Entrepreneur_11
JOM
23/11/2016
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)## CODIGO UNI PAIS EDAD
## Min. : 11002 UPM :412 España :412 Min. :19.00
## 1st Qu.: 21059 MILAN :245 Italia :727 1st Qu.:22.00
## 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
## GENERO CARRERA CURSO PASNAC
## Hombre:1173 Organización :456 último:1773 Min. :1.000
## Mujer : 600 Industriales :587 1st Qu.:2.000
## QuÃmica :330 Median :2.000
## Ing. Civil :400 Mean :2.617
## Ing. Informática: 0 3rd Qu.:3.000
## Max. :6.000
##
## PASNACPADRE PASNACMADRE CLASESOCIAL ClaseSocial2
## Min. :1.000 Min. :1.000 Baja : 61 Baja-MedioBaja:797
## 1st Qu.:2.000 1st Qu.:2.000 Media-Baja:736 Alta-MedioAlta:976
## Median :2.000 Median :2.000 Media-Alta:929
## Mean :2.605 Mean :2.608 Alta : 47
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000
##
## ESTPADRE ESTMADRE OCUPACIONPADRE
## Sin estudios:139 Sin estudios:154 Funcionario:405
## Secundaria :480 Secundaria :547 Empleado :633
## FP :395 FP :400 Empresario :453
## Universidad :759 Universidad :672 Desempleado: 63
## Otro :219
##
##
## OCUPACIONMADRE ENTORNONEG V2.1Individualismo V2.2Feminidad
## Funcionario:438 Min. :0.000 Min. :1.000 Muy Masculino:121
## Empleado :537 1st Qu.:0.000 1st Qu.:3.000 Masculino :225
## Empresario :210 Median :1.000 Median :4.000 Medio :520
## Desempleado:298 Mean :0.727 Mean :3.813 Femenino :627
## Otro :290 3rd Qu.:1.000 3rd Qu.:5.000 Muy femenino :280
## Max. :2.000 Max. :6.000
##
## V2.2MasculinidadINV V2.3NoAversin V2.3.AversinINV
## Muy femenino :280 Alta aversión:331 Baja aversión: 97
## Femenino :627 Aversión :542 Poca aversión:301
## Medio :520 Medio :502 Medio :502
## Masculino :225 Poca aversión:301 Aversión :542
## Muy Masculino:121 Baja aversión: 97 Alta aversión:331
##
##
## V2.4Colectivismo V2.4IndividualismoINV
## Muy Individualista: 83 Muy Colectivista :270
## Individualista :197 Colectivista :693
## Medio :530 Medio :530
## Colectivista :693 Individualista :197
## Muy Colectivista :270 Muy Individualista: 83
##
##
## V2.5Aversin V2.6Masculinidad V2.7Aversin
## Baja aversión:110 Muy femenino : 29 Baja aversión: 27
## Poca aversión:301 Femenino : 98 Poca aversión:120
## Medio :473 Medio :389 Medio :413
## Aversión :493 Masculino :726 Aversión :741
## Alta aversión:396 Muy Masculino:531 Alta aversión:472
##
##
## V2.8Individualismo V2.9Innovacin V2.10IT
## Muy Colectivista : 29 Min. :1.000 Min. :1.000
## Colectivista :127 1st Qu.:2.000 1st Qu.:1.000
## Medio :386 Median :3.000 Median :2.000
## Individualista :635 Mean :3.092 Mean :2.389
## Muy Individualista:596 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000
##
## V3.1ACTFuturoAtractivo V3.2ACTOpciones MediaACT V3.3CONTFcil
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.500 1st Qu.:3.000
## Median :5.000 Median :4.000 Median :4.500 Median :4.000
## Mean :4.917 Mean :4.245 Mean :4.581 Mean :3.669
## 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## V3.4CONTPuedo MediaCONT V3.5IEListo V3.6IEMeta
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.500 1st Qu.:2.000 1st Qu.:2.000
## Median :4.000 Median :3.500 Median :3.000 Median :4.000
## Mean :3.812 Mean :3.741 Mean :3.467 Mean :3.699
## 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
##
## V3.7IEHarTodo V3.8IEConvencido V3.9IEPensarSerio V3.10IEFirmeIntencin
## 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 :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.671 Mean :3.635 Mean :3.676 Mean :3.633
## 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
##
## MediaIE V3.11NSFamilia V3.12NSAmigos V3.12NSCompaeros
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.167 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :3.333 Median :6.000 Median :6.000 Median :5.000
