Entrepreneur_22
JOM
11 junio, 2020
Introducción
Lo primero se procede a la carga de paquetes que puedan ser necesarios: * foreign para poder leer los ficheros de SPSS * lavaan para construir modelos SEM
Carga de datos
Se procede a leer los resultados de las encuestas a un data frame llamado ddd y cuyo resumen se muestra a continuación, previa eliminación de los registros que contengan datos NA:
setwd('/home/jb/git/Entrepreneur/')
# ddd = read.spss(file="Emprendimiento2016.sav",to.data.frame=TRUE)
# save(ddd,file="Emprendimiento2016.RData")
load(file="Emprendimiento2016.RData")
# summary(ddd)
jdx=apply(is.na(ddd),1,sum) == 0
ddna=ddd[jdx,]
colnames(ddna)= iconv(colnames(ddna),'utf-8','ascii',sub='')
# summary(ddna)
ddf = ddna
for (i in (1:length(colnames(ddf)))[-c(1,4)]) {
ddf[,i]=as.ordered(ddf[,i])
}
#
ddnaddf=ddna
ddnaddf[,'V2.4IndividualismoINV']=as.numeric(ddna[,'V2.4IndividualismoINV'])
obs.ddf=c('V2.1Individualismo','V2.4IndividualismoINV','V2.8Individualismo',
'V2.2MasculinidadINV','V2.6Masculinidad',
'V2.3.AversinINV','V2.5Aversin','V2.7Aversin',
'V3.1ACTFuturoAtractivo', 'V3.2ACTOpciones',
'V3.3CONTFcil', 'V3.4CONTPuedo',
'V3.11NSFamilia', 'V3.12NSAmigos','V3.12NSCompaeros','V3.14NSSociedad',
'V3.5IEListo', 'V3.6IEMeta','V3.7IEHarTodo', 'V3.8IEConvencido',
'V3.9IEPensarSerio','V3.10IEFirmeIntencin')
# cr.poly=polychoric(ddnaddf[,obs.ddf])
# alpha(cr.poly$rho)
#
belief=63
ddnaddf[,belief]=NA
colnames(ddnaddf)[belief]="BELIEFS"
ddnaddf[ddnaddf$PAIS=="Alemania","BELIEFS"]=1
ddnaddf[ddnaddf$PAIS=="Suecia","BELIEFS"]=1
ddnaddf[ddnaddf$PAIS=="España","BELIEFS"]=2
ddnaddf[ddnaddf$PAIS=="Italia","BELIEFS"]=2
ddnaddf[ddnaddf$PAIS=="México","BELIEFS"]=2
# De la excell de Gustavo
ddnaddf[ddnaddf$PAIS=="Alemania","EcFree"]=rnorm(sum(ddnaddf$PAIS=="Alemania"),mean=73.4, sd=0.5)
ddnaddf[ddnaddf$PAIS=="Suecia","EcFree"]=rnorm(sum(ddnaddf$PAIS=="Suecia"),mean=73.1, sd=0.5)
ddnaddf[ddnaddf$PAIS=="España","EcFree"]=rnorm(sum(ddnaddf$PAIS=="España"),mean=67.2, sd=0.6)
ddnaddf[ddnaddf$PAIS=="Italia","EcFree"]=rnorm(sum(ddnaddf$PAIS=="Italia"),mean=60.9, sd=0.5)
ddnaddf[ddnaddf$PAIS=="México","EcFree"]=rnorm(sum(ddnaddf$PAIS=="México"),mean=66.8, sd=0.5)
ddnaddf[ddnaddf$PAIS=="Alemania","PPP"]=rnorm(sum(ddnaddf$PAIS=="Alemania"),mean=47.012, sd=0.5)
ddnaddf[ddnaddf$PAIS=="Suecia","PPP"]=rnorm(sum(ddnaddf$PAIS=="Suecia"),mean=47.046, sd=1.5)
ddnaddf[ddnaddf$PAIS=="España","PPP"]=rnorm(sum(ddnaddf$PAIS=="España"),mean=33.526, sd=0.5)
ddnaddf[ddnaddf$PAIS=="Italia","PPP"]=rnorm(sum(ddnaddf$PAIS=="Italia"),mean=36.195, sd=0.5)
ddnaddf[ddnaddf$PAIS=="México","PPP"]=rnorm(sum(ddnaddf$PAIS=="México"),mean=18.046, sd=0.95)
ddnaddf[ddnaddf$PAIS=="Alemania","Gini"]=rnorm(sum(ddnaddf$PAIS=="Alemania"),mean=31.7, sd=0.5)
ddnaddf[ddnaddf$PAIS=="Suecia","Gini"]=rnorm(sum(ddnaddf$PAIS=="Suecia"),mean=28.4, sd=0.5)
ddnaddf[ddnaddf$PAIS=="España","Gini"]=rnorm(sum(ddnaddf$PAIS=="España"),mean=36.1, sd=0.5)
ddnaddf[ddnaddf$PAIS=="Italia","Gini"]=rnorm(sum(ddnaddf$PAIS=="Italia"),mean=34.7, sd=0.5)
ddnaddf[ddnaddf$PAIS=="México","Gini"]=rnorm(sum(ddnaddf$PAIS=="México"),mean=48.7, sd=0.5)
ddnaddf[ddnaddf$PAIS=="Alemania","IDV"]=round(rnorm(sum(ddnaddf$PAIS=="Alemania"),mean=51, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Suecia","IDV"]=round(rnorm(sum(ddnaddf$PAIS=="Suecia"),mean=71, sd=1),0)
ddnaddf[ddnaddf$PAIS=="España","IDV"]=round(rnorm(sum(ddnaddf$PAIS=="España"),mean=67, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Italia","IDV"]=round(rnorm(sum(ddnaddf$PAIS=="Italia"),mean=76, sd=1),0)
ddnaddf[ddnaddf$PAIS=="México","IDV"]=round(rnorm(sum(ddnaddf$PAIS=="México"),mean=30, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Alemania","MAS"]=round(rnorm(sum(ddnaddf$PAIS=="Alemania"),mean=42, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Suecia","MAS"]=round(rnorm(sum(ddnaddf$PAIS=="Suecia"),mean=5, sd=2),0)
ddnaddf[ddnaddf$PAIS=="España","MAS"]=round(rnorm(sum(ddnaddf$PAIS=="España"),mean=66, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Italia","MAS"]=round(rnorm(sum(ddnaddf$PAIS=="Italia"),mean=70, sd=0.5),0)
ddnaddf[ddnaddf$PAIS=="México","MAS"]=round(rnorm(sum(ddnaddf$PAIS=="México"),mean=69, sd=0.5),0)
