Entrepreneur-extravars_02
JOM
17/7/2016
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
## Warning: replacing previous import 'lme4::sigma' by 'stats::sigma' when
## loading 'pbkrtest'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('~/git/Entrepreneur/')
ddd = read.spss(file="Emprendimiento2016.sav",to.data.frame=TRUE)
# 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])
}
#
#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
Datos con rango numérico
#
my_hist3d(ddna[,31],ddna[,32],nclass=20)
#
AJZEN.model = ' AtB =~ V3.1ACTFuturoAtractivo + V3.2ACTOpciones
SN =~ V3.11NSFamilia + V3.12NSAmigos + V3.12NSCompaeros + V3.14NSSociedad
PBC =~ V3.3CONTFcil + V3.4CONTPuedo '
fitn = cfa(AJZEN.model, data =ddna)
summary(fitn,fit.measures=TRUE)## lavaan (0.5-20) converged normally after 44 iterations
##
## Number of observations 1773
##
## Estimator ML
## Minimum Function Test Statistic 407.123
## Degrees of freedom 17
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 7856.530
## Degrees of freedom 28
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.950
## Tucker-Lewis Index (TLI) 0.918
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -22955.627
## Loglikelihood unrestricted model (H1) -22752.065
##
## Number of free parameters 19
## Akaike (AIC) 45949.253
## Bayesian (BIC) 46053.381
## Sample-size adjusted Bayesian (BIC) 45993.020
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.114
## 90 Percent Confidence Interval 0.104 0.123
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.053
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## AtB =~
## V3.1ACTFtrAtrc 1.000
## V3.2ACTOpcions 1.126 0.030 36.986 0.000
## SN =~
## V3.11NSFamilia 1.000
## V3.12NSAmigos 1.232 0.037 33.598 0.000
## V3.12NSCompars 1.199 0.037 32.502 0.000
## V3.14NSSociedd 1.018 0.039 26.227 0.000
## PBC =~
## V3.3CONTFcil 1.000
## V3.4CONTPuedo 0.913 0.032 28.546 0.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## AtB ~~
## SN 0.515 0.045 11.516 0.000
## PBC 1.236 0.066 18.714 0.000
## SN ~~
## PBC 0.464 0.045 10.357 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## V3.1ACTFtrAtrc 0.677 0.048 14.059 0.000
## V3.2ACTOpcions 0.326 0.055 5.937 0.000
## V3.11NSFamilia 1.226 0.047 26.280 0.000
## V3.12NSAmigos 0.418 0.030 13.742 0.000
## V3.12NSCompars 0.678 0.035 19.480 0.000
## V3.14NSSociedd 1.518 0.056 26.881 0.000
## V3.3CONTFcil 0.484 0.059 8.162 0.000
## V3.4CONTPuedo 0.948 0.057 16.562 0.000
## AtB 1.928 0.095 20.395 0.000
## SN 1.193 0.073 16.257 0.000
## PBC 1.963 0.099 19.896 0.000semPaths(fitn)
#
AJZEN.model2 = ' AtB =~ V3.1ACTFuturoAtractivo
SN =~ V3.12NSCompaeros
PBC =~ V3.4CONTPuedo '
fitn2 = cfa(AJZEN.model2, data =ddna)
summary(fitn2,fit.measures=TRUE)## lavaan (0.5-20) converged normally after 50 iterations
##
## Number of observations 1773
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
## Minimum Function Value 0.0000000000000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 478.358
## Degrees of freedom 3
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9773.034
## Loglikelihood unrestricted model (H1) -9773.034
##
## Number of free parameters 6
## Akaike (AIC) 19558.068
## Bayesian (BIC) 19590.951
## Sample-size adjusted Bayesian (BIC) 19571.889
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## AtB =~