## Mean :3.630 Mean :5.506 Mean :5.415 Mean :5.124
## 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 MediaNS V4.1FINPROP V4.2ADM
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.250 1st Qu.:2.000 1st Qu.:1.000
## Median :5.000 Median :5.250 Median :2.000 Median :2.000
## Mean :4.818 Mean :5.216 Mean :2.399 Mean :2.223
## 3rd Qu.:6.000 3rd Qu.:6.250 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :7.000 Max. :7.000 Max. :5.000 Max. :5.000
##
## V4.3JUV V4.4CREDIT V4.5FINPUB V4.6JUV
## Min. :1.0 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.0 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.0 Median :2.000 Median :2.000 Median :3.000
## Mean :2.9 Mean :2.403 Mean :2.342 Mean :3.094
## 3rd Qu.:4.0 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000
## 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
## 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
#
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 = 0.001347Análisis 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
Se va a constuir ahora un modelo de interdependencia con hipótesis de regresión sobre EI
#
mcv = round(cov(ddna[,c("V3.1ACTFuturoAtractivo","V3.2ACTOpciones","V3.11NSFamilia",
"V3.12NSAmigos","V3.12NSCompaeros","V3.14NSSociedad",
"V3.3CONTFcil","V3.4CONTPuedo","V3.5IEListo","V3.6IEMeta",
"V3.7IEHarTodo","V3.8IEConvencido","V3.9IEPensarSerio")]),1)
#
obs.variables=c('V3.1ACTFuturoAtractivo', 'V3.2ACTOpciones',
'V3.11NSFamilia', 'V3.12NSAmigos', 'V3.14NSSociedad',
'V3.3CONTFcil', 'V3.4CONTPuedo', 'V3.6IEMeta',
'V3.8IEConvencido', 'V3.10IEFirmeIntencin')
#
data = as.matrix(ddna[,obs.variables])
mcv = round(cov(data),1)
print(xtable(mcv),type="html")| V3.1ACTFuturoAtractivo | V3.2ACTOpciones | V3.11NSFamilia | V3.12NSAmigos | V3.14NSSociedad | V3.3CONTFcil | V3.4CONTPuedo | V3.6IEMeta | V3.8IEConvencido | V3.10IEFirmeIntencin | |
|---|---|---|---|---|---|---|---|---|---|---|
| V3.1ACTFuturoAtractivo | 2.60 | 2.20 | 0.90 | 0.70 | 0.50 | 1.20 | 1.10 | 1.80 | 1.70 | 1.90 |
| V3.2ACTOpciones | 2.20 | 2.80 | 0.90 | 0.70 | 0.50 | 1.40 | 1.30 | 2.10 | 2.00 | 2.10 |
| V3.11NSFamilia | 0.90 | 0.90 | 2.40 | 1.60 | 1.00 | 0.70 | 0.80 | 0.90 | 0.90 | 1.00 |
| V3.12NSAmigos | 0.70 | 0.70 | 1.60 | 2.20 | 1.40 | 0.50 | 0.60 | 0.60 | 0.50 | 0.60 |
| V3.14NSSociedad | 0.50 | 0.50 | 1.00 | 1.40 | 2.80 | 0.40 | 0.40 | 0.40 | 0.30 | 0.40 |
| V3.3CONTFcil | 1.20 | 1.40 | 0.70 | 0.50 | 0.40 | 2.40 | 1.80 | 1.50 | 1.50 | 1.70 |
| V3.4CONTPuedo | 1.10 | 1.30 | 0.80 | 0.60 | 0.40 | 1.80 | 2.60 | 1.60 | 1.50 | 1.60 |
| V3.6IEMeta | 1.80 | 2.10 | 0.90 | 0.60 | 0.40 | 1.50 | 1.60 | 3.30 | 2.80 | 2.90 |
| V3.8IEConvencido | 1.70 | 2.00 | 0.90 | 0.50 | 0.30 | 1.50 | 1.50 | 2.80 | 3.40 | 3.10 |
| V3.10IEFirmeIntencin | 1.90 | 2.10 | 1.00 | 0.60 | 0.40 | 1.70 | 1.60 | 2.90 | 3.10 | 3.80 |
##
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. :7.000EI.AJZEN.model13 = '
# Variables Latentes
CHI =~ V2.8Individualismo
CHM =~ V2.2MasculinidadINV + V2.6Masculinidad
CRA =~ V2.5Aversin + V2.7Aversin
AtB =~ V3.1ACTFuturoAtractivo + V3.2ACTOpciones
SN =~ V3.11NSFamilia + V3.12NSAmigos + V3.14NSSociedad
PBC =~ V3.3CONTFcil + V3.4CONTPuedo
EI =~ V3.6IEMeta + V3.8IEConvencido + V3.10IEFirmeIntencin
# Regresión
SN ~ CHI + CRA
AtB ~ CHI + CHM + CRA + SN
PBC ~ CHI + CHM + SN
EI ~ SN + PBC + AtB
# Correlación de Residuos
CHI ~~ CHI
CHM ~~ CHM
CRA ~~ CRA
AtB ~~ AtB
PBC ~~ PBC
SN ~~ SN
EI ~~ 1 * EI
'
fitm13 = lavaan::sem(EI.AJZEN.model13, data =ddnaddf[,obs.variables2], std.lv=TRUE,
sample.cov=mcv, estimator = "ML", start="Mplus",
missing = "listwise", bootstrap=10000)
summary(fitm13,fit.measures=TRUE,rsquare=TRUE)## lavaan (0.5-20) converged normally after 110 iterations
