad 15.059 0.053 284.094 0.000 15.059 9.022
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.432 0.071 6.109 0.000 0.432 0.278
## .siqs02 1.015 0.067 15.233 0.000 1.015 0.527
## .siqs03 0.336 0.079 4.244 0.000 0.336 0.184
## .siqs04 0.804 0.069 11.623 0.000 0.804 0.507
## .siqs05 0.854 0.073 11.704 0.000 0.854 0.481
## .siqs06 0.460 0.074 6.180 0.000 0.460 0.321
## .siqs07 0.227 0.072 3.171 0.002 0.227 0.184
## .siqs08 0.221 0.070 3.146 0.002 0.221 0.187
## .siqs09 0.613 0.065 9.468 0.000 0.613 0.460
## .tpqp02 0.608 0.056 10.816 0.000 0.608 0.507
## .tpqp05 0.578 0.058 9.923 0.000 0.578 0.377
## .tpqp12 0.609 0.061 10.021 0.000 0.609 0.304
## .tpqp16 0.588 0.057 10.394 0.000 0.588 0.473
## .tpqp01 0.773 0.057 13.572 0.000 0.773 0.515
## .tpqp09 0.488 0.059 8.227 0.000 0.488 0.298
## .tpqp11 0.585 0.059 9.888 0.000 0.585 0.344
## .tpqp14 0.254 0.057 4.416 0.000 0.254 0.237
## .tpqp03 0.832 0.055 15.164 0.000 0.832 0.703
## .tpqp07 0.752 0.057 13.090 0.000 0.752 0.480
## .tpqp10 0.338 0.058 5.883 0.000 0.338 0.290
## .tpqp13 0.692 0.060 11.563 0.000 0.692 0.358
## .tpqp04 0.636 0.058 11.037 0.000 0.636 0.428
## .tpqp06 0.371 0.058 6.412 0.000 0.371 0.294
## .tpqp08 0.413 0.058 7.079 0.000 0.413 0.300
## .tpqp15 0.503 0.059 8.463 0.000 0.503 0.301
## .tpqm02 0.658 0.056 11.683 0.000 0.658 0.545
## .tpqm05 0.430 0.057 7.563 0.000 0.430 0.402
## .tpqm12 0.489 0.059 8.231 0.000 0.489 0.320
## .tpqm16 0.270 0.055 4.923 0.000 0.270 0.455
## .tpqm01 0.534 0.055 9.626 0.000 0.534 0.566
## .tpqm09 0.463 0.058 7.964 0.000 0.463 0.353
## .tpqm11 0.465 0.059 7.940 0.000 0.465 0.335
## .tpqm14 0.207 0.057 3.643 0.000 0.207 0.240
## .tpqm03 0.759 0.055 13.882 0.000 0.759 0.730
## .tpqm07 0.588 0.056 10.532 0.000 0.588 0.556
## .tpqm10 0.194 0.057 3.408 0.001 0.194 0.226
## .tpqm13 0.680 0.060 11.431 0.000 0.680 0.390
## .tpqm04 0.436 0.056 7.835 0.000 0.436 0.491
## .tpqm06 0.312 0.057 5.427 0.000 0.312 0.297
## .tpqm08 0.327 0.056 5.805 0.000 0.327 0.369
## .tpqm15 0.495 0.059 8.401 0.000 0.495 0.338
## .it 0.279 0.039 7.100 0.000 0.249 0.249
## .cc 0.429 0.035 12.185 0.000 0.549 0.549
## .ia 0.158 0.043 3.652 0.000 0.157 0.157
## .SIQS 0.672 0.031 21.605 0.000 0.798 0.798
## .LTp 0.265 0.012 22.208 0.000 0.449 0.449
## .LTm 0.096 0.012 7.826 0.000 0.175 0.175
## LT 0.452 0.019 23.911 0.000 1.000 1.000
## edad 2.786 0.053 52.557 0.000 2.786 1.000semPaths(# Argumentos globales
sem.config, what="diagram", whatLabels="std",layout="tree2", residuals = F,
rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Varones; 2=Mujeres.
# [Grupo 0] SIQS = 0 + .44 * LTc
# [Grupo 1] SIQS = 0 + .312 * LTc
graf <- data.frame(sexo=rep(0:1,2),
"LT (std)"=c(rep(min(c(scale(datos$tpqm_LT),scale(datos$tpqp_LT)),na.rm = T),2),rep(max(c(scale(datos$tpqm_LT),scale(datos$tpqp_LT)),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$sexo[i]==0,
.44*graf$`LT (std)`[i],
.312*graf$`LT (std)`[i])
}
graf %>%
ggplot(aes(x=`LT (std)`, y=`siqs (std)`, color=as.factor(sexo), group=as.factor(sexo)))+
geom_line()+
scale_color_manual(name="Sex",
values = 2:3,
labels = c("females",
"males"))
Edad
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTp =~ tpqp02 + tpqp05 + tpqp12 + tpqp16 + tpqp01 + tpqp09 + tpqp11 + tpqp14 + tpqp03 + tpqp07 + tpqp10 + tpqp13 + tpqp04 + tpqp06 + tpqp08 + tpqp15
LTm =~ tpqm02 + tpqm05 + tpqm12 + tpqm16 + tpqm01 + tpqm09 + tpqm11 + tpqm14 + tpqm03 + tpqm07 + tpqm10 + tpqm13 + tpqm04 + tpqm06 + tpqm08 + tpqm15
LT =~ LTm + LTp
# REGRESIONES
SIQS ~ LT + sexo
'
# Configural model
sem.config <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config 1626 2363.4
## sem.metric 1665 3260.2 896.81 0.258956 39 < 2.2e-16 ***
## sem.scalar 1699 3296.2 36.06 0.013606 34 0.3722
## sem.strict 1740 3454.8 158.52 0.093480 41 9.943e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config 2363.374† 1626 NA .037† .993† .993† .058†
## sem.metric 3260.181 1665 NA .054 .985 .985 .068
## sem.scalar 3296.245 1699 NA .054 .985 .985 .068
## sem.strict 3454.761 1740 NA .055 .984 .984 .070
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config 39 0.017 -0.008 -0.008 0.010
## sem.scalar - sem.metric 34 -0.001 0.000 0.000 0.000
## sem.strict - sem.scalar 41 0.001 -0.001 -0.001 0.002El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para el padre
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
sem.config %>%
summary(standardized=T)## lavaan 0.6.16 ended normally after 97 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 264
##
## Number of observations per group:
## 2 302
## 1 354
##
## Model Test User Model:
##
## Test statistic 2363.374
## Degrees of freedom 1626
## P-value (Unknown) NA
## Test statistic for each group:
## 2 1241.883
## 1 1121.491
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.897 0.836
## siqs02 0.906 0.041 21.867 0.000 0.813 0.699
## siqs03 1.102 0.047 23.227 0.000 0.989 0.836
## cc =~
## siqs04 1.000 1.082 0.789
## siqs05 0.942 0.055 17.132 0.000 1.020 0.733
## siqs06 0.704 0.043 16.450 0.000 0.762 0.644
## ia =~
## siqs07 1.000 0.878 0.880
## siqs08 0.971 0.046 20.952 0.000 0.852 0.884
## siqs09 0.915 0.045 20.534 0.000 0.804 0.726
## SIQS =~
## it 1.000 0.935 0.935
## cc 0.605 0.036 17.009 0.000 0.469 0.469
## ia 0.895 0.049 18.423 0.000 0.855 0.855
## LTp =~
## tpqp02 1.000 0.726 0.687
## tpqp05 1.106 0.031 35.729 0.000 0.803 0.761
## tpqp12 1.589 0.040 40.227 0.000 1.154 0.809
## tpqp16 1.120 0.031 35.913 0.000 0.813 0.751
## tpqp01 1.059 0.030 35.081 0.000 0.769 0.723
## tpqp09 1.246 0.033 37.401 0.000 0.905 0.798
## tpqp11 1.196 0.032 36.843 0.000 0.868 0.738
## tpqp14 1.180 0.032 36.656 0.000 0.856 0.871
## tpqp03 0.858 0.027 31.655 0.000 0.623 0.579
## tpqp07 1.336 0.035 38.298 0.000 0.970 0.758
## tpqp10 1.140 0.032 36.177 0.000 0.828 0.853
## tpqp13 1.397 0.036 38.835 0.000 1.014 0.790
## tpqp04 1.152 0.032 36.327 0.000 0.837 0.739
## tpqp06 1.198 0.032 36.875 0.000 0.870 0.833
## tpqp08 1.208 0.033 36.990 0.000 0.877 0.823
## tpqp15 1.408 0.036 38.930 0.000 1.023 0.837
## LTm =~
## tpqm02 1.000 0.675 0.646
## tpqm05 0.763 0.030 25.503 0.000 0.515 0.616
## tpqm12 1.333 0.040 32.966 0.000 0.900 0.805
## tpqm16 0.842 0.031 26.998 0.000 0.569 0.746
## tpqm01 0.811 0.031 26.437 0.000 0.548 0.652
## tpqm09 0.983 0.034 29.214 0.000 0.664 0.713
## tpqm11 1.163 0.037 31.405 0.000 0.785 0.745
## tpqm14 1.136 0.037 31.124 0.000 0.767 0.863
## tpqm03 0.819 0.031 26.582 0.000 0.553 0.544
## tpqm07 1.022 0.034 29.753 0.000 0.690 0.647
## tpqm10 1.085 0.036 30.540 0.000 0.733 0.851
## tpqm13 1.309 0.040 32.778 0.000 0.884 0.770
## tpqm04 0.897 0.032 27.919 0.000 0.606 0.699
## tpqm06 1.154 0.037 31.316 0.000 0.780 0.816
## tpqm08 0.889 0.032 27.798 0.000 0.601 0.706
## tpqm15 1.340 0.041 33.028 0.000 0.905 0.830
## LT =~
## LTm 1.000 1.024 1.024
## LTp 0.645 0.035 18.440 0.000 0.614 0.614
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.435 0.023 18.570 0.000 0.359 0.359
## sexo 0.300 0.116 2.586 0.010 0.357 0.179
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 4.732 0.084 56.280 0.000 4.732 4.408
## .siqs02 4.833 0.080 60.400 0.000 4.833 4.157
## .siqs03 4.564 0.089 51.449 0.000 4.564 3.858
## .siqs04 4.209 0.069 61.386 0.000 4.209 3.071
## .siqs05 4.172 0.067 61.852 0.000 4.172 3.000
## .siqs06 4.773 0.063 75.373 0.000 4.773 4.036
## .siqs07 5.122 0.080 64.386 0.000 5.122 5.132
## .siqs08 5.113 0.078 65.167 0.000 5.113 5.303
## .siqs09 4.859 0.076 63.558 0.000 4.859 4.389
## .tpqp02 5.308 0.058 92.102 0.000 5.308 5.021
## .tpqp05 5.464 0.058 94.802 0.000 5.464 5.178
## .tpqp12 4.851 0.058 84.173 0.000 4.851 3.401
## .tpqp16 5.440 0.058 94.400 0.000 5.440 5.028
## .tpqp01 5.371 0.058 93.193 0.000 5.371 5.050
## .tpqp09 5.411 0.058 93.883 0.000 5.411 4.771
## .tpqp11 5.192 0.058 90.091 0.000 5.192 4.412
## .tpqp14 5.523 0.058 95.836 0.000 5.523 5.614
## .tpqp03 5.275 0.058 91.527 0.000 5.275 4.904
## .tpqp07 5.053 0.058 87.677 0.000 5.053 3.948
## .tpqp10 5.520 0.058 95.779 0.000 5.520 5.689
## .tpqp13 5.132 0.058 89.056 0.000 5.132 3.995
## .tpqp04 5.285 0.058 91.699 0.000 5.285 4.670
## .tpqp06 5.447 0.058 94.515 0.000 5.447 5.213
## .tpqp08 5.424 0.058 94.113 0.000 5.424 5.090
## .tpqp15 5.126 0.058 88.941 0.000 5.126 4.196
## .tpqm02 5.411 0.058 93.883 0.000 5.411 5.175
## .tpqm05 5.672 0.058 98.422 0.000 5.672 6.782
## .tpqm12 5.328 0.058 92.446 0.000 5.328 4.763
## .tpqm16 5.666 0.058 98.307 0.000 5.666 7.426
## .tpqm01 5.546 0.058 96.238 0.000 5.546 6.596
## .tpqm09 5.626 0.058 97.617 0.000 5.626 6.045
## .tpqm11 5.371 0.058 93.193 0.000 5.371 5.095
## .tpqm14 5.606 0.058 97.273 0.000 5.606 6.303
## .tpqm03 5.268 0.058 91.412 0.000 5.268 5.181
## .tpqm07 5.278 0.058 91.584 0.000 5.278 4.947
## .tpqm10 5.586 0.058 96.928 0.000 5.586 6.486
## .tpqm13 5.301 0.058 91.987 0.000 5.301 4.613
## .tpqm04 5.566 0.058 96.583 0.000 5.566 6.421
## .tpqm06 5.560 0.058 96.468 0.000 5.560 5.823
## .tpqm08 5.566 0.058 96.583 0.000 5.566 6.538
## .tpqm15 5.325 0.058 92.389 0.000 5.325 4.881
## sexo 0.507 0.058 8.791 0.000 0.507 1.012
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.348 0.078 4.452 0.000 0.348 0.302
## .siqs02 0.691 0.074 9.388 0.000 0.691 0.511
## .siqs03 0.422 0.084 5.005 0.000 0.422 0.301
## .siqs04 0.709 0.103 6.851 0.000 0.709 0.377
## .siqs05 0.894 0.097 9.242 0.000 0.894 0.462
## .siqs06 0.818 0.077 10.668 0.000 0.818 0.585
## .siqs07 0.225 0.080 2.819 0.005 0.225 0.226
## .siqs08 0.203 0.078 2.602 0.009 0.203 0.218
## .siqs09 0.579 0.075 7.707 0.000 0.579 0.473
## .tpqp02 0.590 0.062 9.589 0.000 0.590 0.528
## .tpqp05 0.468 0.062 7.501 0.000 0.468 0.421
## .tpqp12 0.703 0.068 10.354 0.000 0.703 0.346
## .tpqp16 0.510 0.063 8.144 0.000 0.510 0.435
## .tpqp01 0.540 0.062 8.696 0.000 0.540 0.477
## .tpqp09 0.468 0.064 7.333 0.000 0.468 0.364
## .tpqp11 0.631 0.063 9.976 0.000 0.631 0.456
## .tpqp14 0.234 0.063 3.712 0.000 0.234 0.242
## .tpqp03 0.768 0.061 12.700 0.000 0.768 0.664
## .tpqp07 0.697 0.065 10.767 0.000 0.697 0.426
## .tpqp10 0.256 0.063 4.075 0.000 0.256 0.272
## .tpqp13 0.621 0.065 9.490 0.000 0.621 0.376
## .tpqp04 0.581 0.063 9.234 0.000 0.581 0.453
## .tpqp06 0.335 0.063 5.288 0.000 0.335 0.307
## .tpqp08 0.366 0.063 5.765 0.000 0.366 0.322
## .tpqp15 0.447 0.066 6.809 0.000 0.447 0.299
## .tpqm02 0.637 0.062 10.340 0.000 0.637 0.583
## .tpqm05 0.434 0.060 7.241 0.000 0.434 0.620
## .tpqm12 0.441 0.065 6.797 0.000 0.441 0.352
## .tpqm16 0.258 0.060 4.276 0.000 0.258 0.444
## .tpqm01 0.407 0.060 6.755 0.000 0.407 0.575
## .tpqm09 0.426 0.061 6.924 0.000 0.426 0.491
## .tpqm11 0.495 0.063 7.842 0.000 0.495 0.445
## .tpqm14 0.202 0.063 3.216 0.001 0.202 0.255
## .tpqm03 0.728 0.060 12.076 0.000 0.728 0.704
## .tpqm07 0.662 0.062 10.704 0.000 0.662 0.581
## .tpqm10 0.205 0.062 3.281 0.001 0.205 0.276
## .tpqm13 0.539 0.065 8.336 0.000 0.539 0.408
## .tpqm04 0.384 0.061 6.321 0.000 0.384 0.512
## .tpqm06 0.304 0.063 4.827 0.000 0.304 0.333
## .tpqm08 0.364 0.061 5.990 0.000 0.364 0.502
## .tpqm15 0.370 0.065 5.702 0.000 0.370 0.311
## .it 0.102 0.048 2.129 0.033 0.126 0.126
## .cc 0.913 0.075 12.187 0.000 0.780 0.780
## .ia 0.208 0.047 4.404 0.000 0.269 0.269
## .SIQS 0.590 0.046 12.908 0.000 0.839 0.839
## .LTp 0.328 0.016 19.939 0.000 0.623 0.623
## .LTm -0.023 0.022 -1.012 0.311 -0.050 -0.050
## LT 0.479 0.031 15.344 0.000 1.000 1.000
## sexo 0.251 0.058 4.352 0.000 0.251 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.986 0.878
## siqs02 0.962 0.032 29.837 0.000 0.949 0.717
## siqs03 1.146 0.037 31.328 0.000 1.130 0.915
## cc =~
## siqs04 1.000 0.821 0.669
## siqs05 1.166 0.045 25.897 0.000 0.957 0.731
## siqs06 1.142 0.044 25.754 0.000 0.938 0.796
## ia =~
## siqs07 1.000 0.800 0.863
## siqs08 1.087 0.040 27.364 0.000 0.870 0.886
## siqs09 0.955 0.037 26.036 0.000 0.765 0.699
## SIQS =~
## it 1.000 0.818 0.818
## cc 0.779 0.032 24.295 0.000 0.766 0.766
## ia 0.908 0.036 25.435 0.000 0.915 0.915
## LTp =~
## tpqp02 1.000 0.629 0.645
## tpqp05 1.498 0.039 38.059 0.000 0.943 0.831
## tpqp12 1.670 0.043 39.152 0.000 1.051 0.808
## tpqp16 1.167 0.033 34.975 0.000 0.734 0.743
## tpqp01 1.203 0.034 35.384 0.000 0.757 0.680
## tpqp09 1.621 0.042 38.870 0.000 1.020 0.833
## tpqp11 1.600 0.041 38.737 0.000 1.007 0.798
## tpqp14 1.262 0.035 36.028 0.000 0.794 0.827
## tpqp03 0.940 0.030 31.718 0.000 0.591 0.534
## tpqp07 1.184 0.034 35.172 0.000 0.745 0.668
## tpqp10 1.317 0.036 36.561 0.000 0.828 0.818
## tpqp13 1.676 0.043 39.185 0.000 1.055 0.835
## tpqp04 1.349 0.037 36.859 0.000 0.849 0.780
## tpqp06 1.267 0.035 36.078 0.000 0.797 0.788
## tpqp08 1.436 0.038 37.593 0.000 0.904 0.790
## tpqp15 1.536 0.040 38.324 0.000 0.966 0.789
## LTm =~
## tpqm02 1.000 0.591 0.622
## tpqm05 1.235 0.040 31.197 0.000 0.730 0.787
## tpqm12 1.468 0.044 33.021 0.000 0.867 0.780
## tpqm16 0.698 0.030 23.301 0.000 0.412 0.674
## tpqm01 1.012 0.035 28.702 0.000 0.598 0.627
## tpqm09 1.433 0.044 32.786 0.000 0.847 0.841
## tpqm11 1.406 0.043 32.598 0.000 0.831 0.809
## tpqm14 1.155 0.038 30.403 0.000 0.683 0.843
## tpqm03 0.862 0.033 26.448 0.000 0.509 0.512
## tpqm07 1.055 0.036 29.257 0.000 0.624 0.602
## tpqm10 1.095 0.037 29.733 0.000 0.647 0.808
## tpqm13 1.563 0.047 33.600 0.000 0.924 0.796
## tpqm04 0.913 0.033 27.286 0.000 0.540 0.685
## tpqm06 1.222 0.039 31.073 0.000 0.722 0.805
## tpqm08 1.276 0.040 31.561 0.000 0.754 0.786
## tpqm15 1.400 0.043 32.557 0.000 0.828 0.754
## LT =~
## LTm 1.000 0.838 0.838
## LTp 0.981 0.041 23.777 0.000 0.772 0.772
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.792 0.031 25.438 0.000 0.487 0.487
## sexo 0.389 0.115 3.368 0.001 0.482 0.237
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 4.823 0.074 64.868 0.000 4.823 4.291
## .siqs02 4.685 0.073 64.184 0.000 4.685 3.541
## .siqs03 4.669 0.080 58.520 0.000 4.669 3.781
## .siqs04 4.522 0.067 67.602 0.000 4.522 3.687
## .siqs05 4.482 0.071 63.008 0.000 4.482 3.424
## .siqs06 4.928 0.071 69.896 0.000 4.928 4.186
## .siqs07 5.261 0.071 73.988 0.000 5.261 5.675
## .siqs08 5.192 0.074 70.280 0.000 5.192 5.286
## .siqs09 4.903 0.070 70.307 0.000 4.903 4.483
## .tpqp02 5.455 0.053 102.475 0.000 5.455 5.593
## .tpqp05 5.342 0.053 100.352 0.000 5.342 4.712
## .tpqp12 5.008 0.053 94.090 0.000 5.008 3.851
## .tpqp16 5.506 0.053 103.430 0.000 5.506 5.573
## .tpqp01 5.325 0.053 100.034 0.000 5.325 4.782
## .tpqp09 5.325 0.053 100.034 0.000 5.325 4.347
## .tpqp11 5.178 0.053 97.274 0.000 5.178 4.105
## .tpqp14 5.537 0.053 104.014 0.000 5.537 5.761
## .tpqp03 5.232 0.053 98.283 0.000 5.232 4.724
## .tpqp07 5.229 0.053 98.229 0.000 5.229 4.692
## .tpqp10 5.486 0.053 103.059 0.000 5.486 5.414
## .tpqp13 5.192 0.053 97.540 0.000 5.192 4.111
## .tpqp04 5.311 0.053 99.768 0.000 5.311 4.881
## .tpqp06 5.449 0.053 102.369 0.000 5.449 5.384
## .tpqp08 5.331 0.053 100.140 0.000 5.331 4.658
## .tpqp15 5.189 0.053 97.487 0.000 5.189 4.234
## .tpqm02 5.492 0.053 103.165 0.000 5.492 5.782
## .tpqm05 5.585 0.053 104.916 0.000 5.585 6.018
## .tpqm12 5.373 0.053 100.936 0.000 5.373 4.830
## .tpqm16 5.729 0.053 107.623 0.000 5.729 9.370
## .tpqm01 5.446 0.053 102.316 0.000 5.446 5.707
## .tpqm09 5.551 0.053 104.279 0.000 5.551 5.515
## .tpqm11 5.373 0.053 100.936 0.000 5.373 5.228
## .tpqm14 5.619 0.053 105.553 0.000 5.619 6.938
## .tpqm03 5.325 0.053 100.034 0.000 5.325 5.350
## .tpqm07 5.331 0.053 100.140 0.000 5.331 5.149
## .tpqm10 5.633 0.053 105.818 0.000 5.633 7.036
## .tpqm13 5.356 0.053 100.618 0.000 5.356 4.616
## .tpqm04 5.621 0.053 105.606 0.000 5.621 7.132
## .tpqm06 5.582 0.053 104.863 0.000 5.582 6.222
## .tpqm08 5.508 0.053 103.483 0.000 5.508 5.746
## .tpqm15 5.359 0.053 100.671 0.000 5.359 4.881
## sexo 0.412 0.053 7.748 0.000 0.412 0.837
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.290 0.071 4.083 0.000 0.290 0.230
## .siqs02 0.850 0.069 12.243 0.000 0.850 0.485
## .siqs03 0.247 0.079 3.137 0.002 0.247 0.162
## .siqs04 0.830 0.067 12.366 0.000 0.830 0.552
## .siqs05 0.798 0.074 10.779 0.000 0.798 0.466
## .siqs06 0.507 0.073 6.954 0.000 0.507 0.366
## .siqs07 0.219 0.067 3.264 0.001 0.219 0.255
## .siqs08 0.208 0.071 2.934 0.003 0.208 0.216
## .siqs09 0.612 0.065 9.348 0.000 0.612 0.511
## .tpqp02 0.555 0.056 9.936 0.000 0.555 0.584
## .tpqp05 0.397 0.059 6.687 0.000 0.397 0.309
## .tpqp12 0.587 0.061 9.638 0.000 0.587 0.347
## .tpqp16 0.436 0.057 7.675 0.000 0.436 0.447
## .tpqp01 0.667 0.057 11.685 0.000 0.667 0.538
## .tpqp09 0.459 0.060 7.607 0.000 0.459 0.306
## .tpqp11 0.578 0.060 9.605 0.000 0.578 0.363
## .tpqp14 0.293 0.058 5.088 0.000 0.293 0.317
## .tpqp03 0.877 0.056 15.780 0.000 0.877 0.715
## .tpqp07 0.687 0.057 12.059 0.000 0.687 0.553
## .tpqp10 0.340 0.058 5.881 0.000 0.340 0.332
## .tpqp13 0.482 0.061 7.919 0.000 0.482 0.303
## .tpqp04 0.463 0.058 7.968 0.000 0.463 0.391
## .tpqp06 0.388 0.058 6.752 0.000 0.388 0.379
## .tpqp08 0.493 0.059 8.386 0.000 0.493 0.377
## .tpqp15 0.568 0.060 9.529 0.000 0.568 0.378
## .tpqm02 0.553 0.056 9.882 0.000 0.553 0.613
## .tpqm05 0.328 0.057 5.714 0.000 0.328 0.381
## .tpqm12 0.485 0.059 8.183 0.000 0.485 0.392
## .tpqm16 0.204 0.055 3.739 0.000 0.204 0.545
## .tpqm01 0.553 0.056 9.873 0.000 0.553 0.607
## .tpqm09 0.296 0.059 5.020 0.000 0.296 0.292
## .tpqm11 0.365 0.059 6.222 0.000 0.365 0.346
## .tpqm14 0.190 0.057 3.334 0.001 0.190 0.289
## .tpqm03 0.731 0.055 13.238 0.000 0.731 0.738
## .tpqm07 0.683 0.056 12.135 0.000 0.683 0.637
## .tpqm10 0.222 0.057 3.933 0.000 0.222 0.347
## .tpqm13 0.493 0.060 8.205 0.000 0.493 0.366
## .tpqm04 0.330 0.055 5.944 0.000 0.330 0.531
## .tpqm06 0.283 0.057 4.940 0.000 0.283 0.352
## .tpqm08 0.351 0.058 6.080 0.000 0.351 0.382
## .tpqm15 0.520 0.059 8.865 0.000 0.520 0.432
## .it 0.322 0.038 8.483 0.000 0.331 0.331
## .cc 0.279 0.032 8.780 0.000 0.413 0.413
## .ia 0.104 0.036 2.920 0.003 0.163 0.163
## .SIQS 0.460 0.031 15.007 0.000 0.707 0.707
## .LTp 0.160 0.010 16.215 0.000 0.404 0.404
## .LTm 0.104 0.009 11.274 0.000 0.297 0.297
## LT 0.245 0.014 17.818 0.000 1.000 1.000
## sexo 0.243 0.053 4.565 0.000 0.243 1.000semPaths(# Argumentos globales
sem.config, what="diagram", whatLabels="std",layout="tree2", residuals = F,
rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Late; 2=Early.
# [Grupo 1] SIQS = 0 + .487 * LTc
# [Grupo 2] SIQS = 0 + .359 * LTc
graf <- data.frame(edad=rep(1:2,2),
"LT (std)"=c(rep(min(c(scale(datos$tpqm_LT),scale(datos$tpqp_LT)),na.rm = T),2),rep(max(c(scale(datos$tpqm_LT),scale(datos$tpqp_LT)),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$edad[i]==1,
.487*graf$`LT (std)`[i],
.359*graf$`LT (std)`[i])
}
graf %>%
ggplot(aes(x=`LT (std)`, y=`siqs (std)`, color=as.factor(edad), group=as.factor(edad)))+
geom_line()+
scale_color_manual(name="Age stage",
values = 2:3,
labels = c("early",
"late"))
Contexto Total
Probé también el modelo de contexto familiar y entrenador en simultaneo.
Sexo
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTp =~ tpqp02 + tpqp05 + tpqp12 + tpqp16 + tpqp01 + tpqp09 + tpqp11 + tpqp14 + tpqp03 + tpqp07 + tpqp10 + tpqp13 + tpqp04 + tpqp06 + tpqp08 + tpqp15
LTm =~ tpqm02 + tpqm05 + tpqm12 + tpqm16 + tpqm01 + tpqm09 + tpqm11 + tpqm14 + tpqm03 + tpqm07 + tpqm10 + tpqm13 + tpqm04 + tpqm06 + tpqm08 + tpqm15
LT =~ LTm + LTp
LTc =~ tcq02 + tcq05 + tcq12 + tcq16 + tcq01 + tcq09 + tcq11 + tcq14 + tcq03 + tcq07 + tcq10 + tcq13 + tcq04 + tcq06 + tcq08 + tcq15
# REGRESIONES
SIQS ~ LT + LTc + edad
'
# Configural model
sem.config.sexo <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config.sexo,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config.sexo 3174 6591.7
## sem.metric 3228 7629.8 1038.08 0.23571 54 < 2e-16 ***
## sem.scalar 3277 7701.0 71.13 0.03711 49 0.02106 *
## sem.strict 3334 8095.0 394.04 0.13427 57 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config.sexo 6591.747† 3174 NA .057† .981† .980† .057†
## sem.metric 7629.830 3228 NA .065 .975 .975 .064
## sem.scalar 7700.964 3277 NA .064 .975 .975 .064
## sem.strict 8095.001 3334 NA .066 .973 .973 .067
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config.sexo 54 0.007 -0.006 -0.005 0.007
## sem.scalar - sem.metric 49 0.000 0.000 0.000 0.000
## sem.strict - sem.scalar 57 0.002 -0.002 -0.001 0.003El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para ambos contextos sociales
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
# sem.config %>%
# summary(standardized=T)
semPaths(# Argumentos globales
sem.config.sexo, what="diagram", whatLabels="std",layout="tree2", residuals = F,
rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Varones; 2=Mujeres.
