Análisis de Karina

Paquetería

library(MASS)
library(tidyverse)
library(tidymodels)
library(haven)
library(magrittr)
library(psych)
library(lavaan)
library(lavaanPlot)
library(rsq)
library(knitr)

## Funciones para organizar las salidas del lavaan

tidy_lavaan <- function(lavaan_obj){
  lavaan_obj %>%
  tidy() %>%
  select(c(1, 3:6)) %>%
  mutate_if(is.numeric, round, 3) %>%
  rename(Ruta = term,
         Estimado = estimate,
         Error_Std = std.error,
         Estadistico = statistic,
         valor_p = p.value) %>%
  kable()
}

glance_lavaan <- function(lavaan_obj){
  lavaan_obj %>%
  glance() %>%
  select(1:7) %>%
  rename(AGFI = agfi,
         CFI = cfi,
         Ji_Cuad = chisq,
         n_par = npar,
         RMSEA = rmsea) %>%
  kable()
}

Regresión Lineal log-Gamma

Moviendo la escala de DERSETOT para que sean todos valores positivos, se observa que se ajusta a una distribución gamma.

karina <- read_csv('karina.csv')
karina %<>% mutate(DERSETOT = DERSETOT + 1)

fit.params <- fitdistr(karina$DERSETOT,
                       'gamma',
                       lower = c(0, 0))

karina %>% ggplot(aes(DERSETOT)) +
  geom_histogram(aes(DERSETOT, ..density..)) +
  geom_line(aes(DERSETOT,
                dgamma(karina$DERSETOT,
                       fit.params$estimate['shape'],
                       fit.params$estimate['rate'])),
            color = 'blue',
            size = 1) +
  labs(y = NULL)

Modelo Lineal Generalizado Gamma con función liga log. Se eliminaron ya las variables que no aportaban al modelo.

reg_karina <- glm(DERSETOT ~
                    I(genero == 1) +    ## con respecto a genero == 2
                    EPASTOT*EVI +
                    EPASTOT*ANS +
                    EPASTOT*SEG,
                   karina,
                   family = Gamma(link = 'log'))

reg_karina %>%
  tidy() %>%
  mutate_if(is.numeric, round, 3) %>%
  rename(Ruta = term,
         Estimado = estimate,
         Error_Std = std.error,
         Estadistico = statistic,
         valor_p = p.value) %>%
  kable()

Ruta	Estimado	Error_Std	Estadistico	valor_p
(Intercept)	1.534	0.456	3.368	0.001
I(genero == 1)TRUE	-0.078	0.031	-2.501	0.013
EPASTOT	0.027	0.007	4.201	0.000
EVI	0.104	0.057	1.823	0.069
ANS	0.306	0.045	6.789	0.000
SEG	-0.055	0.065	-0.842	0.400
EPASTOT:EVI	-0.001	0.001	-1.342	0.180
EPASTOT:ANS	-0.002	0.001	-3.685	0.000
EPASTOT:SEG	-0.001	0.001	-0.716	0.474

reg_karina %>%
  glance() %>%
  kable()

null.deviance	df.null	logLik	AIC	BIC	deviance	df.residual	nobs
401.0018	1039	-4179.927	8379.854	8429.324	248.8656	1031	1040

coef_det <- rsq(reg_karina,
                adj = TRUE,
                type = 'v') %>%
  round(3)
paste('R² = ', coef_det)

## [1] "R² =  0.435"

Análisis Factoriales Exploratorios

egna <- read_sav('egna.sav') %>%
  select(3:17)

# Corriendo los 5 modelos

for (x in 1:5){
  fa_kar <- fa(egna,
               nfactors = x,
               n.obs = 1264,
               rotate = 'oblimin',
               oblique.scores = TRUE,
               fm = 'pa',
               cor = 'poly')
  print('Factores a extraer:')
  print(fa_kar$factors)
  print(fa_kar$RMSEA)
  print('BIC')
  print(fa_kar$BIC)
  print('R²')
  print(fa_kar$R2)
  print('__________________________________________')
}

## [1] "Factores a extraer:"
## [1] 1
##      RMSEA      lower      upper confidence 
##  0.2009550  0.1961553  0.2059598  0.9000000 
## [1] "BIC"
## [1] 4041.249
## [1] "R²"
##       PA1 
## 0.8772212 
## [1] "__________________________________________"
## [1] "Factores a extraer:"
## [1] 2
##      RMSEA      lower      upper confidence 
##  0.1754232  0.1701807  0.1808660  0.9000000 
## [1] "BIC"
## [1] 2489.473
## [1] "R²"
##       PA1       PA2 
## 0.9021080 0.7631871 
## [1] "__________________________________________"
## [1] "Factores a extraer:"
## [1] 3
##      RMSEA      lower      upper confidence 
##  0.1423888  0.1366027  0.1483766  0.9000000 
## [1] "BIC"
## [1] 1227.606
## [1] "R²"
##       PA1       PA2       PA3 
## 0.9186277 0.8193543 0.7553243 
## [1] "__________________________________________"
## [1] "Factores a extraer:"
## [1] 4
##      RMSEA      lower      upper confidence 
## 0.10479077 0.09830904 0.11149818 0.90000000 
## [1] "BIC"
## [1] 394.6825
## [1] "R²"
##       PA1       PA2       PA3       PA4 
## 0.9365999 0.8624118 0.8106007 0.7055646 
## [1] "__________________________________________"
## [1] "Factores a extraer:"
## [1] 5
##      RMSEA      lower      upper confidence 
## 0.08907822 0.08172206 0.09671049 0.90000000 
## [1] "BIC"
## [1] 155.5402
## [1] "R²"
##       PA1       PA2       PA3       PA4       PA5 
## 0.9438379 0.9076278 0.8552566 0.7217529 0.5458479 
## [1] "__________________________________________"

# Cargas del mejor modelo

fa_kar_5 <- fa(egna, 
   nfactors = 5,
   n.obs = 1264,
   rotate = 'oblimin',
   fm = 'pa',
   oblique.scores = TRUE,
   cor = 'poly')

summary(fa_kar_5)

## 
## Factor analysis with Call: fa(r = egna, nfactors = 5, n.obs = 1264, rotate = "oblimin", 
##     fm = "pa", oblique.scores = TRUE, cor = "poly")
## 
## Test of the hypothesis that 5 factors are sufficient.
## The degrees of freedom for the model is 40  and the objective function was  0.35 
## The number of observations was  1264  with Chi Square =  441.22  with prob <  4.7e-69 
## 
## The root mean square of the residuals (RMSA) is  0.02 
## The df corrected root mean square of the residuals is  0.04 
## 
## Tucker Lewis Index of factoring reliability =  0.884
## RMSEA index =  0.089  and the 10 % confidence intervals are  0.082 0.097
## BIC =  155.54

loadings(fa_kar_5)