##
## Number of observations 1773
##
## Estimator ML
## Minimum Function Test Statistic 764.719
## Degrees of freedom 76
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 13153.299
## Degrees of freedom 105
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.947
## Tucker-Lewis Index (TLI) 0.927
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -40888.835
## Loglikelihood unrestricted model (H1) -40506.476
##
## Number of free parameters 44
## Akaike (AIC) 81865.670
## Bayesian (BIC) 82106.809
## Sample-size adjusted Bayesian (BIC) 81967.024
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.071
## 90 Percent Confidence Interval 0.067 0.076
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.067
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## CHI =~
## V2.8Individlsm 0.991 0.017 59.548 0.000
## CHM =~
## V2.2MsclnddINV 0.250 0.031 8.107 0.000
## V2.6Masculindd -0.023 0.026 -0.898 0.369
## CRA =~
## V2.5Aversin 0.388 0.070 5.576 0.000
## V2.7Aversin 0.874 0.145 6.025 0.000
## AtB =~
## V3.1ACTFtrAtrc 0.947 0.067 14.111 0.000
## V3.2ACTOpcions 1.087 0.077 14.169 0.000
## SN =~
## V3.11NSFamilia 1.129 0.035 32.326 0.000
## V3.12NSAmigos 1.354 0.033 41.033 0.000
## V3.14NSSociedd 0.972 0.038 25.520 0.000
## PBC =~
## V3.3CONTFcil 0.610 0.171 3.567 0.000
## V3.4CONTPuedo 0.586 0.164 3.574 0.000
## EI =~
## V3.6IEMeta 0.958 0.024 39.845 0.000
## V3.8IEConvencd 1.000 0.024 41.367 0.000
## V3.10IEFrmIntn 1.037 0.025 40.786 0.000
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## SN ~
## CHI 0.170 0.026 6.434 0.000
## CRA 0.061 0.030 2.055 0.040
## AtB ~
## CHI 0.453 0.130 3.488 0.000
## CHM 1.005 0.155 6.506 0.000
## CRA 0.129 0.044 2.920 0.003
## SN 0.484 0.050 9.598 0.000
## PBC ~
## CHI 0.864 0.414 2.088 0.037
## CHM 2.052 0.755 2.719 0.007
## SN 0.690 0.202 3.414 0.001
## EI ~
## SN -0.036 0.031 -1.142 0.253
## PBC 0.253 0.073 3.462 0.001
## AtB 0.671 0.057 11.845 0.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## CHI ~~
## CHM -0.409 0.099 -4.135 0.000
## CRA 0.172 0.037 4.658 0.000
## CHM ~~
## CRA -0.128 0.039 -3.270 0.001
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## CHI 1.000
## CHM 1.000
## CRA 1.000
## AtB 1.000
## PBC 1.000
## SN 1.000
## EI 1.000
## V2.8Individlsm 0.000
## V2.2MsclnddINV 1.158 0.040 29.116 0.000
## V2.6Masculindd 0.880 0.030 29.769 0.000
## V2.5Aversin 1.253 0.065 19.277 0.000
## V2.7Aversin 0.121 0.252 0.480 0.631
## V3.1ACTFtrAtrc 0.712 0.036 19.699 0.000
## V3.2ACTOpcions 0.281 0.037 7.613 0.000
## V3.11NSFamilia 1.099 0.053 20.733 0.000
## V3.12NSAmigos 0.331 0.055 6.006 0.000
## V3.14NSSociedd 1.775 0.067 26.628 0.000
## V3.3CONTFcil 0.584 0.048 12.183 0.000
## V3.4CONTPuedo 0.864 0.050 17.356 0.000
## V3.6IEMeta 0.650 0.029 22.240 0.000
## V3.8IEConvencd 0.454 0.025 17.913 0.000
## V3.10IEFrmIntn 0.596 0.030 19.996 0.000
##
## R-Square:
## Estimate
## AtB 0.525
## PBC 0.800
## SN 0.035
## EI 0.659
## V2.8Individlsm 1.000
## V2.2MsclnddINV 0.051
## V2.6Masculindd 0.001
## V2.5Aversin 0.107
## V2.7Aversin 0.863
## V3.1ACTFtrAtrc 0.726
## V3.2ACTOpcions 0.899
## V3.11NSFamilia 0.546
## V3.12NSAmigos 0.852
## V3.14NSSociedd 0.355
## V3.3CONTFcil 0.761
## V3.4CONTPuedo 0.665
## V3.6IEMeta 0.806
## V3.8IEConvencd 0.866
## V3.10IEFrmIntn 0.841semPaths(fitm13, what="par", ask=FALSE, layout="tree2",residuals=TRUE, style="OpenMx",
sizeMan=8, edge.label.cex=0.9, thresholds = TRUE)
#
fit13Pr = lavaan::sem(EI.AJZEN.model13, data =ddnaddf[ddnaddf[,"BELIEFS"]==1,
obs.variables2], std.lv=TRUE,
sample.cov=mcv, estimator = "ML", start="Mplus",
missing = "listwise", bootstrap=10000)## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING: could not compute standard errors!