# [Grupo 0] SIQS = 0 + .390 * LT
# [Grupo 1] SIQS = 0 + .201 * LT
graf <- data.frame(sexo=rep(0:1,2),
"LT (std)"=c(rep(min(c(scale(datos$tpqm_LT),scale(datos$tpqp_LT)),na.rm = T),2),rep(max(c(scale(datos$tpqm_LT),scale(datos$tpqp_LT)),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$sexo[i]==0,
.390*graf$`LT (std)`[i],
.201*graf$`LT (std)`[i])
}
p1 <- graf %>%
ggplot(aes(x=`LT (std)`, y=`siqs (std)`, color=as.factor(sexo), group=as.factor(sexo)))+
geom_line()+
scale_color_manual(name="Sex",
values = 2:3,
labels = c("females",
"males"))+
ylim(c(min(scale(datos$total_IS)),max(scale(datos$total_IS))))
# [Grupo 0] SIQS = 0 + .284 * LTc
# [Grupo 1] SIQS = 0 + .525 * LTc
graf <- data.frame(sexo=rep(0:1,2),
"LTc (std)"=c(rep(min(scale(datos$tcq_LT),na.rm = T),2),rep(max(scale(datos$tcq_LT),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$sexo[i]==0,
.284*graf$`LTc (std)`[i],
.525*graf$`LTc (std)`[i])
}
p2 <- graf %>%
ggplot(aes(x=`LTc (std)`, y=`siqs (std)`, color=as.factor(sexo), group=as.factor(sexo)))+
geom_line()+
scale_color_manual(name="Sex",
values = 2:3,
labels = c("females",
"males"))+
ylim(c(min(scale(datos$total_IS)),max(scale(datos$total_IS))))
grid.arrange(p1,p2, nrow=1)
Edad
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTp =~ tpqp02 + tpqp05 + tpqp12 + tpqp16 + tpqp01 + tpqp09 + tpqp11 + tpqp14 + tpqp03 + tpqp07 + tpqp10 + tpqp13 + tpqp04 + tpqp06 + tpqp08 + tpqp15
LTm =~ tpqm02 + tpqm05 + tpqm12 + tpqm16 + tpqm01 + tpqm09 + tpqm11 + tpqm14 + tpqm03 + tpqm07 + tpqm10 + tpqm13 + tpqm04 + tpqm06 + tpqm08 + tpqm15
LT =~ LTm + LTp
LTc =~ tcq02 + tcq05 + tcq12 + tcq16 + tcq01 + tcq09 + tcq11 + tcq14 + tcq03 + tcq07 + tcq10 + tcq13 + tcq04 + tcq06 + tcq08 + tcq15
# REGRESIONES
SIQS ~ LT + LTc + sexo
'
# Configural model
sem.config.edad <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Model comparison
semTools::compareFit(sem.config.edad,
sem.metric,
sem.scalar,
sem.strict) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config.edad 3174 5373.0
## sem.metric 3228 6720.6 1347.57 0.270248 54 < 2e-16 ***
## sem.scalar 3277 6793.4 72.75 0.038442 49 0.01542 *
## sem.strict 3334 7115.7 322.32 0.119127 57 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config.edad 5373.037† 3174 NA .046† .987† .987† .057†
## sem.metric 6720.611 3228 NA .058 .980 .979 .064
## sem.scalar 6793.361 3277 NA .057 .980 .979 .064
## sem.strict 7115.682 3334 NA .059 .978 .978 .066
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config.edad 54 0.011 -0.008 -0.007 0.007
## sem.scalar - sem.metric 49 0.000 0.000 0.000 0.000
## sem.strict - sem.scalar 57 0.002 -0.002 -0.001 0.001El modelo métrico ya difiere significativamente del modelo configural, por lo que no se verifica la invariaza en el modelo completo para ambos contextos
Estimaciones para el modelo configural (el que asume menos igualdades entre los grupos)
# sem.config %>%
# summary(standardized=T)
semPaths(# Argumentos globales
sem.config.edad, what="diagram", whatLabels="std",layout="tree2", residuals = F,
rotation = 2, width=50, height=35,exoVar = F,
# Etiquetas
curvePivot=T, curvature = .5
)
Nota. 1=Late; 2=Early.
sem.config.edad %>% summary(standardized=T)## lavaan 0.6.16 ended normally after 116 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 364
##
## Number of observations per group:
## 2 302
## 1 354
##
## Model Test User Model:
##
## Test statistic 5373.037
## Degrees of freedom 3174
## P-value (Unknown) NA
## Test statistic for each group:
## 2 2700.585
## 1 2672.453
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.907 0.845
## siqs02 0.858 0.034 24.869 0.000 0.778 0.669
## siqs03 1.117 0.041 27.395 0.000 1.013 0.856
## cc =~
## siqs04 1.000 1.068 0.779
## siqs05 0.931 0.047 19.896 0.000 0.995 0.715
## siqs06 0.745 0.039 18.874 0.000 0.796 0.673
## ia =~
## siqs07 1.000 0.857 0.858
## siqs08 1.014 0.042 23.961 0.000 0.869 0.901
## siqs09 0.945 0.040 23.340 0.000 0.809 0.731
## SIQS =~
## it 1.000 0.916 0.916
## cc 0.655 0.031 20.822 0.000 0.509 0.509
## ia 0.868 0.038 22.679 0.000 0.842 0.842
## LTp =~
## tpqp02 1.000 0.724 0.685
## tpqp05 1.107 0.031 35.800 0.000 0.801 0.760
## tpqp12 1.601 0.040 40.367 0.000 1.158 0.812
## tpqp16 1.120 0.031 35.966 0.000 0.810 0.749
## tpqp01 1.063 0.030 35.177 0.000 0.769 0.723
## tpqp09 1.244 0.033 37.437 0.000 0.900 0.794
## tpqp11 1.200 0.032 36.949 0.000 0.868 0.738
## tpqp14 1.180 0.032 36.712 0.000 0.854 0.868
## tpqp03 0.863 0.027 31.784 0.000 0.624 0.580
## tpqp07 1.352 0.035 38.503 0.000 0.978 0.764
## tpqp10 1.139 0.031 36.213 0.000 0.824 0.850
## tpqp13 1.405 0.036 38.964 0.000 1.017 0.792
## tpqp04 1.160 0.032 36.470 0.000 0.839 0.742
## tpqp06 1.193 0.032 36.871 0.000 0.864 0.826
## tpqp08 1.209 0.033 37.056 0.000 0.875 0.821
## tpqp15 1.418 0.036 39.067 0.000 1.026 0.840
## LTm =~
## tpqm02 1.000 0.674 0.645
## tpqm05 0.750 0.029 25.421 0.000 0.505 0.604
## tpqm12 1.329 0.040 33.199 0.000 0.896 0.801
## tpqm16 0.842 0.031 27.199 0.000 0.568 0.744
## tpqm01 0.813 0.030 26.667 0.000 0.548 0.652
## tpqm09 0.975 0.033 29.332 0.000 0.658 0.707
## tpqm11 1.162 0.037 31.649 0.000 0.784 0.744
## tpqm14 1.136 0.036 31.367 0.000 0.766 0.861
## tpqm03 0.832 0.031 27.018 0.000 0.561 0.552
## tpqm07 1.028 0.034 30.056 0.000 0.693 0.650
## tpqm10 1.090 0.035 30.841 0.000 0.735 0.854
## tpqm13 1.310 0.040 33.045 0.000 0.884 0.769
## tpqm04 0.901 0.032 28.193 0.000 0.607 0.701
## tpqm06 1.162 0.037 31.647 0.000 0.784 0.821
## tpqm08 0.892 0.032 28.050 0.000 0.601 0.706
## tpqm15 1.349 0.040 33.359 0.000 0.910 0.834
## LT =~
## LTm 1.000 0.987 0.987
## LTp 0.694 0.033 21.153 0.000 0.638 0.638
## LTc =~
## tcq02 1.000 1.012 0.734
## tcq05 0.982 0.023 42.347 0.000 0.994 0.777
## tcq12 0.717 0.020 36.109 0.000 0.726 0.487
## tcq16 1.064 0.024 43.729 0.000 1.077 0.776
## tcq01 1.036 0.024 43.282 0.000 1.048 0.827
## tcq09 0.926 0.022 41.264 0.000 0.937 0.694
## tcq11 0.915 0.022 41.035 0.000 0.926 0.713
## tcq14 0.849 0.021 39.583 0.000 0.859 0.724
## tcq03 0.720 0.020 36.179 0.000 0.728 0.603
## tcq07 0.706 0.020 35.778 0.000 0.715 0.632
## tcq10 0.579 0.018 31.493 0.000 0.586 0.439
## tcq13 0.823 0.021 38.967 0.000 0.833 0.709
## tcq04 1.022 0.024 43.053 0.000 1.034 0.800
## tcq06 0.993 0.023 42.544 0.000 1.005 0.801
## tcq08 0.858 0.022 39.802 0.000 0.868 0.757
## tcq15 0.876 0.022 40.211 0.000 0.887 0.746
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.390 0.019 20.166 0.000 0.312 0.312
## LTc 0.272 0.011 25.280 0.000 0.332 0.332
## sexo 0.292 0.114 2.557 0.011 0.352 0.176
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.099 0.004 22.917 0.000 0.147 0.147
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 4.736 0.083 56.783 0.000 4.736 4.411
## .siqs02 4.843 0.077 62.535 0.000 4.843 4.166
## .siqs03 4.566 0.089 51.533 0.000 4.566 3.860
## .siqs04 4.204 0.070 60.152 0.000 4.204 3.067
## .siqs05 4.168 0.068 60.940 0.000 4.168 2.998
## .siqs06 4.766 0.065 73.602 0.000 4.766 4.029
## .siqs07 5.130 0.078 65.890 0.000 5.130 5.140
## .siqs08 5.115 0.078 65.285 0.000 5.115 5.304
## .siqs09 4.862 0.076 64.017 0.000 4.862 4.392
## .tpqp02 5.308 0.058 92.102 0.000 5.308 5.021
## .tpqp05 5.464 0.058 94.802 0.000 5.464 5.178
## .tpqp12 4.851 0.058 84.173 0.000 4.851 3.401
## .tpqp16 5.440 0.058 94.400 0.000 5.440 5.028
## .tpqp01 5.371 0.058 93.193 0.000 5.371 5.050
## .tpqp09 5.411 0.058 93.883 0.000 5.411 4.771
## .tpqp11 5.192 0.058 90.091 0.000 5.192 4.412
## .tpqp14 5.523 0.058 95.836 0.000 5.523 5.614
## .tpqp03 5.275 0.058 91.527 0.000 5.275 4.904
## .tpqp07 5.053 0.058 87.677 0.000 5.053 3.948
## .tpqp10 5.520 0.058 95.779 0.000 5.520 5.689
## .tpqp13 5.132 0.058 89.056 0.000 5.132 3.995
## .tpqp04 5.285 0.058 91.699 0.000 5.285 4.670
## .tpqp06 5.447 0.058 94.515 0.000 5.447 5.213
## .tpqp08 5.424 0.058 94.113 0.000 5.424 5.090
## .tpqp15 5.126 0.058 88.941 0.000 5.126 4.196
## .tpqm02 5.411 0.058 93.883 0.000 5.411 5.175
## .tpqm05 5.672 0.058 98.422 0.000 5.672 6.782
## .tpqm12 5.328 0.058 92.446 0.000 5.328 4.763
## .tpqm16 5.666 0.058 98.307 0.000 5.666 7.426
## .tpqm01 5.546 0.058 96.238 0.000 5.546 6.596
## .tpqm09 5.626 0.058 97.617 0.000 5.626 6.045
## .tpqm11 5.371 0.058 93.193 0.000 5.371 5.095
## .tpqm14 5.606 0.058 97.273 0.000 5.606 6.303
## .tpqm03 5.268 0.058 91.412 0.000 5.268 5.181
## .tpqm07 5.278 0.058 91.584 0.000 5.278 4.947
## .tpqm10 5.586 0.058 96.928 0.000 5.586 6.486
## .tpqm13 5.301 0.058 91.987 0.000 5.301 4.613
## .tpqm04 5.566 0.058 96.583 0.000 5.566 6.421
## .tpqm06 5.560 0.058 96.468 0.000 5.560 5.823
## .tpqm08 5.566 0.058 96.583 0.000 5.566 6.538
## .tpqm15 5.325 0.058 92.389 0.000 5.325 4.881
## .tcq02 3.977 0.058 69.004 0.000 3.977 2.883
## .tcq05 4.788 0.058 83.081 0.000 4.788 3.743
## .tcq12 3.854 0.058 66.878 0.000 3.854 2.588
## .tcq16 4.682 0.058 81.242 0.000 4.682 3.374
## .tcq01 4.603 0.058 79.863 0.000 4.603 3.628
## .tcq09 4.897 0.058 84.977 0.000 4.897 3.630
## .tcq11 4.589 0.058 79.634 0.000 4.589 3.536
## .tcq14 4.772 0.058 82.794 0.000 4.772 4.024
## .tcq03 4.646 0.058 80.610 0.000 4.646 3.846
## .tcq07 4.818 0.058 83.598 0.000 4.818 4.260
## .tcq10 4.384 0.058 76.071 0.000 4.384 3.281
## .tcq13 4.517 0.058 78.370 0.000 4.517 3.845
## .tcq04 4.795 0.058 83.196 0.000 4.795 3.708
## .tcq06 4.712 0.058 81.760 0.000 4.712 3.755
## .tcq08 5.073 0.058 88.022 0.000 5.073 4.420
## .tcq15 4.844 0.058 84.058 0.000 4.844 4.074
## sexo 0.507 0.058 8.791 0.000 0.507 1.012
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.330 0.076 4.350 0.000 0.330 0.286
## .siqs02 0.746 0.070 10.693 0.000 0.746 0.552
## .siqs03 0.373 0.082 4.525 0.000 0.373 0.266
## .siqs04 0.737 0.095 7.750 0.000 0.737 0.392
## .siqs05 0.944 0.089 10.649 0.000 0.944 0.488
## .siqs06 0.766 0.076 10.122 0.000 0.766 0.547
## .siqs07 0.262 0.076 3.460 0.001 0.262 0.263
## .siqs08 0.175 0.077 2.290 0.022 0.175 0.189
## .siqs09 0.571 0.073 7.793 0.000 0.571 0.466
## .tpqp02 0.594 0.061 9.657 0.000 0.594 0.531
## .tpqp05 0.471 0.062 7.550 0.000 0.471 0.423
## .tpqp12 0.692 0.068 10.195 0.000 0.692 0.340
## .tpqp16 0.514 0.063 8.223 0.000 0.514 0.439
## .tpqp01 0.540 0.062 8.708 0.000 0.540 0.477
## .tpqp09 0.476 0.064 7.470 0.000 0.476 0.370
## .tpqp11 0.631 0.063 9.974 0.000 0.631 0.456
## .tpqp14 0.239 0.063 3.791 0.000 0.239 0.247
## .tpqp03 0.767 0.060 12.680 0.000 0.767 0.663
## .tpqp07 0.681 0.065 10.506 0.000 0.681 0.416
## .tpqp10 0.262 0.063 4.180 0.000 0.262 0.278
## .tpqp13 0.616 0.065 9.417 0.000 0.616 0.373
## .tpqp04 0.576 0.063 9.169 0.000 0.576 0.450
## .tpqp06 0.346 0.063 5.479 0.000 0.346 0.317
## .tpqp08 0.370 0.063 5.833 0.000 0.370 0.325
## .tpqp15 0.440 0.066 6.708 0.000 0.440 0.295
## .tpqm02 0.639 0.062 10.379 0.000 0.639 0.584
## .tpqm05 0.444 0.060 7.426 0.000 0.444 0.635
## .tpqm12 0.448 0.065 6.926 0.000 0.448 0.358
## .tpqm16 0.260 0.060 4.301 0.000 0.260 0.446
## .tpqm01 0.407 0.060 6.757 0.000 0.407 0.575
## .tpqm09 0.434 0.061 7.073 0.000 0.434 0.501
## .tpqm11 0.497 0.063 7.892 0.000 0.497 0.447
## .tpqm14 0.204 0.063 3.253 0.001 0.204 0.258
## .tpqm03 0.719 0.060 11.928 0.000 0.719 0.696
## .tpqm07 0.658 0.062 10.654 0.000 0.658 0.578
## .tpqm10 0.201 0.062 3.228 0.001 0.201 0.271
## .tpqm13 0.540 0.064 8.380 0.000 0.540 0.409
## .tpqm04 0.383 0.061 6.296 0.000 0.383 0.509
## .tpqm06 0.298 0.063 4.729 0.000 0.298 0.327
## .tpqm08 0.363 0.061 5.982 0.000 0.363 0.501
## .tpqm15 0.363 0.065 5.588 0.000 0.363 0.305
## .tcq02 0.879 0.067 13.214 0.000 0.879 0.462
## .tcq05 0.648 0.066 9.794 0.000 0.648 0.396
## .tcq12 1.691 0.062 27.228 0.000 1.691 0.762
## .tcq16 0.766 0.068 11.305 0.000 0.766 0.398
## .tcq01 0.510 0.067 7.585 0.000 0.510 0.317
## .tcq09 0.942 0.065 14.449 0.000 0.942 0.518
## .tcq11 0.828 0.065 12.735 0.000 0.828 0.492
## .tcq14 0.668 0.064 10.451 0.000 0.668 0.475
## .tcq03 0.928 0.062 14.942 0.000 0.928 0.636
## .tcq07 0.768 0.062 12.399 0.000 0.768 0.601
## .tcq10 1.442 0.061 23.821 0.000 1.442 0.808
## .tcq13 0.687 0.064 10.800 0.000 0.687 0.497
## .tcq04 0.602 0.067 8.989 0.000 0.602 0.360
## .tcq06 0.565 0.066 8.506 0.000 0.565 0.359
## .tcq08 0.563 0.064 8.779 0.000 0.563 0.427
## .tcq15 0.628 0.064 9.753 0.000 0.628 0.444
## .it 0.132 0.044 3.008 0.003 0.161 0.161
## .cc 0.846 0.065 12.924 0.000 0.741 0.741
## .ia 0.214 0.042 5.114 0.000 0.292 0.292
## .SIQS 0.505 0.036 14.116 0.000 0.731 0.731
## .LTp 0.311 0.015 20.370 0.000 0.593 0.593
## .LTm 0.012 0.017 0.696 0.487 0.026 0.026
## LT 0.443 0.026 16.860 0.000 1.000 1.000
## LTc 1.024 0.033 30.799 0.000 1.000 1.000
## sexo 0.251 0.058 4.352 0.000 0.251 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.021 0.908
## siqs02 0.892 0.026 33.852 0.000 0.910 0.688
## siqs03 1.109 0.030 36.529 0.000 1.133 0.917
## cc =~
## siqs04 1.000 0.817 0.666
## siqs05 1.204 0.038 31.386 0.000 0.984 0.752
## siqs06 1.123 0.037 30.691 0.000 0.917 0.779
## ia =~
## siqs07 1.000 0.812 0.876
## siqs08 1.057 0.033 31.891 0.000 0.858 0.873
## siqs09 0.942 0.031 30.377 0.000 0.765 0.699
## SIQS =~
## it 1.000 0.791 0.791
## cc 0.815 0.027 29.835 0.000 0.807 0.807
## ia 0.903 0.029 31.068 0.000 0.899 0.899
## LTp =~
## tpqp02 1.000 0.631 0.647
## tpqp05 1.480 0.038 38.774 0.000 0.934 0.824
## tpqp12 1.662 0.042 39.985 0.000 1.049 0.806
## tpqp16 1.162 0.033 35.678 0.000 0.733 0.742
## tpqp01 1.193 0.033 36.054 0.000 0.753 0.676
## tpqp09 1.611 0.041 39.676 0.000 1.016 0.830
## tpqp11 1.584 0.040 39.506 0.000 1.000 0.792
## tpqp14 1.256 0.034 36.763 0.000 0.793 0.825
## tpqp03 0.956 0.029 32.693 0.000 0.603 0.545
## tpqp07 1.176 0.033 35.852 0.000 0.742 0.666
## tpqp10 1.324 0.035 37.441 0.000 0.835 0.824
## tpqp13 1.672 0.042 40.038 0.000 1.055 0.835
## tpqp04 1.354 0.036 37.725 0.000 0.854 0.785
## tpqp06 1.260 0.034 36.806 0.000 0.795 0.786
## tpqp08 1.441 0.037 38.471 0.000 0.909 0.794
## tpqp15 1.544 0.039 39.239 0.000 0.974 0.795
## LTm =~
## tpqm02 1.000 0.581 0.612
## tpqm05 1.258 0.040 31.598 0.000 0.731 0.788
## tpqm12 1.480 0.044 33.317 0.000 0.860 0.773
## tpqm16 0.702 0.030 23.506 0.000 0.408 0.667
## tpqm01 1.023 0.035 29.009 0.000 0.594 0.623
## tpqm09 1.456 0.044 33.153 0.000 0.846 0.841
## tpqm11 1.443 0.044 33.067 0.000 0.839 0.816
## tpqm14 1.172 0.038 30.763 0.000 0.681 0.841
## tpqm03 0.888 0.033 27.025 0.000 0.516 0.518
## tpqm07 1.074 0.036 29.662 0.000 0.624 0.603
## tpqm10 1.124 0.037 30.239 0.000 0.653 0.816
## tpqm13 1.577 0.047 33.902 0.000 0.916 0.790
## tpqm04 0.937 0.034 27.793 0.000 0.544 0.691
## tpqm06 1.216 0.039 31.199 0.000 0.706 0.787
## tpqm08 1.303 0.041 31.999 0.000 0.758 0.790
## tpqm15 1.450 0.044 33.119 0.000 0.843 0.768
## LT =~
## LTm 1.000 0.821 0.821
## LTp 1.043 0.040 26.381 0.000 0.789 0.789
## LTc =~
## tcq02 1.000 0.931 0.634
## tcq05 0.930 0.022 42.958 0.000 0.866 0.727
## tcq12 0.873 0.021 41.648 0.000 0.813 0.501
## tcq16 1.081 0.024 45.833 0.000 1.006 0.805
## tcq01 0.924 0.022 42.834 0.000 0.861 0.692
## tcq09 0.851 0.021 41.126 0.000 0.793 0.710
## tcq11 1.013 0.023 44.644 0.000 0.943 0.754
## tcq14 0.994 0.022 44.269 0.000 0.925 0.805
## tcq03 0.831 0.020 40.611 0.000 0.774 0.626
## tcq07 0.898 0.021 42.234 0.000 0.836 0.676
## tcq10 0.772 0.020 39.005 0.000 0.719 0.540
## tcq13 1.037 0.023 45.074 0.000 0.965 0.750
## tcq04 0.938 0.022 43.132 0.000 0.873 0.744
## tcq06 1.020 0.023 44.774 0.000 0.950 0.768
## tcq08 0.916 0.021 42.653 0.000 0.853 0.784
## tcq15 1.031 0.023 44.979 0.000 0.960 0.775
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.650 0.025 25.834 0.000 0.384 0.384
## LTc 0.327 0.011 30.500 0.000 0.377 0.377
## sexo 0.390 0.116 3.368 0.001 0.482 0.238
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.121 0.004 28.884 0.000 0.272 0.272
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 4.822 0.074 64.785 0.000 4.822 4.291
## .siqs02 4.696 0.071 66.487 0.000 4.696 3.549
## .siqs03 4.675 0.079 59.549 0.000 4.675 3.785
## .siqs04 4.516 0.068 66.317 0.000 4.516 3.682
## .siqs05 4.469 0.074 60.570 0.000 4.469 3.414
## .siqs06 4.923 0.071 68.918 0.000 4.923 4.182
## .siqs07 5.262 0.071 74.080 0.000 5.262 5.675
## .siqs08 5.197 0.073 71.383 0.000 5.197 5.291
## .siqs09 4.906 0.069 70.844 0.000 4.906 4.485
## .tpqp02 5.455 0.053 102.475 0.000 5.455 5.593
## .tpqp05 5.342 0.053 100.352 0.000 5.342 4.712
## .tpqp12 5.008 0.053 94.090 0.000 5.008 3.851
## .tpqp16 5.506 0.053 103.430 0.000 5.506 5.573
## .tpqp01 5.325 0.053 100.034 0.000 5.325 4.782
## .tpqp09 5.325 0.053 100.034 0.000 5.325 4.347
## .tpqp11 5.178 0.053 97.274 0.000 5.178 4.105
## .tpqp14 5.537 0.053 104.014 0.000 5.537 5.761
## .tpqp03 5.232 0.053 98.283 0.000 5.232 4.724
## .tpqp07 5.229 0.053 98.229 0.000 5.229 4.692
## .tpqp10 5.486 0.053 103.059 0.000 5.486 5.414
## .tpqp13 5.192 0.053 97.540 0.000 5.192 4.111
## .tpqp04 5.311 0.053 99.768 0.000 5.311 4.881
## .tpqp06 5.449 0.053 102.369 0.000 5.449 5.384
## .tpqp08 5.331 0.053 100.140 0.000 5.331 4.658
## .tpqp15 5.189 0.053 97.487 0.000 5.189 4.234
## .tpqm02 5.492 0.053 103.165 0.000 5.492 5.782
## .tpqm05 5.585 0.053 104.916 0.000 5.585 6.018
## .tpqm12 5.373 0.053 100.936 0.000 5.373 4.830
## .tpqm16 5.729 0.053 107.623 0.000 5.729 9.370
## .tpqm01 5.446 0.053 102.316 0.000 5.446 5.707
## .tpqm09 5.551 0.053 104.279 0.000 5.551 5.515
## .tpqm11 5.373 0.053 100.936 0.000 5.373 5.228
## .tpqm14 5.619 0.053 105.553 0.000 5.619 6.938
## .tpqm03 5.325 0.053 100.034 0.000 5.325 5.350
## .tpqm07 5.331 0.053 100.140 0.000 5.331 5.149
## .tpqm10 5.633 0.053 105.818 0.000 5.633 7.036
## .tpqm13 5.356 0.053 100.618 0.000 5.356 4.616
## .tpqm04 5.621 0.053 105.606 0.000 5.621 7.132
## .tpqm06 5.582 0.053 104.863 0.000 5.582 6.222
## .tpqm08 5.508 0.053 103.483 0.000 5.508 5.746
## .tpqm15 5.359 0.053 100.671 0.000 5.359 4.881
## .tcq02 4.280 0.053 80.399 0.000 4.280 2.914
## .tcq05 5.124 0.053 96.266 0.000 5.124 4.303
## .tcq12 3.698 0.053 69.466 0.000 3.698 2.282
## .tcq16 5.040 0.053 94.674 0.000 5.040 4.032
## .tcq01 4.833 0.053 90.800 0.000 4.833 3.889
## .tcq09 5.257 0.053 98.760 0.000 5.257 4.711
## .tcq11 5.020 0.053 94.302 0.000 5.020 4.011
## .tcq14 5.073 0.053 95.311 0.000 5.073 4.415
## .tcq03 4.938 0.053 92.763 0.000 4.938 3.997
## .tcq07 4.915 0.053 92.339 0.000 4.915 3.976
## .tcq10 4.562 0.053 85.705 0.000 4.562 3.428
## .tcq13 4.749 0.053 89.208 0.000 4.749 3.690
## .tcq04 5.017 0.053 94.249 0.000 5.017 4.276
## .tcq06 4.952 0.053 93.029 0.000 4.952 4.003
## .tcq08 5.311 0.053 99.768 0.000 5.311 4.881
## .tcq15 5.040 0.053 94.674 0.000 5.040 4.069
## sexo 0.412 0.053 7.748 0.000 0.412 0.837
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 0.221 0.070 3.144 0.002 0.221 0.175
## .siqs02 0.921 0.066 13.997 0.000 0.921 0.526
## .siqs03 0.242 0.076 3.191 0.001 0.242 0.159
## .siqs04 0.836 0.065 12.959 0.000 0.836 0.556
## .siqs05 0.745 0.072 10.321 0.000 0.745 0.435
## .siqs06 0.545 0.069 7.927 0.000 0.545 0.393
## .siqs07 0.200 0.066 3.029 0.002 0.200 0.233
## .siqs08 0.229 0.068 3.344 0.001 0.229 0.237
## .siqs09 0.612 0.064 9.555 0.000 0.612 0.512
## .tpqp02 0.553 0.056 9.913 0.000 0.553 0.582
## .tpqp05 0.413 0.059 7.008 0.000 0.413 0.322
## .tpqp12 0.591 0.061 9.764 0.000 0.591 0.350
## .tpqp16 0.439 0.057 7.738 0.000 0.439 0.450
## .tpqp01 0.673 0.057 11.832 0.000 0.673 0.543
## .tpqp09 0.467 0.060 7.779 0.000 0.467 0.312
## .tpqp11 0.592 0.060 9.898 0.000 0.592 0.372
## .tpqp14 0.295 0.057 5.152 0.000 0.295 0.320
## .tpqp03 0.863 0.056 15.524 0.000 0.863 0.703
## .tpqp07 0.692 0.057 12.179 0.000 0.692 0.557
## .tpqp10 0.329 0.058 5.699 0.000 0.329 0.321
## .tpqp13 0.483 0.061 7.959 0.000 0.483 0.303
## .tpqp04 0.454 0.058 7.826 0.000 0.454 0.384
## .tpqp06 0.392 0.057 6.835 0.000 0.392 0.383
## .tpqp08 0.483 0.059 8.236 0.000 0.483 0.369
## .tpqp15 0.553 0.060 9.292 0.000 0.553 0.368
## .tpqm02 0.564 0.056 10.121 0.000 0.564 0.626
## .tpqm05 0.327 0.057 5.700 0.000 0.327 0.379
## .tpqm12 0.497 0.059 8.437 0.000 0.497 0.402
## .tpqm16 0.208 0.054 3.811 0.000 0.208 0.555
## .tpqm01 0.557 0.056 9.974 0.000 0.557 0.612
## .tpqm09 0.297 0.059 5.062 0.000 0.297 0.293
## .tpqm11 0.353 0.059 6.015 0.000 0.353 0.334
## .tpqm14 0.192 0.057 3.378 0.001 0.192 0.292
## .tpqm03 0.724 0.055 13.118 0.000 0.724 0.731
## .tpqm07 0.682 0.056 12.140 0.000 0.682 0.636
## .tpqm10 0.214 0.056 3.798 0.000 0.214 0.334
## .tpqm13 0.506 0.060 8.476 0.000 0.506 0.376
## .tpqm04 0.325 0.055 5.859 0.000 0.325 0.523
## .tpqm06 0.306 0.057 5.363 0.000 0.306 0.380
## .tpqm08 0.345 0.058 5.994 0.000 0.345 0.376
## .tpqm15 0.494 0.059 8.426 0.000 0.494 0.410
## .tcq02 1.290 0.060 21.580 0.000 1.290 0.598
## .tcq05 0.668 0.059 11.356 0.000 0.668 0.471
## .tcq12 1.965 0.058 33.780 0.000 1.965 0.749
## .tcq16 0.550 0.061 9.029 0.000 0.550 0.352
## .tcq01 0.804 0.059 13.675 0.000 0.804 0.521
## .tcq09 0.617 0.058 10.652 0.000 0.617 0.495
## .tcq11 0.676 0.060 11.279 0.000 0.676 0.432
## .tcq14 0.464 0.060 7.782 0.000 0.464 0.352
## .tcq03 0.927 0.058 16.068 0.000 0.927 0.608
## .tcq07 0.830 0.058 14.195 0.000 0.830 0.543
## .tcq10 1.254 0.057 21.973 0.000 1.254 0.708
## .tcq13 0.724 0.060 12.019 0.000 0.724 0.437
## .tcq04 0.614 0.059 10.412 0.000 0.614 0.446
## .tcq06 0.628 0.060 10.457 0.000 0.628 0.410
## .tcq08 0.456 0.059 7.773 0.000 0.456 0.385
## .tcq15 0.612 0.060 10.160 0.000 0.612 0.399
## .it 0.389 0.038 10.205 0.000 0.374 0.374
## .cc 0.233 0.030 7.898 0.000 0.349 0.349
## .ia 0.126 0.035 3.603 0.000 0.192 0.192
## .SIQS 0.376 0.026 14.522 0.000 0.576 0.576
## .LTp 0.150 0.009 17.255 0.000 0.377 0.377
## .LTm 0.110 0.008 13.742 0.000 0.326 0.326
## LT 0.228 0.012 18.832 0.000 1.000 1.000
## LTc 0.867 0.027 31.889 0.000 1.000 1.000
## sexo 0.243 0.053 4.565 0.000 0.243 1.000# [Grupo 1] SIQS = 0 + .384 * LT
# [Grupo 2] SIQS = 0 + .312 * LT
graf <- data.frame(edad=rep(1:2,2),
"LT (std)"=c(rep(min(c(scale(datos$tpqm_LT),scale(datos$tpqp_LT)),na.rm = T),2),rep(max(c(scale(datos$tpqm_LT),scale(datos$tpqp_LT)),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$edad[i]==1,
.384*graf$`LT (std)`[i],
.312*graf$`LT (std)`[i])
}
p3 <- graf %>%
ggplot(aes(x=`LT (std)`, y=`siqs (std)`, color=as.factor(edad), group=as.factor(edad)))+
geom_line()+
scale_color_manual(name="Age stage",
values = 2:3,
labels = c("early",
"late"))+
ylim(c(min(scale(datos$total_IS)),max(scale(datos$total_IS))))
# [Grupo 1] SIQS = 0 + .377 * LTc
# [Grupo 2] SIQS = 0 + .332 * LTc
graf <- data.frame(edad=rep(1:2,2),
"LTc (std)"=c(rep(min(scale(datos$tcq_LT),na.rm = T),2),rep(max(scale(datos$tcq_LT),na.rm = T),2)),
"siqs (std)"=rep(NA,4),
check.names = F)
for(i in 1:4){
graf$`siqs (std)`[i] <- ifelse(graf$edad[i]==1,
.377*graf$`LTc (std)`[i],
.332*graf$`LTc (std)`[i])
}
p4 <- graf %>%
ggplot(aes(x=`LTc (std)`, y=`siqs (std)`, color=as.factor(edad), group=as.factor(edad)))+
geom_line()+
scale_color_manual(name="Age stage",
values = 2:3,
labels = c("early",
"late"))+
ylim(c(min(scale(datos$total_IS)),max(scale(datos$total_IS))))
grid.arrange(p3, p4, nrow=1)
INVARIANZA DEL SEM
Agregué la comparación de modelos primero igualando las medias de las variables latentes, luego agregando la igualdad de varianzas de las latentes y finalmente la igualdad de covarianzas entre latentes. No tenía en claro si estos tres modelos debían compararse con el modelo de la invarianza estricta o con el configural. Por las dudas, hice ambas comparaciones. En ambos caminos parecería que el SEM es invariante.