## 
## Loadings:
##        PA1    PA2    PA3    PA4    PA5   
## egna1   0.657 -0.325        -0.293       
## egna2   0.421  0.138  0.528        -0.173
## egna3   0.579 -0.227 -0.104  0.347 -0.112
## egna4   0.553  0.502 -0.335 -0.122 -0.106
## egna5   0.493  0.194                0.262
## egna6   0.725 -0.405 -0.171  0.332       
## egna7   0.681 -0.405        -0.332       
## egna8   0.554  0.629 -0.449 -0.163 -0.219
## egna9   0.507  0.371  0.587        -0.124
## egna10  0.416  0.137                0.382
## egna11  0.607 -0.407  0.119 -0.351       
## egna12  0.517  0.297 -0.159              
## egna13  0.445  0.277  0.133  0.101  0.407
## egna14  0.736 -0.297 -0.236  0.247       
## egna15  0.514  0.153  0.424        -0.130
## 
##                  PA1   PA2   PA3   PA4   PA5
## SS loadings    4.859 1.788 1.276 0.678 0.536
## Proportion Var 0.324 0.119 0.085 0.045 0.036
## Cumulative Var 0.324 0.443 0.528 0.573 0.609

fa.diagram(fa_kar_5,
           labels = TRUE,
           simple = FALSE)

# Correlaciones entre factores

fa_kar_cor <- as.data.frame(fa_kar_5$r.scores)
fa_kar_cor %>%
  rownames_to_column() %>%
  rename(Correlaciones = rowname) %>%
  mutate_if(is.numeric, round, 3) %>%
  kable()

Correlaciones	PA1	PA2	PA3	PA4	PA5
PA1	1.000	0.007	-0.032	-0.007	-0.059
PA2	0.007	1.000	-0.054	-0.058	-0.097
PA3	-0.032	-0.054	1.000	0.028	0.078
PA4	-0.007	-0.058	0.028	1.000	0.007
PA5	-0.059	-0.097	0.078	0.007	1.000

Análisis Factorial Confirmatorio

Se consideran solo aquellos reactivos con carga factorial absoluta mayor a 0.3.

egna_model <- 'pa1 =~ egna3 + egna6 + egna14
               pa2 =~ egna4 + egna8 + egna12
               pa3 =~ egna2 + egna9 + egna15
               pa4 =~ egna1 + egna7 + egna11
               pa5 =~ egna5 + egna10 + egna13'
egna_fit <- cfa(egna_model,
                egna)

tidy_lavaan(egna_fit)

Ruta	Estimado	Error_Std	Estadistico	valor_p
pa1 =~ egna3	1.000	0.000	NA	NA
pa1 =~ egna6	1.202	0.066	18.204	0
pa1 =~ egna14	1.053	0.059	17.963	0
pa2 =~ egna4	1.000	0.000	NA	NA
pa2 =~ egna8	1.014	0.046	21.956	0
pa2 =~ egna12	0.590	0.037	16.036	0
pa3 =~ egna2	1.000	0.000	NA	NA
pa3 =~ egna9	1.368	0.086	15.937	0
pa3 =~ egna15	1.024	0.066	15.584	0
pa4 =~ egna1	1.000	0.000	NA	NA
pa4 =~ egna7	1.484	0.081	18.421	0
pa4 =~ egna11	1.451	0.082	17.755	0
pa5 =~ egna5	1.000	0.000	NA	NA
pa5 =~ egna10	0.841	0.081	10.341	0
pa5 =~ egna13	0.990	0.088	11.231	0
egna3 ~~ egna3	0.213	0.010	21.819	0
egna6 ~~ egna6	0.088	0.007	13.104	0
egna14 ~~ egna14	0.097	0.006	16.353	0
egna4 ~~ egna4	0.125	0.011	11.300	0
egna8 ~~ egna8	0.113	0.011	10.148	0
egna12 ~~ egna12	0.280	0.012	23.470	0
egna2 ~~ egna2	0.284	0.014	19.697	0
egna9 ~~ egna9	0.187	0.017	10.818	0
egna15 ~~ egna15	0.280	0.015	19.291	0
egna1 ~~ egna1	0.105	0.005	19.749	0
egna7 ~~ egna7	0.118	0.008	14.570	0
egna11 ~~ egna11	0.179	0.010	18.354	0
egna5 ~~ egna5	0.192	0.011	17.751	0
egna10 ~~ egna10	0.258	0.012	21.380	0
egna13 ~~ egna13	0.239	0.012	19.390	0
pa1 ~~ pa1	0.109	0.011	10.111	0
pa2 ~~ pa2	0.255	0.017	14.663	0
pa3 ~~ pa3	0.162	0.016	9.925	0
pa4 ~~ pa4	0.073	0.007	11.030	0
pa5 ~~ pa5	0.092	0.011	8.133	0
pa1 ~~ pa2	0.049	0.006	7.527	0
pa1 ~~ pa3	0.032	0.005	5.918	0
pa1 ~~ pa4	0.056	0.005	11.556	0
pa1 ~~ pa5	0.041	0.005	7.839	0
pa2 ~~ pa3	0.062	0.008	7.591	0
pa2 ~~ pa4	0.029	0.005	5.652	0
pa2 ~~ pa5	0.079	0.008	9.861	0
pa3 ~~ pa4	0.033	0.005	7.145	0
pa3 ~~ pa5	0.063	0.007	8.979	0
pa4 ~~ pa5	0.030	0.004	7.192	0

glance_lavaan(egna_fit)

AGFI	AIC	BIC	CFI	Ji_Cuad	n_par	RMSEA
0.9550418	27913.95	28119.63	0.9548931	294.5847	40	0.046066

# Gráfica sin covarianzas

lavaanPlot(name = 'Modelo',
                       egna_fit,
                       node_options = list(shape = "box",
                                           fontname = "Helvetica"),
                       edge_options = list(color = "grey"),
                       coefs = TRUE,
                       stand = TRUE,
                       stars = TRUE)

# Gráfica con covarianzas

lavaanPlot(name = 'Modelo',
                       egna_fit,
                       node_options = list(shape = "box",
                                           fontname = "Helvetica"),
                       edge_options = list(color = "grey"),
                       coefs = TRUE,
                       stand = TRUE,
                       covs = TRUE,
                       stars = TRUE)

Ecuaciones estructurales

kar_sem <- read_csv("sem.csv")

kar_reg <- 'SP =~ cpas3 + cpas4 + cpas5 + cpas8 + cpas11 + cpas13 + cpas14 + cpas16 + cpas19 + cpas20 + cpas21 + cpas23 + cpas26
            USB =~ cpas1 + cpas7 + cpas9 + cpas2
            