## lavaan NOTE: this may be a symptom that the model is not identified.## Warning in lav_object_post_check(lavobject): lavaan WARNING: some estimated
## variances are negative## Warning in lav_object_post_check(lavobject): lavaan WARNING: observed
## variable error term matrix (theta) is not positive definite; use
## inspect(fit,"theta") to investigate.summary(fit13Pr,fit.measures=TRUE,rsquare=TRUE)## lavaan (0.5-20) converged normally after 1504 iterations
##
## Number of observations 434
##
## Estimator ML
## Minimum Function Test Statistic 186.775
## Degrees of freedom 76
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 3331.969
## Degrees of freedom 105
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.966
## Tucker-Lewis Index (TLI) 0.953
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9703.564
## Loglikelihood unrestricted model (H1) -9610.177
##
## Number of free parameters 44
## Akaike (AIC) 19495.128
## Bayesian (BIC) 19674.342
## Sample-size adjusted Bayesian (BIC) 19534.710
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058
## 90 Percent Confidence Interval 0.048 0.069
## P-value RMSEA <= 0.05 0.102
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.054
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## CHI =~
## V2.8Individlsm 0.922 NA
## CHM =~
## V2.2MsclnddINV -0.086 NA
## V2.6Masculindd 0.128 NA
## CRA =~
## V2.5Aversin 2.100 NA
## V2.7Aversin 0.180 NA
## AtB =~
## V3.1ACTFtrAtrc 0.014 NA
## V3.2ACTOpcions 0.017 NA
## SN =~
## V3.11NSFamilia 1.053 NA
## V3.12NSAmigos 1.202 NA
## V3.14NSSociedd 0.957 NA
## PBC =~
## V3.3CONTFcil 0.970 NA
## V3.4CONTPuedo 0.843 NA
## EI =~
## V3.6IEMeta 0.923 NA
## V3.8IEConvencd 1.000 NA
## V3.10IEFrmIntn 1.038 NA
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## SN ~
## CHI 0.156 NA
## CRA -0.055 NA
## AtB ~
## CHI -147.703 NA
## CHM 174.621 NA
## CRA -2.876 NA
## SN 30.419 NA
## PBC ~
## CHI -1.501 NA
## CHM 1.769 NA
## SN 0.488 NA
## EI ~
## SN -0.155 NA
## PBC 0.328 NA
## AtB 0.011 NA
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## CHI ~~
## CHM 0.850 NA
## CRA -0.022 NA
## CHM ~~
## CRA 0.021 NA
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## CHI 1.000
## CHM 1.000
## CRA 1.000
## AtB 1.000
## PBC 1.000
## SN 1.000
## EI 1.000
## V2.8Individlsm 0.000
## V2.2MsclnddINV 0.875 NA
## V2.6Masculindd 0.833 NA
## V2.5Aversin -2.928 NA
## V2.7Aversin 0.838 NA
## V3.1ACTFtrAtrc 0.747 NA
## V3.2ACTOpcions 0.133 NA
## V3.11NSFamilia 1.073 NA
## V3.12NSAmigos 0.349 NA
## V3.14NSSociedd 1.512 NA
## V3.3CONTFcil 0.354 NA
## V3.4CONTPuedo 0.988 NA
## V3.6IEMeta 0.630 NA
## V3.8IEConvencd 0.439 NA
## V3.10IEFrmIntn 0.473 NA
##
## R-Square:
## Estimate
## AtB 1.000
## PBC 0.526
## SN 0.027
## EI 0.653
## V2.8Individlsm 1.000
## V2.2MsclnddINV 0.008
## V2.6Masculindd 0.019
## V2.5Aversin NA
## V2.7Aversin 0.037
## V3.1ACTFtrAtrc 0.703
## V3.2ACTOpcions 0.951
## V3.11NSFamilia 0.515
## V3.12NSAmigos 0.810
## V3.14NSSociedd 0.383
## V3.3CONTFcil 0.849
## V3.4CONTPuedo 0.603
## V3.6IEMeta 0.796
## V3.8IEConvencd 0.868
## V3.10IEFrmIntn 0.868semPaths(fit13Pr, what="par", ask=FALSE, layout="tree2",residuals=TRUE, style="OpenMx",
sizeMan=8, edge.label.cex=0.9, thresholds = TRUE)
#
fit13Ct = lavaan::sem(EI.AJZEN.model13, data =ddnaddf[ddnaddf[,"BELIEFS"]==2,
obs.variables2], std.lv=TRUE,
sample.cov=mcv, estimator = "ML", start="Mplus",
missing = "listwise", bootstrap=10000)