Sexo
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTp =~ tpqp02 + tpqp05 + tpqp12 + tpqp16 + tpqp01 + tpqp09 + tpqp11 + tpqp14 + tpqp03 + tpqp07 + tpqp10 + tpqp13 + tpqp04 + tpqp06 + tpqp08 + tpqp15
LTm =~ tpqm02 + tpqm05 + tpqm12 + tpqm16 + tpqm01 + tpqm09 + tpqm11 + tpqm14 + tpqm03 + tpqm07 + tpqm10 + tpqm13 + tpqm04 + tpqm06 + tpqm08 + tpqm15
LT =~ LTm + LTp
LTc =~ tcq02 + tcq05 + tcq12 + tcq16 + tcq01 + tcq09 + tcq11 + tcq14 + tcq03 + tcq07 + tcq10 + tcq13 + tcq04 + tcq06 + tcq08 + tcq15
# REGRESIONES
SIQS ~ LT + LTc + edaddicot
'
# Configural model
sem.config.sexo <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Latent variables means
sem.lv1means <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals",
"means"))
# Latent variables variances
sem.lv2vars <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals",
"means",
"lv.variances"))
# Latent variables covariances
sem.lv3covars <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "sexo",
group.equal = c("loadings",
"intercepts",
"residuals",
"means",
"lv.variances",
"lv.covariances"))
# Model comparison
semTools::compareFit(sem.config.sexo,
sem.metric,
sem.scalar,
sem.strict,
sem.lv1means,
sem.lv2vars,
sem.lv3covars) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config.sexo 3174 5502.3
## sem.metric 3228 6524.1 1021.78 0.23375 54 < 2e-16 ***
## sem.scalar 3277 6595.2 71.11 0.03709 49 0.02114 *
## sem.strict 3334 6988.9 393.67 0.13419 57 < 2e-16 ***
## sem.lv1means 3342 7500.4 511.52 0.43805 8 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config.sexo 5502.296† 3174 NA .047† .987† .986† .057†
## sem.metric 6524.076 3228 NA .056 .981 .981 .064
## sem.scalar 6595.190 3277 NA .056 .981 .981 .064
## sem.strict 6988.860 3334 NA .058 .979 .979 .067
## sem.lv1means 7500.378 3342 NA .062 .976 .977 .070
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config.sexo 54 0.009 -0.005 -0.005 0.007
## sem.scalar - sem.metric 49 0.000 0.000 0.000 0.000
## sem.strict - sem.scalar 57 0.002 -0.002 -0.002 0.003
## sem.lv1means - sem.strict 8 0.004 -0.003 -0.003 0.003El modelo igualando las varianzas de las variables latentes no convergió y tampoco el que iguala las covarianzas.
# Model comparison
semTools::compareFit(sem.config.sexo,
sem.lv1means,
sem.lv2vars,
sem.lv3covars) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config.sexo 3174 5502.3
## sem.lv1means 3342 7500.4 1998.1 0.18224 168 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config.sexo 5502.296† 3174 NA .047† .987† .986† .057†
## sem.lv1means 7500.378 3342 NA .062 .976 .977 .070
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.lv1means - sem.config.sexo 168 0.014 -0.01 -0.01 0.013Modelo con igualdad de medias
sem.lv1means %>%
summary(fit.measures=T, standardized=T)## lavaan 0.6.16 ended normally after 164 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 364
## Number of equality constraints 168
##
## Number of observations per group:
## 1 299
## 0 357
##
## Model Test User Model:
##
## Test statistic 7500.378
## Degrees of freedom 3342
## P-value (Unknown) NA
## Test statistic for each group:
## 1 3403.373
## 0 4097.005
##
## Model Test Baseline Model:
##
## Test statistic 179629.264
## Degrees of freedom 3306
## P-value NA
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.976
## Tucker-Lewis Index (TLI) 0.977
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062
## 90 Percent confidence interval - lower 0.060
## 90 Percent confidence interval - upper 0.064
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.705 0.807
## siqs02 (.p2.) 0.860 0.020 42.535 0.000 0.606 0.545
## siqs03 (.p3.) 1.124 0.024 47.391 0.000 0.793 0.828
## cc =~
## siqs04 1.000 0.936 0.712
## siqs05 (.p5.) 1.054 0.028 37.359 0.000 0.987 0.723
## siqs06 (.p6.) 0.984 0.027 36.567 0.000 0.921 0.772
## ia =~
## siqs07 1.000 0.650 0.833
## siqs08 (.p8.) 1.002 0.024 41.946 0.000 0.651 0.817
## siqs09 (.p9.) 0.892 0.022 39.808 0.000 0.580 0.591
## SIQS =~
## it 1.000 0.892 0.892
## cc (.11.) 0.794 0.021 37.001 0.000 0.534 0.534
## ia (.12.) 0.956 0.024 39.638 0.000 0.924 0.924
## LTp =~
## tpqp02 1.000 0.578 0.610
## tpqp05 (.14.) 1.273 0.023 54.547 0.000 0.736 0.749
## tpqp12 (.15.) 1.576 0.027 58.238 0.000 0.911 0.746
## tpqp16 (.16.) 1.111 0.021 51.748 0.000 0.642 0.676
## tpqp01 (.17.) 1.107 0.021 51.659 0.000 0.640 0.640
## tpqp09 (.18.) 1.402 0.025 56.329 0.000 0.811 0.765
## tpqp11 (.19.) 1.389 0.025 56.157 0.000 0.803 0.721
## tpqp14 (.20.) 1.203 0.023 53.418 0.000 0.695 0.802
## tpqp03 (.21.) 0.851 0.019 45.445 0.000 0.492 0.469
## tpqp07 (.22.) 1.230 0.023 53.868 0.000 0.711 0.641
## tpqp10 (.23.) 1.210 0.023 53.543 0.000 0.700 0.788
## tpqp13 (.24.) 1.492 0.026 57.378 0.000 0.863 0.762
## tpqp04 (.25.) 1.224 0.023 53.776 0.000 0.708 0.706
## tpqp06 (.26.) 1.228 0.023 53.839 0.000 0.710 0.768
## tpqp08 (.27.) 1.303 0.024 54.991 0.000 0.753 0.752
## tpqp15 (.28.) 1.441 0.025 56.792 0.000 0.833 0.761
## LTm =~
## tpqm02 1.000 0.475 0.526
## tpqm05 (.30.) 1.041 0.023 44.747 0.000 0.494 0.643
## tpqm12 (.31.) 1.384 0.028 49.977 0.000 0.657 0.692
## tpqm16 (.32.) 0.775 0.020 38.261 0.000 0.368 0.607
## tpqm01 (.33.) 0.885 0.021 41.263 0.000 0.420 0.510
## tpqm09 (.34.) 1.231 0.026 47.981 0.000 0.584 0.714
## tpqm11 (.35.) 1.300 0.027 48.942 0.000 0.617 0.702
## tpqm14 (.36.) 1.127 0.024 46.342 0.000 0.535 0.757
## tpqm03 (.37.) 0.788 0.020 38.653 0.000 0.374 0.394
## tpqm07 (.38.) 0.991 0.023 43.727 0.000 0.471 0.486
## tpqm10 (.39.) 1.111 0.024 46.057 0.000 0.528 0.765
## tpqm13 (.40.) 1.419 0.028 50.377 0.000 0.674 0.689
## tpqm04 (.41.) 0.923 0.022 42.211 0.000 0.439 0.600
## tpqm06 (.42.) 1.174 0.025 47.111 0.000 0.557 0.711
## tpqm08 (.43.) 1.053 0.023 44.982 0.000 0.500 0.624
## tpqm15 (.44.) 1.365 0.027 49.760 0.000 0.648 0.697
## LT =~
## LTm 1.000 0.856 0.856
## LTp (.46.) 0.895 0.025 35.447 0.000 0.630 0.630
## LTc =~
## tcq02 1.000 0.850 0.627
## tcq05 (.48.) 0.957 0.016 61.068 0.000 0.813 0.703
## tcq12 (.49.) 0.765 0.014 54.457 0.000 0.650 0.430
## tcq16 (.50.) 1.068 0.017 63.971 0.000 0.907 0.751
## tcq01 (.51.) 0.971 0.016 61.456 0.000 0.825 0.708
## tcq09 (.52.) 0.886 0.015 58.895 0.000 0.753 0.648
## tcq11 (.53.) 0.976 0.016 61.623 0.000 0.830 0.689
## tcq14 (.54.) 0.932 0.015 60.334 0.000 0.792 0.737
## tcq03 (.55.) 0.789 0.014 55.408 0.000 0.670 0.573
## tcq07 (.56.) 0.782 0.014 55.161 0.000 0.665 0.589
## tcq10 (.57.) 0.640 0.013 48.810 0.000 0.544 0.420
## tcq13 (.58.) 0.939 0.016 60.540 0.000 0.798 0.694
## tcq04 (.59.) 0.985 0.016 61.849 0.000 0.837 0.731
## tcq06 (.60.) 1.010 0.016 62.538 0.000 0.858 0.741
## tcq08 (.61.) 0.887 0.015 58.933 0.000 0.754 0.730
## tcq15 (.62.) 0.987 0.016 61.923 0.000 0.839 0.742
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.310 0.024 12.882 0.000 0.201 0.201
## LTc 0.391 0.012 32.724 0.000 0.528 0.528
## edaddicot -0.167 0.076 -2.202 0.028 -0.265 -0.140
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.075 0.003 25.281 0.000 0.216 0.216
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 5.354 0.121 44.183 0.000 5.354 6.125
## .siqs02 (.134) 5.257 0.106 49.547 0.000 5.257 4.730
## .siqs03 (.135) 5.265 0.135 39.087 0.000 5.265 5.494
## .siqs04 (.136) 4.818 0.099 48.590 0.000 4.818 3.668
## .siqs05 (.137) 4.806 0.104 46.341 0.000 4.806 3.524
## .siqs06 (.138) 5.288 0.098 54.072 0.000 5.288 4.433
## .siqs07 (.139) 5.736 0.116 49.288 0.000 5.736 7.354
## .siqs08 (.140) 5.700 0.117 48.895 0.000 5.700 7.152
## .siqs09 (.141) 5.370 0.105 50.973 0.000 5.370 5.470
## .tpqp02 (.142) 5.387 0.039 137.768 0.000 5.387 5.685
## .tpqp05 (.143) 5.398 0.039 138.041 0.000 5.398 5.492
## .tpqp12 (.144) 4.936 0.039 126.228 0.000 4.936 4.037
## .tpqp16 (.145) 5.476 0.039 140.029 0.000 5.476 5.763
## .tpqp01 (.146) 5.346 0.039 136.715 0.000 5.346 5.349
## .tpqp09 (.147) 5.364 0.039 137.183 0.000 5.364 5.061
## .tpqp11 (.148) 5.184 0.039 132.583 0.000 5.184 4.655
## .tpqp14 (.149) 5.530 0.039 141.433 0.000 5.530 6.381
## .tpqp03 (.150) 5.252 0.039 134.299 0.000 5.252 5.007
## .tpqp07 (.151) 5.148 0.039 131.648 0.000 5.148 4.638
## .tpqp10 (.152) 5.501 0.039 140.692 0.000 5.501 6.197
## .tpqp13 (.153) 5.165 0.039 132.076 0.000 5.165 4.559
## .tpqp04 (.154) 5.299 0.039 135.507 0.000 5.299 5.282
## .tpqp06 (.155) 5.448 0.039 139.328 0.000 5.448 5.892
## .tpqp08 (.156) 5.373 0.039 137.418 0.000 5.373 5.362
## .tpqp15 (.157) 5.160 0.039 131.959 0.000 5.160 4.712
## .tpqm02 (.158) 5.454 0.039 139.484 0.000 5.454 6.043
## .tpqm05 (.159) 5.625 0.039 143.850 0.000 5.625 7.314
## .tpqm12 (.160) 5.352 0.039 136.871 0.000 5.352 5.637
## .tpqm16 (.161) 5.700 0.039 145.760 0.000 5.700 9.395
## .tpqm01 (.162) 5.492 0.039 140.458 0.000 5.492 6.670
## .tpqm09 (.163) 5.585 0.039 142.836 0.000 5.585 6.825
## .tpqm11 (.164) 5.372 0.039 137.378 0.000 5.372 6.106
## .tpqm14 (.165) 5.613 0.039 143.538 0.000 5.613 7.937
## .tpqm03 (.166) 5.299 0.039 135.508 0.000 5.299 5.572
## .tpqm07 (.167) 5.306 0.039 135.703 0.000 5.306 5.481
## .tpqm10 (.168) 5.611 0.039 143.499 0.000 5.611 8.138
## .tpqm13 (.169) 5.331 0.039 136.325 0.000 5.331 5.453
## .tpqm04 (.170) 5.596 0.039 143.109 0.000 5.596 7.655
## .tpqm06 (.171) 5.572 0.039 142.486 0.000 5.572 7.105
## .tpqm08 (.172) 5.535 0.039 141.550 0.000 5.535 6.912
## .tpqm15 (.173) 5.343 0.039 136.638 0.000 5.343 5.743
## .tcq02 (.174) 4.140 0.039 105.879 0.000 4.140 3.055
## .tcq05 (.175) 4.969 0.039 127.087 0.000 4.969 4.299
## .tcq12 (.176) 3.770 0.039 96.406 0.000 3.770 2.492
## .tcq16 (.177) 4.875 0.039 124.669 0.000 4.875 4.036
## .tcq01 (.178) 4.727 0.039 120.888 0.000 4.727 4.059
## .tcq09 (.179) 5.091 0.039 130.205 0.000 5.091 4.383
## .tcq11 (.180) 4.822 0.039 123.305 0.000 4.822 4.006
## .tcq14 (.181) 4.934 0.039 126.189 0.000 4.934 4.590
## .tcq03 (.182) 4.803 0.039 122.837 0.000 4.803 4.107
## .tcq07 (.183) 4.870 0.039 124.553 0.000 4.870 4.317
## .tcq10 (.184) 4.480 0.039 114.573 0.000 4.480 3.465
## .tcq13 (.185) 4.642 0.039 118.705 0.000 4.642 4.037
## .tcq04 (.186) 4.915 0.039 125.683 0.000 4.915 4.296
## .tcq06 (.187) 4.841 0.039 123.812 0.000 4.841 4.181
## .tcq08 (.188) 5.201 0.039 133.012 0.000 5.201 5.035
## .tcq15 (.189) 4.950 0.039 126.580 0.000 4.950 4.379
## edaddct 1.507 0.058 26.028 0.000 1.507 2.859
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.267 0.051 5.271 0.000 0.267 0.349
## .siqs02 (.67.) 0.868 0.047 18.540 0.000 0.868 0.702
## .siqs03 (.68.) 0.289 0.055 5.249 0.000 0.289 0.315
## .siqs04 (.69.) 0.850 0.052 16.245 0.000 0.850 0.493
## .siqs05 (.70.) 0.886 0.054 16.311 0.000 0.886 0.477
## .siqs06 (.71.) 0.576 0.052 11.119 0.000 0.576 0.405
## .siqs07 (.72.) 0.186 0.050 3.710 0.000 0.186 0.305
## .siqs08 (.73.) 0.211 0.050 4.207 0.000 0.211 0.332
## .siqs09 (.74.) 0.628 0.047 13.353 0.000 0.628 0.651
## .tpqp02 (.75.) 0.564 0.041 13.663 0.000 0.564 0.628
## .tpqp05 (.76.) 0.424 0.043 9.950 0.000 0.424 0.439
## .tpqp12 (.77.) 0.664 0.045 14.886 0.000 0.664 0.444
## .tpqp16 (.78.) 0.490 0.042 11.731 0.000 0.490 0.543
## .tpqp01 (.79.) 0.590 0.042 14.122 0.000 0.590 0.590
## .tpqp09 (.80.) 0.466 0.043 10.728 0.000 0.466 0.415
## .tpqp11 (.81.) 0.596 0.043 13.752 0.000 0.596 0.480
## .tpqp14 (.82.) 0.268 0.042 6.335 0.000 0.268 0.356
## .tpqp03 (.83.) 0.858 0.041 21.100 0.000 0.858 0.780
## .tpqp07 (.84.) 0.726 0.042 17.129 0.000 0.726 0.589
## .tpqp10 (.85.) 0.298 0.042 7.058 0.000 0.298 0.379
## .tpqp13 (.86.) 0.539 0.044 12.245 0.000 0.539 0.420
## .tpqp04 (.87.) 0.505 0.042 11.930 0.000 0.505 0.502
## .tpqp06 (.88.) 0.351 0.042 8.276 0.000 0.351 0.410
## .tpqp08 (.89.) 0.437 0.043 10.205 0.000 0.437 0.435
## .tpqp15 (.90.) 0.505 0.044 11.571 0.000 0.505 0.421
## .tpqm02 (.91.) 0.589 0.041 14.326 0.000 0.589 0.723
## .tpqm05 (.92.) 0.347 0.041 8.410 0.000 0.347 0.587
## .tpqm12 (.93.) 0.470 0.043 10.915 0.000 0.470 0.521
## .tpqm16 (.94.) 0.233 0.040 5.770 0.000 0.233 0.632
## .tpqm01 (.95.) 0.502 0.041 12.333 0.000 0.502 0.740
## .tpqm09 (.96.) 0.328 0.042 7.778 0.000 0.328 0.490
## .tpqm11 (.97.) 0.393 0.043 9.234 0.000 0.393 0.508
## .tpqm14 (.98.) 0.213 0.042 5.119 0.000 0.213 0.427
## .tpqm03 (.99.) 0.764 0.040 18.938 0.000 0.764 0.845
## .tpqm07 (.100) 0.716 0.041 17.431 0.000 0.716 0.764
## .tpqm10 (.101) 0.197 0.042 4.736 0.000 0.197 0.414
## .tpqm13 (.102) 0.501 0.043 11.598 0.000 0.501 0.525
## .tpqm04 (.103) 0.342 0.041 8.381 0.000 0.342 0.640
## .tpqm06 (.104) 0.304 0.042 7.258 0.000 0.304 0.495
## .tpqm08 (.105) 0.391 0.041 9.464 0.000 0.391 0.610
## .tpqm15 (.106) 0.445 0.043 10.372 0.000 0.445 0.514
## .tcq02 (.107) 1.114 0.044 25.137 0.000 1.114 0.607
## .tcq05 (.108) 0.676 0.044 15.396 0.000 0.676 0.505
## .tcq12 (.109) 1.866 0.042 44.298 0.000 1.866 0.815
## .tcq16 (.110) 0.636 0.045 14.102 0.000 0.636 0.436
## .tcq01 (.111) 0.677 0.044 15.370 0.000 0.677 0.499
## .tcq09 (.112) 0.783 0.043 18.121 0.000 0.783 0.580
## .tcq11 (.113) 0.760 0.044 17.250 0.000 0.760 0.525
## .tcq14 (.114) 0.529 0.044 12.121 0.000 0.529 0.458
## .tcq03 (.115) 0.919 0.042 21.716 0.000 0.919 0.672
## .tcq07 (.116) 0.831 0.042 19.655 0.000 0.831 0.653
## .tcq10 (.117) 1.377 0.041 33.409 0.000 1.377 0.823
## .tcq13 (.118) 0.685 0.044 15.688 0.000 0.685 0.519
## .tcq04 (.119) 0.609 0.044 13.788 0.000 0.609 0.465
## .tcq06 (.120) 0.604 0.044 13.597 0.000 0.604 0.451
## .tcq08 (.121) 0.498 0.043 11.539 0.000 0.498 0.467
## .tcq15 (.122) 0.574 0.044 12.989 0.000 0.574 0.449
## .it 0.102 0.033 3.114 0.002 0.205 0.205
## .cc 0.626 0.036 17.625 0.000 0.715 0.715
## .ia 0.061 0.034 1.816 0.069 0.145 0.145
## .SIQS 0.244 0.018 13.796 0.000 0.616 0.616
## .LTp 0.202 0.007 27.564 0.000 0.604 0.604
## .LTm 0.060 0.006 9.737 0.000 0.267 0.267
## LT 0.165 0.007 24.709 0.000 1.000 1.000
## LTc 0.722 0.017 42.682 0.000 1.000 1.000
## edaddct 0.278 0.057 4.872 0.000 0.278 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.108 0.906
## siqs02 (.p2.) 0.860 0.020 42.535 0.000 0.952 0.715
## siqs03 (.p3.) 1.124 0.024 47.391 0.000 1.245 0.918
## cc =~
## siqs04 1.000 0.916 0.705
## siqs05 (.p5.) 1.054 0.028 37.359 0.000 0.966 0.716
## siqs06 (.p6.) 0.984 0.027 36.567 0.000 0.901 0.765
## ia =~
## siqs07 1.000 0.983 0.916
## siqs08 (.p8.) 1.002 0.024 41.946 0.000 0.985 0.906
## siqs09 (.p9.) 0.892 0.022 39.808 0.000 0.877 0.742
## SIQS =~
## it 1.000 0.807 0.807
## cc (.11.) 0.794 0.021 37.001 0.000 0.775 0.775
## ia (.12.) 0.956 0.024 39.638 0.000 0.869 0.869
## LTp =~
## tpqp02 1.000 0.755 0.709
## tpqp05 (.14.) 1.273 0.023 54.547 0.000 0.961 0.828
## tpqp12 (.15.) 1.576 0.027 58.238 0.000 1.190 0.825
## tpqp16 (.16.) 1.111 0.021 51.748 0.000 0.839 0.768
## tpqp01 (.17.) 1.107 0.021 51.659 0.000 0.835 0.736
## tpqp09 (.18.) 1.402 0.025 56.329 0.000 1.058 0.840
## tpqp11 (.19.) 1.389 0.025 56.157 0.000 1.048 0.805
## tpqp14 (.20.) 1.203 0.023 53.418 0.000 0.908 0.869
## tpqp03 (.21.) 0.851 0.019 45.445 0.000 0.642 0.570
## tpqp07 (.22.) 1.230 0.023 53.868 0.000 0.928 0.737
## tpqp10 (.23.) 1.210 0.023 53.543 0.000 0.913 0.858
## tpqp13 (.24.) 1.492 0.026 57.378 0.000 1.126 0.838
## tpqp04 (.25.) 1.224 0.023 53.776 0.000 0.924 0.793
## tpqp06 (.26.) 1.228 0.023 53.839 0.000 0.927 0.843
## tpqp08 (.27.) 1.303 0.024 54.991 0.000 0.983 0.830
## tpqp15 (.28.) 1.441 0.025 56.792 0.000 1.087 0.837
## LTm =~
## tpqm02 1.000 0.732 0.690
## tpqm05 (.30.) 1.041 0.023 44.747 0.000 0.762 0.791
## tpqm12 (.31.) 1.384 0.028 49.977 0.000 1.013 0.828
## tpqm16 (.32.) 0.775 0.020 38.261 0.000 0.567 0.762
## tpqm01 (.33.) 0.885 0.021 41.263 0.000 0.647 0.675
## tpqm09 (.34.) 1.231 0.026 47.981 0.000 0.901 0.844
## tpqm11 (.35.) 1.300 0.027 48.942 0.000 0.951 0.835
## tpqm14 (.36.) 1.127 0.024 46.342 0.000 0.825 0.873
## tpqm03 (.37.) 0.788 0.020 38.653 0.000 0.577 0.551
## tpqm07 (.38.) 0.991 0.023 43.727 0.000 0.725 0.651
## tpqm10 (.39.) 1.111 0.024 46.057 0.000 0.813 0.878
## tpqm13 (.40.) 1.419 0.028 50.377 0.000 1.038 0.826
## tpqm04 (.41.) 0.923 0.022 42.211 0.000 0.676 0.756
## tpqm06 (.42.) 1.174 0.025 47.111 0.000 0.859 0.842
## tpqm08 (.43.) 1.053 0.023 44.982 0.000 0.770 0.776
## tpqm15 (.44.) 1.365 0.027 49.760 0.000 0.999 0.832
## LT =~
## LTm 1.000 0.879 0.879
## LTp (.46.) 0.895 0.025 35.447 0.000 0.763 0.763
## LTc =~
## tcq02 1.000 1.056 0.707
## tcq05 (.48.) 0.957 0.016 61.068 0.000 1.011 0.776
## tcq12 (.49.) 0.765 0.014 54.457 0.000 0.808 0.509
## tcq16 (.50.) 1.068 0.017 63.971 0.000 1.128 0.816
## tcq01 (.51.) 0.971 0.016 61.456 0.000 1.025 0.780
## tcq09 (.52.) 0.886 0.015 58.895 0.000 0.936 0.727
## tcq11 (.53.) 0.976 0.016 61.623 0.000 1.031 0.764
## tcq14 (.54.) 0.932 0.015 60.334 0.000 0.984 0.804
## tcq03 (.55.) 0.789 0.014 55.408 0.000 0.833 0.656
## tcq07 (.56.) 0.782 0.014 55.161 0.000 0.826 0.672
## tcq10 (.57.) 0.640 0.013 48.810 0.000 0.676 0.499
## tcq13 (.58.) 0.939 0.016 60.540 0.000 0.992 0.768
## tcq04 (.59.) 0.985 0.016 61.849 0.000 1.040 0.800
## tcq06 (.60.) 1.010 0.016 62.538 0.000 1.067 0.808
## tcq08 (.61.) 0.887 0.015 58.933 0.000 0.937 0.799
## tcq15 (.62.) 0.987 0.016 61.923 0.000 1.043 0.809
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.544 0.016 34.841 0.000 0.391 0.391
## LTc 0.242 0.007 34.770 0.000 0.286 0.286
## edaddicot -0.389 0.083 -4.709 0.000 -0.435 -0.170
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.122 0.003 35.302 0.000 0.180 0.180
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 5.354 0.121 44.183 0.000 5.354 4.381
## .siqs02 (.134) 5.257 0.106 49.547 0.000 5.257 3.947
## .siqs03 (.135) 5.265 0.135 39.087 0.000 5.265 3.881
## .siqs04 (.136) 4.818 0.099 48.590 0.000 4.818 3.707
## .siqs05 (.137) 4.806 0.104 46.341 0.000 4.806 3.563
## .siqs06 (.138) 5.288 0.098 54.072 0.000 5.288 4.489
## .siqs07 (.139) 5.736 0.116 49.288 0.000 5.736 5.343
## .siqs08 (.140) 5.700 0.117 48.895 0.000 5.700 5.244
## .siqs09 (.141) 5.370 0.105 50.973 0.000 5.370 4.544
## .tpqp02 (.142) 5.387 0.039 137.768 0.000 5.387 5.060
## .tpqp05 (.143) 5.398 0.039 138.041 0.000 5.398 4.650
## .tpqp12 (.144) 4.936 0.039 126.228 0.000 4.936 3.423
## .tpqp16 (.145) 5.476 0.039 140.029 0.000 5.476 5.013
## .tpqp01 (.146) 5.346 0.039 136.715 0.000 5.346 4.712
## .tpqp09 (.147) 5.364 0.039 137.183 0.000 5.364 4.260
## .tpqp11 (.148) 5.184 0.039 132.583 0.000 5.184 3.983
## .tpqp14 (.149) 5.530 0.039 141.433 0.000 5.530 5.294
## .tpqp03 (.150) 5.252 0.039 134.299 0.000 5.252 4.659
## .tpqp07 (.151) 5.148 0.039 131.648 0.000 5.148 4.086
## .tpqp10 (.152) 5.501 0.039 140.692 0.000 5.501 5.170
## .tpqp13 (.153) 5.165 0.039 132.076 0.000 5.165 3.842
## .tpqp04 (.154) 5.299 0.039 135.507 0.000 5.299 4.545
## .tpqp06 (.155) 5.448 0.039 139.328 0.000 5.448 4.954
## .tpqp08 (.156) 5.373 0.039 137.418 0.000 5.373 4.535
## .tpqp15 (.157) 5.160 0.039 131.959 0.000 5.160 3.972
## .tpqm02 (.158) 5.454 0.039 139.484 0.000 5.454 5.144
## .tpqm05 (.159) 5.625 0.039 143.850 0.000 5.625 5.842
## .tpqm12 (.160) 5.352 0.039 136.871 0.000 5.352 4.377
## .tpqm16 (.161) 5.700 0.039 145.760 0.000 5.700 7.656
## .tpqm01 (.162) 5.492 0.039 140.458 0.000 5.492 5.725
## .tpqm09 (.163) 5.585 0.039 142.836 0.000 5.585 5.233
## .tpqm11 (.164) 5.372 0.039 137.378 0.000 5.372 4.716
## .tpqm14 (.165) 5.613 0.039 143.538 0.000 5.613 5.936
## .tpqm03 (.166) 5.299 0.039 135.508 0.000 5.299 5.059
## .tpqm07 (.167) 5.306 0.039 135.703 0.000 5.306 4.762
## .tpqm10 (.168) 5.611 0.039 143.499 0.000 5.611 6.058
## .tpqm13 (.169) 5.331 0.039 136.325 0.000 5.331 4.241
## .tpqm04 (.170) 5.596 0.039 143.109 0.000 5.596 6.262
## .tpqm06 (.171) 5.572 0.039 142.486 0.000 5.572 5.459
## .tpqm08 (.172) 5.535 0.039 141.550 0.000 5.535 5.578
## .tpqm15 (.173) 5.343 0.039 136.638 0.000 5.343 4.448
## .tcq02 (.174) 4.140 0.039 105.879 0.000 4.140 2.772
## .tcq05 (.175) 4.969 0.039 127.087 0.000 4.969 3.815
## .tcq12 (.176) 3.770 0.039 96.406 0.000 3.770 2.375
## .tcq16 (.177) 4.875 0.039 124.669 0.000 4.875 3.529
## .tcq01 (.178) 4.727 0.039 120.888 0.000 4.727 3.597
## .tcq09 (.179) 5.091 0.039 130.205 0.000 5.091 3.953
## .tcq11 (.180) 4.822 0.039 123.305 0.000 4.822 3.570
## .tcq14 (.181) 4.934 0.039 126.189 0.000 4.934 4.032
## .tcq03 (.182) 4.803 0.039 122.837 0.000 4.803 3.782
## .tcq07 (.183) 4.870 0.039 124.553 0.000 4.870 3.959
## .tcq10 (.184) 4.480 0.039 114.573 0.000 4.480 3.309
## .tcq13 (.185) 4.642 0.039 118.705 0.000 4.642 3.593
## .tcq04 (.186) 4.915 0.039 125.683 0.000 4.915 3.780
## .tcq06 (.187) 4.841 0.039 123.812 0.000 4.841 3.667
## .tcq08 (.188) 5.201 0.039 133.012 0.000 5.201 4.432
## .tcq15 (.189) 4.950 0.039 126.580 0.000 4.950 3.840
## edaddct 1.427 0.053 26.989 0.000 1.427 3.643
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.267 0.051 5.271 0.000 0.267 0.178
