Análisis Diego Bogado
Federico Villalba
Análisis estadístico
Diego Bogado
Para el análisis descriptivo, aquellas variables numéricas continuas que asumieron una distribución normal se reportan como media y desvío estándar (DE). En caso contrario se reportan como mediana y rango intercuartílico (RIQ). Las variables categóricas se reportan como número de presentación y porcentaje (%). Para valorar la normalidad de la muestra se utilizó el test estadístico de Shapiro-Wilk y la evaluación gráfica mediante gráficos de histogramas y gráficos de cajas y bigotes.
Luego del análisis descriptivo, se analizó la consistencia interna del cuestionario Escala de Identidad Deportiva a través del cálculo del coeficiente de alfa de Cronbach (Bland 1997). La evaluación de la consistencia interna permite estimar la confiabilidad de la escala como instrumento de medida a través de un conjunto de ítems que se espera que midan el mismo constructo. Se consideran aceptables valores entre 0.70 y 0.95 (Tavakol & Dennick 2011; COSMIN 2018).
Previo a la exploración de la estructura interna de la escala a través de un Análisis Factorial Exploratorio (Orcan 2018), se calculó la correlación inter ítem mediante el coeficiente de correlación de Spearman. Se considera necesario para el análisis factorial la presencia de correlaciones mayores a 0.30 en la matríz de correlación (Tabachnik & Fidell 2001). Se aplicó el criterio de Kaiser-Meyer-Olkin (KMO) (Kaiser 1960; Kaiser 1970) y el test de esfericidad de Bartlett (Bartlett 1950; Bartlett 1951) para valorar la adecuación del muestreo. La índices de KMO superiores a 0.6 se consideran aceptables como medida de adecuación del modelo. Luego de haber controlado el cumplimiento de supuestos, se realizó la extracción de datos mediante el método de rotación Equamax y el método de extracción de máxima verosimilitud. Se calculó el número más adecuado de factores con extracción Equamax (Lloret 2014). Con el objetivo de analizar los vínculos entre las variables observadas en la escala, y las variables latentes denominadas factores, se llevó a cabo el análisis factorial exploratorio incluyendo todas las variables con un coeficiente de correlación mayor a 0.30.
###Referencias
Bland JM, Altman DG. Statistics notes: Cronbach’s alpha. BMJ. 1997;314(7080):572-572.
Tavakol, Mohsen, and Reg Dennick. 2011. “Making Sense of Cronbach’s Alpha.” International journal of medical education 2: 53–55.
Prinsen CAC, Mokkink LB, Bouter LM, Alonso J, Patrick DL, de Vet HCW, Terwee CB. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res. 2018 May;27(5):1147-1157. -57.
Tabachnick, B. G. and L. S. Fidell (2001). Using multivariate statistics. Needham Heights, MA, Allyn & Bacon.
Orcan F. Exploratory and Confirmatory Factor Analysis: Which One to Use First? Eğitimde Ve Psikolojide Ölçme Ve Değerlendirme Derg. 2018;414-21.
Kaiser HF. The Application of Electronic Computers to Factor Analysis. Educ Psychol Meas. 1960;20(1):141-51.
Kaiser HF. A second generation little jiffy. Psychometrika. 1970;35(4):401-15.
Bartlett MS. Tests of significance in factor analysis. Br J Stat Psychol. 1950;3(2):77-85.
Bartlett MS. A further note on tests of significance in factor analysis. Br J Stat Psychol. 1951;4(1):1-2.
Lloret-Segura, Susana; Ferreres-Traver, Adoración; Hernández-Baeza, Ana; Tomás-Marco, Inés El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada. Anales de Psicología 2014; 30 (3):1151-116.
ANÁLISIS DESCRIPTIVO
Edad
# Edad
boxplot(df$Edad)
hist(df$Edad)
shapiro.test(df$Edad) # Distribución Normal - 0.08117##
## Shapiro-Wilk normality test
##
## data: df$Edad
## W = 0.95183, p-value = 0.08117# No hay evidencia de incumplimiento de la normalidad, sin embargo de forma gráfica no parece serlo.
# Por lo tanto test de la Mediana y RIQ
mifuncion(df$Edad)## Media Desvio Cuantil.25.25% Mediana Cuantil.75.75%
## 30.487805 8.964156 24.000000 30.000000 37.000000
## Min Max Rango1 Rango2 N
## 0.000000 50.000000 0.000000 50.000000 41.000000
## na N_total Porc_NA
## 0.000000 41.000000 0.000000RPE
tab.noby <- tableby(~Deporte, data=df)
summary(tab.noby)##
##
## | | Overall (N=41) |
## |:-----------------------------------------------|:--------------:|
## |**Deporte** | |
## | Atletismo adaptado | 2 (4.9%) |
## | Básquet sobre silla de ruedas | 21 (51.2%) |
## | Natación adaptada | 3 (7.3%) |
## | Tenis de mesa adaptado | 15 (36.6%) |#descriptiva por grupo/by
tab1 <- tableby(Deporte ~ Sexo + Edad + Frecuencia + Horas + RPE + Años, data=df, na.tableby(TRUE)) #paquete arsenal
# si queremos missing, agregar a la sintaxis na.tableby(TRUE)
# si queremos que no haga comparaciones, agregar test=FALSE
#para q se vea bien en la consola
summary(tab1, text=TRUE)##
##
## | | Atletismo adaptado (N=2) | Básquet sobre silla de ruedas (N=21) | Natación adaptada (N=3) | Tenis de mesa adaptado (N=15) | Total (N=41) | p value|
## |:-----------------------------|:------------------------:|:------------------------------------:|:-----------------------:|:-----------------------------:|:--------------:|-------:|
## |Sexo | | | | | | 0.205|
## |- Femenino | 2 (100.0%) | 6 (28.6%) | 1 (33.3%) | 4 (26.7%) | 13 (31.7%) | |
## |- Masculino | 0 (0.0%) | 15 (71.4%) | 2 (66.7%) | 11 (73.3%) | 28 (68.3%) | |
## |Edad | | | | | | 0.070|
## |- Mean (SD) | 21.000 (0.000) | 28.952 (8.840) | 26.333 (5.508) | 34.733 (8.614) | 30.488 (8.964) | |
## |- Range | 21.000 - 21.000 | 0.000 - 40.000 | 20.000 - 30.000 | 22.000 - 50.000 | 0.000 - 50.000 | |
## |Frecuencia | | | | | | 0.331|
## |- 2 veces por semana o menos | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (6.7%) | 1 (2.4%) | |
## |- 3 veces por semana | 0 (0.0%) | 2 (9.5%) | 0 (0.0%) | 5 (33.3%) | 7 (17.1%) | |
## |- 4 veces por semana o más | 2 (100.0%) | 19 (90.5%) | 3 (100.0%) | 9 (60.0%) | 33 (80.5%) | |
## |Horas | | | | | | 0.583|
## |- Entre 1 y 2 horas | 0 (0.0%) | 4 (19.0%) | 0 (0.0%) | 2 (13.3%) | 6 (14.6%) | |
## |- Entre 2 y 3 horas | 0 (0.0%) | 9 (42.9%) | 1 (33.3%) | 8 (53.3%) | 18 (43.9%) | |
## |- Más de 3 horas | 2 (100.0%) | 8 (38.1%) | 2 (66.7%) | 5 (33.3%) | 17 (41.5%) | |
## |RPE | | | | | | 0.169|
## |- Mean (SD) | 8.000 (1.414) | 7.762 (0.889) | 9.333 (0.577) | 8.200 (1.474) | 8.049 (1.182) | |
## |- Range | 7.000 - 9.000 | 6.000 - 10.000 | 9.000 - 10.000 | 5.000 - 10.000 | 5.000 - 10.000 | |
## |Años | | | | | | 0.515|
## |- 2 años o menos | 0 (0.0%) | 2 (9.5%) | 0 (0.0%) | 0 (0.0%) | 2 (4.9%) | |
## |- 3 años | 1 (50.0%) | 2 (9.5%) | 0 (0.0%) | 0 (0.0%) | 3 (7.3%) | |
## |- 4 años | 0 (0.0%) | 2 (9.5%) | 0 (0.0%) | 2 (13.3%) | 4 (9.8%) | |
## |- 5 años | 0 (0.0%) | 2 (9.5%) | 0 (0.0%) | 3 (20.0%) | 5 (12.2%) | |
## |- 6 años o más | 1 (50.0%) | 13 (61.9%) | 3 (100.0%) | 10 (66.7%) | 27 (65.9%) | |SUBGRUPO IDENTIDAD DEPORTIVA (AIMS)
Se extrajo de la base de datos aquellas columnas correspondientes a la Escala de identidad deportiva. A partir de dicha base, se llevó a cabo el cálculo del coeficiente alfa de Cronbach el cual arrojó un valor de 0.739. Dicho valor se encuentra dentro del rango propuesto por Tavakol & Dennick.
df_afe<-df[c(40:49)]
alpha(df_afe) # 0.7391779## [1] 0.7391779Con el objetivo de buscar las correlaciones entre las variables de la Escala, se utilizó el método de correlación de Spearman debido a su mayor robustez. De la aplicación de dicho test, se encontraron correlaciones positivas y negativas siendo las de mayor magnitud la correlación entre el ítem 4 y 5 (0.75), entre el 1 y el 2 (0.74) y entre el 8 y el 9 (0.54). El resto de los valores se muestran en la siguiente matriz.
correlacion<-cor2(df_afe, method ="spearman")## xi
## xi 1.00
## 0.74 1.00
## 0.02 0.22 1.00
## 0.36 0.40 0.33 1.00
## 0.09 0.14 0.37 0.75 1.00
## 0.00 -0.05 0.35 0.22 0.40 1.00
## 0.47 0.46 0.07 0.53 0.34 0.22 1.00
## 0.08 0.22 0.00 0.21 0.26 0.12 0.15 1.00
## -0.19 -0.17 0.22 0.25 0.36 0.42 0.00 0.39 1.00
## 0.26 0.38 -0.01 0.46 0.48 0.19 0.36 0.54 0.19 1.00colnames(correlacion)<-c("ID1","ID2","ID3","ID4","ID5","ID6", "ID7", "ID8", "ID9", "ID10")
rownames(correlacion)<-c("ID1","ID2","ID3","ID4","ID5","ID6", "ID7", "ID8", "ID9", "ID10")
corrplot.mixed(correlacion, upper = "ellipse" , tl.pos = "lt")
#matriz<-hetcor(df_afe)$correlations
#ggcorrplot(mat_cor, type = "lower", hc.order = TRUE)Llevamos a cabo la prueba de adecuación de la muestra mediante el test de KMO y de esfericidad de Bartlett. De dichos test encontramos que la adecuación muestral fueron satisfactorios, con un índice global de KMO de 0.64, con todos los índices individuales ubicados entre 0.44 y 0.77. Por otro lado, la prueba de esfericidad de Bartlett resultó estadísticamente significativo con p-valor < 0.001.