summary(fit13Ct,fit.measures=TRUE,rsquare=TRUE)## lavaan (0.5-20) converged normally after 71 iterations
##
## Number of observations 1339
##
## Estimator ML
## Minimum Function Test Statistic 657.146
## Degrees of freedom 76
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 9986.755
## Degrees of freedom 105
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.941
## Tucker-Lewis Index (TLI) 0.919
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -31019.233
## Loglikelihood unrestricted model (H1) -30690.659
##
## Number of free parameters 44
## Akaike (AIC) 62126.465
## Bayesian (BIC) 62355.251
## Sample-size adjusted Bayesian (BIC) 62215.482
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.076
## 90 Percent Confidence Interval 0.070 0.081
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.071
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## CHI =~
## V2.8Individlsm 1.012 0.020 51.749 0.000
## CHM =~
## V2.2MsclnddINV 0.319 0.037 8.504 0.000
## V2.6Masculindd -0.010 0.030 -0.320 0.749
## CRA =~
## V2.5Aversin 0.410 0.062 6.616 0.000
## V2.7Aversin 0.770 0.103 7.458 0.000
## AtB =~
## V3.1ACTFtrAtrc 0.987 0.059 16.666 0.000
## V3.2ACTOpcions 1.114 0.066 16.797 0.000
## SN =~
## V3.11NSFamilia 1.148 0.040 28.385 0.000
## V3.12NSAmigos 1.402 0.039 36.235 0.000
## V3.14NSSociedd 0.978 0.044 22.038 0.000
## PBC =~
## V3.3CONTFcil 0.605 0.156 3.875 0.000
## V3.4CONTPuedo 0.605 0.156 3.880 0.000
## EI =~
## V3.6IEMeta 0.937 0.028 33.752 0.000
## V3.8IEConvencd 0.960 0.027 34.931 0.000
## V3.10IEFrmIntn 0.999 0.029 34.235 0.000
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## SN ~
## CHI 0.166 0.031 5.354 0.000
## CRA 0.077 0.038 2.033 0.042
## AtB ~
## CHI 0.270 0.095 2.841 0.005
## CHM 0.878 0.120 7.293 0.000
## CRA 0.144 0.053 2.719 0.007
## SN 0.470 0.050 9.468 0.000
## PBC ~
## CHI 0.515 0.267 1.927 0.054
## CHM 1.922 0.642 2.991 0.003
## SN 0.664 0.184 3.613 0.000
## EI ~
## SN 0.003 0.036 0.070 0.944
## PBC 0.294 0.079 3.744 0.000
## AtB 0.683 0.055 12.441 0.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## CHI ~~
## CHM -0.252 0.095 -2.638 0.008
## CRA 0.243 0.042 5.789 0.000
## CHM ~~
## CRA -0.162 0.049 -3.335 0.001
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## CHI 1.000
## CHM 1.000
## CRA 1.000
## AtB 1.000
## PBC 1.000
## SN 1.000
## EI 1.000
## V2.8Individlsm 0.000
## V2.2MsclnddINV 1.182 0.048 24.736 0.000
## V2.6Masculindd 0.876 0.034 25.874 0.000
## V2.5Aversin 1.181 0.064 18.575 0.000
## V2.7Aversin 0.293 0.156 1.881 0.060
## V3.1ACTFtrAtrc 0.701 0.043 16.404 0.000
## V3.2ACTOpcions 0.325 0.044 7.395 0.000
## V3.11NSFamilia 1.116 0.062 18.022 0.000
## V3.12NSAmigos 0.311 0.066 4.679 0.000
## V3.14NSSociedd 1.855 0.079 23.359 0.000
## V3.3CONTFcil 0.673 0.055 12.226 0.000
## V3.4CONTPuedo 0.804 0.058 13.953 0.000
## V3.6IEMeta 0.655 0.034 19.157 0.000
## V3.8IEConvencd 0.442 0.029 15.390 0.000
## V3.10IEFrmIntn 0.641 0.036 17.976 0.000
##
## R-Square:
## Estimate
## AtB 0.492
## PBC 0.796
## SN 0.038
## EI 0.678
## V2.8Individlsm 1.000
## V2.2MsclnddINV 0.079
## V2.6Masculindd 0.000
## V2.5Aversin 0.125
## V2.7Aversin 0.669
## V3.1ACTFtrAtrc 0.732
## V3.2ACTOpcions 0.883
## V3.11NSFamilia 0.551
## V3.12NSAmigos 0.868
## V3.14NSSociedd 0.349
## V3.3CONTFcil 0.727
## V3.4CONTPuedo 0.691
## V3.6IEMeta 0.806
## V3.8IEConvencd 0.866
## V3.10IEFrmIntn 0.829semPaths(fit13Ct, what="par", ask=FALSE, layout="tree2",residuals=TRUE, style="OpenMx",
sizeMan=8, edge.label.cex=0.9, thresholds = TRUE)