## .siqs02 (.67.) 0.868 0.047 18.540 0.000 0.868 0.489
## .siqs03 (.68.) 0.289 0.055 5.249 0.000 0.289 0.157
## .siqs04 (.69.) 0.850 0.052 16.245 0.000 0.850 0.503
## .siqs05 (.70.) 0.886 0.054 16.311 0.000 0.886 0.487
## .siqs06 (.71.) 0.576 0.052 11.119 0.000 0.576 0.415
## .siqs07 (.72.) 0.186 0.050 3.710 0.000 0.186 0.161
## .siqs08 (.73.) 0.211 0.050 4.207 0.000 0.211 0.179
## .siqs09 (.74.) 0.628 0.047 13.353 0.000 0.628 0.449
## .tpqp02 (.75.) 0.564 0.041 13.663 0.000 0.564 0.497
## .tpqp05 (.76.) 0.424 0.043 9.950 0.000 0.424 0.315
## .tpqp12 (.77.) 0.664 0.045 14.886 0.000 0.664 0.319
## .tpqp16 (.78.) 0.490 0.042 11.731 0.000 0.490 0.411
## .tpqp01 (.79.) 0.590 0.042 14.122 0.000 0.590 0.458
## .tpqp09 (.80.) 0.466 0.043 10.728 0.000 0.466 0.294
## .tpqp11 (.81.) 0.596 0.043 13.752 0.000 0.596 0.352
## .tpqp14 (.82.) 0.268 0.042 6.335 0.000 0.268 0.245
## .tpqp03 (.83.) 0.858 0.041 21.100 0.000 0.858 0.675
## .tpqp07 (.84.) 0.726 0.042 17.129 0.000 0.726 0.457
## .tpqp10 (.85.) 0.298 0.042 7.058 0.000 0.298 0.264
## .tpqp13 (.86.) 0.539 0.044 12.245 0.000 0.539 0.298
## .tpqp04 (.87.) 0.505 0.042 11.930 0.000 0.505 0.372
## .tpqp06 (.88.) 0.351 0.042 8.276 0.000 0.351 0.290
## .tpqp08 (.89.) 0.437 0.043 10.205 0.000 0.437 0.311
## .tpqp15 (.90.) 0.505 0.044 11.571 0.000 0.505 0.299
## .tpqm02 (.91.) 0.589 0.041 14.326 0.000 0.589 0.524
## .tpqm05 (.92.) 0.347 0.041 8.410 0.000 0.347 0.374
## .tpqm12 (.93.) 0.470 0.043 10.915 0.000 0.470 0.314
## .tpqm16 (.94.) 0.233 0.040 5.770 0.000 0.233 0.420
## .tpqm01 (.95.) 0.502 0.041 12.333 0.000 0.502 0.545
## .tpqm09 (.96.) 0.328 0.042 7.778 0.000 0.328 0.288
## .tpqm11 (.97.) 0.393 0.043 9.234 0.000 0.393 0.303
## .tpqm14 (.98.) 0.213 0.042 5.119 0.000 0.213 0.239
## .tpqm03 (.99.) 0.764 0.040 18.938 0.000 0.764 0.697
## .tpqm07 (.100) 0.716 0.041 17.431 0.000 0.716 0.577
## .tpqm10 (.101) 0.197 0.042 4.736 0.000 0.197 0.230
## .tpqm13 (.102) 0.501 0.043 11.598 0.000 0.501 0.317
## .tpqm04 (.103) 0.342 0.041 8.381 0.000 0.342 0.428
## .tpqm06 (.104) 0.304 0.042 7.258 0.000 0.304 0.292
## .tpqm08 (.105) 0.391 0.041 9.464 0.000 0.391 0.397
## .tpqm15 (.106) 0.445 0.043 10.372 0.000 0.445 0.308
## .tcq02 (.107) 1.114 0.044 25.137 0.000 1.114 0.500
## .tcq05 (.108) 0.676 0.044 15.396 0.000 0.676 0.398
## .tcq12 (.109) 1.866 0.042 44.298 0.000 1.866 0.741
## .tcq16 (.110) 0.636 0.045 14.102 0.000 0.636 0.333
## .tcq01 (.111) 0.677 0.044 15.370 0.000 0.677 0.392
## .tcq09 (.112) 0.783 0.043 18.121 0.000 0.783 0.472
## .tcq11 (.113) 0.760 0.044 17.250 0.000 0.760 0.417
## .tcq14 (.114) 0.529 0.044 12.121 0.000 0.529 0.353
## .tcq03 (.115) 0.919 0.042 21.716 0.000 0.919 0.570
## .tcq07 (.116) 0.831 0.042 19.655 0.000 0.831 0.549
## .tcq10 (.117) 1.377 0.041 33.409 0.000 1.377 0.751
## .tcq13 (.118) 0.685 0.044 15.688 0.000 0.685 0.411
## .tcq04 (.119) 0.609 0.044 13.788 0.000 0.609 0.360
## .tcq06 (.120) 0.604 0.044 13.597 0.000 0.604 0.347
## .tcq08 (.121) 0.498 0.043 11.539 0.000 0.498 0.362
## .tcq15 (.122) 0.574 0.044 12.989 0.000 0.574 0.346
## .it 0.427 0.035 12.197 0.000 0.348 0.348
## .cc 0.335 0.031 10.694 0.000 0.400 0.400
## .ia 0.237 0.035 6.795 0.000 0.245 0.245
## .SIQS 0.556 0.024 23.299 0.000 0.696 0.696
## .LTp 0.238 0.010 24.280 0.000 0.419 0.419
## .LTm 0.122 0.010 12.473 0.000 0.228 0.228
## LT 0.413 0.015 26.939 0.000 1.000 1.000
## LTc 1.116 0.025 44.606 0.000 1.000 1.000
## edaddct 0.153 0.040 3.819 0.000 0.153 1.000Modelo con igualdad de medias y varianzas
sem.lv2vars %>%
summary(fit.measures=T, standardized=T)## lavaan 0.6.16 did NOT end normally after 79 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 364
## Number of equality constraints 176
##
## Number of observations per group:
## 1 299
## 0 357
##
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.793 0.733
## siqs02 (.p2.) 0.913 NA 0.724 0.598
## siqs03 (.p3.) 0.971 NA 0.770 0.643
## cc =~
## siqs04 1.000 0.907 0.713
## siqs05 (.p5.) 0.954 NA 0.865 0.657
## siqs06 (.p6.) 0.965 NA 0.875 0.687
## ia =~
## siqs07 1.000 0.691 0.681
## siqs08 (.p8.) 0.961 NA 0.664 0.616
## siqs09 (.p9.) 0.911 NA 0.629 0.566
## SIQS =~
## it 1.000 0.644 0.644
## cc (.11.) 0.909 NA 0.512 0.512
## ia (.12.) 1.051 NA 0.777 0.777
## LTp =~
## tpqp02 1.000 0.949 0.783
## tpqp05 (.14.) 0.832 NA 0.789 0.657
## tpqp12 (.15.) 1.069 NA 1.014 0.725
## tpqp16 (.16.) 0.749 NA 0.710 0.617
## tpqp01 (.17.) 0.730 NA 0.692 0.597
## tpqp09 (.18.) 0.926 NA 0.878 0.690
## tpqp11 (.19.) 0.927 NA 0.879 0.682
## tpqp14 (.20.) 0.792 NA 0.752 0.655
## tpqp03 (.21.) 0.613 NA 0.582 0.517
## tpqp07 (.22.) 0.847 NA 0.804 0.644
## tpqp10 (.23.) 0.794 NA 0.753 0.653
## tpqp13 (.24.) 0.997 NA 0.946 0.710
## tpqp04 (.25.) 0.811 NA 0.769 0.643
## tpqp06 (.26.) 0.803 NA 0.762 0.651
## tpqp08 (.27.) 0.864 NA 0.820 0.670
## tpqp15 (.28.) 0.963 NA 0.914 0.701
## LTm =~
## tpqm02 1.000 0.909 0.756
## tpqm05 (.30.) 0.634 NA 0.577 0.546
## tpqm12 (.31.) 0.899 NA 0.817 0.663
## tpqm16 (.32.) 0.493 NA 0.449 0.471
## tpqm01 (.33.) 0.588 NA 0.534 0.510
## tpqm09 (.34.) 0.769 NA 0.699 0.618
## tpqm11 (.35.) 0.827 NA 0.752 0.639
## tpqm14 (.36.) 0.738 NA 0.671 0.620
## tpqm03 (.37.) 0.571 NA 0.519 0.482
## tpqm07 (.38.) 0.706 NA 0.642 0.564
## tpqm10 (.39.) 0.708 NA 0.644 0.604
## tpqm13 (.40.) 0.923 NA 0.840 0.670
## tpqm04 (.41.) 0.591 NA 0.538 0.525
## tpqm06 (.42.) 0.764 NA 0.695 0.622
## tpqm08 (.43.) 0.710 NA 0.645 0.591
## tpqm15 (.44.) 0.898 NA 0.817 0.666
## LT =~
## LTm 1.000 0.971 0.971
## LTp (.46.) 0.740 NA 0.688 0.688
## LTc =~
## tcq02 1.000 1.113 0.758
## tcq05 (.48.) 0.816 NA 0.908 0.692
## tcq12 (.49.) 0.722 NA 0.803 0.577
## tcq16 (.50.) 0.896 NA 0.997 0.723
## tcq01 (.51.) 0.823 NA 0.915 0.693
## tcq09 (.52.) 0.759 NA 0.845 0.659
## tcq11 (.53.) 0.839 NA 0.934 0.696
## tcq14 (.54.) 0.768 NA 0.855 0.678
## tcq03 (.55.) 0.680 NA 0.756 0.608
## tcq07 (.56.) 0.678 NA 0.754 0.615
## tcq10 (.57.) 0.597 NA 0.664 0.532
## tcq13 (.58.) 0.791 NA 0.879 0.678
## tcq04 (.59.) 0.834 NA 0.928 0.703
## tcq06 (.60.) 0.862 NA 0.959 0.715
## tcq08 (.61.) 0.736 NA 0.818 0.666
## tcq15 (.62.) 0.825 NA 0.918 0.700
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.131 NA 0.227 0.227
## LTc 0.303 NA 0.659 0.659
## edaddicot 1.876 NA 3.673 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.174 NA 0.177 0.177
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 1.157 NA 1.157 1.071
## .siqs02 (.134) 1.262 NA 1.262 1.042
## .siqs03 (.135) 1.177 NA 1.177 0.984
## .siqs04 (.136) 1.086 NA 1.086 0.854
## .siqs05 (.137) 1.142 NA 1.142 0.868
## .siqs06 (.138) 1.348 NA 1.348 1.058
## .siqs07 (.139) 1.330 NA 1.330 1.311
## .siqs08 (.140) 1.398 NA 1.398 1.298
## .siqs09 (.141) 1.316 NA 1.316 1.185
## .tpqp02 (.142) 2.113 NA 2.113 1.743
## .tpqp05 (.143) 2.117 NA 2.117 1.761
## .tpqp12 (.144) 1.936 NA 1.936 1.385
## .tpqp16 (.145) 2.148 NA 2.148 1.867
## .tpqp01 (.146) 2.097 NA 2.097 1.809
## .tpqp09 (.147) 2.104 NA 2.104 1.653
## .tpqp11 (.148) 2.034 NA 2.034 1.577
## .tpqp14 (.149) 2.169 NA 2.169 1.890
## .tpqp03 (.150) 2.060 NA 2.060 1.828
## .tpqp07 (.151) 2.019 NA 2.019 1.618
## .tpqp10 (.152) 2.158 NA 2.158 1.869
## .tpqp13 (.153) 2.026 NA 2.026 1.521
## .tpqp04 (.154) 2.078 NA 2.078 1.736
## .tpqp06 (.155) 2.137 NA 2.137 1.827
## .tpqp08 (.156) 2.108 NA 2.108 1.723
## .tpqp15 (.157) 2.024 NA 2.024 1.553
## .tpqm02 (.158) 2.139 NA 2.139 1.779
## .tpqm05 (.159) 2.206 NA 2.206 2.090
## .tpqm12 (.160) 2.099 NA 2.099 1.703
## .tpqm16 (.161) 2.236 NA 2.236 2.345
## .tpqm01 (.162) 2.154 NA 2.154 2.058
## .tpqm09 (.163) 2.191 NA 2.191 1.936
## .tpqm11 (.164) 2.107 NA 2.107 1.790
## .tpqm14 (.165) 2.202 NA 2.202 2.032
## .tpqm03 (.166) 2.078 NA 2.078 1.927
## .tpqm07 (.167) 2.081 NA 2.081 1.828
## .tpqm10 (.168) 2.201 NA 2.201 2.065
## .tpqm13 (.169) 2.091 NA 2.091 1.668
## .tpqm04 (.170) 2.195 NA 2.195 2.144
## .tpqm06 (.171) 2.186 NA 2.186 1.959
## .tpqm08 (.172) 2.171 NA 2.171 1.987
## .tpqm15 (.173) 2.096 NA 2.096 1.709
## .tcq02 (.174) 1.624 NA 1.624 1.106
## .tcq05 (.175) 1.949 NA 1.949 1.484
## .tcq12 (.176) 1.479 NA 1.479 1.062
## .tcq16 (.177) 1.912 NA 1.912 1.387
## .tcq01 (.178) 1.854 NA 1.854 1.404
## .tcq09 (.179) 1.997 NA 1.997 1.557
## .tcq11 (.180) 1.891 NA 1.891 1.409
## .tcq14 (.181) 1.936 NA 1.936 1.535
## .tcq03 (.182) 1.884 NA 1.884 1.516
## .tcq07 (.183) 1.910 NA 1.910 1.557
## .tcq10 (.184) 1.757 NA 1.757 1.407
## .tcq13 (.185) 1.821 NA 1.821 1.404
## .tcq04 (.186) 1.928 NA 1.928 1.460
## .tcq06 (.187) 1.899 NA 1.899 1.416
## .tcq08 (.188) 2.040 NA 2.040 1.661
## .tcq15 (.189) 1.942 NA 1.942 1.481
## edaddct 2.242 NA 2.242 NA
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.540 NA 0.540 0.462
## .siqs02 (.67.) 0.943 NA 0.943 0.643
## .siqs03 (.68.) 0.839 NA 0.839 0.586
## .siqs04 (.69.) 0.795 NA 0.795 0.492
## .siqs05 (.70.) 0.983 NA 0.983 0.568
## .siqs06 (.71.) 0.857 NA 0.857 0.528
## .siqs07 (.72.) 0.551 NA 0.551 0.536
## .siqs08 (.73.) 0.721 NA 0.721 0.620
## .siqs09 (.74.) 0.838 NA 0.838 0.679
## .tpqp02 (.75.) 0.569 NA 0.569 0.387
## .tpqp05 (.76.) 0.822 NA 0.822 0.569
## .tpqp12 (.77.) 0.927 NA 0.927 0.474
## .tpqp16 (.78.) 0.819 NA 0.819 0.619
## .tpqp01 (.79.) 0.865 NA 0.865 0.643
## .tpqp09 (.80.) 0.849 NA 0.849 0.524
## .tpqp11 (.81.) 0.891 NA 0.891 0.535
## .tpqp14 (.82.) 0.752 NA 0.752 0.571
## .tpqp03 (.83.) 0.931 NA 0.931 0.733
## .tpqp07 (.84.) 0.911 NA 0.911 0.585
## .tpqp10 (.85.) 0.765 NA 0.765 0.574
## .tpqp13 (.86.) 0.879 NA 0.879 0.496
## .tpqp04 (.87.) 0.841 NA 0.841 0.587
## .tpqp06 (.88.) 0.788 NA 0.788 0.576
## .tpqp08 (.89.) 0.824 NA 0.824 0.550
## .tpqp15 (.90.) 0.863 NA 0.863 0.508
## .tpqm02 (.91.) 0.619 NA 0.619 0.428
## .tpqm05 (.92.) 0.782 NA 0.782 0.702
## .tpqm12 (.93.) 0.852 NA 0.852 0.561
## .tpqm16 (.94.) 0.708 NA 0.708 0.779
## .tpqm01 (.95.) 0.811 NA 0.811 0.740
## .tpqm09 (.96.) 0.792 NA 0.792 0.618
## .tpqm11 (.97.) 0.820 NA 0.820 0.592
## .tpqm14 (.98.) 0.723 NA 0.723 0.616
## .tpqm03 (.99.) 0.894 NA 0.894 0.768
## .tpqm07 (.100) 0.884 NA 0.884 0.682
## .tpqm10 (.101) 0.722 NA 0.722 0.635
## .tpqm13 (.102) 0.867 NA 0.867 0.551
## .tpqm04 (.103) 0.759 NA 0.759 0.724
## .tpqm06 (.104) 0.763 NA 0.763 0.613
## .tpqm08 (.105) 0.777 NA 0.777 0.651
## .tpqm15 (.106) 0.836 NA 0.836 0.556
## .tcq02 (.107) 0.918 NA 0.918 0.426
## .tcq05 (.108) 0.900 NA 0.900 0.522
## .tcq12 (.109) 1.295 NA 1.295 0.668
## .tcq16 (.110) 0.908 NA 0.908 0.478
## .tcq01 (.111) 0.905 NA 0.905 0.519
## .tcq09 (.112) 0.932 NA 0.932 0.566
## .tcq11 (.113) 0.930 NA 0.930 0.516
## .tcq14 (.114) 0.859 NA 0.859 0.540
## .tcq03 (.115) 0.973 NA 0.973 0.630
## .tcq07 (.116) 0.937 NA 0.937 0.622
## .tcq10 (.117) 1.118 NA 1.118 0.717
## .tcq13 (.118) 0.908 NA 0.908 0.540
## .tcq04 (.119) 0.882 NA 0.882 0.506
## .tcq06 (.120) 0.879 NA 0.879 0.489
## .tcq08 (.121) 0.839 NA 0.839 0.556
## .tcq15 (.122) 0.877 NA 0.877 0.510
## .it (.123) 0.367 NA 0.585 0.585
## .cc (.124) 0.606 NA 0.738 0.738
## .ia (.125) 0.189 NA 0.396 0.396
## .SIQS (.126) 0.120 NA 0.461 0.461
## .LTp (.127) 0.474 NA 0.527 0.527
## .LTm (.128) 0.047 NA 0.057 0.057
## LT (.129) 0.779 NA 1.000 1.000
## LTc (.130) 1.238 NA 1.000 1.000
## edaddct -0.000 NA -0.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.989 0.803
## siqs02 (.p2.) 0.913 NA 0.903 0.681
## siqs03 (.p3.) 0.971 NA 0.960 0.723
## cc =~
## siqs04 1.000 1.054 0.763
## siqs05 (.p5.) 0.954 NA 1.005 0.712
## siqs06 (.p6.) 0.965 NA 1.017 0.739
## ia =~
## siqs07 1.000 0.929 0.781
## siqs08 (.p8.) 0.961 NA 0.893 0.725
## siqs09 (.p9.) 0.911 NA 0.846 0.679
## SIQS =~
## it 1.000 0.790 0.790
## cc (.11.) 0.909 NA 0.674 0.674
## ia (.12.) 1.051 NA 0.884 0.884
## LTp =~
## tpqp02 1.000 0.949 0.783
## tpqp05 (.14.) 0.832 NA 0.789 0.657
## tpqp12 (.15.) 1.069 NA 1.014 0.725
## tpqp16 (.16.) 0.749 NA 0.710 0.617
## tpqp01 (.17.) 0.730 NA 0.692 0.597
## tpqp09 (.18.) 0.926 NA 0.878 0.690
## tpqp11 (.19.) 0.927 NA 0.879 0.682
## tpqp14 (.20.) 0.792 NA 0.752 0.655
## tpqp03 (.21.) 0.613 NA 0.582 0.517
## tpqp07 (.22.) 0.847 NA 0.804 0.644
## tpqp10 (.23.) 0.794 NA 0.753 0.653
## tpqp13 (.24.) 0.997 NA 0.946 0.710
## tpqp04 (.25.) 0.811 NA 0.769 0.643
## tpqp06 (.26.) 0.803 NA 0.762 0.651
## tpqp08 (.27.) 0.864 NA 0.820 0.670
## tpqp15 (.28.) 0.963 NA 0.914 0.701
## LTm =~
## tpqm02 1.000 0.909 0.756
## tpqm05 (.30.) 0.634 NA 0.577 0.546
## tpqm12 (.31.) 0.899 NA 0.817 0.663
## tpqm16 (.32.) 0.493 NA 0.449 0.471
## tpqm01 (.33.) 0.588 NA 0.534 0.510
## tpqm09 (.34.) 0.769 NA 0.699 0.618
## tpqm11 (.35.) 0.827 NA 0.752 0.639
## tpqm14 (.36.) 0.738 NA 0.671 0.620
## tpqm03 (.37.) 0.571 NA 0.519 0.482
## tpqm07 (.38.) 0.706 NA 0.642 0.564
## tpqm10 (.39.) 0.708 NA 0.644 0.604
## tpqm13 (.40.) 0.923 NA 0.840 0.670
## tpqm04 (.41.) 0.591 NA 0.538 0.525
## tpqm06 (.42.) 0.764 NA 0.695 0.622
## tpqm08 (.43.) 0.710 NA 0.645 0.591
## tpqm15 (.44.) 0.898 NA 0.817 0.666
## LT =~
## LTm 1.000 0.971 0.971
## LTp (.46.) 0.740 NA 0.688 0.688
## LTc =~
## tcq02 1.000 1.113 0.758
## tcq05 (.48.) 0.816 NA 0.908 0.692
## tcq12 (.49.) 0.722 NA 0.803 0.577
## tcq16 (.50.) 0.896 NA 0.997 0.723
## tcq01 (.51.) 0.823 NA 0.915 0.693
## tcq09 (.52.) 0.759 NA 0.845 0.659
## tcq11 (.53.) 0.839 NA 0.934 0.696
## tcq14 (.54.) 0.768 NA 0.855 0.678
## tcq03 (.55.) 0.680 NA 0.756 0.608
## tcq07 (.56.) 0.678 NA 0.754 0.615
## tcq10 (.57.) 0.597 NA 0.664 0.532
## tcq13 (.58.) 0.791 NA 0.879 0.678
## tcq04 (.59.) 0.834 NA 0.928 0.703
## tcq06 (.60.) 0.862 NA 0.959 0.715
## tcq08 (.61.) 0.736 NA 0.818 0.666
## tcq15 (.62.) 0.825 NA 0.918 0.700
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.441 NA 0.498 0.498
## LTc 0.277 NA 0.395 0.395
## edaddicot 1.728 NA 2.212 0.568
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.190 NA 0.193 0.193
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 1.157 NA 1.157 0.939
## .siqs02 (.134) 1.262 NA 1.262 0.952
## .siqs03 (.135) 1.177 NA 1.177 0.887
## .siqs04 (.136) 1.086 NA 1.086 0.787
## .siqs05 (.137) 1.142 NA 1.142 0.809
## .siqs06 (.138) 1.348 NA 1.348 0.980
## .siqs07 (.139) 1.330 NA 1.330 1.119
## .siqs08 (.140) 1.398 NA 1.398 1.135
## .siqs09 (.141) 1.316 NA 1.316 1.056
## .tpqp02 (.142) 2.113 NA 2.113 1.743
## .tpqp05 (.143) 2.117 NA 2.117 1.761
## .tpqp12 (.144) 1.936 NA 1.936 1.385
## .tpqp16 (.145) 2.148 NA 2.148 1.867
## .tpqp01 (.146) 2.097 NA 2.097 1.809
## .tpqp09 (.147) 2.104 NA 2.104 1.653
## .tpqp11 (.148) 2.034 NA 2.034 1.577
## .tpqp14 (.149) 2.169 NA 2.169 1.890
## .tpqp03 (.150) 2.060 NA 2.060 1.828
## .tpqp07 (.151) 2.019 NA 2.019 1.618
## .tpqp10 (.152) 2.158 NA 2.158 1.869
## .tpqp13 (.153) 2.026 NA 2.026 1.521
## .tpqp04 (.154) 2.078 NA 2.078 1.736
## .tpqp06 (.155) 2.137 NA 2.137 1.827
## .tpqp08 (.156) 2.108 NA 2.108 1.723
## .tpqp15 (.157) 2.024 NA 2.024 1.553
## .tpqm02 (.158) 2.139 NA 2.139 1.779
## .tpqm05 (.159) 2.206 NA 2.206 2.090
## .tpqm12 (.160) 2.099 NA 2.099 1.703
## .tpqm16 (.161) 2.236 NA 2.236 2.345
## .tpqm01 (.162) 2.154 NA 2.154 2.058
## .tpqm09 (.163) 2.191 NA 2.191 1.936
## .tpqm11 (.164) 2.107 NA 2.107 1.790
## .tpqm14 (.165) 2.202 NA 2.202 2.032
## .tpqm03 (.166) 2.078 NA 2.078 1.927
## .tpqm07 (.167) 2.081 NA 2.081 1.828
## .tpqm10 (.168) 2.201 NA 2.201 2.065
## .tpqm13 (.169) 2.091 NA 2.091 1.668
## .tpqm04 (.170) 2.195 NA 2.195 2.144
## .tpqm06 (.171) 2.186 NA 2.186 1.959
## .tpqm08 (.172) 2.171 NA 2.171 1.987
## .tpqm15 (.173) 2.096 NA 2.096 1.709
## .tcq02 (.174) 1.624 NA 1.624 1.106
## .tcq05 (.175) 1.949 NA 1.949 1.484
## .tcq12 (.176) 1.479 NA 1.479 1.062
## .tcq16 (.177) 1.912 NA 1.912 1.387
## .tcq01 (.178) 1.854 NA 1.854 1.404
## .tcq09 (.179) 1.997 NA 1.997 1.557
## .tcq11 (.180) 1.891 NA 1.891 1.409
## .tcq14 (.181) 1.936 NA 1.936 1.535
## .tcq03 (.182) 1.884 NA 1.884 1.516
## .tcq07 (.183) 1.910 NA 1.910 1.557
## .tcq10 (.184) 1.757 NA 1.757 1.407
## .tcq13 (.185) 1.821 NA 1.821 1.404
## .tcq04 (.186) 1.928 NA 1.928 1.460
## .tcq06 (.187) 1.899 NA 1.899 1.416
## .tcq08 (.188) 2.040 NA 2.040 1.661
## .tcq15 (.189) 1.942 NA 1.942 1.481
## edaddct 2.335 NA 2.335 9.096
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.540 NA 0.540 0.356
## .siqs02 (.67.) 0.943 NA 0.943 0.536
## .siqs03 (.68.) 0.839 NA 0.839 0.477
## .siqs04 (.69.) 0.795 NA 0.795 0.417
## .siqs05 (.70.) 0.983 NA 0.983 0.493
## .siqs06 (.71.) 0.857 NA 0.857 0.453
## .siqs07 (.72.) 0.551 NA 0.551 0.390
## .siqs08 (.73.) 0.721 NA 0.721 0.475
## .siqs09 (.74.) 0.838 NA 0.838 0.539
## .tpqp02 (.75.) 0.569 NA 0.569 0.387
## .tpqp05 (.76.) 0.822 NA 0.822 0.569
## .tpqp12 (.77.) 0.927 NA 0.927 0.474
## .tpqp16 (.78.) 0.819 NA 0.819 0.619
## .tpqp01 (.79.) 0.865 NA 0.865 0.643
## .tpqp09 (.80.) 0.849 NA 0.849 0.524
## .tpqp11 (.81.) 0.891 NA 0.891 0.535
## .tpqp14 (.82.) 0.752 NA 0.752 0.571
## .tpqp03 (.83.) 0.931 NA 0.931 0.733
## .tpqp07 (.84.) 0.911 NA 0.911 0.585
## .tpqp10 (.85.) 0.765 NA 0.765 0.574
## .tpqp13 (.86.) 0.879 NA 0.879 0.496
## .tpqp04 (.87.) 0.841 NA 0.841 0.587
## .tpqp06 (.88.) 0.788 NA 0.788 0.576
## .tpqp08 (.89.) 0.824 NA 0.824 0.550
## .tpqp15 (.90.) 0.863 NA 0.863 0.508
## .tpqm02 (.91.) 0.619 NA 0.619 0.428
## .tpqm05 (.92.) 0.782 NA 0.782 0.702
## .tpqm12 (.93.) 0.852 NA 0.852 0.561
## .tpqm16 (.94.) 0.708 NA 0.708 0.779
## .tpqm01 (.95.) 0.811 NA 0.811 0.740
## .tpqm09 (.96.) 0.792 NA 0.792 0.618
## .tpqm11 (.97.) 0.820 NA 0.820 0.592
## .tpqm14 (.98.) 0.723 NA 0.723 0.616
## .tpqm03 (.99.) 0.894 NA 0.894 0.768
## .tpqm07 (.100) 0.884 NA 0.884 0.682
## .tpqm10 (.101) 0.722 NA 0.722 0.635
## .tpqm13 (.102) 0.867 NA 0.867 0.551
## .tpqm04 (.103) 0.759 NA 0.759 0.724
## .tpqm06 (.104) 0.763 NA 0.763 0.613
## .tpqm08 (.105) 0.777 NA 0.777 0.651
## .tpqm15 (.106) 0.836 NA 0.836 0.556
## .tcq02 (.107) 0.918 NA 0.918 0.426
## .tcq05 (.108) 0.900 NA 0.900 0.522
## .tcq12 (.109) 1.295 NA 1.295 0.668
## .tcq16 (.110) 0.908 NA 0.908 0.478
## .tcq01 (.111) 0.905 NA 0.905 0.519
## .tcq09 (.112) 0.932 NA 0.932 0.566
## .tcq11 (.113) 0.930 NA 0.930 0.516
## .tcq14 (.114) 0.859 NA 0.859 0.540
## .tcq03 (.115) 0.973 NA 0.973 0.630
## .tcq07 (.116) 0.937 NA 0.937 0.622
## .tcq10 (.117) 1.118 NA 1.118 0.717
## .tcq13 (.118) 0.908 NA 0.908 0.540
## .tcq04 (.119) 0.882 NA 0.882 0.506
## .tcq06 (.120) 0.879 NA 0.879 0.489
## .tcq08 (.121) 0.839 NA 0.839 0.556
## .tcq15 (.122) 0.877 NA 0.877 0.510
## .it (.123) 0.367 NA 0.376 0.376
## .cc (.124) 0.606 NA 0.546 0.546
## .ia (.125) 0.189 NA 0.219 0.219
## .SIQS (.126) 0.120 NA 0.197 0.197
## .LTp (.127) 0.474 NA 0.527 0.527
## .LTm (.128) 0.047 NA 0.057 0.057
## LT (.129) 0.779 NA 1.000 1.000
## LTc (.130) 1.238 NA 1.000 1.000
## edaddct 0.066 NA 0.066 1.000Modelo con igualdad de medias, varianzas y covarianzas
sem.lv3covars %>%
summary(fit.measures=T, standardized=T)## lavaan 0.6.16 did NOT end normally after 76 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 364
## Number of equality constraints 177
##
## Number of observations per group:
## 1 299
## 0 357
##
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.001 0.832
## siqs02 (.p2.) 0.939 NA 0.940 0.704
## siqs03 (.p3.) 1.033 NA 1.034 0.771
## cc =~
## siqs04 1.000 0.902 0.718
## siqs05 (.p5.) 0.944 NA 0.852 0.653
## siqs06 (.p6.) 0.938 NA 0.846 0.706
## ia =~
## siqs07 1.000 0.688 0.735
## siqs08 (.p8.) 0.910 NA 0.625 0.643
## siqs09 (.p9.) 0.856 NA 0.589 0.558
## SIQS =~
## it 1.000 0.848 0.848
## cc (.11.) 0.741 NA 0.697 0.697
## ia (.12.) 0.852 NA 1.052 1.052
## LTp =~
## tpqp02 1.000 0.762 0.794
## tpqp05 (.14.) 0.941 NA 0.717 0.654
## tpqp12 (.15.) 1.173 NA 0.893 0.697
## tpqp16 (.16.) 0.857 NA 0.653 0.617
## tpqp01 (.17.) 0.832 NA 0.634 0.585
## tpqp09 (.18.) 1.035 NA 0.789 0.679
## tpqp11 (.19.) 1.035 NA 0.789 0.662
## tpqp14 (.20.) 0.903 NA 0.688 0.673
## tpqp03 (.21.) 0.703 NA 0.535 0.493
## tpqp07 (.22.) 0.948 NA 0.722 0.619
## tpqp10 (.23.) 0.905 NA 0.689 0.667
## tpqp13 (.24.) 1.103 NA 0.841 0.691
## tpqp04 (.25.) 0.916 NA 0.698 0.634
## tpqp06 (.26.) 0.913 NA 0.695 0.658
## tpqp08 (.27.) 0.973 NA 0.741 0.666
## tpqp15 (.28.) 1.068 NA 0.814 0.686
## LTm =~
## tpqm02 1.000 0.901 0.818
## tpqm05 (.30.) 0.709 NA 0.639 0.625
## tpqm12 (.31.) 0.991 NA 0.893 0.721
## tpqm16 (.32.) 0.561 NA 0.505 0.576
## tpqm01 (.33.) 0.654 NA 0.589 0.579
## tpqm09 (.34.) 0.853 NA 0.769 0.691
## tpqm11 (.35.) 0.915 NA 0.824 0.705
## tpqm14 (.36.) 0.825 NA 0.743 0.715
## tpqm03 (.37.) 0.630 NA 0.568 0.528
## tpqm07 (.38.) 0.780 NA 0.703 0.616
## tpqm10 (.39.) 0.791 NA 0.713 0.700
## tpqm13 (.40.) 1.012 NA 0.912 0.721
## tpqm04 (.41.) 0.662 NA 0.596 0.610
## tpqm06 (.42.) 0.850 NA 0.765 0.704
## tpqm08 (.43.) 0.790 NA 0.712 0.670
## tpqm15 (.44.) 0.988 NA 0.890 0.726
## LT =~
## LTm 1.000 0.968 0.968
## LTp (.46.) 0.611 NA 0.700 0.700
## LTc =~
## tcq02 1.000 0.994 0.740
## tcq05 (.48.) 0.796 NA 0.791 0.659
## tcq12 (.49.) 0.673 NA 0.669 0.477
## tcq16 (.50.) 0.859 NA 0.854 0.686
## tcq01 (.51.) 0.797 NA 0.793 0.658
## tcq09 (.52.) 0.743 NA 0.738 0.619
## tcq11 (.53.) 0.807 NA 0.803 0.653
## tcq14 (.54.) 0.753 NA 0.748 0.653
## tcq03 (.55.) 0.675 NA 0.671 0.566
## tcq07 (.56.) 0.673 NA 0.669 0.578
## tcq10 (.57.) 0.580 NA 0.576 0.463
## tcq13 (.58.) 0.766 NA 0.762 0.640
## tcq04 (.59.) 0.808 NA 0.803 0.672
## tcq06 (.60.) 0.831 NA 0.826 0.684
## tcq08 (.61.) 0.731 NA 0.727 0.651
## tcq15 (.62.) 0.795 NA 0.791 0.667
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.316 NA 0.324 0.324