ddnaddf[ddnaddf$PAIS=="Alemania","UAI"]=round(rnorm(sum(ddnaddf$PAIS=="Alemania"),mean=86, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Suecia","UAI"]=round(rnorm(sum(ddnaddf$PAIS=="Suecia"),mean=29, sd=2),0)
ddnaddf[ddnaddf$PAIS=="España","UAI"]=round(rnorm(sum(ddnaddf$PAIS=="España"),mean=65, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Italia","UAI"]=round(rnorm(sum(ddnaddf$PAIS=="Italia"),mean=75, sd=1),0)
ddnaddf[ddnaddf$PAIS=="México","UAI"]=round(rnorm(sum(ddnaddf$PAIS=="México"),mean=82, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Alemania","CAT"]=round(rnorm(sum(ddnaddf$PAIS=="Alemania"),mean=29, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Suecia","CAT"]=round(rnorm(sum(ddnaddf$PAIS=="Suecia"),mean=5, sd=2),0)
ddnaddf[ddnaddf$PAIS=="España","CAT"]=round(rnorm(sum(ddnaddf$PAIS=="España"),mean=92, sd=1),0)
ddnaddf[ddnaddf$PAIS=="Italia","CAT"]=round(rnorm(sum(ddnaddf$PAIS=="Italia"),mean=94, sd=1),0)
ddnaddf[ddnaddf$PAIS=="México","CAT"]=round(rnorm(sum(ddnaddf$PAIS=="México"),mean=85, sd=1),0)
# Datos de Gustavo incorporados
kruskal.test(ddnaddf[,63],ddnaddf[,42])##
## Kruskal-Wallis rank sum test
##
## data: ddnaddf[, 63] and ddnaddf[, 42]
## Kruskal-Wallis chi-squared = 37.568, df = 6, p-value = 1.364e-06chisq.test(table(ddnaddf[,63],ddnaddf[,42]))##
## Pearson's Chi-squared test
##
## data: table(ddnaddf[, 63], ddnaddf[, 42])
## X-squared = 37.589, df = 6, p-value = 1.351e-06#
kruskal.test(ddna[,5],ddna[,42])##
## Kruskal-Wallis rank sum test
##
## data: ddna[, 5] and ddna[, 42]
## Kruskal-Wallis chi-squared = 27.427, df = 6, p-value = 0.0001204kruskal.test(ddna[,2],ddna[,42])##
## Kruskal-Wallis rank sum test
##
## data: ddna[, 2] and ddna[, 42]
## Kruskal-Wallis chi-squared = 20.236, df = 6, p-value = 0.002514kruskal.test(ddna[,5],ddna[,42])##
## Kruskal-Wallis rank sum test
##
## data: ddna[, 5] and ddna[, 42]
## Kruskal-Wallis chi-squared = 27.427, df = 6, p-value = 0.0001204chisq.test(table(ddna[,5],ddna[,42]))##
## Pearson's Chi-squared test
##
## data: table(ddna[, 5], ddna[, 42])
## X-squared = 27.443, df = 6, p-value = 0.0001196chisq.test(table(ddna[,5],ddna[,36]))##
## Pearson's Chi-squared test
##
## data: table(ddna[, 5], ddna[, 36])
## X-squared = 9.7832, df = 12, p-value = 0.635chisq.test(table(ddna[,5],ddna[,38]))##
## Pearson's Chi-squared test
##
## data: table(ddna[, 5], ddna[, 38])
## X-squared = 21.744, df = 6, p-value = 0.001347chisq.test(table(ddna[,5],ddna[,39]))##
## Pearson's Chi-squared test
##
## data: table(ddna[, 5], ddna[, 39])
## X-squared = 10.39, df = 6, p-value = 0.1092chisq.test(table(ddna[,5],ddna[,40]))##
## Pearson's Chi-squared test
##
## data: table(ddna[, 5], ddna[, 40])
## X-squared = 11.3, df = 6, p-value = 0.07952chisq.test(table(ddna[,5],ddna[,38]))##
## Pearson's Chi-squared test
##
## data: table(ddna[, 5], ddna[, 38])
## X-squared = 21.744, df = 6, p-value = 0.001347#
summary(ddnaddf)## CODIGO UNI PAIS EDAD GENERO
## Min. : 11002 UPM :412 España :412 Min. :19.00 Hombre:1173
## 1st Qu.: 21059 MILAN :245 Italia :727 1st Qu.:22.00 Mujer : 600
## Median : 41075 TUB :213 México :200 Median :23.00
## Mean : 42867 PARMA :202 Suecia :221 Mean :23.61
## 3rd Qu.: 63016 MEXICO :200 Alemania:213 3rd Qu.:25.00
## Max. :150452 POLIBA :140 Max. :52.00
## (Other):361
## CARRERA CURSO PASNAC PASNACPADRE
## Organización :456 último:1773 Min. :1.000 Min. :1.000
## Industriales :587 1st Qu.:2.000 1st Qu.:2.000
## Química :330 Median :2.000 Median :2.000
## Ing. Civil :400 Mean :2.617 Mean :2.605
## Ing. Informática: 0 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000
##
## PASNACMADRE CLASESOCIAL ClaseSocial2 ESTPADRE
## Min. :1.000 Baja : 61 Baja-MedioBaja:797 Sin estudios:139
## 1st Qu.:2.000 Media-Baja:736 Alta-MedioAlta:976 Secundaria :480
## Median :2.000 Media-Alta:929 FP :395
## Mean :2.608 Alta : 47 Universidad :759
## 3rd Qu.:3.000
## Max. :6.000
##
## ESTMADRE OCUPACIONPADRE OCUPACIONMADRE ENTORNONEG
## Sin estudios:154 Funcionario:405 Funcionario:438 Min. :0.000
## Secundaria :547 Empleado :633 Empleado :537 1st Qu.:0.000
## FP :400 Empresario :453 Empresario :210 Median :1.000
## Universidad :672 Desempleado: 63 Desempleado:298 Mean :0.727
## Otro :219 Otro :290 3rd Qu.:1.000
## Max. :2.000
##
## V2.1Individualismo V2.2Feminidad V2.2MasculinidadINV
## Min. :1.000 