            NAC =~ derse10 + derse12 + derse14 + derse15 + derse18  + derse19
            ME =~ derse11 + derse13 + derse16 + derse17 + derse22 + derse23
            CON =~ derse1 + derse2 + derse6 + derse7 + derse9
            CLA =~ derse3 + derse4 + derse5 + derse8
            
            EVI =~ eea1 + eea4 + eea7 + eea10 + eea13 + eea16 + eea19
            ANS =~ eea2 + eea5 + eea8 + eea11 + eea14 + eea17 + eea20 + eea21
            SEG =~ eea3 + eea6 + eea9 + eea12 + eea15 + eea18
          
            SP ~ EVI + ANS + SEG
            USB ~ EVI + ANS + SEG
            NAC ~ EVI + ANS + SEG + SP + USB
            ME ~ EVI + ANS + SEG + SP + USB
            CON ~ EVI + ANS + SEG + SP + USB
            CLA ~ EVI + ANS + SEG + SP + USB'

kar_fit <- sem(kar_reg,
               kar_sem)

tidy_lavaan(kar_fit)

Ruta	Estimado	Error_Std	Estadistico	valor_p
SP =~ cpas3	1.000	0.000	NA	NA
SP =~ cpas4	0.977	0.083	11.721	0.000
SP =~ cpas5	1.141	0.089	12.801	0.000
SP =~ cpas8	1.041	0.086	12.072	0.000
SP =~ cpas11	1.190	0.086	13.771	0.000
SP =~ cpas13	1.071	0.087	12.367	0.000
SP =~ cpas14	1.374	0.095	14.524	0.000
SP =~ cpas16	1.053	0.082	12.917	0.000
SP =~ cpas19	1.494	0.102	14.699	0.000
SP =~ cpas20	1.044	0.091	11.511	0.000
SP =~ cpas21	1.428	0.097	14.709	0.000
SP =~ cpas23	1.232	0.090	13.649	0.000
SP =~ cpas26	1.236	0.096	12.832	0.000
USB =~ cpas1	1.000	0.000	NA	NA
USB =~ cpas7	1.452	0.106	13.646	0.000
USB =~ cpas9	1.586	0.116	13.638	0.000
USB =~ cpas2	0.495	0.065	7.613	0.000
NAC =~ derse10	1.000	0.000	NA	NA
NAC =~ derse12	1.041	0.051	20.249	0.000
NAC =~ derse14	1.179	0.057	20.702	0.000
NAC =~ derse15	1.231	0.057	21.780	0.000
NAC =~ derse18	0.800	0.046	17.284	0.000
NAC =~ derse19	1.174	0.055	21.198	0.000
ME =~ derse11	1.000	0.000	NA	NA
ME =~ derse13	1.052	0.049	21.449	0.000
ME =~ derse16	1.096	0.051	21.362	0.000
ME =~ derse17	0.954	0.047	20.231	0.000
ME =~ derse22	0.826	0.047	17.740	0.000
ME =~ derse23	1.148	0.053	21.462	0.000
CON =~ derse1	1.000	0.000	NA	NA
CON =~ derse2	0.754	0.047	16.177	0.000
CON =~ derse6	0.573	0.052	11.050	0.000
CON =~ derse7	1.222	0.060	20.407	0.000
CON =~ derse9	0.957	0.055	17.436	0.000
CLA =~ derse3	1.000	0.000	NA	NA
CLA =~ derse4	1.207	0.065	18.435	0.000
CLA =~ derse5	1.416	0.073	19.477	0.000
CLA =~ derse8	1.293	0.069	18.661	0.000
EVI =~ eea1	1.000	0.000	NA	NA
EVI =~ eea4	1.012	0.049	20.717	0.000
EVI =~ eea7	0.692	0.056	12.451	0.000
EVI =~ eea10	0.857	0.057	15.147	0.000
EVI =~ eea13	0.940	0.048	19.523	0.000
EVI =~ eea16	1.110	0.052	21.507	0.000
EVI =~ eea19	0.866	0.053	16.427	0.000
ANS =~ eea2	1.000	0.000	NA	NA
ANS =~ eea5	1.151	0.075	15.411	0.000
ANS =~ eea8	1.291	0.083	15.584	0.000
ANS =~ eea11	1.119	0.076	14.693	0.000
ANS =~ eea14	1.133	0.075	15.120	0.000
ANS =~ eea17	1.139	0.075	15.170	0.000
ANS =~ eea20	1.217	0.073	16.564	0.000
ANS =~ eea21	1.605	0.092	17.432	0.000
SEG =~ eea3	1.000	0.000	NA	NA
SEG =~ eea6	0.753	0.065	11.671	0.000
SEG =~ eea9	0.734	0.064	11.505	0.000
SEG =~ eea12	1.040	0.071	14.560	0.000
SEG =~ eea15	0.984	0.075	13.181	0.000
SEG =~ eea18	1.021	0.069	14.691	0.000
SP ~ EVI	0.014	0.032	0.441	0.660
SP ~ ANS	0.405	0.042	9.662	0.000
SP ~ SEG	0.035	0.043	0.814	0.416
USB ~ EVI	0.083	0.039	2.144	0.032
USB ~ ANS	0.267	0.040	6.735	0.000
USB ~ SEG	0.099	0.052	1.920	0.055
NAC ~ EVI	0.013	0.029	0.437	0.662
NAC ~ ANS	0.328	0.038	8.579	0.000
NAC ~ SEG	-0.115	0.040	-2.891	0.004
NAC ~ SP	0.415	0.045	9.249	0.000
NAC ~ USB	0.008	0.034	0.224	0.823
ME ~ EVI	-0.024	0.030	-0.825	0.409
ME ~ ANS	0.273	0.037	7.463	0.000
ME ~ SEG	-0.117	0.040	-2.911	0.004
ME ~ SP	0.399	0.044	9.038	0.000
ME ~ USB	0.063	0.035	1.812	0.070
CON ~ EVI	0.003	0.031	0.101	0.920
CON ~ ANS	0.084	0.035	2.388	0.017
CON ~ SEG	-0.331	0.046	-7.161	0.000
CON ~ SP	0.323	0.043	7.579	0.000
CON ~ USB	-0.068	0.037	-1.864	0.062
CLA ~ EVI	0.019	0.025	0.743	0.458
CLA ~ ANS	0.234	0.032	7.297	0.000
CLA ~ SEG	-0.154	0.036	-4.336	0.000
CLA ~ SP	0.327	0.038	8.549	0.000
CLA ~ USB	-0.013	0.030	-0.445	0.656
cpas3 ~~ cpas3	2.206	0.101	21.787	0.000
cpas4 ~~ cpas4	2.400	0.110	21.912	0.000
cpas5 ~~ cpas5	2.266	0.105	21.515	0.000
cpas8 ~~ cpas8	2.428	0.111	21.803	0.000
cpas11 ~~ cpas11	1.692	0.081	20.925	0.000
cpas13 ~~ cpas13	2.327	0.107	21.698	0.000
cpas14 ~~ cpas14	1.594	0.079	20.141	0.000
cpas16 ~~ cpas16	1.850	0.086	21.460	0.000
cpas19 ~~ cpas19	1.718	0.086	19.883	0.000
cpas20 ~~ cpas20	2.929	0.133	21.970	0.000
cpas21 ~~ cpas21	1.562	0.079	19.867	0.000
cpas23 ~~ cpas23	1.911	0.091	21.019	0.000
cpas26 ~~ cpas26	2.629	0.122	21.501	0.000
cpas1 ~~ cpas1	1.589	0.086	18.475	0.000
cpas7 ~~ cpas7	1.751	0.125	14.009	0.000
cpas9 ~~ cpas9	1.784	0.141	12.696	0.000
cpas2 ~~ cpas2	1.957	0.089	21.983	0.000
derse10 ~~ derse10	1.109	0.052	21.151	0.000
derse12 ~~ derse12	0.703	0.035	19.964	0.000
derse14 ~~ derse14	0.793	0.041	19.566	0.000
derse15 ~~ derse15	0.608	0.033	18.176	0.000
derse18 ~~ derse18	0.846	0.039	21.417	0.000
derse19 ~~ derse19	0.676	0.036	19.024	0.000
derse11 ~~ derse11	0.919	0.045	20.638	0.000
derse13 ~~ derse13	0.571	0.030	18.918	0.000
derse16 ~~ derse16	0.634	0.033	19.010	0.000
derse17 ~~ derse17	0.639	0.032	19.963	0.000
derse22 ~~ derse22	0.824	0.039	21.159	0.000
derse23 ~~ derse23	0.677	0.036	18.903	0.000
derse1 ~~ derse1	0.621	0.036	17.440	0.000
derse2 ~~ derse2	0.722	0.036	20.234	0.000
derse6 ~~ derse6	1.192	0.054	21.914	0.000
derse7 ~~ derse7	0.654	0.043	15.226	0.000
derse9 ~~ derse9	0.896	0.046	19.446	0.000
derse3 ~~ derse3	0.932	0.044	20.976	0.000
derse4 ~~ derse4	0.607	0.033	18.642	0.000
derse5 ~~ derse5	0.523	0.033	16.081	0.000
derse8 ~~ derse8	0.638	0.035	18.248	0.000
eea1 ~~ eea1	1.458	0.074	19.631	0.000
eea4 ~~ eea4	0.964	0.054	17.844	0.000
eea7 ~~ eea7	2.643	0.120	21.977	0.000
eea10 ~~ eea10	2.358	0.110	21.377	0.000
eea13 ~~ eea13	1.131	0.059	19.177	0.000
eea16 ~~ eea16	0.907	0.055	16.470	0.000
eea19 ~~ eea19	1.863	0.089	20.957	0.000
eea2 ~~ eea2	2.034	0.096	21.180	0.000
eea5 ~~ eea5	1.916	0.093	20.515	0.000
eea8 ~~ eea8	2.277	0.112	20.380	0.000
eea11 ~~ eea11	2.263	0.108	20.973	0.000
eea14 ~~ eea14	2.038	0.098	20.718	0.000
eea17 ~~ eea17	2.026	0.098	20.685	0.000
eea20 ~~ eea20	1.422	0.074	19.342	0.000
eea21 ~~ eea21	1.669	0.094	17.670	0.000
eea3 ~~ eea3	1.575	0.080	19.602	0.000
eea6 ~~ eea6	1.513	0.072	20.927	0.000
eea9 ~~ eea9	1.511	0.072	21.016	0.000
eea12 ~~ eea12	1.093	0.061	17.786	0.000
eea15 ~~ eea15	1.630	0.082	19.812	0.000
eea18 ~~ eea18	0.992	0.057	17.478	0.000
SP ~~ SP	0.562	0.071	7.929	0.000
USB ~~ USB	0.686	0.083	8.269	0.000
NAC ~~ NAC	0.430	0.041	10.551	0.000
ME ~~ ME	0.440	0.040	11.008	0.000
CON ~~ CON	0.442	0.040	11.064	0.000
CLA ~~ CLA	0.308	0.032	9.525	0.000
EVI ~~ EVI	1.232	0.106	11.612	0.000
ANS ~~ ANS	0.941	0.102	9.257	0.000
SEG ~~ SEG	0.758	0.086	8.821	0.000
EVI ~~ ANS	0.146	0.040	3.625	0.000
EVI ~~ SEG	-0.502	0.050	-9.963	0.000
ANS ~~ SEG	-0.071	0.033	-2.151	0.032
NAC ~~ ME	0.302	0.026	11.685	0.000
NAC ~~ CON	0.097	0.020	4.864	0.000
NAC ~~ CLA	0.211	0.021	10.080	0.000
ME ~~ CON	0.083	0.020	4.208	0.000
ME ~~ CLA	0.176	0.019	9.070	0.000
CON ~~ CLA	0.191	0.020	9.337	0.000