## V3.1ACTFtrAtrc 1.000
## SN =~
## V3.12NSCompars 1.000
## PBC =~
## V3.4CONTPuedo 1.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## AtB ~~
## SN 0.614 0.061 10.058 0.000
## PBC 1.084 0.067 16.231 0.000
## SN ~~
## PBC 0.528 0.060 8.743 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## V3.1ACTFtrAtrc 0.000
## V3.12NSCompars 0.000
## V3.4CONTPuedo 0.000
## AtB 2.605 0.087 29.774 0.000
## SN 2.394 0.080 29.774 0.000
## PBC 2.586 0.087 29.774 0.000semPaths(fitn2)
#
AJZEN.model3 = ' AtB =~ V3.1ACTFuturoAtractivo
SN =~ V3.12NSCompaeros + V3.12NSAmigos
PBC =~ V3.4CONTPuedo '
fitn3 = cfa(AJZEN.model3, data =ddna)
summary(fitn3,fit.measures=TRUE)## lavaan (0.5-20) converged normally after 72 iterations
##
## Number of observations 1773
##
## Estimator ML
## Minimum Function Test Statistic 1.162
## Degrees of freedom 1
## P-value (Chi-square) 0.281
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2057.401
## Degrees of freedom 6
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12210.551
## Loglikelihood unrestricted model (H1) -12209.970
##
## Number of free parameters 9
## Akaike (AIC) 24439.103
## Bayesian (BIC) 24488.427
## Sample-size adjusted Bayesian (BIC) 24459.834
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.010
## 90 Percent Confidence Interval 0.000 0.065
## P-value RMSEA <= 0.05 0.849
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.004
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## AtB =~
## V3.1ACTFtrAtrc 1.000
## SN =~
## V3.12NSCompars 1.000
## V3.12NSAmigos 1.145 0.065 17.513 0.000
## PBC =~
## V3.4CONTPuedo 1.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## AtB ~~
## SN 0.628 0.060 10.504 0.000
## PBC 1.084 0.067 16.231 0.000
## SN ~~
## PBC 0.505 0.056 8.976 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## V3.1ACTFtrAtrc 0.000
## V3.12NSCompars 0.865 0.088 9.886 0.000
## V3.12NSAmigos 0.224 0.109 2.063 0.039
## V3.4CONTPuedo 0.000
## AtB 2.605 0.087 29.774 0.000
## SN 1.529 0.112 13.712 0.000
## PBC 2.586 0.087 29.774 0.000#Datos con rango categórico endógeno
#
ddfr = ddf[c("UNI","V3.1ACTFuturoAtractivo","V3.2ACTOpciones",
"V3.11NSFamilia","V3.12NSAmigos","V3.12NSCompaeros",
"V3.14NSSociedad","V3.3CONTFcil","V3.4CONTPuedo")]
fitc = cfa(AJZEN.model, data =ddfr)
summary(fitc,fit.measures=TRUE)## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 1773
##
## Estimator DWLS Robust
## Minimum Function Test Statistic 361.422 636.168
## Degrees of freedom 17 17
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.573
## Shift parameter 5.492
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 56768.670 28237.892
## Degrees of freedom 28 28
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.994 0.978
## Tucker-Lewis Index (TLI) 0.990 0.964
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.107 0.143
## 90 Percent Confidence Interval 0.097 0.117 0.134 0.153
## P-value RMSEA <= 0.05 0.000 0.000
##
## Weighted Root Mean Square Residual:
##
## WRMR 2.181 2.181
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## AtB =~