Muy Masculino:121 Muy femenino :280
## 1st Qu.:3.000 Masculino :225 Femenino :627
## Median :4.000 Medio :520 Medio :520
## Mean :3.813 Femenino :627 Masculino :225
## 3rd Qu.:5.000 Muy femenino :280 Muy Masculino:121
## Max. :6.000
##
## V2.3NoAversin V2.3.AversinINV V2.4Colectivismo
## Alta aversión:331 Baja aversión: 97 Muy Individualista: 83
## Aversión :542 Poca aversión:301 Individualista :197
## Medio :502 Medio :502 Medio :530
## Poca aversión:301 Aversión :542 Colectivista :693
## Baja aversión: 97 Alta aversión:331 Muy Colectivista :270
##
##
## V2.4IndividualismoINV V2.5Aversin V2.6Masculinidad
## Min. :1.000 Baja aversión:110 Muy femenino : 29
## 1st Qu.:2.000 Poca aversión:301 Femenino : 98
## Median :2.000 Medio :473 Medio :389
## Mean :2.509 Aversión :493 Masculino :726
## 3rd Qu.:3.000 Alta aversión:396 Muy Masculino:531
## Max. :5.000
##
## V2.7Aversin V2.8Individualismo V2.9Innovacin
## Baja aversión: 27 Muy Colectivista : 29 Min. :1.000
## Poca aversión:120 Colectivista :127 1st Qu.:2.000
## Medio :413 Medio :386 Median :3.000
## Aversión :741 Individualista :635 Mean :3.092
## Alta aversión:472 Muy Individualista:596 3rd Qu.:4.000
## Max. :5.000
##
## V2.10IT V3.1ACTFuturoAtractivo V3.2ACTOpciones MediaACT
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.500
## Median :2.000 Median :5.000 Median :4.000 Median :4.500
## Mean :2.389 Mean :4.917 Mean :4.245 Mean :4.581
## 3rd Qu.:3.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
## Max. :5.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## V3.3CONTFcil V3.4CONTPuedo MediaCONT V3.5IEListo
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.500 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :3.500 Median :3.000
## Mean :3.669 Mean :3.812 Mean :3.741 Mean :3.467
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## V3.6IEMeta V3.7IEHarTodo V3.8IEConvencido V3.9IEPensarSerio
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :4.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.699 Mean :3.671 Mean :3.635 Mean :3.676
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## V3.10IEFirmeIntencin MediaIE V3.11NSFamilia V3.12NSAmigos
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.167 1st Qu.:4.000 1st Qu.:4.000
## Median :3.000 Median :3.333 Median :6.000 Median :6.000
## Mean :3.633 Mean :3.630 Mean :5.506 Mean :5.415
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## V3.12NSCompaeros V3.14NSSociedad MediaNS V4.1FINPROP
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.250 1st Qu.:2.000
## Median :5.000 Median :5.000 Median :5.250 Median :2.000
## Mean :5.124 Mean :4.818 Mean :5.216 Mean :2.399
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.250 3rd Qu.:3.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :5.000
##
## V4.2ADM V4.3JUV V4.4CREDIT V4.5FINPUB V4.6JUV
## Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.0 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :3.0 Median :2.000 Median :2.000 Median :3.000
## Mean :2.223 Mean :2.9 Mean :2.403 Mean :2.342 Mean :3.094
## 3rd Qu.:3.000 3rd Qu.:4.0 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.0 Max. :5.000 Max. :5.000 Max. :5.000
##
## V4.7ADM V4.8FINPROP V4.9ADM V4.10JUV
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.346 Mean :2.404 Mean :2.136 Mean :2.508
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## V4.11ADM V4.12EDU V4.13JUV V4.14ADM
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :3.000 Median :2.000
## Mean :2.043 Mean :2.755 Mean :2.633 Mean :2.459
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## BELIEFS EcFree PPP Gini
## Min. :1.000 Min. :59.06 Min. :15.33 Min. :27.15
## 1st Qu.:2.000 1st Qu.:61.06 1st Qu.:33.59 1st Qu.:33.63
## Median :2.000 Median :66.60 Median :36.08 Median :34.86
## Mean :1.755 Mean :66.03 Mean :36.17 Mean :35.47
## 3rd Qu.:2.000 3rd Qu.:68.41 3rd Qu.:37.41 3rd Qu.:35.99
## Max. :2.000 Max. :74.85 Max. :50.63 Max. :49.88
##
## IDV MAS UAI CAT
## Min. :27.00 Min. : 0.00 Min. :24.00 Min. : 0.00
## 1st Qu.:66.00 1st Qu.:64.00 1st Qu.:65.00 1st Qu.:83.00
## Median :70.00 Median :69.00 Median :75.00 Median :92.00
## Mean :65.11 Mean :57.51 Mean :69.06 Mean :73.63
## 3rd Qu.:76.00 3rd Qu.:70.00 3rd Qu.:77.00 3rd Qu.:94.00
## Max. :79.00 Max. :72.00 Max. :89.00 Max. :97.00
## Análisis Factorial Confirmatorio. Modelo de Ajzen
Se procede a crear en modelo de Ajzen y a un análisis confirmatorio de datos. Partimos de las variables como factores