glance_lavaan(kar_fit)

AGFI	AIC	BIC	CFI	Ji_Cuad	n_par	RMSEA
0.8303645	204203.9	204960.8	0.8458782	5291.966	153	0.0467471

# Gráfica

lavaanPlot(name = 'Modelo',
                       kar_fit,
                       node_options = list(shape = "box",
                                           fontname = "Helvetica"),
                       edge_options = list(color = "grey"),
                       coefs = TRUE,
                       stand = TRUE,
                       stars = TRUE)

# Eliminando parámetros no significativos

kar_pns <- 'SP =~ cpas3 + cpas4 + cpas5 + cpas8 + cpas11 + cpas13 + cpas14 + cpas16 + cpas19 + cpas20 + cpas21 + cpas23 + cpas26
            
            NAC =~ derse10 + derse12 + derse14 + derse15 + derse18  + derse19
            ME =~ derse11 + derse13 + derse16 + derse17 + derse22 + derse23
            CON =~ derse1 + derse2 + derse6 + derse7 + derse9
            CLA =~ derse3 + derse4 + derse5 + derse8
            
            ANS =~ eea2 + eea5 + eea8 + eea11 + eea14 + eea17 + eea20 + eea21
            SEG =~ eea3 + eea6 + eea9 + eea12 + eea15 + eea18
          
            SP ~ ANS
            NAC ~ ANS + SEG + SP
            ME ~ ANS + SEG + SP
            CON ~ ANS + SEG + SP
            CLA ~ ANS + SEG + SP'

kar_fns <- sem(kar_pns,
               kar_sem)

tidy_lavaan(kar_fns)