KMO<-KMO(cor(df_afe))
Bartlett<-cortest.bartlett(cor(df_afe), n=nrow(df_afe))
KMO # Medida de adecuación de los datos para el análisis factorial## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(df_afe))
## Overall MSA = 0.64
## MSA for each item =
## ID1 ID2 ID3 ID4 ID5 ID6 ID7 ID8 ID9 ID10
## 0.58 0.52 0.44 0.75 0.70 0.65 0.77 0.57 0.67 0.63Bartlett # Comprueba la H0 que afirma que las variables no están correlacionadas## $chisq
## [1] 162.2623
##
## $p.value
## [1] 3.688479e-15
##
## $df
## [1] 45#Calculo de determinante, donde valores cercanos a cero indican presencia de correlación entre las variables
#det(cor(df_afe))Análisis Factorial. Por medio de esta estrategia, se lleva a cabo la extracción de factores. El Análisis Factorial intenta reducir la cantidad de dimensiones disponibles sin perder información (o perdiendo la menor cantidad de información disponible) por lo cual, la cantidad máxima de factores va a ser igual a la cantidad de ítems que tengamos. En el gráfico siguiente se puede observar cual es el número de factores más adecuado según la mayoría de los métodos empleados.
nfactoresAF<-n_factors(
df_afe,
type = "FA",
rotation = "equamax",
algorithm = "default",
package = c("nFactors", "psych"),
cor = NULL,
safe = TRUE,
n_max = NULL)
plot(nfactoresAF)
nfactoresAFas.data.frame(nfactoresAF)summary(nfactoresAF)El análisis factorial llevado a cabo mostró una estructura de 3 factores que en suma explican el 58% de la varianza total, con una explicación del 24% por parte de primer factor. La siguiente salida muestra los resultados del análisis factorial exploratorio con los factores y las variables que lo componen según el valor de correlación en cada una. A su vez, se muestra en la figura las cargas de cada variable que es explicada por cada factor latente.
afe<-fa(df_afe,nfactors = 3,fm = "ml",rotate ="equamax",cor = "cor")
print(afe,digits = 2,cut = .30,sort=TRUE)## Factor Analysis using method = ml
## Call: fa(r = df_afe, nfactors = 3, rotate = "equamax", fm = "ml", cor = "cor")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item ML2 ML3 ML1 h2 u2 com
## ID5 5 0.92 0.86 0.137 1.1
## ID4 4 0.76 0.37 0.72 0.275 1.5
## ID7 7 0.55 0.38 0.45 0.549 1.8
## ID6 6 0.50 0.29 0.713 1.3
## ID3 3 0.42 0.19 0.810 1.2
## ID9 9 0.40 0.34 0.34 0.655 2.7
## ID2 2 0.91 0.84 0.157 1.1
## ID1 1 0.81 0.66 0.335 1.0
## ID8 8 0.99 1.00 0.005 1.0
## ID10 10 0.30 0.56 0.46 0.536 2.0
##
## ML2 ML3 ML1
## SS loadings 2.41 1.93 1.48
## Proportion Var 0.24 0.19 0.15
## Cumulative Var 0.24 0.43 0.58
## Proportion Explained 0.41 0.33 0.25
## Cumulative Proportion 0.41 0.75 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 4.53 with Chi Square of 162.26
## The degrees of freedom for the model are 18 and the objective function was 0.66
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.1
##
## The harmonic number of observations is 41 with the empirical chi square 15.56 with prob < 0.62
## The total number of observations was 41 with Likelihood Chi Square = 22.49 with prob < 0.21
##
## Tucker Lewis Index of factoring reliability = 0.896
## RMSEA index = 0.074 and the 90 % confidence intervals are 0 0.17
## BIC = -44.35
## Fit based upon off diagonal values = 0.96
## Measures of factor score adequacy
## ML2 ML3 ML1
## Correlation of (regression) scores with factors 0.95 0.94 1.00
## Multiple R square of scores with factors 0.90 0.89 0.99
## Minimum correlation of possible factor scores 0.81 0.78 0.99fa.diagram(afe)
Analisis factorial confirmatorio
Análisis de un factor, es decir, con todas las variables ubicadas bajo el mismo factor.
#Especificación del modelo conceptual - Unifactorial
Unfactor<-'A =~ ID1 + ID2 + ID3 + ID4 + ID5 + ID6 + ID7 + ID8 + ID9 + ID10'
Unfactor <- cfa(Unfactor,orthogonal=FALSE, data=df_afe, estimator="WLSMV",ordered =names(df_afe))
summary(Unfactor, fit.measures=TRUE)## lavaan 0.6-12 ended normally after 24 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 55
##
## Number of observations 41
##
## Model Test User Model:
## Standard Robust
## Test Statistic 107.216 105.341
## Degrees of freedom 35 35
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.178
## Shift parameter 14.317
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 705.910 418.854
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.768
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.891 0.812
## Tucker-Lewis Index (TLI) 0.860 0.758
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.227 0.224
## 90 Percent confidence interval - lower 0.179 0.176
## 90 Percent confidence interval - upper 0.277 0.274
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.221 0.221
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## A =~
## ID1 1.000
## ID2 1.023 0.201 5.090 0.000
## ID3 0.427 0.133 3.218 0.001
## ID4 1.061 0.128 8.298 0.000
## ID5 0.962 0.114 8.410 0.000
## ID6 0.503 0.152 3.318 0.001
## ID7 0.752 0.121 6.213 0.000
## ID8 0.586 0.159 3.698 0.000
## ID9 0.386 0.152 2.545 0.011
## ID10 0.771 0.132 5.830 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .ID1 0.000
## .ID2 0.000
## .ID3 0.000
## .ID4 0.000
## .ID5 0.000
## .ID6 0.000
## .ID7 0.000
## .ID8 0.000
## .ID9 0.000
## .ID10 0.000
## A 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## ID1|t1 -1.657 0.337 -4.919 0.000
## ID1|t2 -1.052 0.244 -4.318 0.000
## ID1|t3 -0.476 0.207 -2.304 0.021
## ID2|t1 -0.859 0.227 -3.782 0.000
## ID2|t2 -0.408 0.204 -1.999 0.046
## ID3|t1 -1.971 0.426 -4.625 0.000
## ID3|t2 -1.052 0.244 -4.318 0.000
## ID3|t3 -0.546 0.209 -2.607 0.009
## ID3|t4 -0.031 0.198 -0.154 0.877
## ID3|t5 0.408 0.204 1.999 0.046
## ID3|t6 0.694 0.217 3.204 0.001
## ID4|t1 -1.052 0.244 -4.318 0.000
## ID4|t2 -0.092 0.198 -0.463 0.644
## ID4|t3 0.279 0.201 1.387 0.166
## ID5|t1 -1.971 0.426 -4.625 0.000
## ID5|t2 -1.052 0.244 -4.318 0.000
## ID5|t3 -0.216 0.200 -1.079 0.281
## ID5|t4 0.343 0.202 1.693 0.090
## ID6|t1 -1.453 0.296 -4.900 0.000
## ID6|t2 -1.052 0.244 -4.318 0.000
## ID6|t3 -0.694 0.217 -3.204 0.001
## ID6|t4 -0.092 0.198 -0.463 0.644
## ID6|t5 0.408 0.204 1.999 0.046
## ID7|t1 -1.971 0.426 -4.625 0.000
## ID7|t2 -1.657 0.337 -4.919 0.000
## ID7|t3 -0.694 0.217 -3.204 0.001
## ID7|t4 0.153 0.199 0.771 0.441
## ID7|t5 0.694 0.217 3.204 0.001
## ID8|t1 -1.657 0.337 -4.919 0.000
## ID8|t2 -1.453 0.296 -4.900 0.000
## ID8|t3 -1.052 0.244 -4.318 0.000
## ID8|t4 -0.774 0.221 -3.497 0.000
## ID8|t5 -0.408 0.204 -1.999 0.046
## ID8|t6 0.153 0.199 0.771 0.441
## ID9|t1 -0.618 0.213 -2.907 0.004
## ID9|t2 -0.343 0.202 -1.693 0.090
## ID9|t3 -0.092 0.198 -0.463 0.644
## ID9|t4 0.618 0.213 2.907 0.004
## ID9|t5 1.165 0.256 4.557 0.000
## ID10|t1 -1.453 0.296 -4.900 0.000
## ID10|t2 -1.052 0.244 -4.318 0.000
## ID10|t3 -0.774 0.221 -3.497 0.000
## ID10|t4 -0.476 0.207 -2.304 0.021
## ID10|t5 -0.153 0.199 -0.771 0.441
## ID10|t6 0.343 0.202 1.693 0.090
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .ID1 0.293
## .ID2 0.259
## .ID3 0.871
## .ID4 0.203
## .ID5 0.345
## .ID6 0.821
## .ID7 0.600
## .ID8 0.757
## .ID9 0.894
## .ID10 0.580
## A 0.707 0.151 4.680 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## ID1 1.000
## ID2 1.000
## ID3 1.000
## ID4 1.000
## ID5 1.000
## ID6 1.000
## ID7 1.000
## ID8 1.000
## ID9 1.000
## ID10 1.000fitMeasures(Unfactor)## npar fmin
## 55.000 1.308
## chisq df
## 107.216 35.000
## pvalue chisq.scaled
## 0.000 105.341
## df.scaled pvalue.scaled
## 35.000 0.000
## chisq.scaling.factor baseline.chisq
## 1.178 705.910
## baseline.df baseline.pvalue
## 45.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 418.854 45.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 1.768
## cfi tli
## 0.891 0.860
## nnfi rfi
## 0.860 0.805
## nfi pnfi
## 0.848 0.660
## ifi rni
## 0.892 0.891
## cfi.scaled tli.scaled
## 0.812 0.758
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.758 NA
## rfi.scaled nfi.scaled
## 0.677 0.749
## ifi.scaled rni.scaled
## 0.817 0.812
## rni.robust rmsea
## NA 0.227
## rmsea.ci.lower rmsea.ci.upper
## 0.179 0.277
## rmsea.pvalue rmsea.scaled
## 0.000 0.224
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.176 0.274
## rmsea.pvalue.scaled rmsea.robust
## 0.000 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmr
## NA 0.203
## rmr_nomean srmr
## 0.221 0.221
## srmr_bentler srmr_bentler_nomean
## 0.203 0.221
## crmr crmr_nomean
## 0.221 0.244
## srmr_mplus srmr_mplus_nomean
## NA NA
## cn_05 cn_01
## 19.580 22.393
## gfi agfi
## 0.911 0.772
## pgfi mfi
## 0.354 0.405semPaths(Unfactor, intercepts = FALSE,
edge.label.cex=1.5,
optimizeLatRes = TRUE,
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt=5,
edge.color ="black",
esize = 6,
label.prop=2,
sizeLat = 6,"std",
layout="tree2",
rotation = 2)
#Especificación del modelo conceptual - Trifactorial
Tresfactores<-'A =~ ID5 + ID4 + ID6 + ID7 + ID9 + ID3
B =~ ID2 + ID1 + ID4 + ID7
C =~ ID 8 + ID9 + ID10'
AFCtres <- cfa(Tresfactores,orthogonal=FALSE, data=df_afe, estimator="WLSMV",ordered =names(df_afe))
summary(AFCtres, fit.measures=TRUE)## lavaan 0.6-12 ended normally after 34 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 61