Modelado del caso de Ajzen con Ecuaciones Estructurales
#
ddold = ddna
ddnaddf[,"V2.8Individualismo"]= as.numeric(ddna[,"V2.8Individualismo"])
ddnaddf[,"V2.2MasculinidadINV"]= as.numeric(ddna[,"V2.2MasculinidadINV"])
ddnaddf[,"V2.6Masculinidad"]= as.numeric(ddna[,"V2.6Masculinidad"])
ddnaddf[,"V2.3.AversinINV"]= as.numeric(ddna[,"V2.3.AversinINV"])
ddnaddf[,"V2.5Aversin"]= as.numeric(ddna[,"V2.5Aversin"])
ddnaddf[,"V2.7Aversin"]= as.numeric(ddna[,"V2.7Aversin"])
#
obs.variables2=c('V3.1ACTFuturoAtractivo', 'V3.2ACTOpciones','V2.1Individualismo',
'V3.11NSFamilia', 'V3.12NSAmigos', 'V3.14NSSociedad',
'V3.3CONTFcil', 'V3.4CONTPuedo', 'V3.6IEMeta', 'V3.8IEConvencido',
'V3.10IEFirmeIntencin', 'V2.8Individualismo',
'V2.2MasculinidadINV', 'V2.6Masculinidad',
'V2.5Aversin','V2.7Aversin')
#
mcv = round(cov(ddnaddf[,obs.variables2]),1)Resumen de las variables para protestantes
summary(ddnaddf[ddnaddf[,"BELIEFS"]==1,obs.ddf])## V2.1Individualismo V2.4IndividualismoINV V2.8Individualismo
## Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.00 1st Qu.:3.000
## Median :4.000 Median :2.00 Median :4.000
## Mean :3.982 Mean :2.27 Mean :3.984
## 3rd Qu.:5.000 3rd Qu.:3.00 3rd Qu.:5.000
## Max. :6.000 Max. :5.00 Max. :5.000
## V2.2MasculinidadINV V2.6Masculinidad V2.3.AversinINV V2.5Aversin
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.00 1st Qu.:2.000
## Median :2.000 Median :4.000 Median :3.00 Median :3.000
## Mean :2.267 Mean :3.735 Mean :3.09 Mean :3.168
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000
## V2.7Aversin V3.1ACTFuturoAtractivo V3.2ACTOpciones V3.3CONTFcil
## Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.0 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :5.0 Median :4.000 Median :4.000
## Mean :3.758 Mean :4.8 Mean :4.124 Mean :3.749
## 3rd Qu.:4.000 3rd Qu.:6.0 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :7.0 Max. :7.000 Max. :7.000
## V3.4CONTPuedo V3.11NSFamilia V3.12NSAmigos V3.12NSCompaeros
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:4.000
## Median :4.000 Median :6.000 Median :6.000 Median :5.000
## Mean :3.947 Mean :5.528 Mean :5.475 Mean :5.014
## 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## V3.14NSSociedad V3.5IEListo V3.6IEMeta V3.7IEHarTodo
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :5.000 Median :3.000 Median :3.000 Median :3.000
## Mean :4.763 Mean :3.007 Mean :3.396 Mean :3.097
## 3rd Qu.:6.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## V3.8IEConvencido V3.9IEPensarSerio V3.10IEFirmeIntencin
## Min. :1.00 Min. :1.00 Min. :1.000
## 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.000
## Median :3.00 Median :3.00 Median :3.000
## Mean :3.15 Mean :3.41 Mean :3.279
## 3rd Qu.:4.00 3rd Qu.:5.00 3rd Qu.:5.000
## Max. :7.00 Max. :7.00 Max. :7.000Resumen de las variables para Católicos
summary(ddnaddf[ddnaddf[,"BELIEFS"]==2,obs.ddf])## V2.1Individualismo V2.4IndividualismoINV V2.8Individualismo
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:3.000
## Median :4.000 Median :2.000 Median :4.000
## Mean :3.758 Mean :2.587 Mean :3.907
## 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000
## V2.2MasculinidadINV V2.6Masculinidad V2.3.AversinINV V2.5Aversin
## Min. :1.0 Min. :1.000 Min. :1.0 Min. :1.000
## 1st Qu.:2.0 1st Qu.:3.000 1st Qu.:3.0 1st Qu.:3.000
## Median :3.0 Median :4.000 Median :4.0 Median :4.000
## Mean :2.7 Mean :3.981 Mean :3.5 Mean :3.516
## 3rd Qu.:3.0 3rd Qu.:5.000 3rd Qu.:4.0 3rd Qu.:4.000
## Max. :5.0 Max. :5.000 Max. :5.0 Max. :5.000
## V2.7Aversin V3.1ACTFuturoAtractivo V3.2ACTOpciones V3.3CONTFcil
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:2.000
## Median :4.000 Median :5.000 Median :4.000 Median :4.000
## Mean :3.883 Mean :4.955 Mean :4.285 Mean :3.643
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.500 3rd Qu.:5.000
## Max. :5.000 Max. :7.000 Max. :7.000 Max. :7.000
## V3.4CONTPuedo V3.11NSFamilia V3.12NSAmigos V3.12NSCompaeros
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00