#Como el modelo de Protestantes es inestable para AtB, vamos a reformular ese modelo
#
EI.AJZEN.model23 = '
# Variables Latentes
CHI =~ V2.8Individualismo
CHM =~ V2.2MasculinidadINV + V2.6Masculinidad
CRA =~ V2.5Aversin + V2.7Aversin
SN =~ V3.11NSFamilia + V3.12NSAmigos + V3.14NSSociedad
PBC =~ V3.3CONTFcil + V3.4CONTPuedo
EI =~ V3.6IEMeta + V3.8IEConvencido + V3.10IEFirmeIntencin
# Regresión
SN ~ CHI + CRA
PBC ~ CHI + CHM + SN
EI ~ SN + PBC
# Correlación de Residuos
CHI ~~ CHI
CHM ~~ CHM
CRA ~~ CRA
PBC ~~ PBC
SN ~~ SN
EI ~~ 1 * EI
'
fitm23 = lavaan::sem(EI.AJZEN.model23, data =ddnaddf[,obs.variables2], std.lv=TRUE,
sample.cov=mcv, estimator = "ML", start="Mplus",
missing = "listwise", bootstrap=10000)## Warning in lavaan::lavaan(model = EI.AJZEN.model23, data = ddnaddf[,
## obs.variables2], : lavaan WARNING: model has NOT converged!summary(fitm23,fit.measures=TRUE,rsquare=TRUE)## ** WARNING ** lavaan (0.5-20) did NOT converge after 10000 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Number of observations 1773
##
## Estimator ML
## Minimum Function Test Statistic NA
## Degrees of freedom NA
## P-value NA## Warning in .local(object, ...): lavaan WARNING: fit measures not available if model did not converge##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## CHI =~
## V2.8Individlsm 0.991 NA
## CHM =~
## V2.2MsclnddINV 0.004 NA
## V2.6Masculindd 34.883 NA
## CRA =~
## V2.5Aversin 0.541 NA
## V2.7Aversin 0.615 NA
## SN =~
## V3.11NSFamilia 1.121 NA
## V3.12NSAmigos 1.356 NA
## V3.14NSSociedd 0.967 NA
## PBC =~
## V3.3CONTFcil 1.279 NA
## V3.4CONTPuedo 1.262 NA
## EI =~
## V3.6IEMeta 1.184 NA
## V3.8IEConvencd 1.251 NA
## V3.10IEFrmIntn 1.293 NA
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## SN ~
## CHI 0.159 NA
## CRA 0.098 NA
## PBC ~
## CHI 0.016 NA
## CHM 0.001 NA
## SN 0.319 NA
## EI ~
## SN 0.098 NA
## PBC 0.863 NA
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## CHI ~~
## CHM 0.005 NA
## CRA 0.213 NA
## CHM ~~
## CRA 0.016 NA
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## CHI 1.000
## CHM 1.000
## CRA 1.000
## PBC 1.000
## SN 1.000
## EI 1.000
## V2.8Individlsm 0.000
## V2.2MsclnddINV 1.220 NA
## V2.6Masculindd -1215.958 NA
## V2.5Aversin 1.111 NA
## V2.7Aversin 0.507 NA
## V3.11NSFamilia 1.110 NA
## V3.12NSAmigos 0.314 NA
## V3.14NSSociedd 1.779 NA
## V3.3CONTFcil 0.630 NA
## V3.4CONTPuedo 0.816 NA
## V3.6IEMeta 0.691 NA
## V3.8IEConvencd 0.425 NA
## V3.10IEFrmIntn 0.586 NA
##
## R-Square:
## Estimate
## PBC 0.098
## SN 0.040
## EI 0.472
## V2.8Individlsm 1.000
## V2.2MsclnddINV 0.000
## V2.6Masculindd NA
## V2.5Aversin 0.208
## V2.7Aversin 0.427
## V3.11NSFamilia 0.541
## V3.12NSAmigos 0.859
## V3.14NSSociedd 0.354
## V3.3CONTFcil 0.742
## V3.4CONTPuedo 0.684
## V3.6IEMeta 0.793
## V3.8IEConvencd 0.875
## V3.10IEFrmIntn 0.844semPaths(fitm23, what="par", ask=FALSE, layout="tree2",residuals=TRUE, style="OpenMx",
sizeMan=8, edge.label.cex=0.9, thresholds = TRUE)
#
fit23Pr = lavaan::sem(EI.AJZEN.model23, data =ddnaddf[ddnaddf[,"BELIEFS"]==1,
obs.variables2], std.lv=TRUE,
sample.cov=mcv, estimator = "ML", start="Mplus",
missing = "listwise", bootstrap=10000)## Warning in lav_object_post_check(lavobject): lavaan WARNING: some estimated