##
## Number of observations 41
##
## Model Test User Model:
## Standard Robust
## Test Statistic 27.307 41.374
## Degrees of freedom 29 29
## P-value (Chi-square) 0.555 0.064
## Scaling correction factor 0.865
## Shift parameter 9.790
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 705.910 418.854
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.768
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.967
## Tucker-Lewis Index (TLI) 1.004 0.949
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.103
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.112 0.170
## P-value RMSEA <= 0.05 0.688 0.130
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.108 0.108
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## A =~
## ID5 1.000
## ID4 0.751 0.117 6.401 0.000
## ID6 0.585 0.133 4.389 0.000
## ID7 0.379 0.129 2.944 0.003
## ID9 0.507 0.169 2.995 0.003
## ID3 0.428 0.104 4.108 0.000
## B =~
## ID2 1.000
## ID1 1.013 0.237 4.269 0.000
## ID4 0.518 0.132 3.931 0.000
## ID7 0.631 0.142 4.450 0.000
## C =~
## ID8 1.000
## ID9 0.033 0.296 0.111 0.912
## ID10 1.609 0.710 2.268 0.023
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## A ~~
## B 0.047 0.178 0.264 0.792
## C 0.281 0.128 2.193 0.028
## B ~~
## C 0.206 0.119 1.735 0.083
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .ID5 0.000
## .ID4 0.000
## .ID6 0.000
## .ID7 0.000
## .ID9 0.000
## .ID3 0.000
## .ID2 0.000
## .ID1 0.000
## .ID8 0.000
## .ID10 0.000
## A 0.000
## B 0.000
## C 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## ID5|t1 -1.971 0.426 -4.625 0.000
## ID5|t2 -1.052 0.244 -4.318 0.000
## ID5|t3 -0.216 0.200 -1.079 0.281
## ID5|t4 0.343 0.202 1.693 0.090
## ID4|t1 -1.052 0.244 -4.318 0.000
## ID4|t2 -0.092 0.198 -0.463 0.644
## ID4|t3 0.279 0.201 1.387 0.166
## ID6|t1 -1.453 0.296 -4.900 0.000
## ID6|t2 -1.052 0.244 -4.318 0.000
## ID6|t3 -0.694 0.217 -3.204 0.001
## ID6|t4 -0.092 0.198 -0.463 0.644
## ID6|t5 0.408 0.204 1.999 0.046
## ID7|t1 -1.971 0.426 -4.625 0.000
## ID7|t2 -1.657 0.337 -4.919 0.000
## ID7|t3 -0.694 0.217 -3.204 0.001
## ID7|t4 0.153 0.199 0.771 0.441
## ID7|t5 0.694 0.217 3.204 0.001
## ID9|t1 -0.618 0.213 -2.907 0.004
## ID9|t2 -0.343 0.202 -1.693 0.090
## ID9|t3 -0.092 0.198 -0.463 0.644
## ID9|t4 0.618 0.213 2.907 0.004
## ID9|t5 1.165 0.256 4.557 0.000
## ID3|t1 -1.971 0.426 -4.625 0.000
## ID3|t2 -1.052 0.244 -4.318 0.000
## ID3|t3 -0.546 0.209 -2.607 0.009
## ID3|t4 -0.031 0.198 -0.154 0.877
## ID3|t5 0.408 0.204 1.999 0.046
## ID3|t6 0.694 0.217 3.204 0.001
## ID2|t1 -0.859 0.227 -3.782 0.000
## ID2|t2 -0.408 0.204 -1.999 0.046
## ID1|t1 -1.657 0.337 -4.919 0.000
## ID1|t2 -1.052 0.244 -4.318 0.000
## ID1|t3 -0.476 0.207 -2.304 0.021
## ID8|t1 -1.657 0.337 -4.919 0.000
## ID8|t2 -1.453 0.296 -4.900 0.000
## ID8|t3 -1.052 0.244 -4.318 0.000
## ID8|t4 -0.774 0.221 -3.497 0.000
## ID8|t5 -0.408 0.204 -1.999 0.046
## ID8|t6 0.153 0.199 0.771 0.441
## ID10|t1 -1.453 0.296 -4.900 0.000
## ID10|t2 -1.052 0.244 -4.318 0.000
## ID10|t3 -0.774 0.221 -3.497 0.000
## ID10|t4 -0.476 0.207 -2.304 0.021
## ID10|t5 -0.153 0.199 -0.771 0.441
## ID10|t6 0.343 0.202 1.693 0.090
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .ID5 -0.020
## .ID4 0.153
## .ID6 0.651
## .ID7 0.482
## .ID9 0.728
## .ID3 0.813
## .ID2 0.125
## .ID1 0.101
## .ID8 0.601
## .ID10 -0.034
## A 1.020 0.164 6.215 0.000
## B 0.875 0.216 4.053 0.000
## C 0.399 0.209 1.912 0.056
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## ID5 1.000
## ID4 1.000
## ID6 1.000
## ID7 1.000
## ID9 1.000
## ID3 1.000
## ID2 1.000
## ID1 1.000
## ID8 1.000
## ID10 1.000fitMeasures(AFCtres)## npar fmin
## 61.000 0.333
## chisq df
## 27.307 29.000
## pvalue chisq.scaled
## 0.555 41.374
## df.scaled pvalue.scaled
## 29.000 0.064
## chisq.scaling.factor baseline.chisq
## 0.865 705.910
## baseline.df baseline.pvalue
## 45.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 418.854 45.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 1.768
## cfi tli
## 1.000 1.004
## nnfi rfi
## 1.004 0.940
## nfi pnfi
## 0.961 0.620
## ifi rni
## 1.003 1.003
## cfi.scaled tli.scaled
## 0.967 0.949
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.949 NA
## rfi.scaled nfi.scaled
## 0.847 0.901
## ifi.scaled rni.scaled
## 0.968 0.967
## rni.robust rmsea
## NA 0.000
## rmsea.ci.lower rmsea.ci.upper
## 0.000 0.112
## rmsea.pvalue rmsea.scaled
## 0.688 0.103
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.000 0.170
## rmsea.pvalue.scaled rmsea.robust
## 0.130 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.000 NA
## rmsea.pvalue.robust rmr
## NA 0.099
## rmr_nomean srmr
## 0.108 0.108
## srmr_bentler srmr_bentler_nomean
## 0.099 0.108
## crmr crmr_nomean
## 0.108 0.119
## srmr_mplus srmr_mplus_nomean
## NA NA
## cn_05 cn_01
## 63.339 73.638
## gfi agfi
## 0.977 0.930
## pgfi mfi
## 0.315 1.021semPaths(AFCtres, intercepts = FALSE,
edge.label.cex=1.5,
optimizeLatRes = TRUE,
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt=1,
edge.color ="black",
esize = 1,
label.prop=1,
sizeLat = 6,
"std",
layout="tree2",
rotation = 2,
weighted = FALSE)
Se realizaron dos modelos de ecuaciones estructurales. Una forma unifactorial y una trifactorial. Se utilizó una rotación Equamax con un método de facorización de máxima verosimilitud. En el análisis trifactorial se obtuvo un Chi-cuadrado de 41.374, con 29 grados de libertd, CFI: 0.967, NFI: 0.961, RMSEA: 0.103. Se muestra el modelo hipotetizado en la figura.
FA3 <- fa(df_afe,nfactors = 3,rotate = "equamax",fm="ml")
print(FA3)## Factor Analysis using method = ml
## Call: fa(r = df_afe, nfactors = 3, rotate = "equamax", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML2 ML3 ML1 h2 u2 com
## ID1 0.07 0.81 -0.02 0.66 0.335 1.0
## ID2 0.06 0.91 0.14 0.84 0.157 1.1
## ID3 0.42 0.10 -0.08 0.19 0.810 1.2
## ID4 0.76 0.37 0.08 0.72 0.275 1.5
## ID5 0.92 -0.03 0.15 0.86 0.137 1.1
## ID6 0.50 -0.15 0.09 0.29 0.713 1.3
## ID7 0.55 0.38 0.06 0.45 0.549 1.8
## ID8 0.05 0.05 0.99 1.00 0.005 1.0
## ID9 0.40 -0.27 0.34 0.34 0.655 2.7
## ID10 0.30 0.25 0.56 0.46 0.536 2.0
##
## ML2 ML3 ML1
## SS loadings 2.41 1.93 1.48
## Proportion Var 0.24 0.19 0.15
## Cumulative Var 0.24 0.43 0.58
## Proportion Explained 0.41 0.33 0.25
## Cumulative Proportion 0.41 0.75 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 4.53 with Chi Square of 162.26
## The degrees of freedom for the model are 18 and the objective function was 0.66
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.1
##
## The harmonic number of observations is 41 with the empirical chi square 15.56 with prob < 0.62
## The total number of observations was 41 with Likelihood Chi Square = 22.49 with prob < 0.21
##
## Tucker Lewis Index of factoring reliability = 0.896
## RMSEA index = 0.074 and the 90 % confidence intervals are 0 0.17
## BIC = -44.35
## Fit based upon off diagonal values = 0.96
## Measures of factor score adequacy
## ML2 ML3 ML1
## Correlation of (regression) scores with factors 0.95 0.94 1.00
## Multiple R square of scores with factors 0.90 0.89 0.99
## Minimum correlation of possible factor scores 0.81 0.78 0.99fa.plot(FA3,cut=0.3,labels=names(df_afe))
print(FA3$loadings,cutoff = 0.3)##
## Loadings:
## ML2 ML3 ML1
## ID1 0.812
## ID2 0.906
## ID3 0.416
## ID4 0.763 0.369
## ID5 0.916
## ID6 0.504
## ID7 0.553 0.376
## ID8 0.995
## ID9 0.396 0.342
## ID10 0.300 0.558
##
## ML2 ML3 ML1
## SS loadings 2.413 1.928 1.485
## Proportion Var 0.241 0.193 0.148
## Cumulative Var 0.241 0.434 0.583fa.diagram(FA3)
SUBGRUPO IDENTIDAD DEPORTIVA (Solo items Identidad Deportiva)
Se extrajo de la base de datos aquellas columnas correspondientes a la Escala de identidad deportiva. A partir de dicha base, se llevó a cabo el cálculo del coeficiente alfa de Cronbach el cual arrojó un valor de 0.6599. Dicho valor se encuentra dentro del rango propuesto por Tavakol & Dennick.
# Esto es el análisis sacando los 3 ítems que no corresponden a Ident Deport según Mosqueda Ortiz
df_afe_id<-df[c(40:44, 47, 49)]
alpha(df_afe_id) # 0.6598875## [1] 0.6598875Con el objetivo de buscar las correlaciones entre las variables de la Escala, se utilizó el método de correlación de Spearman debido a su mayor robustez. De la aplicación de dicho test, se encontraron correlaciones positivas siendo las de mayor magnitud la correlación entre el ítem 4 y 5 (0.75), entre el 1 y el 2 (0.74) y entre el 8 y el 9 (0.54). El resto de los valores se muestran en la siguiente matriz.
# Esto es el análisis sacando los 3 ítems que no corresponden a Ident Deport según Mosqueda Ortiz
correlacion_id<-cor2(df_afe_id, method ="spearman")## xi
## xi 1.00
## 0.74 1.00
## 0.02 0.22 1.00
## 0.36 0.40 0.33 1.00
## 0.09 0.14 0.37 0.75 1.00
## 0.08 0.22 0.00 0.21 0.26 1.00
## 0.26 0.38 -0.01 0.46 0.48 0.54 1.00colnames(correlacion_id)<-c("ID1","ID2","ID3","ID4","ID5", "ID8", "ID10")
rownames(correlacion_id)<-c("ID1","ID2","ID3","ID4","ID5", "ID8", "ID10")
corrplot.mixed(correlacion_id, upper = "ellipse" , tl.pos = "lt")
Llevamos a cabo la prueba de adecuación de la muestra mediante el test de KMO y de esfericidad de Bartlett. De dichos test encontramos que la adecuación muestral fueron satisfactorios, con un índice global de KMO de 0.55, con todos los índices individuales ubicados entre 0.40 y 0.66. Por otro lado, la prueba de esfericidad de Bartlett resultó estadísticamente significativo con p-valor < 0.001.