Ruta	Estimado	Error_Std	Estadistico	valor_p
SP =~ cpas3	1.000	0.000	NA	NA
SP =~ cpas4	0.978	0.084	11.696	0.000
SP =~ cpas5	1.144	0.089	12.790	0.000
SP =~ cpas8	1.043	0.087	12.056	0.000
SP =~ cpas11	1.192	0.087	13.753	0.000
SP =~ cpas13	1.071	0.087	12.336	0.000
SP =~ cpas14	1.377	0.095	14.503	0.000
SP =~ cpas16	1.054	0.082	12.897	0.000
SP =~ cpas19	1.497	0.102	14.675	0.000
SP =~ cpas20	1.046	0.091	11.501	0.000
SP =~ cpas21	1.430	0.097	14.678	0.000
SP =~ cpas23	1.235	0.091	13.629	0.000
SP =~ cpas26	1.239	0.097	12.817	0.000
NAC =~ derse10	1.000	0.000	NA	NA
NAC =~ derse12	1.041	0.051	20.286	0.000
NAC =~ derse14	1.179	0.057	20.733	0.000
NAC =~ derse15	1.231	0.056	21.812	0.000
NAC =~ derse18	0.800	0.046	17.298	0.000
NAC =~ derse19	1.174	0.055	21.226	0.000
ME =~ derse11	1.000	0.000	NA	NA
ME =~ derse13	1.052	0.049	21.685	0.000
ME =~ derse16	1.095	0.051	21.576	0.000
ME =~ derse17	0.953	0.047	20.440	0.000
ME =~ derse22	0.826	0.046	17.922	0.000
ME =~ derse23	1.145	0.053	21.645	0.000
CON =~ derse1	1.000	0.000	NA	NA
CON =~ derse2	0.752	0.047	15.973	0.000
CON =~ derse6	0.574	0.052	10.954	0.000
CON =~ derse7	1.223	0.061	20.174	0.000
CON =~ derse9	0.954	0.055	17.210	0.000
CLA =~ derse3	1.000	0.000	NA	NA
CLA =~ derse4	1.204	0.065	18.423	0.000
CLA =~ derse5	1.413	0.073	19.472	0.000
CLA =~ derse8	1.290	0.069	18.659	0.000
ANS =~ eea2	1.000	0.000	NA	NA
ANS =~ eea5	1.157	0.075	15.432	0.000
ANS =~ eea8	1.295	0.083	15.591	0.000
ANS =~ eea11	1.125	0.076	14.721	0.000
ANS =~ eea14	1.135	0.075	15.103	0.000
ANS =~ eea17	1.143	0.075	15.173	0.000
ANS =~ eea20	1.223	0.074	16.573	0.000
ANS =~ eea21	1.605	0.092	17.396	0.000
SEG =~ eea3	1.000	0.000	NA	NA
SEG =~ eea6	0.705	0.060	11.698	0.000
SEG =~ eea9	0.716	0.060	11.893	0.000
SEG =~ eea12	0.952	0.066	14.535	0.000
SEG =~ eea15	0.985	0.071	13.899	0.000
SEG =~ eea18	0.899	0.063	14.342	0.000
SP ~ ANS	0.384	0.041	9.424	0.000
NAC ~ ANS	0.325	0.036	8.994	0.000
NAC ~ SEG	-0.120	0.030	-3.921	0.000
NAC ~ SP	0.427	0.045	9.493	0.000
ME ~ ANS	0.270	0.034	7.840	0.000
ME ~ SEG	-0.097	0.030	-3.191	0.001
ME ~ SP	0.438	0.045	9.660	0.000
CON ~ ANS	0.079	0.033	2.420	0.016
CON ~ SEG	-0.319	0.036	-8.777	0.000
CON ~ SP	0.282	0.041	6.913	0.000
CLA ~ ANS	0.233	0.030	7.673	0.000
CLA ~ SEG	-0.162	0.028	-5.827	0.000
CLA ~ SP	0.324	0.038	8.587	0.000
cpas3 ~~ cpas3	2.208	0.101	21.790	0.000
cpas4 ~~ cpas4	2.402	0.110	21.913	0.000
cpas5 ~~ cpas5	2.265	0.105	21.509	0.000
cpas8 ~~ cpas8	2.428	0.111	21.801	0.000
cpas11 ~~ cpas11	1.691	0.081	20.919	0.000
cpas13 ~~ cpas13	2.330	0.107	21.701	0.000
cpas14 ~~ cpas14	1.592	0.079	20.128	0.000
cpas16 ~~ cpas16	1.850	0.086	21.458	0.000
cpas19 ~~ cpas19	1.716	0.086	19.871	0.000
cpas20 ~~ cpas20	2.928	0.133	21.968	0.000
cpas21 ~~ cpas21	1.564	0.079	19.867	0.000
cpas23 ~~ cpas23	1.910	0.091	21.014	0.000
cpas26 ~~ cpas26	2.628	0.122	21.497	0.000
derse10 ~~ derse10	1.108	0.052	21.149	0.000
derse12 ~~ derse12	0.702	0.035	19.956	0.000
derse14 ~~ derse14	0.793	0.041	19.565	0.000
derse15 ~~ derse15	0.609	0.033	18.177	0.000
derse18 ~~ derse18	0.846	0.040	21.420	0.000
derse19 ~~ derse19	0.676	0.036	19.028	0.000
derse11 ~~ derse11	0.918	0.045	20.630	0.000
derse13 ~~ derse13	0.569	0.030	18.894	0.000
derse16 ~~ derse16	0.634	0.033	19.009	0.000
derse17 ~~ derse17	0.639	0.032	19.956	0.000
derse22 ~~ derse22	0.824	0.039	21.154	0.000
derse23 ~~ derse23	0.681	0.036	18.937	0.000
derse1 ~~ derse1	0.621	0.036	17.398	0.000
derse2 ~~ derse2	0.723	0.036	20.237	0.000
derse6 ~~ derse6	1.191	0.054	21.906	0.000
derse7 ~~ derse7	0.651	0.043	15.136	0.000
derse9 ~~ derse9	0.899	0.046	19.456	0.000
derse3 ~~ derse3	0.930	0.044	20.961	0.000
derse4 ~~ derse4	0.608	0.033	18.649	0.000
derse5 ~~ derse5	0.524	0.033	16.089	0.000
derse8 ~~ derse8	0.638	0.035	18.241	0.000
eea2 ~~ eea2	2.035	0.096	21.165	0.000
eea5 ~~ eea5	1.906	0.093	20.456	0.000
eea8 ~~ eea8	2.267	0.112	20.327	0.000
eea11 ~~ eea11	2.252	0.108	20.926	0.000
eea14 ~~ eea14	2.037	0.098	20.691	0.000
eea17 ~~ eea17	2.020	0.098	20.644	0.000
eea20 ~~ eea20	1.412	0.073	19.251	0.000
eea21 ~~ eea21	1.670	0.095	17.621	0.000
eea3 ~~ eea3	1.482	0.080	18.560	0.000
eea6 ~~ eea6	1.520	0.073	20.768	0.000
eea9 ~~ eea9	1.484	0.072	20.652	0.000
eea12 ~~ eea12	1.142	0.064	17.802	0.000
eea15 ~~ eea15	1.539	0.082	18.844	0.000
eea18 ~~ eea18	1.096	0.060	18.163	0.000
SP ~~ SP	0.575	0.073	7.925	0.000
NAC ~~ NAC	0.429	0.041	10.551	0.000
ME ~~ ME	0.441	0.040	11.068	0.000
CON ~~ CON	0.445	0.040	11.041	0.000
CLA ~~ CLA	0.309	0.032	9.523	0.000
ANS ~~ ANS	0.940	0.102	9.243	0.000
SEG ~~ SEG	0.850	0.092	9.282	0.000
ANS ~~ SEG	-0.092	0.035	-2.615	0.009
NAC ~~ ME	0.301	0.026	11.658	0.000
NAC ~~ CON	0.096	0.020	4.830	0.000
NAC ~~ CLA	0.211	0.021	10.057	0.000
ME ~~ CON	0.080	0.020	4.038	0.000
ME ~~ CLA	0.175	0.019	8.992	0.000
CON ~~ CLA	0.192	0.021	9.323	0.000