## LTc 0.382 NA 0.448 0.448
## edaddicot 1.620 NA 1.909 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc (.131) 0.457 NA 0.527 0.527
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 1.503 NA 1.503 1.250
## .siqs02 (.134) 1.580 NA 1.580 1.183
## .siqs03 (.135) 1.374 NA 1.374 1.024
## .siqs04 (.136) 1.350 NA 1.350 1.074
## .siqs05 (.137) 1.413 NA 1.413 1.084
## .siqs06 (.138) 1.757 NA 1.757 1.465
## .siqs07 (.139) 1.649 NA 1.649 1.763
## .siqs08 (.140) 1.730 NA 1.730 1.778
## .siqs09 (.141) 1.633 NA 1.633 1.549
## .tpqp02 (.142) 3.585 NA 3.585 3.735
## .tpqp05 (.143) 3.592 NA 3.592 3.274
## .tpqp12 (.144) 3.285 NA 3.285 2.562
## .tpqp16 (.145) 3.644 NA 3.644 3.446
## .tpqp01 (.146) 3.558 NA 3.558 3.283
## .tpqp09 (.147) 3.570 NA 3.570 3.076
## .tpqp11 (.148) 3.450 NA 3.450 2.897
## .tpqp14 (.149) 3.681 NA 3.681 3.603
## .tpqp03 (.150) 3.495 NA 3.495 3.220
## .tpqp07 (.151) 3.426 NA 3.426 2.934
## .tpqp10 (.152) 3.661 NA 3.661 3.540
## .tpqp13 (.153) 3.437 NA 3.437 2.826
## .tpqp04 (.154) 3.526 NA 3.526 3.202
## .tpqp06 (.155) 3.626 NA 3.626 3.430
## .tpqp08 (.156) 3.576 NA 3.576 3.215
## .tpqp15 (.157) 3.434 NA 3.434 2.896
## .tpqm02 (.158) 3.630 NA 3.630 3.298
## .tpqm05 (.159) 3.744 NA 3.744 3.661
## .tpqm12 (.160) 3.562 NA 3.562 2.874
## .tpqm16 (.161) 3.793 NA 3.793 4.324
## .tpqm01 (.162) 3.655 NA 3.655 3.590
## .tpqm09 (.163) 3.717 NA 3.717 3.342
## .tpqm11 (.164) 3.575 NA 3.575 3.056
## .tpqm14 (.165) 3.735 NA 3.735 3.594
## .tpqm03 (.166) 3.527 NA 3.527 3.283
## .tpqm07 (.167) 3.532 NA 3.532 3.095
## .tpqm10 (.168) 3.734 NA 3.734 3.668
## .tpqm13 (.169) 3.548 NA 3.548 2.808
## .tpqm04 (.170) 3.724 NA 3.724 3.811
## .tpqm06 (.171) 3.708 NA 3.708 3.411
## .tpqm08 (.172) 3.684 NA 3.684 3.465
## .tpqm15 (.173) 3.556 NA 3.556 2.900
## .tcq02 (.174) 2.755 NA 2.755 2.051
## .tcq05 (.175) 3.307 NA 3.307 2.756
## .tcq12 (.176) 2.509 NA 2.509 1.790
## .tcq16 (.177) 3.244 NA 3.244 2.606
## .tcq01 (.178) 3.146 NA 3.146 2.611
## .tcq09 (.179) 3.389 NA 3.389 2.842
## .tcq11 (.180) 3.209 NA 3.209 2.611
## .tcq14 (.181) 3.284 NA 3.284 2.867
## .tcq03 (.182) 3.197 NA 3.197 2.696
## .tcq07 (.183) 3.241 NA 3.241 2.800
## .tcq10 (.184) 2.982 NA 2.982 2.396
## .tcq13 (.185) 3.089 NA 3.089 2.597
## .tcq04 (.186) 3.271 NA 3.271 2.735
## .tcq06 (.187) 3.222 NA 3.222 2.670
## .tcq08 (.188) 3.462 NA 3.462 3.100
## .tcq15 (.189) 3.294 NA 3.294 2.779
## edaddct 1.789 NA 1.789 NA
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.445 NA 0.445 0.308
## .siqs02 (.67.) 0.899 NA 0.899 0.504
## .siqs03 (.68.) 0.731 NA 0.731 0.406
## .siqs04 (.69.) 0.764 NA 0.764 0.484
## .siqs05 (.70.) 0.975 NA 0.975 0.573
## .siqs06 (.71.) 0.722 NA 0.722 0.502
## .siqs07 (.72.) 0.402 NA 0.402 0.460
## .siqs08 (.73.) 0.555 NA 0.555 0.587
## .siqs09 (.74.) 0.765 NA 0.765 0.688
## .tpqp02 (.75.) 0.341 NA 0.341 0.370
## .tpqp05 (.76.) 0.689 NA 0.689 0.573
## .tpqp12 (.77.) 0.846 NA 0.846 0.515
## .tpqp16 (.78.) 0.692 NA 0.692 0.619
## .tpqp01 (.79.) 0.773 NA 0.773 0.658
## .tpqp09 (.80.) 0.725 NA 0.725 0.538
## .tpqp11 (.81.) 0.796 NA 0.796 0.561
## .tpqp14 (.82.) 0.571 NA 0.571 0.547
## .tpqp03 (.83.) 0.891 NA 0.891 0.757
## .tpqp07 (.84.) 0.841 NA 0.841 0.617
## .tpqp10 (.85.) 0.594 NA 0.594 0.556
## .tpqp13 (.86.) 0.773 NA 0.773 0.522
## .tpqp04 (.87.) 0.726 NA 0.726 0.599
## .tpqp06 (.88.) 0.634 NA 0.634 0.567
## .tpqp08 (.89.) 0.688 NA 0.688 0.556
## .tpqp15 (.90.) 0.744 NA 0.744 0.529
## .tpqm02 (.91.) 0.400 NA 0.400 0.330
## .tpqm05 (.92.) 0.638 NA 0.638 0.610
## .tpqm12 (.93.) 0.738 NA 0.738 0.481
## .tpqm16 (.94.) 0.515 NA 0.515 0.669
## .tpqm01 (.95.) 0.690 NA 0.690 0.665
## .tpqm09 (.96.) 0.646 NA 0.646 0.522
## .tpqm11 (.97.) 0.689 NA 0.689 0.504
## .tpqm14 (.98.) 0.528 NA 0.528 0.489
## .tpqm03 (.99.) 0.831 NA 0.831 0.721
## .tpqm07 (.100) 0.808 NA 0.808 0.620
## .tpqm10 (.101) 0.529 NA 0.529 0.510
## .tpqm13 (.102) 0.765 NA 0.765 0.480
## .tpqm04 (.103) 0.600 NA 0.600 0.628
## .tpqm06 (.104) 0.596 NA 0.596 0.505
## .tpqm08 (.105) 0.623 NA 0.623 0.552
## .tpqm15 (.106) 0.711 NA 0.711 0.473
## .tcq02 (.107) 0.817 NA 0.817 0.452
## .tcq05 (.108) 0.814 NA 0.814 0.565
## .tcq12 (.109) 1.517 NA 1.517 0.772
## .tcq16 (.110) 0.821 NA 0.821 0.529
## .tcq01 (.111) 0.823 NA 0.823 0.567
## .tcq09 (.112) 0.876 NA 0.876 0.616
## .tcq11 (.113) 0.866 NA 0.866 0.573
## .tcq14 (.114) 0.752 NA 0.752 0.573
## .tcq03 (.115) 0.956 NA 0.956 0.680
## .tcq07 (.116) 0.893 NA 0.893 0.666
## .tcq10 (.117) 1.216 NA 1.216 0.785
## .tcq13 (.118) 0.835 NA 0.835 0.590
## .tcq04 (.119) 0.785 NA 0.785 0.549
## .tcq06 (.120) 0.774 NA 0.774 0.532
## .tcq08 (.121) 0.719 NA 0.719 0.577
## .tcq15 (.122) 0.780 NA 0.780 0.555
## .it (.123) 0.281 NA 0.280 0.280
## .cc (.124) 0.418 NA 0.514 0.514
## .ia (.125) -0.051 NA -0.108 -0.108
## .SIQS (.126) 0.390 NA 0.542 0.542
## .LTp (.127) 0.296 NA 0.511 0.511
## .LTm (.128) 0.052 NA 0.064 0.064
## LT (.129) 0.760 NA 1.000 1.000
## LTc (.130) 0.989 NA 1.000 1.000
## edaddct -0.000 NA -0.000 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.080 0.851
## siqs02 (.p2.) 0.939 NA 1.014 0.730
## siqs03 (.p3.) 1.033 NA 1.116 0.794
## cc =~
## siqs04 1.000 0.951 0.736
## siqs05 (.p5.) 0.944 NA 0.898 0.673
## siqs06 (.p6.) 0.938 NA 0.892 0.724
## ia =~
## siqs07 1.000 0.769 0.772
## siqs08 (.p8.) 0.910 NA 0.700 0.685
## siqs09 (.p9.) 0.856 NA 0.659 0.602
## SIQS =~
## it 1.000 0.871 0.871
## cc (.11.) 0.741 NA 0.733 0.733
## ia (.12.) 0.852 NA 1.042 1.042
## LTp =~
## tpqp02 1.000 0.762 0.794
## tpqp05 (.14.) 0.941 NA 0.717 0.654
## tpqp12 (.15.) 1.173 NA 0.893 0.697
## tpqp16 (.16.) 0.857 NA 0.653 0.617
## tpqp01 (.17.) 0.832 NA 0.634 0.585
## tpqp09 (.18.) 1.035 NA 0.789 0.679
## tpqp11 (.19.) 1.035 NA 0.789 0.662
## tpqp14 (.20.) 0.903 NA 0.688 0.673
## tpqp03 (.21.) 0.703 NA 0.535 0.493
## tpqp07 (.22.) 0.948 NA 0.722 0.619
## tpqp10 (.23.) 0.905 NA 0.689 0.667
## tpqp13 (.24.) 1.103 NA 0.841 0.691
## tpqp04 (.25.) 0.916 NA 0.698 0.634
## tpqp06 (.26.) 0.913 NA 0.695 0.658
## tpqp08 (.27.) 0.973 NA 0.741 0.666
## tpqp15 (.28.) 1.068 NA 0.814 0.686
## LTm =~
## tpqm02 1.000 0.901 0.818
## tpqm05 (.30.) 0.709 NA 0.639 0.625
## tpqm12 (.31.) 0.991 NA 0.893 0.721
## tpqm16 (.32.) 0.561 NA 0.505 0.576
## tpqm01 (.33.) 0.654 NA 0.589 0.579
## tpqm09 (.34.) 0.853 NA 0.769 0.691
## tpqm11 (.35.) 0.915 NA 0.824 0.705
## tpqm14 (.36.) 0.825 NA 0.743 0.715
## tpqm03 (.37.) 0.630 NA 0.568 0.528
## tpqm07 (.38.) 0.780 NA 0.703 0.616
## tpqm10 (.39.) 0.791 NA 0.713 0.700
## tpqm13 (.40.) 1.012 NA 0.912 0.721
## tpqm04 (.41.) 0.662 NA 0.596 0.610
## tpqm06 (.42.) 0.850 NA 0.765 0.704
## tpqm08 (.43.) 0.790 NA 0.712 0.670
## tpqm15 (.44.) 0.988 NA 0.890 0.726
## LT =~
## LTm 1.000 0.968 0.968
## LTp (.46.) 0.611 NA 0.700 0.700
## LTc =~
## tcq02 1.000 0.994 0.740
## tcq05 (.48.) 0.796 NA 0.791 0.659
## tcq12 (.49.) 0.673 NA 0.669 0.477
## tcq16 (.50.) 0.859 NA 0.854 0.686
## tcq01 (.51.) 0.797 NA 0.793 0.658
## tcq09 (.52.) 0.743 NA 0.738 0.619
## tcq11 (.53.) 0.807 NA 0.803 0.653
## tcq14 (.54.) 0.753 NA 0.748 0.653
## tcq03 (.55.) 0.675 NA 0.671 0.566
## tcq07 (.56.) 0.673 NA 0.669 0.578
## tcq10 (.57.) 0.580 NA 0.576 0.463
## tcq13 (.58.) 0.766 NA 0.762 0.640
## tcq04 (.59.) 0.808 NA 0.803 0.672
## tcq06 (.60.) 0.831 NA 0.826 0.684
## tcq08 (.61.) 0.731 NA 0.727 0.651
## tcq15 (.62.) 0.795 NA 0.791 0.667
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.655 NA 0.607 0.607
## LTc -0.033 NA -0.035 -0.035
## edaddicot 1.167 NA 1.240 0.460
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc (.131) 0.457 NA 0.527 0.527
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 1.503 NA 1.503 1.185
## .siqs02 (.134) 1.580 NA 1.580 1.138
## .siqs03 (.135) 1.374 NA 1.374 0.978
## .siqs04 (.136) 1.350 NA 1.350 1.045
## .siqs05 (.137) 1.413 NA 1.413 1.059
## .siqs06 (.138) 1.757 NA 1.757 1.426
## .siqs07 (.139) 1.649 NA 1.649 1.654
## .siqs08 (.140) 1.730 NA 1.730 1.692
## .siqs09 (.141) 1.633 NA 1.633 1.492
## .tpqp02 (.142) 3.585 NA 3.585 3.735
## .tpqp05 (.143) 3.592 NA 3.592 3.274
## .tpqp12 (.144) 3.285 NA 3.285 2.562
## .tpqp16 (.145) 3.644 NA 3.644 3.446
## .tpqp01 (.146) 3.558 NA 3.558 3.283
## .tpqp09 (.147) 3.570 NA 3.570 3.076
## .tpqp11 (.148) 3.450 NA 3.450 2.897
## .tpqp14 (.149) 3.681 NA 3.681 3.603
## .tpqp03 (.150) 3.495 NA 3.495 3.220
## .tpqp07 (.151) 3.426 NA 3.426 2.934
## .tpqp10 (.152) 3.661 NA 3.661 3.540
## .tpqp13 (.153) 3.437 NA 3.437 2.826
## .tpqp04 (.154) 3.526 NA 3.526 3.202
## .tpqp06 (.155) 3.626 NA 3.626 3.430
## .tpqp08 (.156) 3.576 NA 3.576 3.215
## .tpqp15 (.157) 3.434 NA 3.434 2.896
## .tpqm02 (.158) 3.630 NA 3.630 3.298
## .tpqm05 (.159) 3.744 NA 3.744 3.661
## .tpqm12 (.160) 3.562 NA 3.562 2.874
## .tpqm16 (.161) 3.793 NA 3.793 4.324
## .tpqm01 (.162) 3.655 NA 3.655 3.590
## .tpqm09 (.163) 3.717 NA 3.717 3.342
## .tpqm11 (.164) 3.575 NA 3.575 3.056
## .tpqm14 (.165) 3.735 NA 3.735 3.594
## .tpqm03 (.166) 3.527 NA 3.527 3.283
## .tpqm07 (.167) 3.532 NA 3.532 3.095
## .tpqm10 (.168) 3.734 NA 3.734 3.668
## .tpqm13 (.169) 3.548 NA 3.548 2.808
## .tpqm04 (.170) 3.724 NA 3.724 3.811
## .tpqm06 (.171) 3.708 NA 3.708 3.411
## .tpqm08 (.172) 3.684 NA 3.684 3.465
## .tpqm15 (.173) 3.556 NA 3.556 2.900
## .tcq02 (.174) 2.755 NA 2.755 2.051
## .tcq05 (.175) 3.307 NA 3.307 2.756
## .tcq12 (.176) 2.509 NA 2.509 1.790
## .tcq16 (.177) 3.244 NA 3.244 2.606
## .tcq01 (.178) 3.146 NA 3.146 2.611
## .tcq09 (.179) 3.389 NA 3.389 2.842
## .tcq11 (.180) 3.209 NA 3.209 2.611
## .tcq14 (.181) 3.284 NA 3.284 2.867
## .tcq03 (.182) 3.197 NA 3.197 2.696
## .tcq07 (.183) 3.241 NA 3.241 2.800
## .tcq10 (.184) 2.982 NA 2.982 2.396
## .tcq13 (.185) 3.089 NA 3.089 2.597
## .tcq04 (.186) 3.271 NA 3.271 2.735
## .tcq06 (.187) 3.222 NA 3.222 2.670
## .tcq08 (.188) 3.462 NA 3.462 3.100
## .tcq15 (.189) 3.294 NA 3.294 2.779
## edaddct 1.541 NA 1.541 4.160
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.445 NA 0.445 0.276
## .siqs02 (.67.) 0.899 NA 0.899 0.466
## .siqs03 (.68.) 0.731 NA 0.731 0.370
## .siqs04 (.69.) 0.764 NA 0.764 0.458
## .siqs05 (.70.) 0.975 NA 0.975 0.547
## .siqs06 (.71.) 0.722 NA 0.722 0.476
## .siqs07 (.72.) 0.402 NA 0.402 0.404
## .siqs08 (.73.) 0.555 NA 0.555 0.531
## .siqs09 (.74.) 0.765 NA 0.765 0.638
## .tpqp02 (.75.) 0.341 NA 0.341 0.370
## .tpqp05 (.76.) 0.689 NA 0.689 0.573
## .tpqp12 (.77.) 0.846 NA 0.846 0.515
## .tpqp16 (.78.) 0.692 NA 0.692 0.619
## .tpqp01 (.79.) 0.773 NA 0.773 0.658
## .tpqp09 (.80.) 0.725 NA 0.725 0.538
## .tpqp11 (.81.) 0.796 NA 0.796 0.561
## .tpqp14 (.82.) 0.571 NA 0.571 0.547
## .tpqp03 (.83.) 0.891 NA 0.891 0.757
## .tpqp07 (.84.) 0.841 NA 0.841 0.617
## .tpqp10 (.85.) 0.594 NA 0.594 0.556
## .tpqp13 (.86.) 0.773 NA 0.773 0.522
## .tpqp04 (.87.) 0.726 NA 0.726 0.599
## .tpqp06 (.88.) 0.634 NA 0.634 0.567
## .tpqp08 (.89.) 0.688 NA 0.688 0.556
## .tpqp15 (.90.) 0.744 NA 0.744 0.529
## .tpqm02 (.91.) 0.400 NA 0.400 0.330
## .tpqm05 (.92.) 0.638 NA 0.638 0.610
## .tpqm12 (.93.) 0.738 NA 0.738 0.481
## .tpqm16 (.94.) 0.515 NA 0.515 0.669
## .tpqm01 (.95.) 0.690 NA 0.690 0.665
## .tpqm09 (.96.) 0.646 NA 0.646 0.522
## .tpqm11 (.97.) 0.689 NA 0.689 0.504
## .tpqm14 (.98.) 0.528 NA 0.528 0.489
## .tpqm03 (.99.) 0.831 NA 0.831 0.721
## .tpqm07 (.100) 0.808 NA 0.808 0.620
## .tpqm10 (.101) 0.529 NA 0.529 0.510
## .tpqm13 (.102) 0.765 NA 0.765 0.480
## .tpqm04 (.103) 0.600 NA 0.600 0.628
## .tpqm06 (.104) 0.596 NA 0.596 0.505
## .tpqm08 (.105) 0.623 NA 0.623 0.552
## .tpqm15 (.106) 0.711 NA 0.711 0.473
## .tcq02 (.107) 0.817 NA 0.817 0.452
## .tcq05 (.108) 0.814 NA 0.814 0.565
## .tcq12 (.109) 1.517 NA 1.517 0.772
## .tcq16 (.110) 0.821 NA 0.821 0.529
## .tcq01 (.111) 0.823 NA 0.823 0.567
## .tcq09 (.112) 0.876 NA 0.876 0.616
## .tcq11 (.113) 0.866 NA 0.866 0.573
## .tcq14 (.114) 0.752 NA 0.752 0.573
## .tcq03 (.115) 0.956 NA 0.956 0.680
## .tcq07 (.116) 0.893 NA 0.893 0.666
## .tcq10 (.117) 1.216 NA 1.216 0.785
## .tcq13 (.118) 0.835 NA 0.835 0.590
## .tcq04 (.119) 0.785 NA 0.785 0.549
## .tcq06 (.120) 0.774 NA 0.774 0.532
## .tcq08 (.121) 0.719 NA 0.719 0.577
## .tcq15 (.122) 0.780 NA 0.780 0.555
## .it (.123) 0.281 NA 0.241 0.241
## .cc (.124) 0.418 NA 0.462 0.462
## .ia (.125) -0.051 NA -0.086 -0.086
## .SIQS (.126) 0.390 NA 0.441 0.441
## .LTp (.127) 0.296 NA 0.511 0.511
## .LTm (.128) 0.052 NA 0.064 0.064
## LT (.129) 0.760 NA 1.000 1.000
## LTc (.130) 0.989 NA 1.000 1.000
## edaddct 0.137 NA 0.137 1.000Edad
modelo <- '
# Identidad social
it =~ siqs01 + siqs02 + siqs03
cc =~ siqs04 + siqs05 + siqs06
ia =~ siqs07 + siqs08 + siqs09
SIQS =~ it + cc + ia
# Liderazgo transformacional (coach)
LTp =~ tpqp02 + tpqp05 + tpqp12 + tpqp16 + tpqp01 + tpqp09 + tpqp11 + tpqp14 + tpqp03 + tpqp07 + tpqp10 + tpqp13 + tpqp04 + tpqp06 + tpqp08 + tpqp15
LTm =~ tpqm02 + tpqm05 + tpqm12 + tpqm16 + tpqm01 + tpqm09 + tpqm11 + tpqm14 + tpqm03 + tpqm07 + tpqm10 + tpqm13 + tpqm04 + tpqm06 + tpqm08 + tpqm15
LT =~ LTm + LTp
LTc =~ tcq02 + tcq05 + tcq12 + tcq16 + tcq01 + tcq09 + tcq11 + tcq14 + tcq03 + tcq07 + tcq10 + tcq13 + tcq04 + tcq06 + tcq08 + tcq15
# REGRESIONES
SIQS ~ LT + LTc + sexo
'
# Configural model
sem.config.edad <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot")
#sem.config %>%
# summary(fit.measures = TRUE, standardized = TRUE)
# Metric model
sem.metric <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = "loadings")
# Scalar model
sem.scalar <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts"))
# Strict model
sem.strict <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals"))
# Latent variables means
sem.lv1means <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals",
"means"))
# Latent variables variances
sem.lv2vars <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals",
"means",
"lv.variances"))
# Latent variables covariances
sem.lv3covars <- datos %>%
sem(modelo, .,
estimator="ULS",
group = "edaddicot",
group.equal = c("loadings",
"intercepts",
"residuals",
"means",
"lv.variances",
"lv.covariances"))
# Model comparison
semTools::compareFit(sem.config.edad,
sem.metric,
sem.scalar,
sem.strict,
sem.lv1means,
sem.lv2vars,
sem.lv3covars) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config.edad 3174 5373.0
## sem.metric 3228 6720.6 1347.57 0.27025 54 < 2e-16 ***
## sem.scalar 3277 6793.4 72.75 0.03844 49 0.01542 *
## sem.strict 3334 7115.7 322.32 0.11913 57 < 2e-16 ***
## sem.lv1means 3342 7302.2 186.47 0.26080 8 < 2e-16 ***
## sem.lv2vars 3350 7422.1 119.92 0.20653 8 < 2e-16 ***
## sem.lv3covars 3351 7798.0 375.93 1.06914 1 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config.edad 5373.037† 3174 NA .046† .987† .987† .057†
## sem.metric 6720.611 3228 NA .058 .980 .979 .064
## sem.scalar 6793.361 3277 NA .057 .980 .979 .064
## sem.strict 7115.682 3334 NA .059 .978 .978 .066
## sem.lv1means 7302.156 3342 NA .060 .977 .977 .067
## sem.lv2vars 7422.079 3350 NA .061 .976 .977 .067
## sem.lv3covars 7798.004 3351 NA .064 .974 .974 .068
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.metric - sem.config.edad 54 0.011 -0.008 -0.007 0.007
## sem.scalar - sem.metric 49 0.000 0.000 0.000 0.000
## sem.strict - sem.scalar 57 0.002 -0.002 -0.001 0.001
## sem.lv1means - sem.strict 8 0.001 -0.001 -0.001 0.001
## sem.lv2vars - sem.lv1means 8 0.001 -0.001 -0.001 0.000
## sem.lv3covars - sem.lv2vars 1 0.003 -0.002 -0.002 0.001# Model comparison
semTools::compareFit(sem.config.edad,
sem.lv1means,
sem.lv2vars,
sem.lv3covars) %>%
summary()## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## sem.config.edad 3174 5373.0
## sem.lv1means 3342 7302.2 1929.12 0.17877 168 < 2.2e-16 ***
## sem.lv2vars 3350 7422.1 119.92 0.20653 8 < 2.2e-16 ***
## sem.lv3covars 3351 7798.0 375.93 1.06914 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr
## sem.config.edad 5373.037† 3174 NA .046† .987† .987† .057†
## sem.lv1means 7302.156 3342 NA .060 .977 .977 .067
## sem.lv2vars 7422.079 3350 NA .061 .976 .977 .067
## sem.lv3covars 7798.004 3351 NA .064 .974 .974 .068
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr
## sem.lv1means - sem.config.edad 168 0.014 -0.010 -0.009 0.010
## sem.lv2vars - sem.lv1means 8 0.001 -0.001 -0.001 0.000
## sem.lv3covars - sem.lv2vars 1 0.003 -0.002 -0.002 0.001Modelo con igualdad de medias
sem.lv1means %>%
summary(fit.measures=T, standardized=T)## lavaan 0.6.16 ended normally after 160 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 364
## Number of equality constraints 168
##
## Number of observations per group:
## 2 302
## 1 354
##
## Model Test User Model:
##
## Test statistic 7302.156
## Degrees of freedom 3342
## P-value (Unknown) NA
## Test statistic for each group:
## 2 3795.696
## 1 3506.460
##
## Model Test Baseline Model:
##
## Test statistic 175231.346
## Degrees of freedom 3306
## P-value NA
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.977
## Tucker-Lewis Index (TLI) 0.977
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.060
## 90 Percent confidence interval - lower 0.058
## 90 Percent confidence interval - upper 0.062
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.067
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.907 0.865
## siqs02 (.p2.) 0.886 0.021 41.999 0.000 0.804 0.661
## siqs03 (.p3.) 1.116 0.024 45.593 0.000 1.012 0.878
## cc =~
## siqs04 1.000 0.940 0.711
## siqs05 (.p5.) 1.098 0.029 37.616 0.000 1.031 0.748
## siqs06 (.p6.) 0.984 0.027 36.359 0.000 0.925 0.765
## ia =~
## siqs07 1.000 0.849 0.871
## siqs08 (.p8.) 1.037 0.026 39.802 0.000 0.880 0.890
## siqs09 (.p9.) 0.941 0.025 38.269 0.000 0.799 0.719
## SIQS =~
## it 1.000 0.830 0.830
## cc (.11.) 0.777 0.021 36.728 0.000 0.623 0.623
## ia (.12.) 0.900 0.023 38.468 0.000 0.798 0.798
## LTp =~
## tpqp02 1.000 0.679 0.667
## tpqp05 (.14.) 1.297 0.024 52.997 0.000 0.881 0.797
## tpqp12 (.15.) 1.633 0.029 56.685 0.000 1.109 0.811
## tpqp16 (.16.) 1.140 0.023 50.510 0.000 0.774 0.747
## tpqp01 (.17.) 1.129 0.022 50.304 0.000 0.767 0.700
## tpqp09 (.18.) 1.429 0.026 54.678 0.000 0.971 0.815
## tpqp11 (.19.) 1.398 0.026 54.306 0.000 0.949 0.772
## tpqp14 (.20.) 1.219 0.024 51.842 0.000 0.828 0.847
## tpqp03 (.21.) 0.910 0.020 45.522 0.000 0.618 0.564
## tpqp07 (.22.) 1.260 0.024 52.465 0.000 0.855 0.714
## tpqp10 (.23.) 1.232 0.024 52.043 0.000 0.837 0.837
## tpqp13 (.24.) 1.542 0.028 55.863 0.000 1.047 0.818
## tpqp04 (.25.) 1.258 0.024 52.431 0.000 0.854 0.767
## tpqp06 (.26.) 1.227 0.024 51.965 0.000 0.833 0.807
## tpqp08 (.27.) 1.326 0.025 53.403 0.000 0.901 0.808
## tpqp15 (.28.) 1.485 0.027 55.285 0.000 1.008 0.819
## LTm =~
## tpqm02 1.000 0.624 0.626
## tpqm05 (.30.) 1.014 0.025 41.312 0.000 0.633 0.712
## tpqm12 (.31.) 1.410 0.030 46.874 0.000 0.880 0.788
## tpqm16 (.32.) 0.770 0.022 35.678 0.000 0.480 0.700
## tpqm01 (.33.) 0.923 0.023 39.453 0.000 0.576 0.636
## tpqm09 (.34.) 1.224 0.027 44.691 0.000 0.764 0.785
## tpqm11 (.35.) 1.309 0.029 45.763 0.000 0.817 0.784
## tpqm14 (.36.) 1.156 0.026 43.721 0.000 0.721 0.850
## tpqm03 (.37.) 0.860 0.023 38.009 0.000 0.536 0.533
## tpqm07 (.38.) 1.052 0.025 42.014 0.000 0.656 0.625
## tpqm10 (.39.) 1.107 0.026 42.947 0.000 0.691 0.832
## tpqm13 (.40.) 1.450 0.031 47.267 0.000 0.905 0.782
## tpqm04 (.41.) 0.922 0.023 39.433 0.000 0.575 0.696
## tpqm06 (.42.) 1.189 0.027 44.204 0.000 0.742 0.802
## tpqm08 (.43.) 1.106 0.026 42.925 0.000 0.690 0.756
## tpqm15 (.44.) 1.403 0.030 46.796 0.000 0.875 0.798
## LT =~
## LTm 1.000 0.851 0.851
## LTp (.46.) 0.942 0.027 35.088 0.000 0.736 0.736
## LTc =~
## tcq02 1.000 0.976 0.680
## tcq05 (.48.) 0.955 0.016 60.162 0.000 0.933 0.753
## tcq12 (.49.) 0.803 0.015 55.195 0.000 0.784 0.501
## tcq16 (.50.) 1.074 0.017 63.172 0.000 1.048 0.792
## tcq01 (.51.) 0.978 0.016 60.785 0.000 0.955 0.756
## tcq09 (.52.) 0.889 0.015 58.170 0.000 0.868 0.703
## tcq11 (.53.) 0.968 0.016 60.516 0.000 0.945 0.738
## tcq14 (.54.) 0.927 0.016 59.346 0.000 0.905 0.772
## tcq03 (.55.) 0.779 0.014 54.310 0.000 0.761 0.620
## tcq07 (.56.) 0.809 0.015 55.425 0.000 0.790 0.661
## tcq10 (.57.) 0.684 0.014 50.305 0.000 0.668 0.500
## tcq13 (.58.) 0.939 0.016 59.690 0.000 0.917 0.737
## tcq04 (.59.) 0.978 0.016 60.793 0.000 0.955 0.772
## tcq06 (.60.) 1.010 0.016 61.632 0.000 0.986 0.787
## tcq08 (.61.) 0.890 0.015 58.197 0.000 0.869 0.774
## tcq15 (.62.) 0.960 0.016 60.293 0.000 0.937 0.766
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.435 0.016 26.392 0.000 0.306 0.306