KMO_id<-KMO(cor(df_afe_id))
Bartlett_id<-cortest.bartlett(cor(df_afe_id), n=nrow(df_afe_id))
KMO_id## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(df_afe_id))
## Overall MSA = 0.55
## MSA for each item =
## ID1 ID2 ID3 ID4 ID5 ID8 ID10
## 0.51 0.50 0.40 0.66 0.54 0.60 0.59Bartlett_id## $chisq
## [1] 108.2131
##
## $p.value
## [1] 9.891136e-14
##
## $df
## [1] 21#Calculo de determinante, donde valores cercanos a cero indican presencia de correlación entre las variables
#det(cor(df_afe))Análisis Factorial. Por medio de esta estrategia, se lleva a cabo la extracción de factores. El Análisis Factorial intenta reducir la cantidad de dimensiones disponibles sin perder información (o perdiendo la menor cantidad de información disponible) por lo cual, la cantidad máxima de factores va a ser igual a la cantidad de ítems que tengamos. En el gráfico siguiente se puede observar cual es el número de factores más adecuado según la mayoría de los métodos empleados.
nfactoresAF<-n_factors(
df_afe_id,
type = "FA",
rotation = "equamax",
algorithm = "default",
package = c("nFactors", "psych"),
cor = NULL,
safe = TRUE,
n_max = NULL)
plot(nfactoresAF)
nfactoresAFas.data.frame(nfactoresAF)summary(nfactoresAF)El análisis factorial llevado a cabo mostró una estructura de 3 factores que en suma explican el 67% de la varianza total, con una explicación del 24% por parte de primer factor. La siguiente salida muestra los resultados del análisis factorial exploratorio con los factores y las variables que lo componen según el valor de correlación en cada una.
afe_id<-fa(df_afe_id,nfactors = 3,fm = "ml",rotate ="equamax",cor = "cor")
print(afe_id,digits = 2,cut = .30,sort=TRUE)## Factor Analysis using method = ml
## Call: fa(r = df_afe_id, nfactors = 3, rotate = "equamax", fm = "ml",
## cor = "cor")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item ML1 ML3 ML2 h2 u2 com
## ID5 5 0.98 1.00 0.005 1.1
## ID4 4 0.70 0.35 0.65 0.355 1.7
## ID3 3 0.43 0.25 0.754 1.7
## ID2 2 0.93 0.91 0.088 1.1
## ID1 1 0.78 0.61 0.387 1.0
## ID10 7 0.97 1.00 0.005 1.1
## ID8 6 0.58 0.34 0.657 1.1
##
## ML1 ML3 ML2
## SS loadings 1.68 1.67 1.40
## Proportion Var 0.24 0.24 0.20
## Cumulative Var 0.24 0.48 0.68
## Proportion Explained 0.35 0.35 0.30
## Cumulative Proportion 0.35 0.70 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 21 and the objective function was 2.94 with Chi Square of 108.21
## The degrees of freedom for the model are 3 and the objective function was 0.17
##
## The root mean square of the residuals (RMSR) is 0.04
## The df corrected root mean square of the residuals is 0.11
##
## The harmonic number of observations is 41 with the empirical chi square 3.07 with prob < 0.38
## The total number of observations was 41 with Likelihood Chi Square = 5.89 with prob < 0.12
##
## Tucker Lewis Index of factoring reliability = 0.751
## RMSEA index = 0.151 and the 90 % confidence intervals are 0 0.341
## BIC = -5.25
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## ML1 ML3 ML2
## Correlation of (regression) scores with factors 1.00 0.96 1.00
## Multiple R square of scores with factors 0.99 0.92 0.99
## Minimum correlation of possible factor scores 0.99 0.84 0.98Analisis factorial confirmatorio
Análisis de un factor, es decir, con todas las variables ubicadas bajo el mismo factor.
#Especificación del modelo conceptual - Unifactorial
Unfactor_id<-'A =~ ID1 + ID2 + ID3 + ID4 + ID5 + ID8 + ID10'
Unfactor_id <- cfa(Unfactor_id,orthogonal=FALSE, data=df_afe_id, estimator="WLSMV",ordered =names(df_afe_id))
summary(Unfactor_id, fit.measures=TRUE)## lavaan 0.6-12 ended normally after 20 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 37
##
## Number of observations 41
##
## Model Test User Model:
## Standard Robust
## Test Statistic 57.777 60.206
## Degrees of freedom 14 14
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.038
## Shift parameter 4.540
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 501.984 332.133
## Degrees of freedom 21 21
## P-value 0.000 0.000
## Scaling correction factor 1.546
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.909 0.851
## Tucker-Lewis Index (TLI) 0.863 0.777
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.280 0.287
## 90 Percent confidence interval - lower 0.207 0.215
## 90 Percent confidence interval - upper 0.356 0.364
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.222 0.222
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## A =~
## ID1 1.000
## ID2 1.093 0.215 5.094 0.000
## ID3 0.408 0.134 3.047 0.002
## ID4 1.066 0.133 7.995 0.000
## ID5 0.955 0.125 7.625 0.000
## ID8 0.583 0.169 3.452 0.001
## ID10 0.802 0.152 5.293 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .ID1 0.000
## .ID2 0.000
## .ID3 0.000
## .ID4 0.000
## .ID5 0.000
## .ID8 0.000
## .ID10 0.000
## A 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## ID1|t1 -1.657 0.337 -4.919 0.000
## ID1|t2 -1.052 0.244 -4.318 0.000
## ID1|t3 -0.476 0.207 -2.304 0.021
## ID2|t1 -0.859 0.227 -3.782 0.000
## ID2|t2 -0.408 0.204 -1.999 0.046
## ID3|t1 -1.971 0.426 -4.625 0.000
## ID3|t2 -1.052 0.244 -4.318 0.000
## ID3|t3 -0.546 0.209 -2.607 0.009
## ID3|t4 -0.031 0.198 -0.154 0.877
## ID3|t5 0.408 0.204 1.999 0.046
## ID3|t6 0.694 0.217 3.204 0.001
## ID4|t1 -1.052 0.244 -4.318 0.000
## ID4|t2 -0.092 0.198 -0.463 0.644
## ID4|t3 0.279 0.201 1.387 0.166
## ID5|t1 -1.971 0.426 -4.625 0.000
## ID5|t2 -1.052 0.244 -4.318 0.000
## ID5|t3 -0.216 0.200 -1.079 0.281
## ID5|t4 0.343 0.202 1.693 0.090
## ID8|t1 -1.657 0.337 -4.919 0.000
## ID8|t2 -1.453 0.296 -4.900 0.000
## ID8|t3 -1.052 0.244 -4.318 0.000
## ID8|t4 -0.774 0.221 -3.497 0.000
## ID8|t5 -0.408 0.204 -1.999 0.046
## ID8|t6 0.153 0.199 0.771 0.441
## ID10|t1 -1.453 0.296 -4.900 0.000
## ID10|t2 -1.052 0.244 -4.318 0.000
## ID10|t3 -0.774 0.221 -3.497 0.000
## ID10|t4 -0.476 0.207 -2.304 0.021
## ID10|t5 -0.153 0.199 -0.771 0.441
## ID10|t6 0.343 0.202 1.693 0.090
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .ID1 0.310
## .ID2 0.176
## .ID3 0.885
## .ID4 0.216
## .ID5 0.371
## .ID8 0.766
## .ID10 0.556
## A 0.690 0.153 4.496 0.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## ID1 1.000
## ID2 1.000
## ID3 1.000
## ID4 1.000
## ID5 1.000
## ID8 1.000
## ID10 1.000fitMeasures(Unfactor_id)## npar fmin
## 37.000 0.705
## chisq df
## 57.777 14.000
## pvalue chisq.scaled
## 0.000 60.206
## df.scaled pvalue.scaled
## 14.000 0.000
## chisq.scaling.factor baseline.chisq
## 1.038 501.984
## baseline.df baseline.pvalue
## 21.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 332.133 21.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 1.546
## cfi tli
## 0.909 0.863
## nnfi rfi
## 0.863 0.827
## nfi pnfi
## 0.885 0.590
## ifi rni
## 0.910 0.909
## cfi.scaled tli.scaled
## 0.851 0.777
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.777 NA
## rfi.scaled nfi.scaled
## 0.728 0.819
## ifi.scaled rni.scaled
## 0.855 0.851
## rni.robust rmsea
## NA 0.280
## rmsea.ci.lower rmsea.ci.upper
## 0.207 0.356
## rmsea.pvalue rmsea.scaled
## 0.000 0.287
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.215 0.364
## rmsea.pvalue.scaled rmsea.robust
## 0.000 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## NA NA
## rmsea.pvalue.robust rmr
## NA 0.199
## rmr_nomean srmr
## 0.222 0.222
## srmr_bentler srmr_bentler_nomean
## 0.199 0.222
## crmr crmr_nomean
## 0.222 0.257
## srmr_mplus srmr_mplus_nomean
## NA NA
## cn_05 cn_01
## 17.397 21.175
## gfi agfi
## 0.931 0.750
## pgfi mfi
## 0.256 0.579semPaths(Unfactor_id, intercepts = FALSE,
edge.label.cex=1.5,
optimizeLatRes = TRUE,
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt=1,
edge.color ="black",
esize = 1,
label.prop=1,
sizeLat = 6,
"std",
layout="tree2",
rotation = 2,
weighted = FALSE)
#Especificación del modelo conceptual - Trifactorial
Tresfactores_id<-'Identidad Social =~ ID1 + ID2
Exclusividad =~ ID3 + ID4 + ID5
Afectividad negativa =~ ID 8 + ID10'
AFCtres_id <- cfa(Tresfactores_id,orthogonal=FALSE, data=df_afe_id, estimator="WLSMV",ordered =names(df_afe_id))## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 2.169638e-17) is close to zero. This may be a symptom that the