glance_lavaan(kar_fns)

AGFI	AIC	BIC	CFI	Ji_Cuad	n_par	RMSEA
0.8503608	163319.1	163893	0.8713373	3585.958	116	0.0478678

# Gráfica

lavaanPlot(name = 'Modelo',
                       kar_fns,
                       node_options = list(shape = "box",
                                           fontname = "Helvetica"),
                       edge_options = list(color = "grey"),
                       coefs = TRUE,
                       stand = TRUE,
                       stars = TRUE)

# Integrando totales y dimensiones

kar_tot <- 'EPASTOT =~ cpas3 + cpas4 + cpas5 + cpas8 + cpas11 + cpas13 + cpas14 + cpas16 + cpas19 + cpas20 + cpas21 + cpas23 + cpas26 + cpas1 + cpas7 + cpas9 + cpas25
            SP =~ cpas3 + cpas4 + cpas5 + cpas8 + cpas11 + cpas13 + cpas14 + cpas16 + cpas19 + cpas20 + cpas21 + cpas23 + cpas26
            USB =~ cpas1 + cpas7 + cpas9 + cpas25
            
            DERSETOT =~ derse10 + derse12 + derse14 + derse15 + derse18  + derse19 + derse20 + derse21 + derse24 + derse11 + derse13 + derse16 + derse17 + derse22 + derse23 + derse1 + derse2 + derse6 + derse7 + derse9 + derse3 + derse4 + derse5 + derse8      
            NAC =~ derse10 + derse12 + derse14 + derse15 + derse18  + derse19 + derse20 + derse21 + derse24
            ME =~ derse11 + derse13 + derse16 + derse17 + derse22 + derse23
            CON =~ derse1 + derse2 + derse6 + derse7 + derse9
            CLA =~ derse3 + derse4 + derse5 + derse8
                        
            EVI =~ eea1 + eea4 + eea7 + eea10 + eea13 + eea16 + eea19
            ANS =~ eea2 + eea5 + eea8 + eea11 + eea14 + eea17 + eea20 + eea21
            SEG =~ eea3 + eea6 + eea9 + eea12 + eea15 + eea18
          
            SP ~ EVI + ANS + SEG
            USB ~ EVI + ANS + SEG
            NAC ~ EVI + ANS + SEG + SP + USB
            ME ~ EVI + ANS + SEG + SP + USB
            CON ~ EVI + ANS + SEG + SP + USB
            CLA ~ EVI + ANS + SEG + SP + USB
            EPASTOT ~ EVI + ANS + SEG + SP + USB + NAC + ME + CON + CLA
            DERSETOT ~ EVI + ANS + SEG + SP + USB + NAC + ME + CON + CLA + EPASTOT'

kar_ftt <- sem(kar_tot,
               kar_sem)

tidy_lavaan(kar_ftt)