## LTc 0.261 0.008 31.577 0.000 0.339 0.339
## sexo 0.193 0.081 2.394 0.017 0.256 0.132
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.077 0.003 26.446 0.000 0.148 0.148
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 4.762 0.057 83.522 0.000 4.762 4.540
## .siqs02 (.134) 4.744 0.054 88.361 0.000 4.744 3.901
## .siqs03 (.135) 4.602 0.061 75.959 0.000 4.602 3.993
## .siqs04 (.136) 4.352 0.051 85.810 0.000 4.352 3.292
## .siqs05 (.137) 4.308 0.053 81.642 0.000 4.308 3.125
## .siqs06 (.138) 4.829 0.050 95.832 0.000 4.829 3.996
## .siqs07 (.139) 5.181 0.054 95.807 0.000 5.181 5.317
## .siqs08 (.140) 5.138 0.055 93.379 0.000 5.138 5.197
## .siqs09 (.141) 4.867 0.053 92.555 0.000 4.867 4.382
## .tpqp02 (.142) 5.387 0.039 137.769 0.000 5.387 5.294
## .tpqp05 (.143) 5.398 0.039 138.042 0.000 5.398 4.889
## .tpqp12 (.144) 4.936 0.039 126.230 0.000 4.936 3.610
## .tpqp16 (.145) 5.476 0.039 140.030 0.000 5.476 5.279
## .tpqp01 (.146) 5.346 0.039 136.716 0.000 5.346 4.881
## .tpqp09 (.147) 5.364 0.039 137.184 0.000 5.364 4.505
## .tpqp11 (.148) 5.184 0.039 132.584 0.000 5.184 4.217
## .tpqp14 (.149) 5.530 0.039 141.434 0.000 5.530 5.658
## .tpqp03 (.150) 5.252 0.039 134.299 0.000 5.252 4.792
## .tpqp07 (.151) 5.148 0.039 131.649 0.000 5.148 4.295
## .tpqp10 (.152) 5.502 0.039 140.693 0.000 5.502 5.504
## .tpqp13 (.153) 5.165 0.039 132.078 0.000 5.165 4.035
## .tpqp04 (.154) 5.299 0.039 135.508 0.000 5.299 4.757
## .tpqp06 (.155) 5.448 0.039 139.328 0.000 5.448 5.274
## .tpqp08 (.156) 5.373 0.039 137.418 0.000 5.373 4.819
## .tpqp15 (.157) 5.160 0.039 131.961 0.000 5.160 4.192
## .tpqm02 (.158) 5.454 0.039 139.485 0.000 5.454 5.477
## .tpqm05 (.159) 5.625 0.039 143.850 0.000 5.625 6.335
## .tpqm12 (.160) 5.352 0.039 136.872 0.000 5.352 4.793
## .tpqm16 (.161) 5.700 0.039 145.761 0.000 5.700 8.308
## .tpqm01 (.162) 5.492 0.039 140.459 0.000 5.492 6.071
## .tpqm09 (.163) 5.585 0.039 142.837 0.000 5.585 5.737
## .tpqm11 (.164) 5.372 0.039 137.379 0.000 5.372 5.159
## .tpqm14 (.165) 5.613 0.039 143.539 0.000 5.613 6.614
## .tpqm03 (.166) 5.299 0.039 135.508 0.000 5.299 5.268
## .tpqm07 (.167) 5.306 0.039 135.703 0.000 5.306 5.050
## .tpqm10 (.168) 5.611 0.039 143.500 0.000 5.611 6.759
## .tpqm13 (.169) 5.331 0.039 136.327 0.000 5.331 4.609
## .tpqm04 (.170) 5.596 0.039 143.110 0.000 5.596 6.774
## .tpqm06 (.171) 5.572 0.039 142.486 0.000 5.572 6.021
## .tpqm08 (.172) 5.535 0.039 141.550 0.000 5.535 6.069
## .tpqm15 (.173) 5.343 0.039 136.639 0.000 5.343 4.875
## .tcq02 (.174) 4.140 0.039 105.881 0.000 4.140 2.885
## .tcq05 (.175) 4.970 0.039 127.089 0.000 4.970 4.010
## .tcq12 (.176) 3.770 0.039 96.407 0.000 3.770 2.408
## .tcq16 (.177) 4.875 0.039 124.672 0.000 4.875 3.684
## .tcq01 (.178) 4.727 0.039 120.890 0.000 4.727 3.746
## .tcq09 (.179) 5.092 0.039 130.207 0.000 5.092 4.123
## .tcq11 (.180) 4.822 0.039 123.307 0.000 4.822 3.767
## .tcq14 (.181) 4.934 0.039 126.192 0.000 4.934 4.206
## .tcq03 (.182) 4.803 0.039 122.839 0.000 4.803 3.913
## .tcq07 (.183) 4.870 0.039 124.554 0.000 4.870 4.079
## .tcq10 (.184) 4.480 0.039 114.574 0.000 4.480 3.352
## .tcq13 (.185) 4.642 0.039 118.707 0.000 4.642 3.733
## .tcq04 (.186) 4.915 0.039 125.685 0.000 4.915 3.972
## .tcq06 (.187) 4.841 0.039 123.814 0.000 4.841 3.864
## .tcq08 (.188) 5.201 0.039 133.014 0.000 5.201 4.633
## .tcq15 (.189) 4.950 0.039 126.581 0.000 4.950 4.046
## sexo 0.498 0.057 8.696 0.000 0.498 0.965
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.278 0.051 5.416 0.000 0.278 0.253
## .siqs02 (.67.) 0.833 0.048 17.350 0.000 0.833 0.563
## .siqs03 (.68.) 0.304 0.056 5.468 0.000 0.304 0.229
## .siqs04 (.69.) 0.864 0.051 16.794 0.000 0.864 0.495
## .siqs05 (.70.) 0.836 0.055 15.200 0.000 0.836 0.440
## .siqs06 (.71.) 0.605 0.051 11.869 0.000 0.605 0.414
## .siqs07 (.72.) 0.228 0.050 4.595 0.000 0.228 0.241
## .siqs08 (.73.) 0.203 0.051 3.983 0.000 0.203 0.207
## .siqs09 (.74.) 0.595 0.048 12.398 0.000 0.595 0.483
## .tpqp02 (.75.) 0.574 0.041 13.903 0.000 0.574 0.555
## .tpqp05 (.76.) 0.444 0.043 10.349 0.000 0.444 0.364
## .tpqp12 (.77.) 0.640 0.045 14.153 0.000 0.640 0.342
## .tpqp16 (.78.) 0.476 0.042 11.338 0.000 0.476 0.443
## .tpqp01 (.79.) 0.612 0.042 14.593 0.000 0.612 0.510
## .tpqp09 (.80.) 0.476 0.044 10.877 0.000 0.476 0.335
## .tpqp11 (.81.) 0.611 0.044 14.042 0.000 0.611 0.404
## .tpqp14 (.82.) 0.270 0.042 6.366 0.000 0.270 0.283
## .tpqp03 (.83.) 0.819 0.041 20.008 0.000 0.819 0.682
## .tpqp07 (.84.) 0.705 0.043 16.525 0.000 0.705 0.491
## .tpqp10 (.85.) 0.299 0.043 7.043 0.000 0.299 0.300
## .tpqp13 (.86.) 0.542 0.045 12.180 0.000 0.542 0.331
## .tpqp04 (.87.) 0.511 0.043 11.992 0.000 0.511 0.412
## .tpqp06 (.88.) 0.373 0.042 8.779 0.000 0.373 0.349
## .tpqp08 (.89.) 0.432 0.043 10.032 0.000 0.432 0.347
## .tpqp15 (.90.) 0.499 0.044 11.304 0.000 0.499 0.329
## .tpqm02 (.91.) 0.603 0.041 14.588 0.000 0.603 0.608
## .tpqm05 (.92.) 0.388 0.041 9.386 0.000 0.388 0.493
## .tpqm12 (.93.) 0.473 0.044 10.849 0.000 0.473 0.379
## .tpqm16 (.94.) 0.240 0.040 5.938 0.000 0.240 0.510
## .tpqm01 (.95.) 0.487 0.041 11.891 0.000 0.487 0.595
## .tpqm09 (.96.) 0.364 0.042 8.586 0.000 0.364 0.385
## .tpqm11 (.97.) 0.417 0.043 9.719 0.000 0.417 0.385
## .tpqm14 (.98.) 0.200 0.042 4.749 0.000 0.200 0.277
## .tpqm03 (.99.) 0.724 0.041 17.780 0.000 0.724 0.716
## .tpqm07 (.100) 0.673 0.042 16.206 0.000 0.673 0.610
## .tpqm10 (.101) 0.212 0.042 5.079 0.000 0.212 0.308
## .tpqm13 (.102) 0.520 0.044 11.844 0.000 0.520 0.388
## .tpqm04 (.103) 0.352 0.041 8.590 0.000 0.352 0.516
## .tpqm06 (.104) 0.306 0.042 7.240 0.000 0.306 0.357
## .tpqm08 (.105) 0.356 0.042 8.513 0.000 0.356 0.428
## .tpqm15 (.106) 0.436 0.044 10.009 0.000 0.436 0.363
## .tcq02 (.107) 1.106 0.044 24.886 0.000 1.106 0.537
## .tcq05 (.108) 0.666 0.044 15.149 0.000 0.666 0.434
## .tcq12 (.109) 1.836 0.042 43.204 0.000 1.836 0.749
## .tcq16 (.110) 0.652 0.045 14.398 0.000 0.652 0.373
## .tcq01 (.111) 0.681 0.044 15.409 0.000 0.681 0.428
## .tcq09 (.112) 0.772 0.043 17.827 0.000 0.772 0.506
## .tcq11 (.113) 0.746 0.044 16.908 0.000 0.746 0.455
## .tcq14 (.114) 0.557 0.044 12.755 0.000 0.557 0.405
## .tcq03 (.115) 0.928 0.042 21.935 0.000 0.928 0.616
## .tcq07 (.116) 0.803 0.043 18.860 0.000 0.803 0.563
## .tcq10 (.117) 1.341 0.042 32.261 0.000 1.341 0.750
## .tcq13 (.118) 0.706 0.044 16.120 0.000 0.706 0.457
## .tcq04 (.119) 0.619 0.044 14.002 0.000 0.619 0.404
## .tcq06 (.120) 0.598 0.045 13.420 0.000 0.598 0.381
## .tcq08 (.121) 0.506 0.043 11.675 0.000 0.506 0.401
## .tcq15 (.122) 0.618 0.044 14.045 0.000 0.618 0.413
## .it 0.256 0.034 7.566 0.000 0.311 0.311
## .cc 0.541 0.034 16.014 0.000 0.612 0.612
## .ia 0.262 0.033 7.910 0.000 0.364 0.364
## .SIQS 0.421 0.021 19.823 0.000 0.743 0.743
## .LTp 0.211 0.008 24.870 0.000 0.458 0.458
## .LTm 0.108 0.008 13.826 0.000 0.276 0.276
## LT 0.282 0.011 25.533 0.000 1.000 1.000
## LTc 0.953 0.022 43.372 0.000 1.000 1.000
## sexo 0.267 0.056 4.730 0.000 0.267 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.015 0.887
## siqs02 (.p2.) 0.886 0.021 41.999 0.000 0.899 0.702
## siqs03 (.p3.) 1.116 0.024 45.593 0.000 1.132 0.899
## cc =~
## siqs04 1.000 0.867 0.682
## siqs05 (.p5.) 1.098 0.029 37.616 0.000 0.952 0.721
## siqs06 (.p6.) 0.984 0.027 36.359 0.000 0.853 0.739
## ia =~
## siqs07 1.000 0.819 0.864
## siqs08 (.p8.) 1.037 0.026 39.802 0.000 0.849 0.884
## siqs09 (.p9.) 0.941 0.025 38.269 0.000 0.771 0.707
## SIQS =~
## it 1.000 0.831 0.831
## cc (.11.) 0.777 0.021 36.728 0.000 0.756 0.756
## ia (.12.) 0.900 0.023 38.468 0.000 0.926 0.926
## LTp =~
## tpqp02 1.000 0.668 0.661
## tpqp05 (.14.) 1.297 0.024 52.997 0.000 0.867 0.793
## tpqp12 (.15.) 1.633 0.029 56.685 0.000 1.092 0.807
## tpqp16 (.16.) 1.140 0.023 50.510 0.000 0.762 0.741
## tpqp01 (.17.) 1.129 0.022 50.304 0.000 0.754 0.694
## tpqp09 (.18.) 1.429 0.026 54.678 0.000 0.955 0.811
## tpqp11 (.19.) 1.398 0.026 54.306 0.000 0.934 0.767
## tpqp14 (.20.) 1.219 0.024 51.842 0.000 0.815 0.843
## tpqp03 (.21.) 0.910 0.020 45.522 0.000 0.608 0.558
## tpqp07 (.22.) 1.260 0.024 52.465 0.000 0.842 0.708
## tpqp10 (.23.) 1.232 0.024 52.043 0.000 0.823 0.833
## tpqp13 (.24.) 1.542 0.028 55.863 0.000 1.031 0.814
## tpqp04 (.25.) 1.258 0.024 52.431 0.000 0.841 0.762
## tpqp06 (.26.) 1.227 0.024 51.965 0.000 0.820 0.802
## tpqp08 (.27.) 1.326 0.025 53.403 0.000 0.886 0.803
## tpqp15 (.28.) 1.485 0.027 55.285 0.000 0.992 0.815
## LTm =~
## tpqm02 1.000 0.621 0.625
## tpqm05 (.30.) 1.014 0.025 41.312 0.000 0.630 0.711
## tpqm12 (.31.) 1.410 0.030 46.874 0.000 0.876 0.787
## tpqm16 (.32.) 0.770 0.022 35.678 0.000 0.479 0.699
## tpqm01 (.33.) 0.923 0.023 39.453 0.000 0.573 0.635
## tpqm09 (.34.) 1.224 0.027 44.691 0.000 0.761 0.783
## tpqm11 (.35.) 1.309 0.029 45.763 0.000 0.813 0.783
## tpqm14 (.36.) 1.156 0.026 43.721 0.000 0.719 0.849
## tpqm03 (.37.) 0.860 0.023 38.009 0.000 0.534 0.532
## tpqm07 (.38.) 1.052 0.025 42.014 0.000 0.654 0.623
## tpqm10 (.39.) 1.107 0.026 42.947 0.000 0.688 0.831
## tpqm13 (.40.) 1.450 0.031 47.267 0.000 0.901 0.781
## tpqm04 (.41.) 0.922 0.023 39.433 0.000 0.573 0.695
## tpqm06 (.42.) 1.189 0.027 44.204 0.000 0.739 0.801
## tpqm08 (.43.) 1.106 0.026 42.925 0.000 0.687 0.755
## tpqm15 (.44.) 1.403 0.030 46.796 0.000 0.872 0.797
## LT =~
## LTm 1.000 0.861 0.861
## LTp (.46.) 0.942 0.027 35.088 0.000 0.754 0.754
## LTc =~
## tcq02 1.000 0.958 0.673
## tcq05 (.48.) 0.955 0.016 60.162 0.000 0.915 0.746
## tcq12 (.49.) 0.803 0.015 55.195 0.000 0.769 0.493
## tcq16 (.50.) 1.074 0.017 63.172 0.000 1.029 0.787
## tcq01 (.51.) 0.978 0.016 60.785 0.000 0.937 0.750
## tcq09 (.52.) 0.889 0.015 58.170 0.000 0.852 0.696
## tcq11 (.53.) 0.968 0.016 60.516 0.000 0.927 0.732
## tcq14 (.54.) 0.927 0.016 59.346 0.000 0.888 0.766
## tcq03 (.55.) 0.779 0.014 54.310 0.000 0.746 0.613
## tcq07 (.56.) 0.809 0.015 55.425 0.000 0.775 0.654
## tcq10 (.57.) 0.684 0.014 50.305 0.000 0.655 0.493
## tcq13 (.58.) 0.939 0.016 59.690 0.000 0.899 0.731
## tcq04 (.59.) 0.978 0.016 60.793 0.000 0.937 0.766
## tcq06 (.60.) 1.010 0.016 61.632 0.000 0.967 0.781
## tcq08 (.61.) 0.890 0.015 58.197 0.000 0.852 0.768
## tcq15 (.62.) 0.960 0.016 60.293 0.000 0.920 0.760
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.608 0.020 30.809 0.000 0.385 0.385
## LTc 0.328 0.009 34.805 0.000 0.373 0.373
## sexo 0.561 0.113 4.965 0.000 0.665 0.295
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.138 0.004 36.706 0.000 0.270 0.270
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 4.762 0.057 83.522 0.000 4.762 4.164
## .siqs02 (.134) 4.744 0.054 88.361 0.000 4.744 3.703
## .siqs03 (.135) 4.602 0.061 75.959 0.000 4.602 3.654
## .siqs04 (.136) 4.352 0.051 85.810 0.000 4.352 3.423
## .siqs05 (.137) 4.308 0.053 81.642 0.000 4.308 3.264
## .siqs06 (.138) 4.829 0.050 95.832 0.000 4.829 4.182
## .siqs07 (.139) 5.181 0.054 95.807 0.000 5.181 5.462
## .siqs08 (.140) 5.138 0.055 93.379 0.000 5.138 5.345
## .siqs09 (.141) 4.867 0.053 92.555 0.000 4.867 4.462
## .tpqp02 (.142) 5.387 0.039 137.769 0.000 5.387 5.331
## .tpqp05 (.143) 5.398 0.039 138.042 0.000 5.398 4.938
## .tpqp12 (.144) 4.936 0.039 126.230 0.000 4.936 3.647
## .tpqp16 (.145) 5.476 0.039 140.030 0.000 5.476 5.326
## .tpqp01 (.146) 5.346 0.039 136.716 0.000 5.346 4.919
## .tpqp09 (.147) 5.364 0.039 137.184 0.000 5.364 4.553
## .tpqp11 (.148) 5.184 0.039 132.584 0.000 5.184 4.257
## .tpqp14 (.149) 5.530 0.039 141.434 0.000 5.530 5.722
## .tpqp03 (.150) 5.252 0.039 134.299 0.000 5.252 4.816
## .tpqp07 (.151) 5.148 0.039 131.649 0.000 5.148 4.329
## .tpqp10 (.152) 5.502 0.039 140.693 0.000 5.502 5.565
## .tpqp13 (.153) 5.165 0.039 132.078 0.000 5.165 4.077
## .tpqp04 (.154) 5.299 0.039 135.508 0.000 5.299 4.801
## .tpqp06 (.155) 5.448 0.039 139.328 0.000 5.448 5.328
## .tpqp08 (.156) 5.373 0.039 137.418 0.000 5.373 4.869
## .tpqp15 (.157) 5.160 0.039 131.961 0.000 5.160 4.237
## .tpqm02 (.158) 5.454 0.039 139.485 0.000 5.454 5.485
## .tpqm05 (.159) 5.625 0.039 143.850 0.000 5.625 6.348
## .tpqm12 (.160) 5.352 0.039 136.872 0.000 5.352 4.805
## .tpqm16 (.161) 5.700 0.039 145.761 0.000 5.700 8.324
## .tpqm01 (.162) 5.492 0.039 140.459 0.000 5.492 6.081
## .tpqm09 (.163) 5.585 0.039 142.837 0.000 5.585 5.751
## .tpqm11 (.164) 5.372 0.039 137.379 0.000 5.372 5.171
## .tpqm14 (.165) 5.613 0.039 143.539 0.000 5.613 6.632
## .tpqm03 (.166) 5.299 0.039 135.508 0.000 5.299 5.274
## .tpqm07 (.167) 5.306 0.039 135.703 0.000 5.306 5.057
## .tpqm10 (.168) 5.611 0.039 143.500 0.000 5.611 6.777
## .tpqm13 (.169) 5.331 0.039 136.327 0.000 5.331 4.620
## .tpqm04 (.170) 5.596 0.039 143.110 0.000 5.596 6.786
## .tpqm06 (.171) 5.572 0.039 142.486 0.000 5.572 6.036
## .tpqm08 (.172) 5.535 0.039 141.550 0.000 5.535 6.083
## .tpqm15 (.173) 5.343 0.039 136.639 0.000 5.343 4.887
## .tcq02 (.174) 4.140 0.039 105.881 0.000 4.140 2.910
## .tcq05 (.175) 4.970 0.039 127.089 0.000 4.970 4.053
## .tcq12 (.176) 3.770 0.039 96.407 0.000 3.770 2.420
## .tcq16 (.177) 4.875 0.039 124.672 0.000 4.875 3.728
## .tcq01 (.178) 4.727 0.039 120.890 0.000 4.727 3.787
## .tcq09 (.179) 5.092 0.039 130.207 0.000 5.092 4.161
## .tcq11 (.180) 4.822 0.039 123.307 0.000 4.822 3.805
## .tcq14 (.181) 4.934 0.039 126.192 0.000 4.934 4.254
## .tcq03 (.182) 4.803 0.039 122.839 0.000 4.803 3.942
## .tcq07 (.183) 4.870 0.039 124.554 0.000 4.870 4.112
## .tcq10 (.184) 4.480 0.039 114.574 0.000 4.480 3.368
## .tcq13 (.185) 4.642 0.039 118.707 0.000 4.642 3.772
## .tcq04 (.186) 4.915 0.039 125.685 0.000 4.915 4.017
## .tcq06 (.187) 4.841 0.039 123.814 0.000 4.841 3.909
## .tcq08 (.188) 5.201 0.039 133.014 0.000 5.201 4.686
## .tcq15 (.189) 4.950 0.039 126.581 0.000 4.950 4.091
## sexo 0.434 0.051 8.530 0.000 0.434 0.978
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.278 0.051 5.416 0.000 0.278 0.212
## .siqs02 (.67.) 0.833 0.048 17.350 0.000 0.833 0.507
## .siqs03 (.68.) 0.304 0.056 5.468 0.000 0.304 0.192
## .siqs04 (.69.) 0.864 0.051 16.794 0.000 0.864 0.535
## .siqs05 (.70.) 0.836 0.055 15.200 0.000 0.836 0.480
## .siqs06 (.71.) 0.605 0.051 11.869 0.000 0.605 0.454
## .siqs07 (.72.) 0.228 0.050 4.595 0.000 0.228 0.254
## .siqs08 (.73.) 0.203 0.051 3.983 0.000 0.203 0.219
## .siqs09 (.74.) 0.595 0.048 12.398 0.000 0.595 0.500
## .tpqp02 (.75.) 0.574 0.041 13.903 0.000 0.574 0.563
## .tpqp05 (.76.) 0.444 0.043 10.349 0.000 0.444 0.371
## .tpqp12 (.77.) 0.640 0.045 14.153 0.000 0.640 0.349
## .tpqp16 (.78.) 0.476 0.042 11.338 0.000 0.476 0.451
## .tpqp01 (.79.) 0.612 0.042 14.593 0.000 0.612 0.518
## .tpqp09 (.80.) 0.476 0.044 10.877 0.000 0.476 0.343
## .tpqp11 (.81.) 0.611 0.044 14.042 0.000 0.611 0.412
## .tpqp14 (.82.) 0.270 0.042 6.366 0.000 0.270 0.289
## .tpqp03 (.83.) 0.819 0.041 20.008 0.000 0.819 0.689
## .tpqp07 (.84.) 0.705 0.043 16.525 0.000 0.705 0.499
## .tpqp10 (.85.) 0.299 0.043 7.043 0.000 0.299 0.306
## .tpqp13 (.86.) 0.542 0.045 12.180 0.000 0.542 0.338
## .tpqp04 (.87.) 0.511 0.043 11.992 0.000 0.511 0.420
## .tpqp06 (.88.) 0.373 0.042 8.779 0.000 0.373 0.357
## .tpqp08 (.89.) 0.432 0.043 10.032 0.000 0.432 0.355
## .tpqp15 (.90.) 0.499 0.044 11.304 0.000 0.499 0.336
## .tpqm02 (.91.) 0.603 0.041 14.588 0.000 0.603 0.609
## .tpqm05 (.92.) 0.388 0.041 9.386 0.000 0.388 0.494
## .tpqm12 (.93.) 0.473 0.044 10.849 0.000 0.473 0.381
## .tpqm16 (.94.) 0.240 0.040 5.938 0.000 0.240 0.512
## .tpqm01 (.95.) 0.487 0.041 11.891 0.000 0.487 0.597
## .tpqm09 (.96.) 0.364 0.042 8.586 0.000 0.364 0.386
## .tpqm11 (.97.) 0.417 0.043 9.719 0.000 0.417 0.387
## .tpqm14 (.98.) 0.200 0.042 4.749 0.000 0.200 0.279
## .tpqm03 (.99.) 0.724 0.041 17.780 0.000 0.724 0.717
## .tpqm07 (.100) 0.673 0.042 16.206 0.000 0.673 0.612
## .tpqm10 (.101) 0.212 0.042 5.079 0.000 0.212 0.310
## .tpqm13 (.102) 0.520 0.044 11.844 0.000 0.520 0.390
## .tpqm04 (.103) 0.352 0.041 8.590 0.000 0.352 0.518
## .tpqm06 (.104) 0.306 0.042 7.240 0.000 0.306 0.359
## .tpqm08 (.105) 0.356 0.042 8.513 0.000 0.356 0.430
## .tpqm15 (.106) 0.436 0.044 10.009 0.000 0.436 0.365
## .tcq02 (.107) 1.106 0.044 24.886 0.000 1.106 0.547
## .tcq05 (.108) 0.666 0.044 15.149 0.000 0.666 0.443
## .tcq12 (.109) 1.836 0.042 43.204 0.000 1.836 0.756
## .tcq16 (.110) 0.652 0.045 14.398 0.000 0.652 0.381
## .tcq01 (.111) 0.681 0.044 15.409 0.000 0.681 0.437
## .tcq09 (.112) 0.772 0.043 17.827 0.000 0.772 0.516
## .tcq11 (.113) 0.746 0.044 16.908 0.000 0.746 0.464
## .tcq14 (.114) 0.557 0.044 12.755 0.000 0.557 0.414
## .tcq03 (.115) 0.928 0.042 21.935 0.000 0.928 0.625
## .tcq07 (.116) 0.803 0.043 18.860 0.000 0.803 0.572
## .tcq10 (.117) 1.341 0.042 32.261 0.000 1.341 0.757
## .tcq13 (.118) 0.706 0.044 16.120 0.000 0.706 0.466
## .tcq04 (.119) 0.619 0.044 14.002 0.000 0.619 0.414
## .tcq06 (.120) 0.598 0.045 13.420 0.000 0.598 0.390
## .tcq08 (.121) 0.506 0.043 11.675 0.000 0.506 0.410
## .tcq15 (.122) 0.618 0.044 14.045 0.000 0.618 0.422
## .it 0.318 0.033 9.505 0.000 0.309 0.309
## .cc 0.322 0.030 10.735 0.000 0.428 0.428
## .ia 0.095 0.032 3.026 0.002 0.142 0.142
## .SIQS 0.390 0.026 14.777 0.000 0.548 0.548
## .LTp 0.193 0.008 24.036 0.000 0.432 0.432
## .LTm 0.100 0.008 13.335 0.000 0.259 0.259
## LT 0.286 0.011 25.657 0.000 1.000 1.000
## LTc 0.917 0.021 43.484 0.000 1.000 1.000
## sexo 0.196 0.040 4.890 0.000 0.196 1.000Modelo con igualdad de medias y varianzas
sem.lv2vars %>%
summary(fit.measures=T, standardized=T)## lavaan 0.6.16 ended normally after 144 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 364
## Number of equality constraints 176
##
## Number of observations per group:
## 2 302
## 1 354
##
## Model Test User Model:
##
## Test statistic 7422.079
## Degrees of freedom 3350
## P-value (Unknown) NA
## Test statistic for each group:
## 2 3865.516
## 1 3556.563
##
## Model Test Baseline Model:
##
## Test statistic 175231.346
## Degrees of freedom 3306
## P-value NA
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.976
## Tucker-Lewis Index (TLI) 0.977
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.061
## 90 Percent confidence interval - lower 0.059
## 90 Percent confidence interval - upper 0.063
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.067
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.927 0.870