## model is not identified.## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negativesummary(AFCtres_id, fit.measures=TRUE)## lavaan 0.6-12 ended normally after 39 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 40
##
## Number of observations 41
##
## Model Test User Model:
## Standard Robust
## Test Statistic 7.859 13.882
## Degrees of freedom 11 11
## P-value (Chi-square) 0.726 0.240
## Scaling correction factor 0.726
## Shift parameter 3.058
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 501.984 332.133
## Degrees of freedom 21 21
## P-value 0.000 0.000
## Scaling correction factor 1.546
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.991
## Tucker-Lewis Index (TLI) 1.012 0.982
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.081
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.124 0.194
## P-value RMSEA <= 0.05 0.788 0.319
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.087 0.087
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## IdentidadSocial =~
## ID1 1.000
## ID2 1.447 0.612 2.366 0.018
## Exclusividad =~
## ID3 1.000
## ID4 2.380 0.610 3.903 0.000
## ID5 2.134 0.576 3.708 0.000
## Afectividadnegativa =~
## ID8 1.000
## ID10 2.078 1.256 1.655 0.098
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## IdentidadSocial ~~
## Exclusividad 0.116 0.081 1.427 0.154
## Afectividdngtv 0.151 0.109 1.389 0.165
## Exclusividad ~~
## Afectividdngtv 0.103 0.071 1.457 0.145
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .ID1 0.000
## .ID2 0.000
## .ID3 0.000
## .ID4 0.000
## .ID5 0.000
## .ID8 0.000
## .ID10 0.000
## IdentidadSocil 0.000
## Exclusividad 0.000
## Afectividdngtv 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## ID1|t1 -1.657 0.337 -4.919 0.000
## ID1|t2 -1.052 0.244 -4.318 0.000
## ID1|t3 -0.476 0.207 -2.304 0.021
## ID2|t1 -0.859 0.227 -3.782 0.000
## ID2|t2 -0.408 0.204 -1.999 0.046
## ID3|t1 -1.971 0.426 -4.625 0.000
## ID3|t2 -1.052 0.244 -4.318 0.000
## ID3|t3 -0.546 0.209 -2.607 0.009
## ID3|t4 -0.031 0.198 -0.154 0.877
## ID3|t5 0.408 0.204 1.999 0.046
## ID3|t6 0.694 0.217 3.204 0.001
## ID4|t1 -1.052 0.244 -4.318 0.000
## ID4|t2 -0.092 0.198 -0.463 0.644
## ID4|t3 0.279 0.201 1.387 0.166
## ID5|t1 -1.971 0.426 -4.625 0.000
## ID5|t2 -1.052 0.244 -4.318 0.000
## ID5|t3 -0.216 0.200 -1.079 0.281
## ID5|t4 0.343 0.202 1.693 0.090
## ID8|t1 -1.657 0.337 -4.919 0.000
## ID8|t2 -1.453 0.296 -4.900 0.000
## ID8|t3 -1.052 0.244 -4.318 0.000
## ID8|t4 -0.774 0.221 -3.497 0.000
## ID8|t5 -0.408 0.204 -1.999 0.046
## ID8|t6 0.153 0.199 0.771 0.441
## ID10|t1 -1.453 0.296 -4.900 0.000
## ID10|t2 -1.052 0.244 -4.318 0.000
## ID10|t3 -0.774 0.221 -3.497 0.000
## ID10|t4 -0.476 0.207 -2.304 0.021
## ID10|t5 -0.153 0.199 -0.771 0.441
## ID10|t6 0.343 0.202 1.693 0.090
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .ID1 0.387
## .ID2 -0.283
## .ID3 0.839
## .ID4 0.089
## .ID5 0.268
## .ID8 0.692
## .ID10 -0.330
## IdentidadSocil 0.613 0.272 2.257 0.024
## Exclusividad 0.161 0.081 1.973 0.048
## Afectividdngtv 0.308 0.212 1.452 0.146
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## ID1 1.000
## ID2 1.000
## ID3 1.000
## ID4 1.000
## ID5 1.000
## ID8 1.000
## ID10 1.000fitMeasures(AFCtres_id)## npar fmin
## 40.000 0.096
## chisq df
## 7.859 11.000
## pvalue chisq.scaled
## 0.726 13.882
## df.scaled pvalue.scaled
## 11.000 0.240
## chisq.scaling.factor baseline.chisq
## 0.726 501.984
## baseline.df baseline.pvalue
## 21.000 0.000
## baseline.chisq.scaled baseline.df.scaled
## 332.133 21.000
## baseline.pvalue.scaled baseline.chisq.scaling.factor
## 0.000 1.546
## cfi tli
## 1.000 1.012
## nnfi rfi
## 1.012 0.970
## nfi pnfi
## 0.984 0.516
## ifi rni
## 1.006 1.007
## cfi.scaled tli.scaled
## 0.991 0.982
## cfi.robust tli.robust
## NA NA
## nnfi.scaled nnfi.robust
## 0.982 NA
## rfi.scaled nfi.scaled
## 0.920 0.958
## ifi.scaled rni.scaled
## 0.991 0.991
## rni.robust rmsea
## NA 0.000
## rmsea.ci.lower rmsea.ci.upper
## 0.000 0.124
## rmsea.pvalue rmsea.scaled
## 0.788 0.081
## rmsea.ci.lower.scaled rmsea.ci.upper.scaled
## 0.000 0.194
## rmsea.pvalue.scaled rmsea.robust
## 0.319 NA
## rmsea.ci.lower.robust rmsea.ci.upper.robust
## 0.000 NA
## rmsea.pvalue.robust rmr
## NA 0.078
## rmr_nomean srmr
## 0.087 0.087
## srmr_bentler srmr_bentler_nomean
## 0.078 0.087
## crmr crmr_nomean
## 0.087 0.101
## srmr_mplus srmr_mplus_nomean
## NA NA
## cn_05 cn_01
## 101.140 126.841
## gfi agfi
## 0.991 0.957
## pgfi mfi
## 0.214 1.040semPaths(AFCtres_id,
intercepts = FALSE,
edge.label.cex=1.5,
optimizeLatRes = TRUE,
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt=1,
edge.color ="black",
esize = 1,
label.prop=1,
sizeLat = 6,
"std",
layout="tree2",
rotation = 2,
weighted = FALSE)
El factor latente Identidad Social queda explicado por las dos variables ID1 y 2. El factor latente Exclusividad queda explicado por las dos variables ID5, 4 y 3. El factor latente Afectividad negativa queda explicado por las dos variables ID8 y 10. Lineas enteras de la variable latente explica de forma más fuerte la variable.
FA3_id <- fa(df_afe_id,nfactors = 3,rotate = "equamax",fm="ml")
print(FA3_id)## Factor Analysis using method = ml
## Call: fa(r = df_afe_id, nfactors = 3, rotate = "equamax", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML3 ML2 h2 u2 com
## ID1 0.05 0.78 0.02 0.61 0.387 1.0
## ID2 0.05 0.93 0.19 0.91 0.088 1.1
## ID3 0.43 0.19 -0.16 0.25 0.754 1.7
## ID4 0.70 0.35 0.20 0.65 0.355 1.7
## ID5 0.98 -0.04 0.18 1.00 0.005 1.1
## ID8 0.09 0.06 0.58 0.34 0.657 1.1
## ID10 0.20 0.15 0.97 1.00 0.005 1.1
##
## ML1 ML3 ML2
## SS loadings 1.68 1.67 1.40
## Proportion Var 0.24 0.24 0.20
## Cumulative Var 0.24 0.48 0.68
## Proportion Explained 0.35 0.35 0.30
## Cumulative Proportion 0.35 0.70 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 21 and the objective function was 2.94 with Chi Square of 108.21
## The degrees of freedom for the model are 3 and the objective function was 0.17
##
## The root mean square of the residuals (RMSR) is 0.04
## The df corrected root mean square of the residuals is 0.11
##
## The harmonic number of observations is 41 with the empirical chi square 3.07 with prob < 0.38
## The total number of observations was 41 with Likelihood Chi Square = 5.89 with prob < 0.12
##
## Tucker Lewis Index of factoring reliability = 0.751
## RMSEA index = 0.151 and the 90 % confidence intervals are 0 0.341
## BIC = -5.25
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## ML1 ML3 ML2
## Correlation of (regression) scores with factors 1.00 0.96 1.00
## Multiple R square of scores with factors 0.99 0.92 0.99
## Minimum correlation of possible factor scores 0.99 0.84 0.98fa.plot(FA3_id,cut=0.3,labels=names(df_afe))
print(FA3_id$loadings,cutoff = 0.3)##
## Loadings:
## ML1 ML3 ML2
## ID1 0.780
## ID2 0.934
## ID3 0.426
## ID4 0.695 0.348
## ID5 0.980
## ID8 0.575
## ID10 0.966
##
## ML1 ML3 ML2
## SS loadings 1.678 1.668 1.402
## Proportion Var 0.240 0.238 0.200
## Cumulative Var 0.240 0.478 0.678fa.diagram(FA3_id)
PARTICIPATION MOTIVATION INVENTORY
El siguiente análisis se llevó a cabo siguiendo lo reportado por el trabajo de Miguel Ángel Torralba 2017.
Se extrajo de la base de datos aquellas columnas correspondientes a la Escala de identidad deportiva. A partir de dicha base, se llevó a cabo el cálculo del coeficiente alfa de Cronbach el cual arrojó un valor de 0.848. Dicho valor se encuentra dentro del rango propuesto por Tavakol & Dennick.
Se extrajo de la base de datos aquellas columnas correspondientes al cuestionario Participation Motivation Inventory (Gill et al. 1983). A partir de dicha base, se llevó a cabo el cálculo del coeficiente alfa de Cronbach el cual arrojó un valor de 0.848. Dicho valor se encuentra dentro del rango propuesto por Tavakol & Dennick.
df_pmi<-df[c(10:39)]
alpha(df_pmi) # 0.847609## [1] 0.847609En la siguiente tabla se reportan las diferencias entre ambos sexos de las variables edad, deporte, clasificación, frecuencia, horas, RPE, años, federación y el puntaje de cada ítem del cuestionario. Debido a la variabilidad de respuestas en el ítem clasificación, se recomienda subagruparlos o reetiquetarlos para disminuir la cantidad de categorías de dicha variable.