## Median :4.000 Median :6.000 Median :6.000 Median :5.00
## Mean :3.768 Mean :5.499 Mean :5.396 Mean :5.16
## 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:6.00
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.00
## V3.14NSSociedad V3.5IEListo V3.6IEMeta V3.7IEHarTodo
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :5.000 Median :3.000 Median :4.000 Median :4.000
## Mean :4.836 Mean :3.616 Mean :3.798 Mean :3.857
## 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## V3.8IEConvencido V3.9IEPensarSerio V3.10IEFirmeIntencin
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :4.000
## Mean :3.792 Mean :3.763 Mean :3.748
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000Preparación del modelo 2020
Nos referimos a EI ~ ATB + PBC, con
#
# V3.5IEListo" "V3.6IEMeta" "V3.7IEHarTodo"
# "V3.8IEConvencido" "V3.9IEPensarSerio" "V3.10IEFirmeIntencin"
ddnaddf[,"EI"] =as.numeric(apply(
ddnaddf[,c("V3.5IEListo","V3.6IEMeta",
"V3.7IEHarTodo","V3.8IEConvencido",
"V3.9IEPensarSerio","V3.10IEFirmeIntencin")],
1,mean))
ddnaddf[,"ATB"] = as.numeric(apply(
ddnaddf[,c("V3.1ACTFuturoAtractivo",
"V3.2ACTOpciones")],
1,mean))
ddnaddf[,"PBC"] = as.numeric(apply(
ddnaddf[,c("V3.11NSFamilia",
"V3.12NSAmigos", "V3.12NSCompaeros",
"V3.14NSSociedad")],
1,mean))
ddnaddf[,"SN"] = as.numeric(apply(
ddnaddf[,c("V3.3CONTFcil","V3.4CONTPuedo")],
1,mean))
#Ahora se modela el Esquema propuesto por G Morales el 18/4/2020
#
# PBC =~ V3.3CONTFcil + V3.4CONTPuedo
# EI =~ V3.6IEMeta + V3.8IEConvencido + V3.10IEFirmeIntencin
obs.variables99 = c("EcFree","PPP","Gini","IDV","MAS",
"UAI","CAT","ATB","PBC","SN","EI")
dat = ddnaddf[,obs.variables99]
EI.AJZEN.model99 = '
# Variables Latentes
Context =~ EcFree + PPP + Gini
Cultural =~ MAS + UAI + CAT + IDV
Religion =~ CAT
Cognitive=~ ATB + PBC + SN
Supportive=~ SN
Ent =~ EI
#
EcFree ~~ EcFree
PPP ~~ PPP
CAT ~~ CAT
SN ~~ SN
Gini ~~ Gini
MAS ~~ MAS
ATB ~~ ATB
EI ~~ EI
Religion ~~ Religion
Supportive ~~ Supportive
Context ~~ Cultural
# Equations
Ent ~ Context + Cultural + Cognitive + Religion + Supportive
'
mcv = round(cov(dat),1)
# mod='EI =~ EcFree + PPP + Gini + IDV + MAS + UAI + CAT + ATB + PBC + SN'
# + Context + Cultural + Cognitive + Religion + Supportive
fit <- lavaan::cfa(EI.AJZEN.model99, data= dat)## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(fit, fit.measures=FALSE)## lavaan 0.6-5 did NOT end normally after 10000 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 39
##
## Number of observations 1773
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Context =~
## EcFree 1.000
## PPP 4.854 NA
## Gini -2.687 NA
## Cultural =~
## MAS 1.000
## UAI -0.062 NA
## CAT -5.660 NA
## IDV 0.007 NA
## Religion =~
## CAT 1.000
## Cognitive =~
## ATB 1.000
## PBC 0.321 NA
## SN 0.003 NA
## Supportive =~
## SN 1.000
## Ent =~
## EI 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Ent ~
## Context 2.523 NA
## Cultural -3.244 NA
## Cognitive -546.312 NA
## Religion 0.253 NA
## Supportive 900.826 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Context ~~
## Cultural -19.753 NA
## Religion -151.147 NA
## Cognitive -0.306 NA
## Supportive -0.224 NA
## Cultural ~~
## Religion -7209.098 NA
## Cognitive -0.753 NA
## Supportive -3.327 NA
## Religion ~~
## Cognitive -5.124 NA
## Supportive -24.615 NA
## Cognitive ~~
## Supportive 1.336 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EcFree 21.082 NA
## .PPP -7.773 NA
## .CAT -22134.968 NA
## .SN 1.371 NA
## .Gini 4.705 NA
## .MAS 1714.104 NA
## .ATB 0.368 NA
## .EI -231.716 NA
## Religion -14290.637 NA
## Supportive 0.808 NA
## .UAI 277.092 NA
## .IDV 217.768 NA
## .PBC 1.490 NA
## Context 3.276 NA
## Cultural -1375.491 NA
## Cognitive 2.200 NA
## .Ent -113.180 NA# reliability(fit)
txtRound(parameterEstimates(fit, ci = TRUE, level = 0.95),
digits=3, excl.cols=1)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 Context =~ EcFree 1e+00 0.000 1.000 1.000