Ruta	Estimado	Error_Std	Estadistico	valor_p
EPASTOT =~ cpas3	1.000	0.000	NA	NA
EPASTOT =~ cpas4	0.797	0.584	1.363	0.173
EPASTOT =~ cpas5	0.554	1.905	0.291	0.771
EPASTOT =~ cpas8	0.866	0.574	1.508	0.132
EPASTOT =~ cpas11	0.663	1.664	0.398	0.690
EPASTOT =~ cpas13	0.665	1.341	0.496	0.620
EPASTOT =~ cpas14	0.240	3.683	0.065	0.948
EPASTOT =~ cpas16	-0.076	3.747	-0.020	0.984
EPASTOT =~ cpas19	0.796	2.232	0.357	0.721
EPASTOT =~ cpas20	0.430	1.982	0.217	0.828
EPASTOT =~ cpas21	0.917	1.537	0.597	0.551
EPASTOT =~ cpas23	-0.233	4.943	-0.047	0.962
EPASTOT =~ cpas26	0.519	2.294	0.226	0.821
EPASTOT =~ cpas1	0.922	1.343	0.687	0.492
EPASTOT =~ cpas7	1.113	2.645	0.421	0.674
EPASTOT =~ cpas9	1.349	3.631	0.371	0.710
EPASTOT =~ cpas25	1.331	3.360	0.396	0.692
SP =~ cpas3	1.000	0.000	NA	NA
SP =~ cpas4	-0.556	1.710	-0.325	0.745
SP =~ cpas5	-3.913	5.687	-0.688	0.491
SP =~ cpas8	-0.461	1.674	-0.276	0.783
SP =~ cpas11	-3.239	4.918	-0.659	0.510
SP =~ cpas13	-2.477	3.955	-0.626	0.531
SP =~ cpas14	-8.406	11.190	-0.751	0.453
SP =~ cpas16	-8.874	11.487	-0.773	0.440
SP =~ cpas19	-4.440	6.607	-0.672	0.502
SP =~ cpas20	-4.219	5.967	-0.707	0.480
SP =~ cpas21	-2.684	4.465	-0.601	0.548
SP =~ cpas23	-11.840	15.199	-0.779	0.436
SP =~ cpas26	-4.863	6.885	-0.706	0.480
USB =~ cpas1	1.000	0.000	NA	NA
USB =~ cpas7	2.300	0.339	6.778	0.000
USB =~ cpas9	3.242	0.465	6.973	0.000
USB =~ cpas25	2.961	0.420	7.051	0.000
DERSETOT =~ derse10	1.000	0.000	NA	NA
DERSETOT =~ derse12	1.285	0.184	6.984	0.000
DERSETOT =~ derse14	1.314	0.144	9.117	0.000
DERSETOT =~ derse15	1.318	0.104	12.615	0.000
DERSETOT =~ derse18	1.045	0.156	6.704	0.000
DERSETOT =~ derse19	1.271	0.068	18.683	0.000
DERSETOT =~ derse20	1.330	0.096	13.835	0.000
DERSETOT =~ derse21	1.136	0.159	7.151	0.000
DERSETOT =~ derse24	1.352	0.198	6.837	0.000
DERSETOT =~ derse11	0.793	0.381	2.082	0.037
DERSETOT =~ derse13	0.938	0.321	2.920	0.003
DERSETOT =~ derse16	0.956	0.337	2.836	0.005
DERSETOT =~ derse17	0.945	0.207	4.576	0.000
DERSETOT =~ derse22	0.944	0.137	6.880	0.000
DERSETOT =~ derse23	1.232	0.221	5.570	0.000
DERSETOT =~ derse1	0.547	5.701	0.096	0.924
DERSETOT =~ derse2	0.259	5.049	0.051	0.959
DERSETOT =~ derse6	0.028	4.588	0.006	0.995
DERSETOT =~ derse7	0.641	7.085	0.090	0.928
DERSETOT =~ derse9	0.335	6.359	0.053	0.958
DERSETOT =~ derse3	-0.770	78.227	-0.010	0.992
DERSETOT =~ derse4	-3.892	218.623	-0.018	0.986
DERSETOT =~ derse5	-3.862	226.322	-0.017	0.986
DERSETOT =~ derse8	-3.110	188.704	-0.016	0.987
NAC =~ derse10	1.000	0.000	NA	NA
NAC =~ derse12	-0.173	0.080	-2.149	0.032
NAC =~ derse14	0.242	0.076	3.185	0.001
NAC =~ derse15	0.626	0.073	8.606	0.000
NAC =~ derse18	-0.179	0.076	-2.352	0.019
NAC =~ derse19	0.998	0.089	11.173	0.000
NAC =~ derse20	0.711	0.070	10.141	0.000
NAC =~ derse21	-0.106	0.075	-1.400	0.161
NAC =~ derse24	-0.220	0.086	-2.565	0.010
ME =~ derse11	1.000	0.000	NA	NA
ME =~ derse13	0.820	0.067	12.260	0.000
ME =~ derse16	0.864	0.071	12.212	0.000
ME =~ derse17	0.467	0.054	8.618	0.000
ME =~ derse22	0.170	0.052	3.251	0.001
ME =~ derse23	0.443	0.054	8.194	0.000
CON =~ derse1	1.000	0.000	NA	NA
CON =~ derse2	0.889	0.064	13.995	0.000
CON =~ derse6	0.811	0.070	11.500	0.000
CON =~ derse7	1.243	0.078	16.013	0.000
CON =~ derse9	1.119	0.076	14.811	0.000
CLA =~ derse3	1.000	0.000	NA	NA
CLA =~ derse4	2.797	0.475	5.893	0.000
CLA =~ derse5	2.895	0.488	5.927	0.000
CLA =~ derse8	2.414	0.414	5.829	0.000
EVI =~ eea1	1.000	0.000	NA	NA
EVI =~ eea4	1.012	0.049	20.757	0.000
EVI =~ eea7	0.690	0.055	12.427	0.000
EVI =~ eea10	0.856	0.056	15.145	0.000
EVI =~ eea13	0.940	0.048	19.556	0.000
EVI =~ eea16	1.110	0.052	21.546	0.000
EVI =~ eea19	0.862	0.053	16.398	0.000
ANS =~ eea2	1.000	0.000	NA	NA
ANS =~ eea5	1.158	0.075	15.416	0.000
ANS =~ eea8	1.294	0.083	15.550	0.000
ANS =~ eea11	1.129	0.077	14.723	0.000
ANS =~ eea14	1.138	0.075	15.106	0.000
ANS =~ eea17	1.146	0.076	15.175	0.000
ANS =~ eea20	1.225	0.074	16.563	0.000
ANS =~ eea21	1.608	0.093	17.380	0.000
SEG =~ eea3	1.000	0.000	NA	NA
SEG =~ eea6	0.751	0.064	11.712	0.000
SEG =~ eea9	0.738	0.064	11.616	0.000
SEG =~ eea12	1.031	0.071	14.572	0.000
SEG =~ eea15	0.985	0.074	13.271	0.000
SEG =~ eea18	1.009	0.069	14.688	0.000
SP ~ EVI	-0.003	0.011	-0.234	0.815
SP ~ ANS	-0.047	0.213	-0.220	0.826
SP ~ SEG	-0.004	0.009	-0.468	0.640
USB ~ EVI	0.063	0.031	2.033	0.042
USB ~ ANS	-0.085	0.450	-0.190	0.849
USB ~ SEG	0.053	0.029	1.837	0.066
NAC ~ EVI	0.069	0.035	1.988	0.047
NAC ~ ANS	-0.037	0.048	-0.769	0.442
NAC ~ SEG	0.090	0.047	1.901	0.057
NAC ~ SP	-0.419	1.410	-0.298	0.766
NAC ~ USB	-0.185	0.215	-0.861	0.389
ME ~ EVI	-0.032	0.037	-0.854	0.393
ME ~ ANS	-0.031	0.100	-0.313	0.755
ME ~ SEG	-0.027	0.068	-0.400	0.689
ME ~ SP	-1.513	3.430	-0.441	0.659
ME ~ USB	0.072	0.577	0.125	0.901
CON ~ EVI	0.008	0.081	0.103	0.918
CON ~ ANS	-0.055	1.946	-0.028	0.978
CON ~ SEG	-0.261	0.535	-0.488	0.626
CON ~ SP	-0.795	8.769	-0.091	0.928