tab.noby <- tableby(Sexo~., data=df)
summary(tab.noby)##
##
## | | Femenino (N=13) | Masculino (N=28) | Total (N=41) | p value|
## |:-----------------------------------------------|:---------------:|:----------------:|:--------------:|-------:|
## |**Edad** | | | | 0.981|
## | Mean (SD) | 30.538 (6.489) | 30.464 (10.017) | 30.488 (8.964) | |
## | Range | 21.000 - 41.000 | 0.000 - 50.000 | 0.000 - 50.000 | |
## |**Deporte** | | | | 0.205|
## | Atletismo adaptado | 2 (15.4%) | 0 (0.0%) | 2 (4.9%) | |
## | Básquet sobre silla de ruedas | 6 (46.2%) | 15 (53.6%) | 21 (51.2%) | |
## | Natación adaptada | 1 (7.7%) | 2 (7.1%) | 3 (7.3%) | |
## | Tenis de mesa adaptado | 4 (30.8%) | 11 (39.3%) | 15 (36.6%) | |
## |**Clasificacion** | | | | 0.585|
## | 1 | 1 (7.7%) | 3 (10.7%) | 4 (9.8%) | |
## | 1,0 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | 1.5 | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## | 2 | 0 (0.0%) | 2 (7.1%) | 2 (4.9%) | |
## | 2.5 | 1 (7.7%) | 2 (7.1%) | 3 (7.3%) | |
## | 3 | 1 (7.7%) | 4 (14.3%) | 5 (12.2%) | |
## | 3.5 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | 4 | 2 (15.4%) | 2 (7.1%) | 4 (9.8%) | |
## | 4,5 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | 7 | 1 (7.7%) | 1 (3.6%) | 2 (4.9%) | |
## | 8 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | C3 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | C5 | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## | Categoria 2 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | Clase 2 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | Clase 3 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | Clase 4 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | Clase 5 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | Clase 7 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | F 35 | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## | F2 | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## | F3 | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## | F41 | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## | Ocho | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | S5 SB4 y SM5 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | S6 | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | S9 | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## |**Frecuencia** | | | | 0.639|
## | 2 veces por semana o menos | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | 3 veces por semana | 3 (23.1%) | 4 (14.3%) | 7 (17.1%) | |
## | 4 veces por semana o más | 10 (76.9%) | 23 (82.1%) | 33 (80.5%) | |
## |**Horas** | | | | 0.154|
## | Entre 1 y 2 horas | 2 (15.4%) | 4 (14.3%) | 6 (14.6%) | |
## | Entre 2 y 3 horas | 3 (23.1%) | 15 (53.6%) | 18 (43.9%) | |
## | Más de 3 horas | 8 (61.5%) | 9 (32.1%) | 17 (41.5%) | |
## |**RPE** | | | | 0.035|
## | Mean (SD) | 8.615 (1.044) | 7.786 (1.166) | 8.049 (1.182) | |
## | Range | 7.000 - 10.000 | 5.000 - 10.000 | 5.000 - 10.000 | |
## |**Años** | | | | 0.108|
## | 2 años o menos | 1 (7.7%) | 1 (3.6%) | 2 (4.9%) | |
## | 3 años | 3 (23.1%) | 0 (0.0%) | 3 (7.3%) | |
## | 4 años | 1 (7.7%) | 3 (10.7%) | 4 (9.8%) | |
## | 5 años | 1 (7.7%) | 4 (14.3%) | 5 (12.2%) | |
## | 6 años o más | 7 (53.8%) | 20 (71.4%) | 27 (65.9%) | |
## |**Federacion** | | | | 0.677|
## | Adima | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | Faba | 1 (7.7%) | 2 (7.1%) | 3 (7.3%) | |
## | FABA | 5 (38.5%) | 12 (42.9%) | 17 (41.5%) | |
## | Fadecir | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | Fadepac | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## | Fadesir | 2 (15.4%) | 2 (7.1%) | 4 (9.8%) | |
## | Fatema | 2 (15.4%) | 6 (21.4%) | 8 (19.5%) | |
## | FATEMA | 1 (7.7%) | 3 (10.7%) | 4 (9.8%) | |
## | Fecoteme | 0 (0.0%) | 1 (3.6%) | 1 (2.4%) | |
## | FECOTEME | 1 (7.7%) | 0 (0.0%) | 1 (2.4%) | |
## |**M1** | | | | 0.335|
## | Mean (SD) | 1.000 (0.000) | 1.071 (0.262) | 1.049 (0.218) | |
## | Range | 1.000 - 1.000 | 1.000 - 2.000 | 1.000 - 2.000 | |
## |**M2** | | | | 0.569|
## | Mean (SD) | 1.846 (0.689) | 1.964 (0.576) | 1.927 (0.608) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M3** | | | | 0.063|
## | Mean (SD) | 1.538 (0.519) | 1.214 (0.499) | 1.317 (0.521) | |
## | Range | 1.000 - 2.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M4** | | | | 0.393|
## | Mean (SD) | 1.846 (0.801) | 2.071 (0.766) | 2.000 (0.775) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M5** | | | | 0.302|
## | Mean (SD) | 1.615 (0.650) | 1.857 (0.705) | 1.780 (0.690) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M6** | | | | 0.477|
## | Mean (SD) | 1.231 (0.439) | 1.357 (0.559) | 1.317 (0.521) | |
## | Range | 1.000 - 2.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M7** | | | | 0.156|
## | Mean (SD) | 1.231 (0.439) | 1.571 (0.790) | 1.463 (0.711) | |
## | Range | 1.000 - 2.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M8** | | | | 0.801|
## | Mean (SD) | 1.308 (0.480) | 1.357 (0.621) | 1.341 (0.575) | |
## | Range | 1.000 - 2.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M9** | | | | 0.560|
## | Mean (SD) | 2.769 (0.599) | 2.857 (0.356) | 2.829 (0.442) | |
## | Range | 1.000 - 3.000 | 2.000 - 3.000 | 1.000 - 3.000 | |
## |**M10** | | | | 0.350|
## | Mean (SD) | 1.231 (0.599) | 1.429 (0.634) | 1.366 (0.623) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M11** | | | | 0.046|
## | Mean (SD) | 1.615 (0.506) | 2.071 (0.716) | 1.927 (0.685) | |
## | Range | 1.000 - 2.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M12** | | | | 0.835|
## | Mean (SD) | 1.692 (0.751) | 1.643 (0.678) | 1.659 (0.693) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M13** | | | | 0.702|
## | Mean (SD) | 2.385 (0.768) | 2.286 (0.763) | 2.317 (0.756) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M14** | | | | 0.756|
## | Mean (SD) | 2.077 (0.862) | 2.000 (0.667) | 2.024 (0.724) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M15** | | | | 0.094|
## | Mean (SD) | 1.077 (0.277) | 1.321 (0.476) | 1.244 (0.435) | |
## | Range | 1.000 - 2.000 | 1.000 - 2.000 | 1.000 - 2.000 | |
## |**M16** | | | | 0.027|
## | Mean (SD) | 1.385 (0.650) | 1.964 (0.793) | 1.780 (0.791) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M17** | | | | 0.046|
## | Mean (SD) | 1.231 (0.439) | 1.679 (0.723) | 1.537 (0.674) | |
## | Range | 1.000 - 2.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M18** | | | | 0.130|
## | Mean (SD) | 1.077 (0.277) | 1.357 (0.621) | 1.268 (0.549) | |
## | Range | 1.000 - 2.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M19** | | | | 0.463|
## | Mean (SD) | 1.846 (0.801) | 2.036 (0.744) | 1.976 (0.758) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M20** | | | | 0.421|
## | Mean (SD) | 1.154 (0.376) | 1.071 (0.262) | 1.098 (0.300) | |
## | Range | 1.000 - 2.000 | 1.000 - 2.000 | 1.000 - 2.000 | |
## |**M21** | | | | 0.634|
## | Mean (SD) | 1.769 (0.725) | 1.893 (0.786) | 1.854 (0.760) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M22** | | | | 0.769|
## | Mean (SD) | 1.077 (0.277) | 1.107 (0.315) | 1.098 (0.300) | |
## | Range | 1.000 - 2.000 | 1.000 - 2.000 | 1.000 - 2.000 | |
## |**M23** | | | | 0.951|
## | Mean (SD) | 1.077 (0.277) | 1.071 (0.262) | 1.073 (0.264) | |
## | Range | 1.000 - 2.000 | 1.000 - 2.000 | 1.000 - 2.000 | |
## |**M24** | | | | 0.499|
## | Mean (SD) | 1.231 (0.439) | 1.143 (0.356) | 1.171 (0.381) | |
## | Range | 1.000 - 2.000 | 1.000 - 2.000 | 1.000 - 2.000 | |
## |**M25** | | | | 0.553|
## | Mean (SD) | 2.846 (0.376) | 2.750 (0.518) | 2.780 (0.475) | |
## | Range | 2.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M26** | | | | 0.288|
## | Mean (SD) | 1.077 (0.277) | 1.214 (0.418) | 1.171 (0.381) | |
## | Range | 1.000 - 2.000 | 1.000 - 2.000 | 1.000 - 2.000 | |
## |**M27** | | | | 0.973|
## | Mean (SD) | 2.385 (0.650) | 2.393 (0.737) | 2.390 (0.703) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M28** | | | | 0.635|
## | Mean (SD) | 1.385 (0.768) | 1.286 (0.535) | 1.317 (0.610) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M29** | | | | 0.303|
## | Mean (SD) | 1.308 (0.480) | 1.500 (0.577) | 1.439 (0.550) | |
## | Range | 1.000 - 2.