## siqs02 (.p2.) 0.884 0.021 41.966 0.000 0.820 0.668
## siqs03 (.p3.) 1.116 0.024 45.586 0.000 1.034 0.883
## cc =~
## siqs04 1.000 0.871 0.682
## siqs05 (.p5.) 1.101 0.030 37.229 0.000 0.959 0.723
## siqs06 (.p6.) 0.998 0.028 36.123 0.000 0.869 0.748
## ia =~
## siqs07 1.000 0.794 0.855
## siqs08 (.p8.) 1.041 0.026 39.713 0.000 0.827 0.879
## siqs09 (.p9.) 0.943 0.025 38.167 0.000 0.749 0.697
## SIQS =~
## it 1.000 0.820 0.820
## cc (.11.) 0.763 0.021 36.538 0.000 0.666 0.666
## ia (.12.) 0.890 0.023 38.440 0.000 0.852 0.852
## LTp =~
## tpqp02 1.000 0.673 0.664
## tpqp05 (.14.) 1.299 0.025 52.980 0.000 0.874 0.796
## tpqp12 (.15.) 1.634 0.029 56.637 0.000 1.099 0.808
## tpqp16 (.16.) 1.140 0.023 50.467 0.000 0.767 0.743
## tpqp01 (.17.) 1.130 0.022 50.269 0.000 0.760 0.697
## tpqp09 (.18.) 1.432 0.026 54.658 0.000 0.963 0.814
## tpqp11 (.19.) 1.401 0.026 54.294 0.000 0.942 0.770
## tpqp14 (.20.) 1.220 0.024 51.809 0.000 0.821 0.845
## tpqp03 (.21.) 0.911 0.020 45.501 0.000 0.613 0.561
## tpqp07 (.22.) 1.259 0.024 52.406 0.000 0.847 0.710
## tpqp10 (.23.) 1.234 0.024 52.020 0.000 0.830 0.835
## tpqp13 (.24.) 1.543 0.028 55.825 0.000 1.038 0.816
## tpqp04 (.25.) 1.259 0.024 52.400 0.000 0.847 0.764
## tpqp06 (.26.) 1.227 0.024 51.923 0.000 0.826 0.804
## tpqp08 (.27.) 1.328 0.025 53.373 0.000 0.893 0.806
## tpqp15 (.28.) 1.486 0.027 55.252 0.000 1.000 0.817
## LTm =~
## tpqm02 1.000 0.623 0.626
## tpqm05 (.30.) 1.014 0.025 41.306 0.000 0.631 0.711
## tpqm12 (.31.) 1.411 0.030 46.881 0.000 0.878 0.788
## tpqm16 (.32.) 0.770 0.022 35.678 0.000 0.479 0.699
## tpqm01 (.33.) 0.922 0.023 39.451 0.000 0.574 0.635
## tpqm09 (.34.) 1.224 0.027 44.688 0.000 0.762 0.784
## tpqm11 (.35.) 1.309 0.029 45.764 0.000 0.815 0.784
## tpqm14 (.36.) 1.157 0.026 43.724 0.000 0.720 0.850
## tpqm03 (.37.) 0.860 0.023 38.014 0.000 0.535 0.533
## tpqm07 (.38.) 1.052 0.025 42.011 0.000 0.655 0.624
## tpqm10 (.39.) 1.107 0.026 42.951 0.000 0.689 0.831
## tpqm13 (.40.) 1.450 0.031 47.269 0.000 0.903 0.781
## tpqm04 (.41.) 0.921 0.023 39.425 0.000 0.574 0.695
## tpqm06 (.42.) 1.189 0.027 44.206 0.000 0.740 0.801
## tpqm08 (.43.) 1.106 0.026 42.926 0.000 0.688 0.756
## tpqm15 (.44.) 1.403 0.030 46.802 0.000 0.873 0.798
## LT =~
## LTm 1.000 0.857 0.857
## LTp (.46.) 0.941 0.027 35.063 0.000 0.746 0.746
## LTc =~
## tcq02 1.000 0.966 0.677
## tcq05 (.48.) 0.954 0.016 60.115 0.000 0.922 0.748
## tcq12 (.49.) 0.804 0.015 55.233 0.000 0.777 0.498
## tcq16 (.50.) 1.073 0.017 63.139 0.000 1.037 0.789
## tcq01 (.51.) 0.976 0.016 60.730 0.000 0.943 0.752
## tcq09 (.52.) 0.887 0.015 58.103 0.000 0.857 0.698
## tcq11 (.53.) 0.968 0.016 60.509 0.000 0.936 0.735
## tcq14 (.54.) 0.928 0.016 59.359 0.000 0.897 0.769
## tcq03 (.55.) 0.780 0.014 54.325 0.000 0.754 0.616
## tcq07 (.56.) 0.810 0.015 55.450 0.000 0.783 0.658
## tcq10 (.57.) 0.685 0.014 50.317 0.000 0.662 0.496
## tcq13 (.58.) 0.941 0.016 59.735 0.000 0.909 0.735
## tcq04 (.59.) 0.977 0.016 60.740 0.000 0.944 0.767
## tcq06 (.60.) 1.010 0.016 61.617 0.000 0.976 0.784
## tcq08 (.61.) 0.890 0.015 58.179 0.000 0.860 0.771
## tcq15 (.62.) 0.961 0.016 60.308 0.000 0.929 0.764
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.437 0.016 27.081 0.000 0.307 0.307
## LTc 0.272 0.008 32.386 0.000 0.346 0.346
## sexo 0.216 0.080 2.705 0.007 0.284 0.151
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.077 0.003 26.401 0.000 0.149 0.149
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 4.773 0.055 86.008 0.000 4.773 4.478
## .siqs02 (.134) 4.754 0.052 90.762 0.000 4.754 3.872
## .siqs03 (.135) 4.613 0.059 78.450 0.000 4.613 3.938
## .siqs04 (.136) 4.362 0.049 88.399 0.000 4.362 3.414
## .siqs05 (.137) 4.319 0.051 84.274 0.000 4.319 3.255
## .siqs06 (.138) 4.838 0.049 98.126 0.000 4.838 4.168
## .siqs07 (.139) 5.192 0.053 98.850 0.000 5.192 5.592
## .siqs08 (.140) 5.149 0.053 96.285 0.000 5.149 5.479
## .siqs09 (.141) 4.877 0.051 95.214 0.000 4.877 4.536
## .tpqp02 (.142) 5.387 0.039 137.769 0.000 5.387 5.314
## .tpqp05 (.143) 5.398 0.039 138.042 0.000 5.398 4.915
## .tpqp12 (.144) 4.936 0.039 126.230 0.000 4.936 3.630
## .tpqp16 (.145) 5.476 0.039 140.030 0.000 5.476 5.304
## .tpqp01 (.146) 5.346 0.039 136.716 0.000 5.346 4.901
## .tpqp09 (.147) 5.364 0.039 137.184 0.000 5.364 4.531
## .tpqp11 (.148) 5.184 0.039 132.584 0.000 5.184 4.238
## .tpqp14 (.149) 5.530 0.039 141.434 0.000 5.530 5.692
## .tpqp03 (.150) 5.252 0.039 134.299 0.000 5.252 4.805
## .tpqp07 (.151) 5.148 0.039 131.649 0.000 5.148 4.313
## .tpqp10 (.152) 5.502 0.039 140.693 0.000 5.502 5.536
## .tpqp13 (.153) 5.165 0.039 132.078 0.000 5.165 4.057
## .tpqp04 (.154) 5.299 0.039 135.508 0.000 5.299 4.781
## .tpqp06 (.155) 5.448 0.039 139.328 0.000 5.448 5.303
## .tpqp08 (.156) 5.373 0.039 137.418 0.000 5.373 4.846
## .tpqp15 (.157) 5.160 0.039 131.961 0.000 5.160 4.216
## .tpqm02 (.158) 5.454 0.039 139.485 0.000 5.454 5.481
## .tpqm05 (.159) 5.625 0.039 143.850 0.000 5.625 6.342
## .tpqm12 (.160) 5.352 0.039 136.873 0.000 5.352 4.799
## .tpqm16 (.161) 5.700 0.039 145.761 0.000 5.700 8.316
## .tpqm01 (.162) 5.492 0.039 140.459 0.000 5.492 6.076
## .tpqm09 (.163) 5.585 0.039 142.837 0.000 5.585 5.744
## .tpqm11 (.164) 5.372 0.039 137.379 0.000 5.372 5.166
## .tpqm14 (.165) 5.613 0.039 143.539 0.000 5.613 6.624
## .tpqm03 (.166) 5.299 0.039 135.508 0.000 5.299 5.271
## .tpqm07 (.167) 5.306 0.039 135.703 0.000 5.306 5.054
## .tpqm10 (.168) 5.611 0.039 143.500 0.000 5.611 6.768
## .tpqm13 (.169) 5.331 0.039 136.327 0.000 5.331 4.615
## .tpqm04 (.170) 5.596 0.039 143.110 0.000 5.596 6.781
## .tpqm06 (.171) 5.572 0.039 142.486 0.000 5.572 6.029
## .tpqm08 (.172) 5.535 0.039 141.550 0.000 5.535 6.076
## .tpqm15 (.173) 5.343 0.039 136.639 0.000 5.343 4.881
## .tcq02 (.174) 4.140 0.039 105.881 0.000 4.140 2.899
## .tcq05 (.175) 4.970 0.039 127.088 0.000 4.970 4.033
## .tcq12 (.176) 3.770 0.039 96.407 0.000 3.770 2.415
## .tcq16 (.177) 4.875 0.039 124.672 0.000 4.875 3.707
## .tcq01 (.178) 4.727 0.039 120.890 0.000 4.727 3.768
## .tcq09 (.179) 5.092 0.039 130.207 0.000 5.092 4.144
## .tcq11 (.180) 4.822 0.039 123.307 0.000 4.822 3.787
## .tcq14 (.181) 4.934 0.039 126.192 0.000 4.934 4.232
## .tcq03 (.182) 4.803 0.039 122.839 0.000 4.803 3.929
## .tcq07 (.183) 4.870 0.039 124.554 0.000 4.870 4.097
## .tcq10 (.184) 4.480 0.039 114.574 0.000 4.480 3.360
## .tcq13 (.185) 4.642 0.039 118.707 0.000 4.642 3.754
## .tcq04 (.186) 4.915 0.039 125.685 0.000 4.915 3.996
## .tcq06 (.187) 4.841 0.039 123.814 0.000 4.841 3.888
## .tcq08 (.188) 5.201 0.039 133.014 0.000 5.201 4.662
## .tcq15 (.189) 4.950 0.039 126.581 0.000 4.950 4.070
## sexo 0.472 0.057 8.317 0.000 0.472 0.888
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.277 0.051 5.400 0.000 0.277 0.244
## .siqs02 (.67.) 0.835 0.048 17.406 0.000 0.835 0.554
## .siqs03 (.68.) 0.303 0.056 5.431 0.000 0.303 0.221
## .siqs04 (.69.) 0.874 0.051 17.007 0.000 0.874 0.535
## .siqs05 (.70.) 0.841 0.055 15.294 0.000 0.841 0.478
## .siqs06 (.71.) 0.593 0.051 11.555 0.000 0.593 0.440
## .siqs07 (.72.) 0.231 0.050 4.656 0.000 0.231 0.268
## .siqs08 (.73.) 0.200 0.051 3.927 0.000 0.200 0.227
## .siqs09 (.74.) 0.595 0.048 12.383 0.000 0.595 0.515
## .tpqp02 (.75.) 0.575 0.041 13.922 0.000 0.575 0.560
## .tpqp05 (.76.) 0.442 0.043 10.318 0.000 0.442 0.367
## .tpqp12 (.77.) 0.641 0.045 14.192 0.000 0.641 0.347
## .tpqp16 (.78.) 0.477 0.042 11.360 0.000 0.477 0.448
## .tpqp01 (.79.) 0.612 0.042 14.603 0.000 0.612 0.515
## .tpqp09 (.80.) 0.474 0.044 10.836 0.000 0.474 0.338
## .tpqp11 (.81.) 0.609 0.044 13.981 0.000 0.609 0.407
## .tpqp14 (.82.) 0.270 0.042 6.369 0.000 0.270 0.286
## .tpqp03 (.83.) 0.819 0.041 20.007 0.000 0.819 0.686
## .tpqp07 (.84.) 0.707 0.043 16.578 0.000 0.707 0.496
## .tpqp10 (.85.) 0.299 0.043 7.028 0.000 0.299 0.303
## .tpqp13 (.86.) 0.542 0.045 12.181 0.000 0.542 0.335
## .tpqp04 (.87.) 0.511 0.043 11.990 0.000 0.511 0.416
## .tpqp06 (.88.) 0.374 0.042 8.798 0.000 0.374 0.354
## .tpqp08 (.89.) 0.432 0.043 10.024 0.000 0.432 0.351
## .tpqp15 (.90.) 0.498 0.044 11.292 0.000 0.498 0.333
## .tpqm02 (.91.) 0.603 0.041 14.589 0.000 0.603 0.609
## .tpqm05 (.92.) 0.389 0.041 9.394 0.000 0.389 0.494
## .tpqm12 (.93.) 0.472 0.044 10.835 0.000 0.472 0.380
## .tpqm16 (.94.) 0.240 0.040 5.939 0.000 0.240 0.511
## .tpqm01 (.95.) 0.487 0.041 11.893 0.000 0.487 0.596
## .tpqm09 (.96.) 0.365 0.042 8.594 0.000 0.365 0.386
## .tpqm11 (.97.) 0.417 0.043 9.721 0.000 0.417 0.386
## .tpqm14 (.98.) 0.200 0.042 4.748 0.000 0.200 0.278
## .tpqm03 (.99.) 0.724 0.041 17.778 0.000 0.724 0.716
## .tpqm07 (.100) 0.674 0.042 16.211 0.000 0.674 0.611
## .tpqm10 (.101) 0.212 0.042 5.076 0.000 0.212 0.309
## .tpqm13 (.102) 0.519 0.044 11.842 0.000 0.519 0.389
## .tpqm04 (.103) 0.352 0.041 8.598 0.000 0.352 0.517
## .tpqm06 (.104) 0.306 0.042 7.239 0.000 0.306 0.358
## .tpqm08 (.105) 0.356 0.042 8.515 0.000 0.356 0.429
## .tpqm15 (.106) 0.435 0.044 9.999 0.000 0.435 0.363
## .tcq02 (.107) 1.106 0.044 24.878 0.000 1.106 0.542
## .tcq05 (.108) 0.668 0.044 15.199 0.000 0.668 0.440
## .tcq12 (.109) 1.834 0.043 43.142 0.000 1.834 0.752
## .tcq16 (.110) 0.654 0.045 14.436 0.000 0.654 0.378
## .tcq01 (.111) 0.684 0.044 15.477 0.000 0.684 0.434
## .tcq09 (.112) 0.775 0.043 17.898 0.000 0.775 0.513
## .tcq11 (.113) 0.745 0.044 16.894 0.000 0.745 0.460
## .tcq14 (.114) 0.555 0.044 12.715 0.000 0.555 0.408
## .tcq03 (.115) 0.927 0.042 21.907 0.000 0.927 0.620
## .tcq07 (.116) 0.801 0.043 18.820 0.000 0.801 0.567
## .tcq10 (.117) 1.340 0.042 32.241 0.000 1.340 0.754
## .tcq13 (.118) 0.702 0.044 16.030 0.000 0.702 0.459
## .tcq04 (.119) 0.622 0.044 14.065 0.000 0.622 0.411
## .tcq06 (.120) 0.598 0.045 13.419 0.000 0.598 0.386
## .tcq08 (.121) 0.506 0.043 11.680 0.000 0.506 0.406
## .tcq15 (.122) 0.616 0.044 13.998 0.000 0.616 0.417
## .it (.123) 0.281 0.028 9.970 0.000 0.328 0.328
## .cc (.124) 0.422 0.027 15.639 0.000 0.556 0.556
## .ia (.125) 0.173 0.027 6.506 0.000 0.275 0.275
## .SIQS (.126) 0.423 0.020 21.620 0.000 0.732 0.732
## .LTp (.127) 0.201 0.008 25.674 0.000 0.444 0.444
## .LTm (.128) 0.103 0.007 14.823 0.000 0.266 0.266
## LT (.129) 0.284 0.011 25.925 0.000 1.000 1.000
## LTc (.130) 0.934 0.021 44.176 0.000 1.000 1.000
## sexo 0.282 0.056 5.022 0.000 0.282 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.000 0.885
## siqs02 (.p2.) 0.884 0.021 41.966 0.000 0.885 0.696
## siqs03 (.p3.) 1.116 0.024 45.586 0.000 1.116 0.897
## cc =~
## siqs04 1.000 0.917 0.700
## siqs05 (.p5.) 1.101 0.030 37.229 0.000 1.010 0.740
## siqs06 (.p6.) 0.998 0.028 36.123 0.000 0.915 0.765
## ia =~
## siqs07 1.000 0.862 0.873
## siqs08 (.p8.) 1.041 0.026 39.713 0.000 0.897 0.895
## siqs09 (.p9.) 0.943 0.025 38.167 0.000 0.813 0.725
## SIQS =~
## it 1.000 0.848 0.848
## cc (.11.) 0.763 0.021 36.538 0.000 0.706 0.706
## ia (.12.) 0.890 0.023 38.440 0.000 0.876 0.876
## LTp =~
## tpqp02 1.000 0.673 0.664
## tpqp05 (.14.) 1.299 0.025 52.980 0.000 0.874 0.796
## tpqp12 (.15.) 1.634 0.029 56.637 0.000 1.099 0.808
## tpqp16 (.16.) 1.140 0.023 50.467 0.000 0.767 0.743
## tpqp01 (.17.) 1.130 0.022 50.269 0.000 0.760 0.697
## tpqp09 (.18.) 1.432 0.026 54.658 0.000 0.963 0.814
## tpqp11 (.19.) 1.401 0.026 54.294 0.000 0.942 0.770
## tpqp14 (.20.) 1.220 0.024 51.809 0.000 0.821 0.845
## tpqp03 (.21.) 0.911 0.020 45.501 0.000 0.613 0.561
## tpqp07 (.22.) 1.259 0.024 52.406 0.000 0.847 0.710
## tpqp10 (.23.) 1.234 0.024 52.020 0.000 0.830 0.835
## tpqp13 (.24.) 1.543 0.028 55.825 0.000 1.038 0.816
## tpqp04 (.25.) 1.259 0.024 52.400 0.000 0.847 0.764
## tpqp06 (.26.) 1.227 0.024 51.923 0.000 0.826 0.804
## tpqp08 (.27.) 1.328 0.025 53.373 0.000 0.893 0.806
## tpqp15 (.28.) 1.486 0.027 55.252 0.000 1.000 0.817
## LTm =~
## tpqm02 1.000 0.623 0.626
## tpqm05 (.30.) 1.014 0.025 41.306 0.000 0.631 0.711
## tpqm12 (.31.) 1.411 0.030 46.881 0.000 0.878 0.788
## tpqm16 (.32.) 0.770 0.022 35.678 0.000 0.479 0.699
## tpqm01 (.33.) 0.922 0.023 39.451 0.000 0.574 0.635
## tpqm09 (.34.) 1.224 0.027 44.688 0.000 0.762 0.784
## tpqm11 (.35.) 1.309 0.029 45.764 0.000 0.815 0.784
## tpqm14 (.36.) 1.157 0.026 43.724 0.000 0.720 0.850
## tpqm03 (.37.) 0.860 0.023 38.014 0.000 0.535 0.533
## tpqm07 (.38.) 1.052 0.025 42.011 0.000 0.655 0.624
## tpqm10 (.39.) 1.107 0.026 42.951 0.000 0.689 0.831
## tpqm13 (.40.) 1.450 0.031 47.269 0.000 0.903 0.781
## tpqm04 (.41.) 0.921 0.023 39.425 0.000 0.574 0.695
## tpqm06 (.42.) 1.189 0.027 44.206 0.000 0.740 0.801
## tpqm08 (.43.) 1.106 0.026 42.926 0.000 0.688 0.756
## tpqm15 (.44.) 1.403 0.030 46.802 0.000 0.873 0.798
## LT =~
## LTm 1.000 0.857 0.857
## LTp (.46.) 0.941 0.027 35.063 0.000 0.746 0.746
## LTc =~
## tcq02 1.000 0.966 0.677
## tcq05 (.48.) 0.954 0.016 60.115 0.000 0.922 0.748
## tcq12 (.49.) 0.804 0.015 55.233 0.000 0.777 0.498
## tcq16 (.50.) 1.073 0.017 63.139 0.000 1.037 0.789
## tcq01 (.51.) 0.976 0.016 60.730 0.000 0.943 0.752
## tcq09 (.52.) 0.887 0.015 58.103 0.000 0.857 0.698
## tcq11 (.53.) 0.968 0.016 60.509 0.000 0.936 0.735
## tcq14 (.54.) 0.928 0.016 59.359 0.000 0.897 0.769
## tcq03 (.55.) 0.780 0.014 54.325 0.000 0.754 0.616
## tcq07 (.56.) 0.810 0.015 55.450 0.000 0.783 0.658
## tcq10 (.57.) 0.685 0.014 50.317 0.000 0.662 0.496
## tcq13 (.58.) 0.941 0.016 59.735 0.000 0.909 0.735
## tcq04 (.59.) 0.977 0.016 60.740 0.000 0.944 0.767
## tcq06 (.60.) 1.010 0.016 61.617 0.000 0.976 0.784
## tcq08 (.61.) 0.890 0.015 58.179 0.000 0.860 0.771
## tcq15 (.62.) 0.961 0.016 60.308 0.000 0.929 0.764
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.613 0.019 31.512 0.000 0.386 0.386
## LTc 0.320 0.009 35.215 0.000 0.365 0.365
## sexo 0.459 0.094 4.899 0.000 0.541 0.233
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc 0.139 0.004 36.744 0.000 0.269 0.269
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 4.773 0.055 86.008 0.000 4.773 4.223
## .siqs02 (.134) 4.754 0.052 90.762 0.000 4.754 3.738
## .siqs03 (.135) 4.613 0.059 78.450 0.000 4.613 3.708
## .siqs04 (.136) 4.362 0.049 88.399 0.000 4.362 3.331
## .siqs05 (.137) 4.319 0.051 84.274 0.000 4.319 3.166
## .siqs06 (.138) 4.838 0.049 98.126 0.000 4.838 4.046
## .siqs07 (.139) 5.192 0.053 98.850 0.000 5.192 5.261
## .siqs08 (.140) 5.149 0.053 96.285 0.000 5.149 5.138
## .siqs09 (.141) 4.877 0.051 95.214 0.000 4.877 4.352
## .tpqp02 (.142) 5.387 0.039 137.769 0.000 5.387 5.314
## .tpqp05 (.143) 5.398 0.039 138.042 0.000 5.398 4.915
## .tpqp12 (.144) 4.936 0.039 126.230 0.000 4.936 3.630
## .tpqp16 (.145) 5.476 0.039 140.030 0.000 5.476 5.304
## .tpqp01 (.146) 5.346 0.039 136.716 0.000 5.346 4.901
## .tpqp09 (.147) 5.364 0.039 137.184 0.000 5.364 4.531
## .tpqp11 (.148) 5.184 0.039 132.584 0.000 5.184 4.238
## .tpqp14 (.149) 5.530 0.039 141.434 0.000 5.530 5.692
## .tpqp03 (.150) 5.252 0.039 134.299 0.000 5.252 4.805
## .tpqp07 (.151) 5.148 0.039 131.649 0.000 5.148 4.313
## .tpqp10 (.152) 5.502 0.039 140.693 0.000 5.502 5.536
## .tpqp13 (.153) 5.165 0.039 132.078 0.000 5.165 4.057
## .tpqp04 (.154) 5.299 0.039 135.508 0.000 5.299 4.781
## .tpqp06 (.155) 5.448 0.039 139.328 0.000 5.448 5.303
## .tpqp08 (.156) 5.373 0.039 137.418 0.000 5.373 4.846
## .tpqp15 (.157) 5.160 0.039 131.961 0.000 5.160 4.216
## .tpqm02 (.158) 5.454 0.039 139.485 0.000 5.454 5.481
## .tpqm05 (.159) 5.625 0.039 143.850 0.000 5.625 6.342
## .tpqm12 (.160) 5.352 0.039 136.873 0.000 5.352 4.799
## .tpqm16 (.161) 5.700 0.039 145.761 0.000 5.700 8.316
## .tpqm01 (.162) 5.492 0.039 140.459 0.000 5.492 6.076
## .tpqm09 (.163) 5.585 0.039 142.837 0.000 5.585 5.744
## .tpqm11 (.164) 5.372 0.039 137.379 0.000 5.372 5.166
## .tpqm14 (.165) 5.613 0.039 143.539 0.000 5.613 6.624
## .tpqm03 (.166) 5.299 0.039 135.508 0.000 5.299 5.271
## .tpqm07 (.167) 5.306 0.039 135.703 0.000 5.306 5.054
## .tpqm10 (.168) 5.611 0.039 143.500 0.000 5.611 6.768
## .tpqm13 (.169) 5.331 0.039 136.327 0.000 5.331 4.615
## .tpqm04 (.170) 5.596 0.039 143.110 0.000 5.596 6.781
## .tpqm06 (.171) 5.572 0.039 142.486 0.000 5.572 6.029
## .tpqm08 (.172) 5.535 0.039 141.550 0.000 5.535 6.076
## .tpqm15 (.173) 5.343 0.039 136.639 0.000 5.343 4.881
## .tcq02 (.174) 4.140 0.039 105.881 0.000 4.140 2.899
## .tcq05 (.175) 4.970 0.039 127.088 0.000 4.970 4.033
## .tcq12 (.176) 3.770 0.039 96.407 0.000 3.770 2.415
## .tcq16 (.177) 4.875 0.039 124.672 0.000 4.875 3.707
## .tcq01 (.178) 4.727 0.039 120.890 0.000 4.727 3.768
## .tcq09 (.179) 5.092 0.039 130.207 0.000 5.092 4.144
## .tcq11 (.180) 4.822 0.039 123.307 0.000 4.822 3.787
## .tcq14 (.181) 4.934 0.039 126.192 0.000 4.934 4.232
## .tcq03 (.182) 4.803 0.039 122.839 0.000 4.803 3.929
## .tcq07 (.183) 4.870 0.039 124.554 0.000 4.870 4.097
## .tcq10 (.184) 4.480 0.039 114.574 0.000 4.480 3.360
## .tcq13 (.185) 4.642 0.039 118.707 0.000 4.642 3.754
## .tcq04 (.186) 4.915 0.039 125.685 0.000 4.915 3.996
## .tcq06 (.187) 4.841 0.039 123.814 0.000 4.841 3.888
## .tcq08 (.188) 5.201 0.039 133.014 0.000 5.201 4.662
## .tcq15 (.189) 4.950 0.039 126.581 0.000 4.950 4.070
## sexo 0.476 0.050 9.561 0.000 0.476 1.106
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.277 0.051 5.400 0.000 0.277 0.217
## .siqs02 (.67.) 0.835 0.048 17.406 0.000 0.835 0.516
## .siqs03 (.68.) 0.303 0.056 5.431 0.000 0.303 0.195
## .siqs04 (.69.) 0.874 0.051 17.007 0.000 0.874 0.510
## .siqs05 (.70.) 0.841 0.055 15.294 0.000 0.841 0.452
## .siqs06 (.71.) 0.593 0.051 11.555 0.000 0.593 0.415
## .siqs07 (.72.) 0.231 0.050 4.656 0.000 0.231 0.237
## .siqs08 (.73.) 0.200 0.051 3.927 0.000 0.200 0.199
## .siqs09 (.74.) 0.595 0.048 12.383 0.000 0.595 0.474
## .tpqp02 (.75.) 0.575 0.041 13.922 0.000 0.575 0.560
## .tpqp05 (.76.) 0.442 0.043 10.318 0.000 0.442 0.367
## .tpqp12 (.77.) 0.641 0.045 14.192 0.000 0.641 0.347
## .tpqp16 (.78.) 0.477 0.042 11.360 0.000 0.477 0.448
## .tpqp01 (.79.) 0.612 0.042 14.603 0.000 0.612 0.515
## .tpqp09 (.80.) 0.474 0.044 10.836 0.000 0.474 0.338
## .tpqp11 (.81.) 0.609 0.044 13.981 0.000 0.609 0.407
## .tpqp14 (.82.) 0.270 0.042 6.369 0.000 0.270 0.286
## .tpqp03 (.83.) 0.819 0.041 20.007 0.000 0.819 0.686
## .tpqp07 (.84.) 0.707 0.043 16.578 0.000 0.707 0.496
## .tpqp10 (.85.) 0.299 0.043 7.028 0.000 0.299 0.303
## .tpqp13 (.86.) 0.542 0.045 12.181 0.000 0.542 0.335
## .tpqp04 (.87.) 0.511 0.043 11.990 0.000 0.511 0.416
## .tpqp06 (.88.) 0.374 0.042 8.798 0.000 0.374 0.354
## .tpqp08 (.89.) 0.432 0.043 10.024 0.000 0.432 0.351
## .tpqp15 (.90.) 0.498 0.044 11.292 0.000 0.498 0.333
## .tpqm02 (.91.) 0.603 0.041 14.589 0.000 0.603 0.609
## .tpqm05 (.92.) 0.389 0.041 9.394 0.000 0.389 0.494
## .tpqm12 (.93.) 0.472 0.044 10.835 0.000 0.472 0.380
## .tpqm16 (.94.) 0.240 0.040 5.939 0.000 0.240 0.511
## .tpqm01 (.95.) 0.487 0.041 11.893 0.000 0.487 0.596
## .tpqm09 (.96.) 0.365 0.042 8.594 0.000 0.365 0.386
## .tpqm11 (.97.) 0.417 0.043 9.721 0.000 0.417 0.386
## .tpqm14 (.98.) 0.200 0.042 4.748 0.000 0.200 0.278
## .tpqm03 (.99.) 0.724 0.041 17.778 0.000 0.724 0.716
## .tpqm07 (.100) 0.674 0.042 16.211 0.000 0.674 0.611
## .tpqm10 (.101) 0.212 0.042 5.076 0.000 0.212 0.309
## .tpqm13 (.102) 0.519 0.044 11.842 0.000 0.519 0.389
## .tpqm04 (.103) 0.352 0.041 8.598 0.000 0.352 0.517
## .tpqm06 (.104) 0.306 0.042 7.239 0.000 0.306 0.358
## .tpqm08 (.105) 0.356 0.042 8.515 0.000 0.356 0.429
## .tpqm15 (.106) 0.435 0.044 9.999 0.000 0.435 0.363
## .tcq02 (.107) 1.106 0.044 24.878 0.000 1.106 0.542
## .tcq05 (.108) 0.668 0.044 15.199 0.000 0.668 0.440
## .tcq12 (.109) 1.834 0.043 43.142 0.000 1.834 0.752
## .tcq16 (.110) 0.654 0.045 14.436 0.000 0.654 0.378
## .tcq01 (.111) 0.684 0.044 15.477 0.000 0.684 0.434
## .tcq09 (.112) 0.775 0.043 17.898 0.000 0.775 0.513
## .tcq11 (.113) 0.745 0.044 16.894 0.000 0.745 0.460
## .tcq14 (.114) 0.555 0.044 12.715 0.000 0.555 0.408
## .tcq03 (.115) 0.927 0.042 21.907 0.000 0.927 0.620
## .tcq07 (.116) 0.801 0.043 18.820 0.000 0.801 0.567
## .tcq10 (.117) 1.340 0.042 32.241 0.000 1.340 0.754
## .tcq13 (.118) 0.702 0.044 16.030 0.000 0.702 0.459
## .tcq04 (.119) 0.622 0.044 14.065 0.000 0.622 0.411
## .tcq06 (.120) 0.598 0.045 13.419 0.000 0.598 0.386
## .tcq08 (.121) 0.506 0.043 11.680 0.000 0.506 0.406
## .tcq15 (.122) 0.616 0.044 13.998 0.000 0.616 0.417
## .it (.123) 0.281 0.028 9.970 0.000 0.281 0.281
## .cc (.124) 0.422 0.027 15.639 0.000 0.502 0.502
## .ia (.125) 0.173 0.027 6.506 0.000 0.233 0.233
## .SIQS (.126) 0.423 0.020 21.620 0.000 0.588 0.588
## .LTp (.127) 0.201 0.008 25.674 0.000 0.444 0.444
## .LTm (.128) 0.103 0.007 14.823 0.000 0.266 0.266
## LT (.129) 0.284 0.011 25.925 0.000 1.000 1.000
## LTc (.130) 0.934 0.021 44.176 0.000 1.000 1.000
## sexo 0.185 0.041 4.523 0.000 0.185 1.000Modelo con igualdad de medias, varianzas y covarianzas
sem.lv3covars %>%
summary(fit.measures=T, standardized=T)## lavaan 0.6.16 ended normally after 142 iterations
##
## Estimator ULS
## Optimization method NLMINB
## Number of model parameters 364
## Number of equality constraints 177
##
## Number of observations per group:
## 2 302
## 1 354
##
## Model Test User Model:
##
## Test statistic 7798.004
## Degrees of freedom 3351
## P-value (Unknown) NA
## Test statistic for each group:
## 2 4041.157
## 1 3756.848
##
## Model Test Baseline Model:
##
## Test statistic 175231.346
## Degrees of freedom 3306
## P-value NA
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.974
## Tucker-Lewis Index (TLI) 0.974
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.064
## 90 Percent confidence interval - lower 0.062
## 90 Percent confidence interval - upper 0.066
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.068
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 0.924 0.869