## V3.1ACTFtrAtrc 1.000
## V3.2ACTOpcions 1.033 0.019 54.991 0.000
## SN =~
## V3.11NSFamilia 1.000
## V3.12NSAmigos 1.170 0.015 77.383 0.000
## V3.12NSCompars 1.134 0.013 84.656 0.000
## V3.14NSSociedd 0.927 0.016 56.941 0.000
## PBC =~
## V3.3CONTFcil 1.000
## V3.4CONTPuedo 0.911 0.023 40.393 0.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## AtB ~~
## SN 0.276 0.016 17.449 0.000
## PBC 0.528 0.016 33.262 0.000
## SN ~~
## PBC 0.239 0.017 14.387 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|)
## V3.1ACTFtrAtrc 0.000
## V3.2ACTOpcions 0.000
## V3.11NSFamilia 0.000
## V3.12NSAmigos 0.000
## V3.12NSCompars 0.000
## V3.14NSSociedd 0.000
## V3.3CONTFcil 0.000
## V3.4CONTPuedo 0.000
## AtB 0.000
## SN 0.000
## PBC 0.000
##
## Thresholds:
## Estimate Std.Err Z-value P(>|z|)
## V3.1ACTFtrAt|1 -1.866 0.059 -31.694 0.000
## V3.1ACTFtrAt|2 -1.312 0.041 -31.813 0.000
## V3.1ACTFtrAt|3 -0.876 0.034 -25.514 0.000
## V3.1ACTFtrAt|4 -0.344 0.030 -11.302 0.000
## V3.1ACTFtrAt|5 0.236 0.030 7.852 0.000
## V3.1ACTFtrAt|6 0.869 0.034 25.385 0.000
## V3.2ACTOpcns|1 -1.611 0.049 -32.816 0.000
## V3.2ACTOpcns|2 -0.931 0.035 -26.618 0.000
## V3.2ACTOpcns|3 -0.426 0.031 -13.844 0.000
## V3.2ACTOpcns|4 0.091 0.030 3.062 0.002
## V3.2ACTOpcns|5 0.686 0.032 21.121 0.000
## V3.2ACTOpcns|6 1.283 0.041 31.567 0.000
## V3.11NSFaml|t1 -2.136 0.074 -28.910 0.000
## V3.11NSFaml|t2 -1.654 0.051 -32.747 0.000
## V3.11NSFaml|t3 -1.141 0.038 -30.022 0.000
## V3.11NSFaml|t4 -0.664 0.032 -20.574 0.000
## V3.11NSFaml|t5 -0.219 0.030 -7.283 0.000
## V3.11NSFaml|t6 0.345 0.030 11.350 0.000
## V3.12NSAmgs|t1 -2.096 0.071 -29.404 0.000
## V3.12NSAmgs|t2 -1.677 0.051 -32.690 0.000
## V3.12NSAmgs|t3 -1.197 0.039 -30.707 0.000
## V3.12NSAmgs|t4 -0.671 0.032 -20.757 0.000
## V3.12NSAmgs|t5 -0.121 0.030 -4.059 0.000
## V3.12NSAmgs|t6 0.529 0.031 16.884 0.000
## V3.12NSCmprs|1 -1.973 0.064 -30.760 0.000
## V3.12NSCmprs|2 -1.557 0.047 -32.830 0.000
## V3.12NSCmprs|3 -1.024 0.036 -28.290 0.000
## V3.12NSCmprs|4 -0.440 0.031 -14.267 0.000
## V3.12NSCmprs|5 0.100 0.030 3.347 0.001
## V3.12NSCmprs|6 0.737 0.033 22.387 0.000
## V3.14NSScdd|t1 -1.725 0.053 -32.524 0.000
## V3.14NSScdd|t2 -1.305 0.041 -31.760 0.000
## V3.14NSScdd|t3 -0.855 0.034 -25.083 0.000
## V3.14NSScdd|t4 -0.170 0.030 -5.672 0.000
## V3.14NSScdd|t5 0.299 0.030 9.886 0.000
## V3.14NSScdd|t6 0.833 0.034 24.604 0.000
## V3.3CONTFcl|t1 -1.393 0.043 -32.350 0.000
## V3.3CONTFcl|t2 -0.675 0.032 -20.848 0.000
## V3.3CONTFcl|t3 -0.049 0.030 -1.638 0.101
## V3.3CONTFcl|t4 0.505 0.031 16.185 0.000
## V3.3CONTFcl|t5 1.125 0.038 29.807 0.000
## V3.3CONTFcl|t6 1.706 0.052 32.595 0.000
## V3.4CONTPud|t1 -1.420 0.044 -32.483 0.000
## V3.4CONTPud|t2 -0.735 0.033 -22.342 0.000
## V3.4CONTPud|t3 -0.133 0.030 -4.439 0.000
## V3.4CONTPud|t4 0.377 0.031 12.339 0.000
## V3.4CONTPud|t5 0.955 0.035 27.075 0.000
## V3.4CONTPud|t6 1.688 0.052 32.655 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## V3.1ACTFtrAtrc 0.181
## V3.2ACTOpcions 0.126
## V3.11NSFamilia 0.388
## V3.12NSAmigos 0.162
## V3.12NSCompars 0.213
## V3.14NSSociedd 0.474
## V3.3CONTFcil 0.185
## V3.4CONTPuedo 0.323
## AtB 0.819 0.017 48.280 0.000
## SN 0.612 0.016 38.860 0.000
## PBC 0.815 0.021 38.535 0.000
##
## Scales y*:
## Estimate Std.Err Z-value P(>|z|)
## V3.1ACTFtrAtrc 1.000
## V3.2ACTOpcions 1.000
## V3.11NSFamilia 1.000
## V3.12NSAmigos 1.000
## V3.12NSCompars 1.000
## V3.14NSSociedd 1.000
## V3.3CONTFcil 1.000
## V3.4CONTPuedo 1.000semPaths(fitc)
Modelo confirmatorio con efecto Cultura
Se comienza añadiendo como variables las correspondientes al individualismo y por sugerencia de GM se incorpora a las tres variables endógenas AtB, SN y PBC.