CON ~ USB	-0.126	1.674	-0.075	0.940
CLA ~ EVI	0.041	1.053	0.039	0.969
CLA ~ ANS	0.581	26.921	0.022	0.983
CLA ~ SEG	-0.189	7.389	-0.026	0.980
CLA ~ SP	-2.664	116.810	-0.023	0.982
CLA ~ USB	-0.576	23.128	-0.025	0.980
EPASTOT ~ EVI	0.004	0.307	0.012	0.990
EPASTOT ~ ANS	0.043	0.364	0.118	0.906
EPASTOT ~ SEG	0.056	0.441	0.128	0.898
EPASTOT ~ SP	-4.050	14.207	-0.285	0.776
EPASTOT ~ USB	-0.710	3.042	-0.233	0.816
EPASTOT ~ NAC	0.097	0.092	1.061	0.289
EPASTOT ~ ME	0.013	0.693	0.018	0.985
EPASTOT ~ CON	-0.027	1.994	-0.014	0.989
EPASTOT ~ CLA	0.243	11.084	0.022	0.983
DERSETOT ~ EVI	-0.010	0.394	-0.025	0.980
DERSETOT ~ ANS	0.004	0.791	0.005	0.996
DERSETOT ~ SEG	-0.013	0.890	-0.015	0.988
DERSETOT ~ SP	-0.082	4.517	-0.018	0.986
DERSETOT ~ USB	0.021	0.682	0.031	0.975
DERSETOT ~ NAC	0.002	0.038	0.053	0.958
DERSETOT ~ ME	-0.003	0.167	-0.021	0.984
DERSETOT ~ CON	-0.106	4.904	-0.022	0.983
DERSETOT ~ CLA	0.577	24.394	0.024	0.981
DERSETOT ~ EPASTOT	-0.007	0.248	-0.029	0.977
cpas3 ~~ cpas3	1.948	0.102	19.144	0.000
cpas4 ~~ cpas4	2.290	0.109	21.060	0.000
cpas5 ~~ cpas5	2.255	0.104	21.707	0.000
cpas8 ~~ cpas8	2.290	0.110	20.722	0.000
cpas11 ~~ cpas11	1.690	0.080	21.081	0.000
cpas13 ~~ cpas13	2.281	0.106	21.601	0.000
cpas14 ~~ cpas14	1.459	0.079	18.417	0.000
cpas16 ~~ cpas16	1.542	0.089	17.302	0.000
cpas19 ~~ cpas19	1.707	0.085	20.201	0.000
cpas20 ~~ cpas20	2.924	0.132	22.102	0.000
cpas21 ~~ cpas21	1.554	0.080	19.500	0.000
cpas23 ~~ cpas23	1.249	0.116	10.787	0.000
cpas26 ~~ cpas26	2.635	0.121	21.728	0.000
cpas1 ~~ cpas1	1.547	0.074	21.018	0.000
cpas7 ~~ cpas7	1.961	0.101	19.464	0.000
cpas9 ~~ cpas9	1.402	0.113	12.400	0.000
cpas25 ~~ cpas25	1.114	0.092	12.093	0.000
derse10 ~~ derse10	0.912	0.051	17.910	0.000
derse12 ~~ derse12	0.622	0.033	18.666	0.000
derse14 ~~ derse14	0.882	0.041	21.265	0.000
derse15 ~~ derse15	0.645	0.033	19.688	0.000
derse18 ~~ derse18	0.729	0.036	20.245	0.000
derse19 ~~ derse19	0.426	0.034	12.627	0.000
derse20 ~~ derse20	0.431	0.025	17.049	0.000
derse21 ~~ derse21	0.722	0.036	20.336	0.000
derse24 ~~ derse24	0.660	0.036	18.135	0.000
derse11 ~~ derse11	0.754	0.048	15.683	0.000
derse13 ~~ derse13	0.510	0.032	16.000	0.000
derse16 ~~ derse16	0.583	0.036	16.197	0.000
derse17 ~~ derse17	0.676	0.032	21.004	0.000
derse22 ~~ derse22	0.809	0.037	21.904	0.000
derse23 ~~ derse23	0.672	0.033	20.608	0.000
derse1 ~~ derse1	0.637	0.035	18.113	0.000
derse2 ~~ derse2	0.695	0.036	19.379	0.000
derse6 ~~ derse6	1.115	0.053	20.942	0.000
derse7 ~~ derse7	0.676	0.042	15.966	0.000
derse9 ~~ derse9	0.860	0.047	18.392	0.000
derse3 ~~ derse3	0.920	0.042	21.912	0.000
derse4 ~~ derse4	0.544	0.036	15.044	0.000
derse5 ~~ derse5	0.506	0.036	14.005	0.000
derse8 ~~ derse8	0.656	0.036	18.171	0.000
eea1 ~~ eea1	1.456	0.074	19.633	0.000
eea4 ~~ eea4	0.962	0.054	17.844	0.000
eea7 ~~ eea7	2.646	0.120	21.985	0.000
eea10 ~~ eea10	2.359	0.110	21.385	0.000
eea13 ~~ eea13	1.129	0.059	19.180	0.000
eea16 ~~ eea16	0.906	0.055	16.475	0.000
eea19 ~~ eea19	1.869	0.089	20.980	0.000
eea2 ~~ eea2	2.039	0.096	21.184	0.000
eea5 ~~ eea5	1.906	0.093	20.474	0.000
eea8 ~~ eea8	2.276	0.112	20.368	0.000
eea11 ~~ eea11	2.249	0.107	20.932	0.000
eea14 ~~ eea14	2.033	0.098	20.696	0.000
eea17 ~~ eea17	2.017	0.098	20.649	0.000
eea20 ~~ eea20	1.410	0.073	19.267	0.000
eea21 ~~ eea21	1.670	0.095	17.654	0.000
eea3 ~~ eea3	1.568	0.080	19.557	0.000
eea6 ~~ eea6	1.512	0.072	20.918	0.000
eea9 ~~ eea9	1.503	0.072	20.970	0.000
eea12 ~~ eea12	1.101	0.062	17.867	0.000
eea15 ~~ eea15	1.622	0.082	19.758	0.000
eea18 ~~ eea18	1.003	0.057	17.607	0.000
EPASTOT ~~ EPASTOT	0.440	0.592	0.743	0.458
SP ~~ SP	0.013	0.064	0.207	0.836
USB ~~ USB	0.160	0.266	0.601	0.548
DERSETOT ~~ DERSETOT	0.013	1.177	0.011	0.991
NAC ~~ NAC	0.378	0.061	6.227	0.000
ME ~~ ME	0.467	0.056	8.281	0.000
CON ~~ CON	0.361	0.433	0.834	0.404
CLA ~~ CLA	0.917	82.638	0.011	0.991
EVI ~~ EVI	1.234	0.106	11.627	0.000
ANS ~~ ANS	0.936	0.101	9.228	0.000
SEG ~~ SEG	0.765	0.086	8.868	0.000
EVI ~~ ANS	0.146	0.040	3.642	0.000
EVI ~~ SEG	-0.503	0.050	-9.971	0.000
ANS ~~ SEG	-0.072	0.033	-2.187	0.029

glance_lavaan(kar_ftt)

AGFI	AIC	BIC	CFI	Ji_Cuad	n_par	RMSEA
0.8546414	210792	211845.7	0.8962962	4559.294	213	0.0394711

# Gráfica

lavaanPlot(name = 'Modelo',
                       kar_ftt,
                       node_options = list(shape = "box",
                                           fontname = "Helvetica"),
                       edge_options = list(color = "grey"),
                       coefs = TRUE,
                       stand = TRUE,
                       stars = TRUE)

Referencias

• R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
• Revelle W (2019). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 1.9.12, https://CRAN.R-project.org/package=psych.
• Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.” Journal of Statistical Software, 48(2), 1–36. http://www.jstatsoft.org/v48/i02/.