## siqs02 (.p2.) 0.885 0.021 41.984 0.000 0.818 0.667
## siqs03 (.p3.) 1.116 0.024 45.603 0.000 1.031 0.882
## cc =~
## siqs04 1.000 0.869 0.681
## siqs05 (.p5.) 1.101 0.030 37.198 0.000 0.957 0.722
## siqs06 (.p6.) 0.998 0.028 36.099 0.000 0.867 0.748
## ia =~
## siqs07 1.000 0.792 0.855
## siqs08 (.p8.) 1.041 0.026 39.710 0.000 0.824 0.879
## siqs09 (.p9.) 0.943 0.025 38.158 0.000 0.747 0.696
## SIQS =~
## it 1.000 0.820 0.820
## cc (.11.) 0.762 0.021 36.525 0.000 0.664 0.664
## ia (.12.) 0.889 0.023 38.440 0.000 0.851 0.851
## LTp =~
## tpqp02 1.000 0.672 0.663
## tpqp05 (.14.) 1.300 0.025 52.897 0.000 0.874 0.796
## tpqp12 (.15.) 1.637 0.029 56.560 0.000 1.100 0.809
## tpqp16 (.16.) 1.141 0.023 50.386 0.000 0.767 0.743
## tpqp01 (.17.) 1.131 0.023 50.205 0.000 0.760 0.697
## tpqp09 (.18.) 1.432 0.026 54.552 0.000 0.962 0.813
## tpqp11 (.19.) 1.402 0.026 54.212 0.000 0.943 0.771
## tpqp14 (.20.) 1.221 0.024 51.724 0.000 0.821 0.845
## tpqp03 (.21.) 0.910 0.020 45.396 0.000 0.612 0.560
## tpqp07 (.22.) 1.264 0.024 52.385 0.000 0.850 0.712
## tpqp10 (.23.) 1.232 0.024 51.903 0.000 0.828 0.834
## tpqp13 (.24.) 1.545 0.028 55.739 0.000 1.039 0.816
## tpqp04 (.25.) 1.260 0.024 52.315 0.000 0.847 0.764
## tpqp06 (.26.) 1.227 0.024 51.819 0.000 0.825 0.803
## tpqp08 (.27.) 1.327 0.025 53.263 0.000 0.892 0.804
## tpqp15 (.28.) 1.487 0.027 55.160 0.000 1.000 0.817
## LTm =~
## tpqm02 1.000 0.624 0.627
## tpqm05 (.30.) 1.007 0.024 41.239 0.000 0.628 0.708
## tpqm12 (.31.) 1.408 0.030 46.917 0.000 0.878 0.788
## tpqm16 (.32.) 0.770 0.022 35.734 0.000 0.480 0.701
## tpqm01 (.33.) 0.921 0.023 39.473 0.000 0.574 0.636
## tpqm09 (.34.) 1.219 0.027 44.677 0.000 0.760 0.782
## tpqm11 (.35.) 1.304 0.028 45.772 0.000 0.814 0.782
## tpqm14 (.36.) 1.155 0.026 43.758 0.000 0.720 0.850
## tpqm03 (.37.) 0.859 0.023 38.054 0.000 0.536 0.533
## tpqm07 (.38.) 1.051 0.025 42.047 0.000 0.655 0.624
## tpqm10 (.39.) 1.105 0.026 42.977 0.000 0.689 0.832
## tpqm13 (.40.) 1.448 0.031 47.310 0.000 0.903 0.782
## tpqm04 (.41.) 0.919 0.023 39.440 0.000 0.574 0.695
## tpqm06 (.42.) 1.191 0.027 44.295 0.000 0.743 0.804
## tpqm08 (.43.) 1.102 0.026 42.925 0.000 0.687 0.755
## tpqm15 (.44.) 1.399 0.030 46.825 0.000 0.873 0.797
## LT =~
## LTm 1.000 0.863 0.863
## LTp (.46.) 0.924 0.027 34.859 0.000 0.740 0.740
## LTc =~
## tcq02 1.000 0.965 0.676
## tcq05 (.48.) 0.956 0.016 60.009 0.000 0.923 0.749
## tcq12 (.49.) 0.801 0.015 54.991 0.000 0.774 0.496
## tcq16 (.50.) 1.072 0.017 62.933 0.000 1.035 0.787
## tcq01 (.51.) 0.980 0.016 60.661 0.000 0.946 0.754
## tcq09 (.52.) 0.889 0.015 57.995 0.000 0.858 0.698
## tcq11 (.53.) 0.972 0.016 60.448 0.000 0.938 0.737
## tcq14 (.54.) 0.931 0.016 59.269 0.000 0.898 0.770
## tcq03 (.55.) 0.780 0.014 54.197 0.000 0.753 0.616
## tcq07 (.56.) 0.805 0.015 55.103 0.000 0.777 0.653
## tcq10 (.57.) 0.680 0.014 49.963 0.000 0.656 0.492
## tcq13 (.58.) 0.944 0.016 59.655 0.000 0.911 0.737
## tcq04 (.59.) 0.981 0.016 60.699 0.000 0.947 0.770
## tcq06 (.60.) 1.014 0.016 61.560 0.000 0.979 0.786
## tcq08 (.61.) 0.888 0.015 57.974 0.000 0.857 0.769
## tcq15 (.62.) 0.964 0.016 60.215 0.000 0.930 0.765
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.408 0.016 25.716 0.000 0.290 0.290
## LTc 0.260 0.008 30.863 0.000 0.332 0.332
## sexo 0.283 0.088 3.208 0.001 0.374 0.197
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc (.131) 0.111 0.003 37.638 0.000 0.214 0.214
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 4.748 0.059 80.604 0.000 4.748 4.464
## .siqs02 (.134) 4.732 0.055 85.679 0.000 4.732 3.859
## .siqs03 (.135) 4.586 0.063 73.018 0.000 4.586 3.923
## .siqs04 (.136) 4.344 0.052 84.221 0.000 4.344 3.403
## .siqs05 (.137) 4.299 0.054 79.850 0.000 4.299 3.243
## .siqs06 (.138) 4.820 0.052 93.529 0.000 4.820 4.156
## .siqs07 (.139) 5.170 0.055 93.366 0.000 5.170 5.580
## .siqs08 (.140) 5.127 0.057 90.717 0.000 5.127 5.467
## .siqs09 (.141) 4.857 0.054 90.229 0.000 4.857 4.523
## .tpqp02 (.142) 5.387 0.039 137.769 0.000 5.387 5.314
## .tpqp05 (.143) 5.398 0.039 138.042 0.000 5.398 4.915
## .tpqp12 (.144) 4.936 0.039 126.230 0.000 4.936 3.630
## .tpqp16 (.145) 5.476 0.039 140.030 0.000 5.476 5.304
## .tpqp01 (.146) 5.346 0.039 136.716 0.000 5.346 4.901
## .tpqp09 (.147) 5.364 0.039 137.184 0.000 5.364 4.531
## .tpqp11 (.148) 5.184 0.039 132.584 0.000 5.184 4.238
## .tpqp14 (.149) 5.530 0.039 141.434 0.000 5.530 5.692
## .tpqp03 (.150) 5.252 0.039 134.299 0.000 5.252 4.805
## .tpqp07 (.151) 5.148 0.039 131.649 0.000 5.148 4.313
## .tpqp10 (.152) 5.502 0.039 140.693 0.000 5.502 5.536
## .tpqp13 (.153) 5.165 0.039 132.078 0.000 5.165 4.057
## .tpqp04 (.154) 5.299 0.039 135.508 0.000 5.299 4.781
## .tpqp06 (.155) 5.448 0.039 139.328 0.000 5.448 5.303
## .tpqp08 (.156) 5.373 0.039 137.418 0.000 5.373 4.846
## .tpqp15 (.157) 5.160 0.039 131.961 0.000 5.160 4.216
## .tpqm02 (.158) 5.454 0.039 139.485 0.000 5.454 5.481
## .tpqm05 (.159) 5.625 0.039 143.850 0.000 5.625 6.342
## .tpqm12 (.160) 5.352 0.039 136.873 0.000 5.352 4.799
## .tpqm16 (.161) 5.700 0.039 145.761 0.000 5.700 8.316
## .tpqm01 (.162) 5.492 0.039 140.459 0.000 5.492 6.076
## .tpqm09 (.163) 5.585 0.039 142.837 0.000 5.585 5.744
## .tpqm11 (.164) 5.372 0.039 137.379 0.000 5.372 5.166
## .tpqm14 (.165) 5.613 0.039 143.539 0.000 5.613 6.624
## .tpqm03 (.166) 5.299 0.039 135.508 0.000 5.299 5.271
## .tpqm07 (.167) 5.306 0.039 135.703 0.000 5.306 5.054
## .tpqm10 (.168) 5.611 0.039 143.500 0.000 5.611 6.768
## .tpqm13 (.169) 5.331 0.039 136.327 0.000 5.331 4.615
## .tpqm04 (.170) 5.596 0.039 143.110 0.000 5.596 6.781
## .tpqm06 (.171) 5.572 0.039 142.486 0.000 5.572 6.029
## .tpqm08 (.172) 5.535 0.039 141.550 0.000 5.535 6.076
## .tpqm15 (.173) 5.343 0.039 136.639 0.000 5.343 4.881
## .tcq02 (.174) 4.140 0.039 105.881 0.000 4.140 2.899
## .tcq05 (.175) 4.970 0.039 127.088 0.000 4.970 4.033
## .tcq12 (.176) 3.770 0.039 96.407 0.000 3.770 2.415
## .tcq16 (.177) 4.875 0.039 124.672 0.000 4.875 3.707
## .tcq01 (.178) 4.727 0.039 120.890 0.000 4.727 3.768
## .tcq09 (.179) 5.092 0.039 130.207 0.000 5.092 4.144
## .tcq11 (.180) 4.822 0.039 123.307 0.000 4.822 3.787
## .tcq14 (.181) 4.934 0.039 126.192 0.000 4.934 4.232
## .tcq03 (.182) 4.803 0.039 122.839 0.000 4.803 3.929
## .tcq07 (.183) 4.870 0.039 124.554 0.000 4.870 4.097
## .tcq10 (.184) 4.480 0.039 114.574 0.000 4.480 3.360
## .tcq13 (.185) 4.642 0.039 118.707 0.000 4.642 3.754
## .tcq04 (.186) 4.915 0.039 125.685 0.000 4.915 3.996
## .tcq06 (.187) 4.841 0.039 123.814 0.000 4.841 3.888
## .tcq08 (.188) 5.201 0.039 133.014 0.000 5.201 4.662
## .tcq15 (.189) 4.950 0.039 126.581 0.000 4.950 4.070
## sexo 0.455 0.056 8.084 0.000 0.455 0.864
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.277 0.051 5.405 0.000 0.277 0.245
## .siqs02 (.67.) 0.835 0.048 17.399 0.000 0.835 0.555
## .siqs03 (.68.) 0.303 0.056 5.435 0.000 0.303 0.222
## .siqs04 (.69.) 0.874 0.051 17.004 0.000 0.874 0.536
## .siqs05 (.70.) 0.842 0.055 15.303 0.000 0.842 0.479
## .siqs06 (.71.) 0.593 0.051 11.544 0.000 0.593 0.441
## .siqs07 (.72.) 0.231 0.050 4.656 0.000 0.231 0.269
## .siqs08 (.73.) 0.200 0.051 3.922 0.000 0.200 0.227
## .siqs09 (.74.) 0.595 0.048 12.389 0.000 0.595 0.516
## .tpqp02 (.75.) 0.576 0.041 13.937 0.000 0.576 0.560
## .tpqp05 (.76.) 0.442 0.043 10.310 0.000 0.442 0.367
## .tpqp12 (.77.) 0.638 0.045 14.111 0.000 0.638 0.345
## .tpqp16 (.78.) 0.477 0.042 11.361 0.000 0.477 0.448
## .tpqp01 (.79.) 0.612 0.042 14.581 0.000 0.612 0.514
## .tpqp09 (.80.) 0.476 0.044 10.878 0.000 0.476 0.339
## .tpqp11 (.81.) 0.608 0.044 13.962 0.000 0.608 0.406
## .tpqp14 (.82.) 0.271 0.042 6.375 0.000 0.271 0.287
## .tpqp03 (.83.) 0.820 0.041 20.037 0.000 0.820 0.687
## .tpqp07 (.84.) 0.702 0.043 16.452 0.000 0.702 0.493
## .tpqp10 (.85.) 0.301 0.043 7.089 0.000 0.301 0.305
## .tpqp13 (.86.) 0.541 0.045 12.156 0.000 0.541 0.334
## .tpqp04 (.87.) 0.511 0.043 11.991 0.000 0.511 0.416
## .tpqp06 (.88.) 0.375 0.042 8.835 0.000 0.375 0.356
## .tpqp08 (.89.) 0.434 0.043 10.075 0.000 0.434 0.353
## .tpqp15 (.90.) 0.498 0.044 11.293 0.000 0.498 0.333
## .tpqm02 (.91.) 0.601 0.041 14.544 0.000 0.601 0.607
## .tpqm05 (.92.) 0.392 0.041 9.483 0.000 0.392 0.498
## .tpqm12 (.93.) 0.472 0.044 10.828 0.000 0.472 0.380
## .tpqm16 (.94.) 0.239 0.040 5.911 0.000 0.239 0.508
## .tpqm01 (.95.) 0.487 0.041 11.885 0.000 0.487 0.596
## .tpqm09 (.96.) 0.367 0.042 8.660 0.000 0.367 0.389
## .tpqm11 (.97.) 0.419 0.043 9.767 0.000 0.419 0.388
## .tpqm14 (.98.) 0.199 0.042 4.733 0.000 0.199 0.277
## .tpqm03 (.99.) 0.723 0.041 17.755 0.000 0.723 0.716
## .tpqm07 (.100) 0.673 0.042 16.190 0.000 0.673 0.610
## .tpqm10 (.101) 0.212 0.042 5.071 0.000 0.212 0.309
## .tpqm13 (.102) 0.519 0.044 11.823 0.000 0.519 0.389
## .tpqm04 (.103) 0.352 0.041 8.596 0.000 0.352 0.517
## .tpqm06 (.104) 0.302 0.042 7.137 0.000 0.302 0.353
## .tpqm08 (.105) 0.357 0.042 8.545 0.000 0.357 0.431
## .tpqm15 (.106) 0.437 0.044 10.025 0.000 0.437 0.364
## .tcq02 (.107) 1.108 0.044 24.920 0.000 1.108 0.543
## .tcq05 (.108) 0.666 0.044 15.145 0.000 0.666 0.439
## .tcq12 (.109) 1.839 0.042 43.277 0.000 1.839 0.754
## .tcq16 (.110) 0.659 0.045 14.551 0.000 0.659 0.381
## .tcq01 (.111) 0.679 0.044 15.352 0.000 0.679 0.431
## .tcq09 (.112) 0.773 0.043 17.862 0.000 0.773 0.512
## .tcq11 (.113) 0.740 0.044 16.761 0.000 0.740 0.457
## .tcq14 (.114) 0.553 0.044 12.646 0.000 0.553 0.407
## .tcq03 (.115) 0.927 0.042 21.913 0.000 0.927 0.620
## .tcq07 (.116) 0.810 0.043 19.056 0.000 0.810 0.573
## .tcq10 (.117) 1.347 0.042 32.434 0.000 1.347 0.758
## .tcq13 (.118) 0.699 0.044 15.940 0.000 0.699 0.457
## .tcq04 (.119) 0.615 0.044 13.895 0.000 0.615 0.407
## .tcq06 (.120) 0.592 0.045 13.260 0.000 0.592 0.382
## .tcq08 (.121) 0.510 0.043 11.774 0.000 0.510 0.409
## .tcq15 (.122) 0.613 0.044 13.922 0.000 0.613 0.415
## .it (.123) 0.280 0.028 9.932 0.000 0.328 0.328
## .cc (.124) 0.422 0.027 15.657 0.000 0.559 0.559
## .ia (.125) 0.173 0.027 6.508 0.000 0.277 0.277
## .SIQS (.126) 0.416 0.020 20.457 0.000 0.726 0.726
## .LTp (.127) 0.204 0.008 25.856 0.000 0.452 0.452
## .LTm (.128) 0.099 0.007 14.117 0.000 0.255 0.255
## LT (.129) 0.290 0.011 25.872 0.000 1.000 1.000
## LTc (.130) 0.932 0.021 44.055 0.000 1.000 1.000
## sexo 0.277 0.056 4.952 0.000 0.277 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## it =~
## siqs01 1.000 1.002 0.885
## siqs02 (.p2.) 0.885 0.021 41.984 0.000 0.887 0.696
## siqs03 (.p3.) 1.116 0.024 45.603 0.000 1.118 0.897
## cc =~
## siqs04 1.000 0.918 0.701
## siqs05 (.p5.) 1.101 0.030 37.198 0.000 1.011 0.740
## siqs06 (.p6.) 0.998 0.028 36.099 0.000 0.916 0.766
## ia =~
## siqs07 1.000 0.864 0.874
## siqs08 (.p8.) 1.041 0.026 39.710 0.000 0.899 0.895
## siqs09 (.p9.) 0.943 0.025 38.158 0.000 0.814 0.726
## SIQS =~
## it 1.000 0.849 0.849
## cc (.11.) 0.762 0.021 36.525 0.000 0.706 0.706
## ia (.12.) 0.889 0.023 38.440 0.000 0.876 0.876
## LTp =~
## tpqp02 1.000 0.672 0.663
## tpqp05 (.14.) 1.300 0.025 52.897 0.000 0.874 0.796
## tpqp12 (.15.) 1.637 0.029 56.560 0.000 1.100 0.809
## tpqp16 (.16.) 1.141 0.023 50.386 0.000 0.767 0.743
## tpqp01 (.17.) 1.131 0.023 50.205 0.000 0.760 0.697
## tpqp09 (.18.) 1.432 0.026 54.552 0.000 0.962 0.813
## tpqp11 (.19.) 1.402 0.026 54.212 0.000 0.943 0.771
## tpqp14 (.20.) 1.221 0.024 51.724 0.000 0.821 0.845
## tpqp03 (.21.) 0.910 0.020 45.396 0.000 0.612 0.560
## tpqp07 (.22.) 1.264 0.024 52.385 0.000 0.850 0.712
## tpqp10 (.23.) 1.232 0.024 51.903 0.000 0.828 0.834
## tpqp13 (.24.) 1.545 0.028 55.739 0.000 1.039 0.816
## tpqp04 (.25.) 1.260 0.024 52.315 0.000 0.847 0.764
## tpqp06 (.26.) 1.227 0.024 51.819 0.000 0.825 0.803
## tpqp08 (.27.) 1.327 0.025 53.263 0.000 0.892 0.804
## tpqp15 (.28.) 1.487 0.027 55.160 0.000 1.000 0.817
## LTm =~
## tpqm02 1.000 0.624 0.627
## tpqm05 (.30.) 1.007 0.024 41.239 0.000 0.628 0.708
## tpqm12 (.31.) 1.408 0.030 46.917 0.000 0.878 0.788
## tpqm16 (.32.) 0.770 0.022 35.734 0.000 0.480 0.701
## tpqm01 (.33.) 0.921 0.023 39.473 0.000 0.574 0.636
## tpqm09 (.34.) 1.219 0.027 44.677 0.000 0.760 0.782
## tpqm11 (.35.) 1.304 0.028 45.772 0.000 0.814 0.782
## tpqm14 (.36.) 1.155 0.026 43.758 0.000 0.720 0.850
## tpqm03 (.37.) 0.859 0.023 38.054 0.000 0.536 0.533
## tpqm07 (.38.) 1.051 0.025 42.047 0.000 0.655 0.624
## tpqm10 (.39.) 1.105 0.026 42.977 0.000 0.689 0.832
## tpqm13 (.40.) 1.448 0.031 47.310 0.000 0.903 0.782
## tpqm04 (.41.) 0.919 0.023 39.440 0.000 0.574 0.695
## tpqm06 (.42.) 1.191 0.027 44.295 0.000 0.743 0.804
## tpqm08 (.43.) 1.102 0.026 42.925 0.000 0.687 0.755
## tpqm15 (.44.) 1.399 0.030 46.825 0.000 0.873 0.797
## LT =~
## LTm 1.000 0.863 0.863
## LTp (.46.) 0.924 0.027 34.859 0.000 0.740 0.740
## LTc =~
## tcq02 1.000 0.965 0.676
## tcq05 (.48.) 0.956 0.016 60.009 0.000 0.923 0.749
## tcq12 (.49.) 0.801 0.015 54.991 0.000 0.774 0.496
## tcq16 (.50.) 1.072 0.017 62.933 0.000 1.035 0.787
## tcq01 (.51.) 0.980 0.016 60.661 0.000 0.946 0.754
## tcq09 (.52.) 0.889 0.015 57.995 0.000 0.858 0.698
## tcq11 (.53.) 0.972 0.016 60.448 0.000 0.938 0.737
## tcq14 (.54.) 0.931 0.016 59.269 0.000 0.898 0.770
## tcq03 (.55.) 0.780 0.014 54.197 0.000 0.753 0.616
## tcq07 (.56.) 0.805 0.015 55.103 0.000 0.777 0.653
## tcq10 (.57.) 0.680 0.014 49.963 0.000 0.656 0.492
## tcq13 (.58.) 0.944 0.016 59.655 0.000 0.911 0.737
## tcq04 (.59.) 0.981 0.016 60.699 0.000 0.947 0.770
## tcq06 (.60.) 1.014 0.016 61.560 0.000 0.979 0.786
## tcq08 (.61.) 0.888 0.015 57.974 0.000 0.857 0.769
## tcq15 (.62.) 0.964 0.016 60.215 0.000 0.930 0.765
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## SIQS ~
## LT 0.632 0.019 32.428 0.000 0.400 0.400
## LTc 0.335 0.009 36.884 0.000 0.381 0.381
## sexo 0.492 0.105 4.705 0.000 0.579 0.234
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LT ~~
## LTc (.131) 0.111 0.003 37.638 0.000 0.214 0.214
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.133) 4.748 0.059 80.604 0.000 4.748 4.195
## .siqs02 (.134) 4.732 0.055 85.679 0.000 4.732 3.717
## .siqs03 (.135) 4.586 0.063 73.018 0.000 4.586 3.680
## .siqs04 (.136) 4.344 0.052 84.221 0.000 4.344 3.315
## .siqs05 (.137) 4.299 0.054 79.850 0.000 4.299 3.149
## .siqs06 (.138) 4.820 0.052 93.529 0.000 4.820 4.028
## .siqs07 (.139) 5.170 0.055 93.366 0.000 5.170 5.231
## .siqs08 (.140) 5.127 0.057 90.717 0.000 5.127 5.107
## .siqs09 (.141) 4.857 0.054 90.229 0.000 4.857 4.329
## .tpqp02 (.142) 5.387 0.039 137.769 0.000 5.387 5.314
## .tpqp05 (.143) 5.398 0.039 138.042 0.000 5.398 4.915
## .tpqp12 (.144) 4.936 0.039 126.230 0.000 4.936 3.630
## .tpqp16 (.145) 5.476 0.039 140.030 0.000 5.476 5.304
## .tpqp01 (.146) 5.346 0.039 136.716 0.000 5.346 4.901
## .tpqp09 (.147) 5.364 0.039 137.184 0.000 5.364 4.531
## .tpqp11 (.148) 5.184 0.039 132.584 0.000 5.184 4.238
## .tpqp14 (.149) 5.530 0.039 141.434 0.000 5.530 5.692
## .tpqp03 (.150) 5.252 0.039 134.299 0.000 5.252 4.805
## .tpqp07 (.151) 5.148 0.039 131.649 0.000 5.148 4.313
## .tpqp10 (.152) 5.502 0.039 140.693 0.000 5.502 5.536
## .tpqp13 (.153) 5.165 0.039 132.078 0.000 5.165 4.057
## .tpqp04 (.154) 5.299 0.039 135.508 0.000 5.299 4.781
## .tpqp06 (.155) 5.448 0.039 139.328 0.000 5.448 5.303
## .tpqp08 (.156) 5.373 0.039 137.418 0.000 5.373 4.846
## .tpqp15 (.157) 5.160 0.039 131.961 0.000 5.160 4.216
## .tpqm02 (.158) 5.454 0.039 139.485 0.000 5.454 5.481
## .tpqm05 (.159) 5.625 0.039 143.850 0.000 5.625 6.342
## .tpqm12 (.160) 5.352 0.039 136.873 0.000 5.352 4.799
## .tpqm16 (.161) 5.700 0.039 145.761 0.000 5.700 8.316
## .tpqm01 (.162) 5.492 0.039 140.459 0.000 5.492 6.076
## .tpqm09 (.163) 5.585 0.039 142.837 0.000 5.585 5.744
## .tpqm11 (.164) 5.372 0.039 137.379 0.000 5.372 5.166
## .tpqm14 (.165) 5.613 0.039 143.539 0.000 5.613 6.624
## .tpqm03 (.166) 5.299 0.039 135.508 0.000 5.299 5.271
## .tpqm07 (.167) 5.306 0.039 135.703 0.000 5.306 5.054
## .tpqm10 (.168) 5.611 0.039 143.500 0.000 5.611 6.768
## .tpqm13 (.169) 5.331 0.039 136.327 0.000 5.331 4.615
## .tpqm04 (.170) 5.596 0.039 143.110 0.000 5.596 6.781
## .tpqm06 (.171) 5.572 0.039 142.486 0.000 5.572 6.029
## .tpqm08 (.172) 5.535 0.039 141.550 0.000 5.535 6.076
## .tpqm15 (.173) 5.343 0.039 136.639 0.000 5.343 4.881
## .tcq02 (.174) 4.140 0.039 105.881 0.000 4.140 2.899
## .tcq05 (.175) 4.970 0.039 127.088 0.000 4.970 4.033
## .tcq12 (.176) 3.770 0.039 96.407 0.000 3.770 2.415
## .tcq16 (.177) 4.875 0.039 124.672 0.000 4.875 3.707
## .tcq01 (.178) 4.727 0.039 120.890 0.000 4.727 3.768
## .tcq09 (.179) 5.092 0.039 130.207 0.000 5.092 4.144
## .tcq11 (.180) 4.822 0.039 123.307 0.000 4.822 3.787
## .tcq14 (.181) 4.934 0.039 126.192 0.000 4.934 4.232
## .tcq03 (.182) 4.803 0.039 122.839 0.000 4.803 3.929
## .tcq07 (.183) 4.870 0.039 124.554 0.000 4.870 4.097
## .tcq10 (.184) 4.480 0.039 114.574 0.000 4.480 3.360
## .tcq13 (.185) 4.642 0.039 118.707 0.000 4.642 3.754
## .tcq04 (.186) 4.915 0.039 125.685 0.000 4.915 3.996
## .tcq06 (.187) 4.841 0.039 123.814 0.000 4.841 3.888
## .tcq08 (.188) 5.201 0.039 133.014 0.000 5.201 4.662
## .tcq15 (.189) 4.950 0.039 126.581 0.000 4.950 4.070
## sexo 0.489 0.050 9.779 0.000 0.489 1.211
## .it 0.000 0.000 0.000
## .cc 0.000 0.000 0.000
## .ia 0.000 0.000 0.000
## .SIQS 0.000 0.000 0.000
## .LTp 0.000 0.000 0.000
## .LTm 0.000 0.000 0.000
## LT 0.000 0.000 0.000
## LTc 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .siqs01 (.66.) 0.277 0.051 5.405 0.000 0.277 0.216
## .siqs02 (.67.) 0.835 0.048 17.399 0.000 0.835 0.515
## .siqs03 (.68.) 0.303 0.056 5.435 0.000 0.303 0.195
## .siqs04 (.69.) 0.874 0.051 17.004 0.000 0.874 0.509
## .siqs05 (.70.) 0.842 0.055 15.303 0.000 0.842 0.452
## .siqs06 (.71.) 0.593 0.051 11.544 0.000 0.593 0.414
## .siqs07 (.72.) 0.231 0.050 4.656 0.000 0.231 0.237
## .siqs08 (.73.) 0.200 0.051 3.922 0.000 0.200 0.198
## .siqs09 (.74.) 0.595 0.048 12.389 0.000 0.595 0.473
## .tpqp02 (.75.) 0.576 0.041 13.937 0.000 0.576 0.560
## .tpqp05 (.76.) 0.442 0.043 10.310 0.000 0.442 0.367
## .tpqp12 (.77.) 0.638 0.045 14.111 0.000 0.638 0.345
## .tpqp16 (.78.) 0.477 0.042 11.361 0.000 0.477 0.448
## .tpqp01 (.79.) 0.612 0.042 14.581 0.000 0.612 0.514
## .tpqp09 (.80.) 0.476 0.044 10.878 0.000 0.476 0.339
## .tpqp11 (.81.) 0.608 0.044 13.962 0.000 0.608 0.406
## .tpqp14 (.82.) 0.271 0.042 6.375 0.000 0.271 0.287
## .tpqp03 (.83.) 0.820 0.041 20.037 0.000 0.820 0.687
## .tpqp07 (.84.) 0.702 0.043 16.452 0.000 0.702 0.493
## .tpqp10 (.85.) 0.301 0.043 7.089 0.000 0.301 0.305
## .tpqp13 (.86.) 0.541 0.045 12.156 0.000 0.541 0.334
## .tpqp04 (.87.) 0.511 0.043 11.991 0.000 0.511 0.416
## .tpqp06 (.88.) 0.375 0.042 8.835 0.000 0.375 0.356
## .tpqp08 (.89.) 0.434 0.043 10.075 0.000 0.434 0.353
## .tpqp15 (.90.) 0.498 0.044 11.293 0.000 0.498 0.333
## .tpqm02 (.91.) 0.601 0.041 14.544 0.000 0.601 0.607
## .tpqm05 (.92.) 0.392 0.041 9.483 0.000 0.392 0.498
## .tpqm12 (.93.) 0.472 0.044 10.828 0.000 0.472 0.380
## .tpqm16 (.94.) 0.239 0.040 5.911 0.000 0.239 0.508
## .tpqm01 (.95.) 0.487 0.041 11.885 0.000 0.487 0.596
## .tpqm09 (.96.) 0.367 0.042 8.660 0.000 0.367 0.389
## .tpqm11 (.97.) 0.419 0.043 9.767 0.000 0.419 0.388
## .tpqm14 (.98.) 0.199 0.042 4.733 0.000 0.199 0.277
## .tpqm03 (.99.) 0.723 0.041 17.755 0.000 0.723 0.716
## .tpqm07 (.100) 0.673 0.042 16.190 0.000 0.673 0.610
## .tpqm10 (.101) 0.212 0.042 5.071 0.000 0.212 0.309
## .tpqm13 (.102) 0.519 0.044 11.823 0.000 0.519 0.389
## .tpqm04 (.103) 0.352 0.041 8.596 0.000 0.352 0.517
## .tpqm06 (.104) 0.302 0.042 7.137 0.000 0.302 0.353
## .tpqm08 (.105) 0.357 0.042 8.545 0.000 0.357 0.431
## .tpqm15 (.106) 0.437 0.044 10.025 0.000 0.437 0.364
## .tcq02 (.107) 1.108 0.044 24.920 0.000 1.108 0.543
## .tcq05 (.108) 0.666 0.044 15.145 0.000 0.666 0.439
## .tcq12 (.109) 1.839 0.042 43.277 0.000 1.839 0.754
## .tcq16 (.110) 0.659 0.045 14.551 0.000 0.659 0.381
## .tcq01 (.111) 0.679 0.044 15.352 0.000 0.679 0.431
## .tcq09 (.112) 0.773 0.043 17.862 0.000 0.773 0.512
## .tcq11 (.113) 0.740 0.044 16.761 0.000 0.740 0.457
## .tcq14 (.114) 0.553 0.044 12.646 0.000 0.553 0.407
## .tcq03 (.115) 0.927 0.042 21.913 0.000 0.927 0.620
## .tcq07 (.116) 0.810 0.043 19.056 0.000 0.810 0.573
## .tcq10 (.117) 1.347 0.042 32.434 0.000 1.347 0.758
## .tcq13 (.118) 0.699 0.044 15.940 0.000 0.699 0.457
## .tcq04 (.119) 0.615 0.044 13.895 0.000 0.615 0.407
## .tcq06 (.120) 0.592 0.045 13.260 0.000 0.592 0.382
## .tcq08 (.121) 0.510 0.043 11.774 0.000 0.510 0.409
## .tcq15 (.122) 0.613 0.044 13.922 0.000 0.613 0.415
## .it (.123) 0.280 0.028 9.932 0.000 0.279 0.279
## .cc (.124) 0.422 0.027 15.657 0.000 0.501 0.501
## .ia (.125) 0.173 0.027 6.508 0.000 0.233 0.233
## .SIQS (.126) 0.416 0.020 20.457 0.000 0.575 0.575
## .LTp (.127) 0.204 0.008 25.856 0.000 0.452 0.452
## .LTm (.128) 0.099 0.007 14.117 0.000 0.255 0.255
## LT (.129) 0.290 0.011 25.872 0.000 1.000 1.000
## LTc (.130) 0.932 0.021 44.055 0.000 1.000 1.000
## sexo 0.163 0.038 4.247 0.000 0.163 1.000