#
AJZEN.model4 = ' AtB =~ V3.1ACTFuturoAtractivo + V3.2ACTOpciones + V2.1Individualismo + V2.8Individualismo + V2.2MasculinidadINV + V2.6Masculinidad + V2.3.AversinINV + V2.5Aversin + V2.7Aversin
SN =~ V3.11NSFamilia + V3.12NSAmigos + V3.12NSCompaeros + V3.14NSSociedad + V2.1Individualismo + V2.8Individualismo + V2.2MasculinidadINV + V2.6Masculinidad + V2.3.AversinINV + V2.5Aversin + V2.7Aversin
PBC =~ V3.3CONTFcil + V3.4CONTPuedo + V2.1Individualismo + V2.8Individualismo + V2.2MasculinidadINV + V2.6Masculinidad + V2.3.AversinINV + V2.5Aversin + V2.7Aversin '
fitn4 = cfa(AJZEN.model4, data =ddna)
summary(fitn4,fit.measures=TRUE)## lavaan (0.5-20) converged normally after 45 iterations
##
## Number of observations 1773
##
## Estimator ML
## Minimum Function Test Statistic 1272.555
## Degrees of freedom 73
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 9282.948
## Degrees of freedom 105
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.869
## Tucker-Lewis Index (TLI) 0.812
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA
## Loglikelihood unrestricted model (H1) NA
##
## Number of free parameters 47
## Akaike (AIC) NA
## Bayesian (BIC) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.096
## 90 Percent Confidence Interval 0.092 0.101
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|)
## AtB =~
## V3.1ACTFtrAtrc 1.000
## V3.2ACTOpcions 1.088 0.026 41.151 0.000
## V2.1Individlsm 0.252 0.023 10.755 0.000
## V2.8Individlsm 0.068 0.025 2.739 0.006
## V2.2MsclnddINV 0.036 0.028 1.276 0.202
## V2.6Masculindd 0.119 0.024 5.013 0.000
## V2.3.AversnINV 0.026 0.029 0.911 0.363
## V2.5Aversin 0.107 0.030 3.519 0.000
## V2.7Aversin 0.123 0.024 5.139 0.000
## SN =~
## V3.11NSFamilia 1.000
## V3.12NSAmigos 1.230 0.037 33.639 0.000
## V3.12NSCompars 1.200 0.037 32.539 0.000
## V3.14NSSociedd 1.018 0.039 26.255 0.000
## V2.1Individlsm 0.031 0.023 1.352 0.176
## V2.8Individlsm 0.147 0.025 5.958 0.000
## V2.2MsclnddINV -0.046 0.027 -1.701 0.089
## V2.6Masculindd 0.092 0.023 3.963 0.000
## V2.3.AversnINV 0.005 0.028 0.189 0.850
## V2.5Aversin -0.028 0.030 -0.938 0.348
## V2.7Aversin 0.068 0.023 2.911 0.004
## PBC =~
## V3.3CONTFcil 1.000
## V3.4CONTPuedo 0.928 0.031 30.075 0.000
## V2.1Individlsm 0.047 0.024 1.984 0.047
## V2.8Individlsm -0.033 0.026 -1.304 0.192
## V2.2MsclnddINV 0.118 0.029 4.099 0.000
## V2.6Masculindd -0.053 0.024 -2.175 0.030
## V2.3.AversnINV -0.160 0.030 -5.369 0.000
## V2.5Aversin -0.097 0.031 -3.092 0.002
## V2.7Aversin -0.119 0.025 -4.817 0.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## AtB ~~
## SN 0.535 0.045 11.765 0.000
## PBC 1.255 0.066 19.154 0.000
## SN ~~
## PBC 0.465 0.045 10.415 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## V3.1ACTFtrAtrc 0.613 0.042 14.436 0.000
## V3.2ACTOpcions 0.416 0.046 9.026 0.000
## V2.1Individlsm 0.804 0.028 29.216 0.000
## V2.8Individlsm 0.945 0.032 29.638 0.000
## V2.2MsclnddINV 1.184 0.040 29.638 0.000
## V2.6Masculindd 0.846 0.029 29.593 0.000
## V2.3.AversnINV 1.242 0.042 29.558 0.000
## V2.5Aversin 1.389 0.047 29.650 0.000
## V2.7Aversin 0.857 0.029 29.469 0.000
## V3.11NSFamilia 1.225 0.047 26.290 0.000
## V3.12NSAmigos 0.423 0.030 14.009 0.000
## V3.12NSCompars 0.676 0.035 19.543 0.000
## V3.14NSSociedd 1.516 0.056 26.883 0.000
## V3.3CONTFcil 0.520 0.055 9.511 0.000
## V3.4CONTPuedo 0.925 0.054 16.998 0.000
## AtB 1.992 0.093 21.463 0.000
## SN 1.194 0.073 16.268 0.000
## PBC 1.927 0.096 20.160 0.000semPaths(fitn4)
A la vista de las estimaciones para los coeficientes resulta evidente que el efecto de la V2.1 es mÃnimo y + en AtB y nulo en el resto y que la V2.8 es nula en todos los casos