## 2 Context =~ PPP 4.854e+00
## 3 Context =~ Gini -2.687e+00
## 4 Cultural =~ MAS 1e+00 0.000 1.000 1.000
## 5 Cultural =~ UAI -6.177
## 6 Cultural =~ CAT -5.66e+00
## 7 Cultural =~ IDV 6.887
## 8 Religion =~ CAT 1e+00 0.000 1.000 1.000
## 9 Cognitive =~ ATB 1e+00 0.000 1.000 1.000
## 10 Cognitive =~ PBC 3.206
## 11 Cognitive =~ SN 3.084
## 12 Supportive =~ SN 1e+00 0.000 1.000 1.000
## 13 Ent =~ EI 1e+00 0.000 1.000 1.000
## 14 EcFree ~~ EcFree 2.1082e+01
## 15 PPP ~~ PPP -7.773e+00
## 16 CAT ~~ CAT -2.213497e+04
## 17 SN ~~ SN 1.371e+00
## 18 Gini ~~ Gini 4.705e+00
## 19 MAS ~~ MAS 1.714104e+03
## 20 ATB ~~ ATB 3.678
## 21 EI ~~ EI -2.31716e+02
## 22 Religion ~~ Religion -1.429064e+04
## 23 Supportive ~~ Supportive 8.076
## 24 Context ~~ Cultural -1.9753e+01
## 25 Ent ~ Context 2.523e+00
## 26 Ent ~ Cultural -3.244e+00
## 27 Ent ~ Cognitive -5.46312e+02
## 28 Ent ~ Religion 2.532
## 29 Ent ~ Supportive 9.00826e+02
## 30 UAI ~~ UAI 2.77092e+02
## 31 IDV ~~ IDV 2.17768e+02
## 32 PBC ~~ PBC 1.49e+00
## 33 Context ~~ Context 3.276e+00
## 34 Cultural ~~ Cultural -1.375491e+03
## 35 Cognitive ~~ Cognitive 2.2e+00
## 36 Ent ~~ Ent -1.1318e+02
## 37 Context ~~ Religion -1.51147e+02
## 38 Context ~~ Cognitive -3.058
## 39 Context ~~ Supportive -2.240
## 40 Cultural ~~ Religion -7.209098e+03
## 41 Cultural ~~ Cognitive -7.534
## 42 Cultural ~~ Supportive -3.327e+00
## 43 Religion ~~ Cognitive -5.124e+00
## 44 Religion ~~ Supportive -2.4615e+01
## 45 Cognitive ~~ Supportive 1.336e+00#
# fitm99 = lavaan::sem(EI.AJZEN.model99, data =dat)
pdf("Gustavo_Model.pdf")
semPaths(fit, style="lisrel",
whatLabels = "std", edge.label.cex = .6, node.label.cex = .6,
label.prop=0.9, edge.label.color = "black", rotation = 4,
equalizeManifests = FALSE, optimizeLatRes = TRUE, node.width = 1.5,
edge.width = 0.5, shapeMan = "rectangle", shapeLat = "ellipse",
shapeInt = "triangle", sizeMan = 4, sizeInt = 2, sizeLat = 4,
curve=2, unCol = "#070b8c")## Warning in sqrt(ETA2): Se han producido NaNs## Warning in sqrt(ETA2): Se han producido NaNs
## Warning in sqrt(ETA2): Se han producido NaNs## Warning in qgraph::qgraph(Edgelist, labels = nLab, bidirectional = Bidir, : The
## following arguments are not documented and likely not arguments of qgraph and
## thus ignored: node.label.cexdev.off()## png
## 2#
#
fitm99 = lavaan::sem(EI.AJZEN.model99, data =dat,
sample.cov=mcv, estimator = "ML",
start="Mplus", std.lv=TRUE,
missing = "listwise", bootstrap=10000)## Warning in lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats, : lavaan WARNING:
## Could not compute standard errors! The information matrix could
## not be inverted. This may be a symptom that the model is not
## identified.## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.summary(fitm99,fit.measures=TRUE,rsquare=TRUE)## lavaan 0.6-5 ended normally after 126 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 39
##
## Number of observations 1773
##
## Model Test User Model:
##
## Test statistic 12709.013
## Degrees of freedom 27
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 32290.463
## Degrees of freedom 55
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.607
## Tucker-Lewis Index (TLI) 0.199
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -51678.277
## Loglikelihood unrestricted model (H1) -45323.771
##
## Akaike (AIC) 103434.554
## Bayesian (BIC) 103648.290
## Sample-size adjusted Bayesian (BIC) 103524.390
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.515
## 90 Percent confidence interval - lower 0.507
## 90 Percent confidence interval - upper 0.522
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.176
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Context =~
## EcFree 1.717 NA
## PPP 8.777 NA
## Gini -4.828 NA
## Cultural =~
## MAS 24.718 NA
## UAI 10.647 NA
## CAT 28.331 NA
## IDV -1.313 NA
## Religion =~
## CAT 1.970 NA
## Cognitive =~
## ATB 1.427 NA
## PBC 0.466 NA
## SN 0.804 NA
## Supportive =~
## SN 0.726 NA
## Ent =~
## EI 0.699 NA
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Ent ~
## Context -0.567 NA
## Cultural -0.051 NA
## Cognitive 1.775 NA
## Religion -0.092 NA
## Supportive 0.712 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Context ~~
## Cultural -0.532 NA
## Religion -3.098 NA
## Cognitive -0.134 NA
## Supportive -0.058 NA
## Cultural ~~
## Religion -0.253 NA
## Cognitive 0.008 NA
## Supportive -0.175 NA
## Religion ~~
## Cognitive 0.201 NA
## Supportive -0.816 NA
## Cognitive ~~
## Supportive 0.104 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EcFree 21.159 NA
## .PPP -8.292 NA
## .CAT 312.256 NA
## .SN 0.860 NA
## .Gini 4.860 NA
## .MAS -144.452 NA
## .ATB 0.395 NA
## .EI 0.299 NA
## Religion 1.000
## Supportive 1.000
## .UAI 158.263 NA
## .IDV 215.976 NA
## .PBC 1.488 NA
## Context 1.000
## Cultural 1.000
## Cognitive 1.000
## .Ent 1.000
##
## R-Square:
## Estimate
## EcFree 0.122
## PPP NA
## CAT 0.714
## SN 0.601
## Gini 0.827
## MAS NA
## ATB 0.838
## EI 0.896
## UAI 0.417
## IDV 0.008
## PBC 0.127
## Ent 0.810semPaths(fitm99,"Standardized", "Estimates",style = "mx",
centerLevels = FALSE, layoutSplit = TRUE)