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**M30** | | | | 0.282|
## | Mean (SD) | 2.077 (0.760) | 1.821 (0.670) | 1.902 (0.700) | |
## | Range | 1.000 - 3.000 | 1.000 - 3.000 | 1.000 - 3.000 | |
## |**ID1** | | | | 0.311|
## | Mean (SD) | 6.692 (0.751) | 6.393 (0.916) | 6.488 (0.870) | |
## | Range | 5.000 - 7.000 | 4.000 - 7.000 | 4.000 - 7.000 | |
## |**ID2** | | | | 0.222|
## | Mean (SD) | 6.692 (0.751) | 6.357 (0.826) | 6.463 (0.809) | |
## | Range | 5.000 - 7.000 | 5.000 - 7.000 | 5.000 - 7.000 | |
## |**ID3** | | | | 0.821|
## | Mean (SD) | 4.538 (2.184) | 4.679 (1.657) | 4.634 (1.813) | |
## | Range | 1.000 - 7.000 | 2.000 - 7.000 | 1.000 - 7.000 | |
## |**ID4** | | | | 0.588|
## | Mean (SD) | 5.923 (1.038) | 5.714 (1.182) | 5.780 (1.129) | |
## | Range | 4.000 - 7.000 | 4.000 - 7.000 | 4.000 - 7.000 | |
## |**ID5** | | | | 0.376|
## | Mean (SD) | 6.000 (1.000) | 5.607 (1.423) | 5.732 (1.304) | |
## | Range | 5.000 - 7.000 | 1.000 - 7.000 | 1.000 - 7.000 | |
## |**ID6** | | | | 0.293|
## | Mean (SD) | 5.769 (1.423) | 5.143 (1.880) | 5.341 (1.755) | |
## | Range | 3.000 - 7.000 | 1.000 - 7.000 | 1.000 - 7.000 | |
## |**ID7** | | | | 0.029|
## | Mean (SD) | 6.000 (1.080) | 5.036 (1.347) | 5.341 (1.334) | |
## | Range | 4.000 - 7.000 | 1.000 - 7.000 | 1.000 - 7.000 | |
## |**ID8** | | | | 0.695|
## | Mean (SD) | 5.769 (1.301) | 5.536 (1.934) | 5.610 (1.745) | |
## | Range | 3.000 - 7.000 | 1.000 - 7.000 | 1.000 - 7.000 | |
## |**ID9** | | | | 0.736|
## | Mean (SD) | 3.154 (1.908) | 3.357 (1.726) | 3.293 (1.764) | |
## | Range | 1.000 - 6.000 | 1.000 - 6.000 | 1.000 - 6.000 | |
## |**ID10** | | | | 0.011|
## | Mean (SD) | 6.308 (0.947) | 4.643 (2.129) | 5.171 (1.986) | |
## | Range | 4.000 - 7.000 | 1.000 - 7.000 | 1.000 - 7.000 | |Con el objetivo de buscar las correlaciones entre las variables del cuestionario, se utilizó el método de correlación de Spearman debido a su mayor robustez. De la aplicación de dicho test, se encontraron correlaciones positivas y negativas siendo las de mayor magnitud la correlación entre el ítem 8 y 18 (0.73), 1 y 22 (0.69), 6 y el 7 (0.62) y entre el 2 y 11 (0.59). El resto de los valores se muestran en la siguiente matriz. (Chequear si me saltee algún valor importante a destacar)
correlacion_pmi<-cor2(df_pmi, method ="spearman")## xi
## xi 1.00
## 0.03 1.00
## 0.10 0.08 1.00
## 0.00 0.43 0.22 1.00
## 0.09 0.27 0.05 0.34 1.00
## 0.10 0.28 0.10 0.56 0.37 1.00
## 0.05 0.29 0.13 0.38 0.45 0.62 1.00
## 0.09 0.13 0.16 0.17 0.14 0.35 0.41 1.00
## -0.22 0.08 -0.19 0.28 0.28 0.12 0.05 -0.04 1.00
## 0.31 0.03 0.05 0.17 0.20 0.22 0.11 0.51 -0.21 1.00
## 0.19 0.59 -0.01 0.37 0.39 0.24 0.44 0.48 0.07 0.16 1.00
## 0.15 -0.17 0.06 -0.03 0.02 0.09 0.07 0.32 -0.08 0.24 0.19
## -0.07 0.25 -0.19 0.15 0.19 0.24 0.08 0.12 0.18 0.09 0.19
## -0.01 0.05 0.15 0.31 0.20 0.34 0.21 0.00 0.03 -0.07 0.00
## 0.14 0.17 0.16 0.30 0.19 0.48 0.58 0.38 0.08 -0.02 0.39
## 0.21 0.25 0.02 0.49 0.32 0.25 0.27 0.22 0.10 0.38 0.41
## 0.21 0.29 0.07 0.28 0.32 0.36 0.55 0.34 -0.06 0.36 0.44
## 0.40 0.30 0.17 0.33 0.26 0.36 0.56 0.73 0.06 0.41 0.58
## 0.01 0.31 0.02 0.00 0.21 0.04 0.27 0.33 -0.21 0.24 0.58
## 0.31 0.05 0.32 0.00 0.10 0.14 0.27 0.30 -0.09 0.12 0.15
## 0.19 -0.17 -0.07 0.27 0.23 0.21 0.13 0.21 0.09 0.32 0.19
## 0.69 0.04 0.14 -0.11 0.10 -0.04 -0.08 0.13 -0.35 0.29 0.15
## 0.37 -0.12 0.22 -0.24 -0.19 0.02 0.14 0.21 -0.44 0.20 0.03
## 0.20 0.37 0.12 0.42 0.16 0.58 0.52 0.60 0.01 0.19 0.43
## -0.17 0.24 0.06 0.16 0.30 0.06 0.13 0.19 0.33 0.00 0.12
## 0.20 -0.04 0.30 0.08 0.06 0.12 0.36 0.38 -0.20 0.23 0.24
## 0.02 0.02 -0.20 0.02 -0.11 0.11 0.05 0.17 0.19 0.14 0.14
## 0.36 -0.19 0.14 -0.12 -0.09 0.14 -0.02 -0.14 -0.05 -0.04 -0.39
## 0.03 0.38 0.05 0.03 0.45 0.06 0.17 0.15 0.21 0.04 0.41
## 0.04 0.34 0.14 0.18 0.01 0.26 0.19 0.35 -0.17 0.28 0.25
##
## 1.00
## -0.05 1.00
## 0.06 0.20 1.00
## 0.05 -0.09 0.06 1.00
## 0.10 0.23 0.10 0.16 1.00
## 0.17 0.10 0.15 0.36 0.50 1.00
## 0.29 0.05 0.08 0.51 0.40 0.60 1.00
## 0.44 0.02 0.00 0.10 0.29 0.54 0.35 1.00
## 0.08 0.19 -0.13 0.39 0.10 0.15 0.39 -0.10 1.00
## 0.57 0.08 0.12 0.04 0.37 0.26 0.26 0.39 0.07 1.00
## 0.16 -0.10 -0.13 0.00 0.10 0.15 0.20 0.23 0.17 0.16 1.00
## 0.28 -0.11 -0.27 0.28 -0.06 0.09 0.07 0.14 0.22 0.05 0.54
## 0.14 0.34 0.26 0.35 0.20 0.29 0.57 0.19 0.29 0.20 0.07
## 0.30 0.17 0.10 0.00 -0.14 0.02 0.12 0.21 -0.04 0.21 -0.04
## 0.32 -0.36 -0.01 0.50 0.04 0.30 0.35 0.28 0.07 0.09 0.29
## 0.37 0.24 -0.09 0.00 0.05 0.26 0.21 0.20 0.17 0.32 0.17
## 0.11 -0.09 0.14 0.20 -0.08 0.14 0.06 -0.32 0.39 -0.07 0.17
## -0.02 0.19 -0.23 -0.03 0.21 0.14 0.19 0.19 0.21 -0.01 0.05
## 0.24 0.20 0.25 0.00 0.16 0.13 0.28 0.33 0.17 0.12 0.17
##
## 1.00
## 0.12 1.00
## -0.09 0.06 1.00
## 0.62 0.14 0.06 1.00
## 0.11 0.21 0.20 -0.07 1.00
## 0.24 0.02 0.02 0.02 0.19 1.00
## -0.05 0.18 0.06 0.00 -0.03 -0.16 1.00
## 0.03 0.35 0.29 -0.03 0.39 -0.07 -0.05 1.00colnames(correlacion_pmi)<-c("M1", "M2", "M3", "M4", "M5", "M6", "M7", "M8", "M9", "M10", "M11", "M12", "M13", "M14", "M15", "M16", "M17", "M18", "M19", "M20", "M21", "M22", "M23", "M24", "M25", "M26", "M27", "M28", "M29", "M30")
rownames(correlacion_pmi)<-c("M1", "M2", "M3", "M4", "M5", "M6", "M7", "M8", "M9", "M10", "M11", "M12", "M13", "M14", "M15", "M16", "M17", "M18", "M19", "M20", "M21", "M22", "M23", "M24", "M25", "M26", "M27", "M28", "M29", "M30")
corrplot.mixed(correlacion_pmi, upper = "ellipse" , tl.pos = "lt")
Llevamos a cabo la prueba de adecuación de la muestra mediante el test de KMO y de esfericidad de Bartlett. De dichos test encontramos que la adecuación muestral según el índice global de KMO no resulta aceptable (0.37), con todos los índices individuales ubicados entre 0.12 y 0.75. Sin embargo, la prueba de esfericidad de Bartlett resultó estadísticamente significativo con p-valor < 0.001.
KMO_pmi<-KMO(cor(df_pmi))
Bartlett_pmi<-cortest.bartlett(cor(df_pmi), n=nrow(df_pmi))
KMO_pmi # Medida de adecuación de los datos para el análisis factorial## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(df_pmi))
## Overall MSA = 0.37
## MSA for each item =
## M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 M16
## 0.32 0.65 0.21 0.49 0.57 0.37 0.43 0.59 0.35 0.48 0.51 0.33 0.17 0.12 0.70 0.39
## M17 M18 M19 M20 M21 M22 M23 M24 M25 M26 M27 M28 M29 M30
## 0.75 0.70 0.41 0.15 0.25 0.29 0.37 0.64 0.12 0.58 0.17 0.12 0.50 0.23Bartlett_pmi # Comprueba la H0 que afirma que las variables no están correlacionadas## $chisq
## [1] 732.0237
##
## $p.value
## [1] 1.801651e-17
##
## $df
## [1] 435#Calculo de determinante, donde valores cercanos a cero indican presencia de correlación entre las variables
#det(cor(df_afe))Análisis Factorial. Por medio de esta estrategia, se lleva a cabo la extracción de factores. El Análisis Factorial intenta reducir la cantidad de dimensiones disponibles sin perder información (o perdiendo la menor cantidad de información disponible) por lo cual, la cantidad máxima de factores va a ser igual a la cantidad de ítems que tengamos. En el gráfico siguiente se puede observar cual es el número de factores más adecuado según la mayoría de los métodos empleados.
nfactoresAF<-n_factors(
df_pmi,
type = "FA",
rotation = "varimax",
algorithm = "mle",
package = c("nFactors", "psych"),
cor = NULL,
safe = TRUE,
n_max = NULL)
plot(nfactoresAF)
nfactoresAFas.data.frame(nfactoresAF)summary(nfactoresAF)El análisis factorial llevado a cabo mostró una estructura de 9 factores que en suma explican el 62% de la varianza total. La siguiente salida muestra los resultados del análisis factorial exploratorio con los factores y las variables que lo componen según el valor de correlación en cada una. A su vez, se muestra en la figura las cargas de cada variable que es explicada por cada factor latente. En la siguiente salida se muestran los valores de cada ítem con su aporte al cada uno de los 9 componentes que describe el análisis factorial realizado.
afe_pmi<-fa(df_pmi,nfactors = 9,fm = "ml",rotate ="varimax",cor = "cor")
print(afe_pmi,digits = 3,sort=TRUE)## Factor Analysis using method = ml
## Call: fa(r = df_pmi, nfactors = 9, rotate = "varimax", fm = "ml", cor = "cor")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item ML9 ML1 ML5 ML7 ML8 ML4 ML3 ML2 ML6 h2