## variances are negative## Warning in lav_object_post_check(lavobject): lavaan WARNING: observed
## variable error term matrix (theta) is not positive definite; use
## inspect(fit,"theta") to investigate.summary(fit23Pr,fit.measures=TRUE,rsquare=TRUE)## lavaan (0.5-20) converged normally after 652 iterations
##
## Number of observations 434
##
## Estimator ML
## Minimum Function Test Statistic 98.886
## Degrees of freedom 56
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2373.062
## Degrees of freedom 78
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.981
## Tucker-Lewis Index (TLI) 0.974
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8489.243
## Loglikelihood unrestricted model (H1) -8439.800
##
## Number of free parameters 35
## Akaike (AIC) 17048.486
## Bayesian (BIC) 17191.042
## Sample-size adjusted Bayesian (BIC) 17079.971
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.042
## 90 Percent Confidence Interval 0.028 0.055
## P-value RMSEA <= 0.05 0.828
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## CHI =~
## V2.8Individlsm 0.922 0.031 29.462 0.000
## CHM =~
## V2.2MsclnddINV 0.022 0.269 0.081 0.936
## V2.6Masculindd 4.173 51.218 0.081 0.935
## CRA =~
## V2.5Aversin 0.684 0.096 7.137 0.000
## V2.7Aversin 0.552 0.076 7.274 0.000
## SN =~
## V3.11NSFamilia 1.057 0.069 15.252 0.000
## V3.12NSAmigos 1.205 0.063 19.269 0.000
## V3.14NSSociedd 0.955 0.074 12.947 0.000
## PBC =~
## V3.3CONTFcil 1.315 0.064 20.450 0.000
## V3.4CONTPuedo 1.163 0.066 17.529 0.000
## EI =~
## V3.6IEMeta 1.126 0.057 19.907 0.000
## V3.8IEConvencd 1.236 0.058 21.268 0.000
## V3.10IEFrmIntn 1.278 0.060 21.181 0.000
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## SN ~
## CHI 0.156 0.053 2.968 0.003
## CRA -0.011 0.070 -0.154 0.878
## PBC ~
## CHI 0.001 0.054 0.011 0.991
## CHM 0.018 0.225 0.081 0.936
## SN 0.354 0.061 5.829 0.000
## EI ~
## SN -0.058 0.063 -0.920 0.358
## PBC 0.913 0.084 10.897 0.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## CHI ~~
## CHM 0.025 0.302 0.081 0.935
## CRA 0.012 0.068 0.171 0.864
## CHM ~~
## CRA 0.091 1.113 0.081 0.935
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## CHI 1.000
## CHM 1.000
## CRA 1.000
## PBC 1.000
## SN 1.000
## EI 1.000
## V2.8Individlsm 0.000
## V2.2MsclnddINV 0.882 0.061 14.467 0.000
## V2.6Masculindd -16.563 427.446 -0.039 0.969
## V2.5Aversin 1.014 0.128 7.890 0.000
## V2.7Aversin 0.565 0.081 7.018 0.000
## V3.11NSFamilia 1.068 0.102 10.423 0.000
## V3.12NSAmigos 0.347 0.096 3.634 0.000
## V3.14NSSociedd 1.519 0.120 12.669 0.000
## V3.3CONTFcil 0.388 0.097 4.004 0.000
## V3.4CONTPuedo 0.963 0.098 9.831 0.000
## V3.6IEMeta 0.671 0.058 11.605 0.000
## V3.8IEConvencd 0.408 0.050 8.209 0.000
## V3.10IEFrmIntn 0.464 0.054 8.551 0.000
##
## R-Square:
## Estimate
## PBC 0.114
## SN 0.024
## EI 0.475
## V2.8Individlsm 1.000
## V2.2MsclnddINV 0.001
## V2.6Masculindd NA
## V2.5Aversin 0.316
## V2.7Aversin 0.350
## V3.11NSFamilia 0.517
## V3.12NSAmigos 0.811
## V3.14NSSociedd 0.381
## V3.3CONTFcil 0.834
## V3.4CONTPuedo 0.613
## V3.6IEMeta 0.782
## V3.8IEConvencd 0.877
## V3.10IEFrmIntn 0.870semPaths(fit23Pr, what="par", ask=FALSE, layout="tree2", residuals=TRUE, style="OpenMx",
sizeMan=8, edge.label.cex=0.9, thresholds = TRUE)
#
fit23Ct = lavaan::sem(EI.AJZEN.model23, data =ddnaddf[ddnaddf[,"BELIEFS"]==2,
obs.variables2], std.lv=TRUE,
sample.cov=mcv, estimator = "ML", start="Mplus",
missing = "listwise", bootstrap=10000)## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING: could not compute standard errors!