#
semCors(fit, layout = "spring", cut = 0.3,
esize = 20, titles = TRUE)## Warning in sqrt(ETA2): Se han producido NaNs## Warning in sqrt(ETA2): Se han producido NaNs
## Warning in sqrt(ETA2): Se han producido NaNs
Ahora se modela el Esquema propuesto por G Morales el 18/4/2020
#
# PBC =~ V3.3CONTFcil + V3.4CONTPuedo
# EI =~ V3.6IEMeta + V3.8IEConvencido + V3.10IEFirmeIntencin
EI.AJZEN.model00 = '
# Variables Latentes
Context =~ EcFree + PPP + Gini
Cultural =~ MAS + UAI + CAT + IDV
Religion =~ CAT
Cognitive=~ ATB + PBC + SN
Ent =~ EI
#
EcFree ~~ EcFree
PPP ~~ PPP
CAT ~~ CAT
SN ~~ SN
Gini ~~ Gini
MAS ~~ MAS
ATB ~~ ATB
EI ~~ EI
Religion ~~ Religion
Context ~~ Cultural
# Equations
Ent ~ Context + Cultural + Cognitive + Religion
'
fit00 <- lavaan::cfa(EI.AJZEN.model00, data= dat)## Warning in lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable, :
## lavaan WARNING: the optimizer warns that a solution has NOT been found!summary(fit00, fit.measures=FALSE)## lavaan 0.6-5 did NOT end normally after 2076 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 33
##
## Number of observations 1773
##
## Model Test User Model:
##
## Test statistic NA
## Degrees of freedom NA
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Context =~
## EcFree 1.000
## PPP 3.340 NA
## Gini -1.914 NA
## Cultural =~
## MAS 1.000
## UAI -8891.951 NA
## CAT -35887.764 NA
## IDV -13195.187 NA
## Religion =~
## CAT 1.000
## Cognitive =~
## ATB 1.000
## PBC 0.364 NA
## SN 0.856 NA
## Ent =~
## EI 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Ent ~
## Context -0.001 NA
## Cultural -188.314 NA
## Cognitive 1.238 NA
## Religion 0.006 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Context ~~
## Cultural -0.000 NA
## Religion -66.521 NA
## Cognitive -0.634 NA
## Cultural ~~
## Religion -0.041 NA
## Cognitive 0.000 NA
## Religion ~~
## Cognitive 7.384 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EcFree 17.547 NA
## .PPP -4.403 NA
## .CAT -117.569 NA
## .SN 1.049 NA
## .Gini 4.137 NA
## .MAS 466.560 NA
## .ATB 0.919 NA
## .EI 0.782 NA
## Religion -788.150 NA
## .UAI 328.257 NA
## .IDV 342.399 NA
## .PBC 1.506 NA
## Context 6.559 NA
## Cultural -0.000 NA
## Cognitive 1.511 NA
## .Ent -0.267 NA# reliability(fit)
txtRound(parameterEstimates(fit00, ci = TRUE, level = 0.95),
digits=3, excl.cols=1)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 Context =~ EcFree 1e+00 0.000 1.000 1.000
## 2 Context =~ PPP 3.34e+00
## 3 Context =~ Gini -1.914e+00
## 4 Cultural =~ MAS 1e+00 0.000 1.000 1.000
## 5 Cultural =~ UAI -8.891951e+03
## 6 Cultural =~ CAT -3.588776e+04
## 7 Cultural =~ IDV -1.319519e+04
## 8 Religion =~ CAT 1e+00 0.000 1.000 1.000
## 9 Cognitive =~ ATB 1e+00 0.000 1.000 1.000
## 10 Cognitive =~ PBC 3.636
## 11 Cognitive =~ SN 8.556
## 12 Ent =~ EI 1e+00 0.000 1.000 1.000
## 13 EcFree ~~ EcFree 1.7547e+01
## 14 PPP ~~ PPP -4.403e+00
## 15 CAT ~~ CAT -1.17569e+02
## 16 SN ~~ SN 1.049e+00
## 17 Gini ~~ Gini 4.137e+00
## 18 MAS ~~ MAS 4.6656e+02
## 19 ATB ~~ ATB 9.192
## 20 EI ~~ EI 7.819
## 21 Religion ~~ Religion -7.8815e+02
## 22 Context ~~ Cultural -2.835
## 23 Ent ~ Context -1.463
## 24 Ent ~ Cultural -1.88314e+02
## 25 Ent ~ Cognitive 1.238e+00
## 26 Ent ~ Religion 6.047
## 27 UAI ~~ UAI 3.28257e+02
## 28 IDV ~~ IDV 3.42399e+02
## 29 PBC ~~ PBC 1.506e+00
## 30 Context ~~ Context 6.559e+00
## 31 Cultural ~~ Cultural -7.163
## 32 Cognitive ~~ Cognitive 1.511e+00
## 33 Ent ~~ Ent -2.671
## 34 Context ~~ Religion -6.6521e+01
## 35 Context ~~ Cognitive -6.340
## 36 Cultural ~~ Religion -4.066
## 37 Cultural ~~ Cognitive 2.447
## 38 Religion ~~ Cognitive 7.384e+00#
# fitm99 = lavaan::sem(EI.AJZEN.model99, data =dat)
pdf("Gustavo_Model00.pdf")
semPaths(fit00, style="lisrel",
whatLabels = "std", edge.label.cex = .6, node.label.cex = .6,
label.prop=0.9, edge.label.color = "black", rotation = 4,
equalizeManifests = FALSE, optimizeLatRes = TRUE, node.width = 1.5,
edge.width = 0.5, shapeMan = "rectangle", shapeLat = "ellipse",
shapeInt = "triangle", sizeMan = 4, sizeInt = 2, sizeLat = 4,
curve=2, unCol = "#070b8c")## Warning in sqrt(ETA2): Se han producido NaNs## Warning in sqrt(ETA2): Se han producido NaNs
## Warning in sqrt(ETA2): Se han producido NaNs## Warning in qgraph::qgraph(Edgelist, labels = nLab, bidirectional = Bidir, : The
## following arguments are not documented and likely not arguments of qgraph and
## thus ignored: node.label.cexdev.off()## png
## 2#
#
fitm00 = lavaan::sem(EI.AJZEN.model00, data =dat,
sample.cov=mcv, estimator = "ML",
start="Mplus", std.lv=TRUE,
missing = "listwise", bootstrap=10000)## Warning in lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats, : lavaan WARNING:
## Could not compute standard errors! The information matrix could
## not be inverted. This may be a symptom that the model is not
## identified.## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.summary(fitm00,fit.measures=TRUE,rsquare=TRUE)## lavaan 0.6-5 ended normally after 104 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 33
##
## Number of observations 1773
##
## Model Test User Model:
##
## Test statistic 12779.802
## Degrees of freedom 33
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 32290.463
## Degrees of freedom 55
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.605
## Tucker-Lewis Index (TLI) 0.341
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -51713.672
## Loglikelihood unrestricted model (H1) -45323.771
##
## Akaike (AIC) 103493.343
## Bayesian (BIC) 103674.197
## Sample-size adjusted Bayesian (BIC) 103569.359
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.467
## 90 Percent confidence interval - lower 0.460
## 90 Percent confidence interval - upper 0.474
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.177
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Context =~
## EcFree 1.724 NA
## PPP 8.771 NA
## Gini -4.831 NA
## Cultural =~
## MAS 24.720 NA
## UAI 10.647 NA
## CAT 28.793 NA
## IDV -1.311 NA
## Religion =~
## CAT 1.333 NA
## Cognitive =~
## ATB 1.231 NA
## PBC 0.446 NA
## SN 1.051 NA
## Ent =~
## EI 0.670 NA
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Ent ~
## Context -0.505 NA
## Cultural -0.058 NA
## Cognitive 2.189 NA
## Religion -0.058 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Context ~~
## Cultural -0.534 NA
## Religion -4.405 NA
## Cognitive -0.155 NA
## Cultural ~~
## Religion -0.723 NA
## Cognitive -0.038 NA
## Religion ~~
## Cognitive -0.013 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EcFree 21.134 NA
## .PPP -8.181 NA
## .CAT 315.185 NA
## .SN 1.051 NA
## .Gini 4.829 NA
## .MAS -144.529 NA
## .ATB 0.915 NA
## .EI 0.123 NA
## Religion 1.000
## .UAI 158.266 NA
## .IDV 215.981 NA
## .PBC 1.506 NA
## Context 1.000
## Cultural 1.000
## Cognitive 1.000
## .Ent 1.000
##
## R-Square:
## Estimate
## EcFree 0.123
## PPP NA
## CAT 0.711
## SN 0.512
## Gini 0.829
## MAS NA
## ATB 0.623
## EI 0.957
## UAI 0.417
## IDV 0.008
## PBC 0.117
## Ent 0.836semPaths(fitm00,"Standardized", "Estimates",style = "mx",
centerLevels = FALSE, layoutSplit = TRUE)
#
semCors(fit00, layout = "spring", cut = 0.3,esize = 20, titles = TRUE)## Warning in sqrt(ETA2): Se han producido NaNs## Warning in sqrt(ETA2): Se han producido NaNs
## Warning in sqrt(ETA2): Se han producido NaNs
=============================================================================
EI.AJZEN.model99 = ’ # Variables Latentes Context =~ EcFree + PPP + Gini Cultural =~ IDV + MAS + UAI + CAT Religion =~ CAT Cognitive=~ ATB + PBC + SN Supportive=~ SN # Regresión EI ~ Context + Cultural + Religion + Cognitive + Supportive # Correlación de Residuos Context ~~ Context Cultural~~ Cultural Religion~~ Religion Cognitive~~ Cognitive Supportive~~ Supportive EI ~~ 1 * EI ’
fitm99 = lavaan::sem(EI.AJZEN.model99, data =ddnaddf[,obs.variables99], std.lv=TRUE, sample.cov=mcv, estimator = “ML”, start=“Mplus”,
missing = “listwise”, bootstrap=10000)
semPaths(fitm99,“Estandardized”, “Estimates”, what=“par”, ask=FALSE, layout=“tree2”,residuals=TRUE, style=“OpenMx”, sizeMan=8, edge.label.cex=0.9)