## lavaan NOTE: this may be a symptom that the model is not identified.## Warning in lav_object_post_check(lavobject): lavaan WARNING: some estimated
## variances are negative## Warning in lav_object_post_check(lavobject): lavaan WARNING: observed
## variable error term matrix (theta) is not positive definite; use
## inspect(fit,"theta") to investigate.summary(fit23Ct,fit.measures=TRUE,rsquare=TRUE)## lavaan (0.5-20) converged normally after 7110 iterations
##
## Number of observations 1339
##
## Estimator ML
## Minimum Function Test Statistic 303.321
## Degrees of freedom 56
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 7467.470
## Degrees of freedom 78
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.967
## Tucker-Lewis Index (TLI) 0.953
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -26970.538
## Loglikelihood unrestricted model (H1) -26818.878
##
## Number of free parameters 35
## Akaike (AIC) 54011.077
## Bayesian (BIC) 54193.066
## Sample-size adjusted Bayesian (BIC) 54081.886
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.057
## 90 Percent Confidence Interval 0.051 0.064
## P-value RMSEA <= 0.05 0.026
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.065
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## CHI =~
## V2.8Individlsm 1.012 NA
## CHM =~
## V2.2MsclnddINV 0.004 NA
## V2.6Masculindd 31.333 NA
## CRA =~
## V2.5Aversin 0.497 NA
## V2.7Aversin 0.614 NA
## SN =~
## V3.11NSFamilia 1.138 NA
## V3.12NSAmigos 1.401 NA
## V3.14NSSociedd 0.971 NA
## PBC =~
## V3.3CONTFcil 1.265 NA
## V3.4CONTPuedo 1.294 NA
## EI =~
## V3.6IEMeta 1.166 NA
## V3.8IEConvencd 1.210 NA
## V3.10IEFrmIntn 1.255 NA
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## SN ~
## CHI 0.148 NA
## CRA 0.132 NA
## PBC ~
## CHI 0.021 NA
## CHM 0.002 NA
## SN 0.309 NA
## EI ~
## SN 0.144 NA
## PBC 0.889 NA
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## CHI ~~
## CHM 0.006 NA
## CRA 0.282 NA
## CHM ~~
## CRA 0.020 NA
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## CHI 1.000
## CHM 1.000
## CRA 1.000
## PBC 1.000
## SN 1.000
## EI 1.000
## V2.8Individlsm 0.000
## V2.2MsclnddINV 1.284 NA
## V2.6Masculindd -980.844 NA
## V2.5Aversin 1.102 NA
## V2.7Aversin 0.509 NA
## V3.11NSFamilia 1.126 NA
## V3.12NSAmigos 0.293 NA
## V3.14NSSociedd 1.859 NA
## V3.3CONTFcil 0.709 NA
## V3.4CONTPuedo 0.757 NA
## V3.6IEMeta 0.697 NA
## V3.8IEConvencd 0.412 NA
## V3.10IEFrmIntn 0.633 NA
##
## R-Square:
## Estimate
## PBC 0.093
## SN 0.048
## EI 0.495
## V2.8Individlsm 1.000
## V2.2MsclnddINV 0.000
## V2.6Masculindd NA
## V2.5Aversin 0.183
## V2.7Aversin 0.425
## V3.11NSFamilia 0.547
## V3.12NSAmigos 0.876
## V3.14NSSociedd 0.348
## V3.3CONTFcil 0.713
## V3.4CONTPuedo 0.709
## V3.6IEMeta 0.794
## V3.8IEConvencd 0.876
## V3.10IEFrmIntn 0.831semPaths(fit23Ct, what="par", ask=FALSE, layout="tree2",residuals=TRUE, style="OpenMx",
sizeMan=8, edge.label.cex=0.9, thresholds = TRUE)
#