## M6 6 0.760 0.341 0.103 0.184 0.032 -0.033 0.076 0.148 0.082 0.774
## M4 4 0.697 0.119 0.254 0.021 -0.016 -0.035 0.025 -0.035 -0.097 0.578
## M7 7 0.531 0.330 0.213 0.468 0.030 -0.106 0.284 -0.097 -0.161 0.784
## M5 5 0.451 0.009 0.228 0.056 0.117 0.055 0.159 -0.128 -0.197 0.356
## M14 14 0.438 -0.001 -0.040 -0.062 0.076 -0.052 0.051 0.123 0.001 0.223
## M8 8 0.098 0.922 0.189 0.201 0.182 -0.051 0.112 -0.040 0.095 0.995
## M18 18 0.227 0.715 0.312 0.180 0.133 0.175 0.323 0.072 -0.147 0.872
## M24 24 0.439 0.547 0.274 0.147 0.075 0.106 -0.026 0.049 0.087 0.616
## M10 10 0.209 0.453 -0.069 -0.122 0.085 0.213 0.282 -0.118 0.387 0.564
## M2 2 0.257 0.043 0.897 -0.039 -0.150 -0.009 0.074 0.060 0.112 0.919
## M11 11 0.145 0.309 0.663 0.204 0.179 0.159 0.195 -0.292 -0.161 0.805
## M29 29 0.089 0.162 0.419 -0.042 -0.019 0.023 0.053 -0.098 -0.092 0.234
## M26 26 -0.052 0.117 -0.015 0.764 0.141 0.117 0.106 -0.084 0.096 0.662
## M15 15 0.252 0.214 0.144 0.686 -0.028 -0.006 0.084 0.170 -0.282 0.718
## M23 23 -0.198 0.045 -0.097 0.610 0.094 0.316 -0.007 0.083 0.395 0.694
## M3 3 0.141 0.049 0.026 0.370 -0.065 0.016 -0.048 0.124 0.151 0.205
## M13 13 0.275 0.148 0.201 -0.335 0.005 -0.093 0.048 0.018 0.030 0.262
## M12 12 0.002 0.067 -0.080 0.173 0.800 0.083 0.015 -0.010 0.133 0.706
## M21 21 0.293 0.056 -0.171 -0.033 0.683 0.210 0.141 -0.203 -0.048 0.693
## M19 19 -0.083 0.084 0.425 0.155 0.525 -0.015 0.496 -0.275 0.205 0.858
## M25 25 0.073 0.097 0.270 0.012 0.421 -0.179 -0.259 0.170 -0.125 0.409
## M27 27 0.026 0.159 0.038 -0.131 0.403 -0.007 0.208 0.216 0.030 0.298
## M1 1 0.039 0.088 0.018 0.111 0.012 0.960 0.066 0.158 -0.015 0.973
## M22 22 -0.158 0.029 0.087 0.183 0.142 0.689 0.056 0.044 0.324 0.673
## M17 17 0.293 0.234 0.158 0.128 0.110 0.036 0.889 0.090 0.041 0.995
## M16 16 0.384 0.131 0.130 -0.066 0.097 0.193 0.391 -0.122 -0.035 0.401
## M28 28 0.080 -0.120 -0.230 0.074 0.003 0.114 0.004 0.950 0.004 0.995
## M20 20 0.059 0.232 0.043 0.235 0.020 0.213 -0.048 0.382 -0.101 0.319
## M9 9 0.255 0.016 0.143 -0.284 -0.028 -0.180 -0.063 0.084 -0.676 0.668
## M30 30 0.184 0.282 0.303 -0.088 0.267 0.000 -0.044 0.054 0.397 0.447
## u2 com
## M6 0.22557 1.72
## M4 0.42182 1.39
## M7 0.21630 4.12
## M5 0.64450 2.71
## M14 0.77664 1.35
## M8 0.00499 1.36
## M18 0.12808 2.65
## M24 0.38421 2.86
## M10 0.43593 4.23
## M2 0.08105 1.29
## M11 0.19539 3.00
## M29 0.76642 1.71
## M26 0.33794 1.28
## M15 0.28246 2.18
## M23 0.30630 2.76
## M3 0.79512 2.11
## M13 0.73825 3.35
## M12 0.29441 1.21
## M21 0.30657 2.09
## M19 0.14175 4.15
## M25 0.59104 3.84
## M27 0.70160 2.83
## M1 0.02677 1.11
## M22 0.32703 1.89
## M17 0.00499 1.56
## M16 0.59865 3.40
## M28 0.00500 1.21
## M20 0.68132 3.44
## M9 0.33204 2.02
## M30 0.55330 4.35
##
## ML9 ML1 ML5 ML7 ML8 ML4 ML3 ML2 ML6
## SS loadings 2.800 2.591 2.388 2.379 2.011 1.867 1.717 1.546 1.396
## Proportion Var 0.093 0.086 0.080 0.079 0.067 0.062 0.057 0.052 0.047
## Cumulative Var 0.093 0.180 0.259 0.339 0.406 0.468 0.525 0.577 0.623
## Proportion Explained 0.150 0.139 0.128 0.127 0.108 0.100 0.092 0.083 0.075
## Cumulative Proportion 0.150 0.288 0.416 0.543 0.651 0.751 0.843 0.925 1.000
##
## Mean item complexity = 2.4
## Test of the hypothesis that 9 factors are sufficient.
##
## The degrees of freedom for the null model are 435 and the objective function was 25.098 with Chi Square of 732.024
## The degrees of freedom for the model are 201 and the objective function was 8.916
##
## The root mean square of the residuals (RMSR) is 0.058
## The df corrected root mean square of the residuals is 0.085
##
## The harmonic number of observations is 41 with the empirical chi square 118.53 with prob < 1
## The total number of observations was 41 with Likelihood Chi Square = 206.56 with prob < 0.379
##
## Tucker Lewis Index of factoring reliability = 0.9178
## RMSEA index = 0.0081 and the 90 % confidence intervals are 0 0.0737
## BIC = -539.868
## Fit based upon off diagonal values = 0.947
## Measures of factor score adequacy
## ML9 ML1 ML5 ML7 ML8
## Correlation of (regression) scores with factors 0.923 0.989 0.959 0.924 0.925
## Multiple R square of scores with factors 0.851 0.979 0.920 0.855 0.856
## Minimum correlation of possible factor scores 0.702 0.957 0.839 0.709 0.711
## ML4 ML3 ML2 ML6
## Correlation of (regression) scores with factors 0.986 0.989 0.993 0.893
## Multiple R square of scores with factors 0.971 0.979 0.985 0.798
## Minimum correlation of possible factor scores 0.943 0.958 0.971 0.595fa.diagram(afe_pmi)
Confirmatorio PMI con 9 factores
factores_pmi<-'uno =~ M8 + M18 + M24 + M10
nueve =~ M6 + M9 + M14 + M5 + M4
tres =~ M17 + M16
siete =~ M15 + M26 + M23 + M3 + M13
cinco =~ M2 + M11 + M29
seis =~ M9 + M30
ocho =~ M12 + M21 + M19 + M25 + M27
cuatro =~ M1 + M22
dos =~ M28 + M20'
AFC <- cfa(factores_pmi,orthogonal=FALSE, data=df_pmi, estimator="WLSMV",ordered =names(df_pmi))## Warning in lavaan::lavaan(model = factores_pmi, data = df_pmi, ordered = names(df_pmi), : lavaan WARNING:
## the optimizer (NLMINB) claimed the model converged, but not all
## elements of the gradient are (near) zero; the optimizer may not
## have found a local solution use check.gradient = FALSE to skip
## this check.## Warning in sqrt(A1[[g]]): Se han producido NaNs## Warning in lavaan::lavaan(model = factores_pmi, data = df_pmi, ordered =
## names(df_pmi), : lavaan WARNING: estimation of the baseline model failed.summary(AFC, fit.measures=TRUE)## Warning in lav_object_summary(object = object, header = header, fit.measures = fit.measures, : lavaan WARNING: fit measures not available if model did not converge## lavaan 0.6-12 did NOT end normally after 1 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 117
##
## Number of observations 41
##
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## uno =~
## M8 1.000
## M18 1.070 NA
## M24 0.756 NA
## M10 0.696 NA
## nueve =~
## M6 1.000
## M9 0.674 NA
## M14 0.640 NA
## M5 0.672 NA
## M4 0.702 NA
## tres =~
## M17 1.000
## M16 0.693 NA
## siete =~
## M15 1.000
## M26 0.701 NA
## M23 0.717 NA
## M3 0.645 NA
## M13 0.582 NA
## cinco =~
## M2 1.000
## M11 0.712 NA
## M29 0.693 NA
## seis =~
## M9 1.000
## M30 0.646 NA
## ocho =~
## M12 1.000
## M21 0.683 NA
## M19 0.644 NA
## M25 0.642 NA
## M27 0.637 NA
## cuatro =~
## M1 1.000
## M22 0.716 NA
## dos =~
## M28 1.000
## M20 0.697 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## uno ~~
## nueve 0.286 NA
## tres 0.342 NA
## siete 0.201 NA
## cinco 0.341 NA
## seis 0.088 NA
## ocho 0.187 NA
## cuatro 0.049 NA
## dos 0.020 NA
## nueve ~~
## tres 0.172 NA
## siete 0.143 NA
## cinco 0.229 NA
## seis -0.015 NA
## ocho 0.081 NA
## cuatro -0.150 NA
## dos 0.007 NA
## tres ~~
## siete 0.067 NA
## cinco 0.159 NA
## seis 0.021 NA
## ocho 0.165 NA
## cuatro 0.027 NA
## dos 0.007 NA
## siete ~~
## cinco 0.086 NA
## seis -0.032 NA
## ocho 0.050 NA
## cuatro 0.053 NA
## dos 0.059 NA
## cinco ~~
## seis 0.068 NA
## ocho 0.133 NA
## cuatro 0.079 NA
## dos 0.016 NA
## seis ~~
## ocho 0.105 NA
## cuatro 0.009 NA
## dos -0.003 NA
## ocho ~~
## cuatro 0.190 NA
## dos -0.012 NA
## cuatro ~~
## dos 0.032 NA
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .M8 0.000
## .M18 0.000
## .M24 0.000
## .M10 0.000
## .M6 0.000
## .M9 0.000
## .M14 0.000
## .M5 0.000
## .M4 0.000
## .M17 0.000
## .M16 0.000
## .M15 0.000
## .M26 0.000
## .M23 0.000
## .M3 0.000
## .M13 0.000
## .M2 0.000
## .M11 0.000
## .M29 0.000
## .M30 0.000
## .M12 0.000
## .M21 0.000
## .M19 0.000
## .M25 0.000
## .M27 0.000
## .M1 0.000
## .M22 0.000
## .M28 0.000
## .M20 0.000
## uno 0.000
## nueve 0.000
## tres 0.000
## siete 0.000
## cinco 0.000
## seis 0.000
## ocho 0.000
## cuatro 0.000
## dos 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## M8|t1 0.027 NA
## M8|t2 0.032 NA
## M18|t1 0.034 NA
## M18|t2 0.032 NA
## M24|t1 0.037 NA
## M10|t1 0.027 NA
## M10|t2 0.036 NA
## M6|t1 0.027 NA
## M6|t2 0.024 NA
## M9|t1 -0.024 NA
## M9|t2 -0.038 NA
## M14|t1 -0.032 NA
## M14|t2 0.030 NA
## M5|t1 -0.018 NA
## M5|t2 0.038 NA
## M4|t1 -0.027 NA
## M4|t2 0.027 NA
## M17|t1 0.008 NA
## M17|t2 0.038 NA
## M16|t1 -0.008 NA
## M16|t2 0.034 NA
## M15|t1 0.032 NA
## M26|t1 0.037 NA
## M23|t1 0.036 NA
## M3|t1 0.027 NA
## M3|t2 0.024 NA
## M13|t1 -0.037 NA
## M13|t2 0.002 NA
## M2|t1 -0.034 NA
## M2|t2 0.038 NA
## M11|t1 -0.030 NA
## M11|t2 0.036 NA
## M29|t1 0.012 NA
## M29|t2 0.024 NA
## M30|t1 -0.027 NA
## M30|t2 0.036 NA
## M12|t1 -0.005 NA
## M12|t2 0.039 NA
## M21|t1 -0.018 NA
## M21|t2 0.034 NA
## M19|t1 -0.027 NA
## M19|t2 0.030 NA
## M25|t1 -0.024 NA
## M25|t2 -0.036 NA
## M27|t1 -0.039 NA
## M27|t2 -0.002 NA
## M1|t1 0.032 NA
## M22|t1 0.038 NA
## M28|t1 0.032 NA
## M28|t2 0.036 NA
## M20|t1 0.038 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .M8 -0.274
## .M18 -0.459
## .M24 0.271
## .M10 0.384
## .M6 0.050
## .M9 -0.374
## .M14 0.611
## .M5 0.571
## .M4 0.532
## .M17 0.005
## .M16 0.522
## .M15 0.070
## .M26 0.543
## .M23 0.522
## .M3 0.613
## .M13 0.685
## .M2 0.001
## .M11 0.493
## .M29 0.521
## .M30 0.599
## .M12 0.086
## .M21 0.573
## .M19 0.621
## .M25 0.624
## .M27 0.629
## .M1 -0.011
## .M22 0.481
## .M28 0.002
## .M20 0.515
## uno 1.274 NA
## nueve 0.950 NA
## tres 0.995 NA
## siete 0.930 NA
## cinco 0.999 NA
## seis 0.962 NA
## ocho 0.914 NA
## cuatro 1.011 NA
## dos 0.998 NA
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## M8 1.000
## M18 1.000
## M24 1.000
## M10 1.000
## M6 1.000
## M9 1.000
## M14 1.000
## M5 1.000
## M4 1.000
## M17 1.000
## M16 1.000
## M15 1.000
## M26 1.000
## M23 1.000
## M3 1.000
## M13 1.000
## M2 1.000
## M11 1.000
## M29 1.000
## M30 1.000
## M12 1.000
## M21 1.000
## M19 1.000
## M25 1.000
## M27 1.000
## M1 1.000
## M22 1.000
## M28 1.000
## M20 1.000semPaths(AFC,
intercepts = FALSE,
edge.label.cex=1.5,
optimizeLatRes = TRUE,
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt=1,
edge.color ="black",
esize = 1,
label.prop=1,
sizeLat = 6,
"std",
layout="tree2",
rotation = 2,
weighted = FALSE)
Se realizó un análisis de confiabilidad de cada uno de los 9 factores encontrados con los siguientes valores de alfa de Chronbach en cada uno de los factores.
uno<-df_pmi[c(8, 18, 24, 10)]
alpha(uno) # 0.8029988## [1] 0.8029988nueve<-df_pmi[c(6, 9, 14, 5, 4)]
alpha(nueve) # 0.669043## [1] 0.669043tres<-df_pmi[c(17, 16)]
alpha(tres) # 0.6676692## [1] 0.6676692siete<-df_pmi[c(15, 26, 23, 3, 13)]
alpha(siete) # 0.103139## [1] 0.103139cinco<-df_pmi[c(2, 11, 29)]
alpha(cinco) # 0.7260198## [1] 0.7260198seis<-df_pmi[c(9, 30)]
alpha(seis) # -0.4867257## [1] -0.4867257ocho<-df_pmi[c(12, 21, 19, 25, 27)]
alpha(ocho) # 0.687806## [1] 0.687806cuatro<-df_pmi[c(1, 22)]
alpha(cuatro) # 0.7914439## [1] 0.7914439dos<-df_pmi[c(28, 20)]
alpha(dos) # 0.4562118## [1] 0.4562118El siguiente análisis es realizado tomando en cuenta los 4 factores reportados por Torralba. No es el análisis que corresponde de manera adecuada a los datos obtenidos. Lo hice simplemente de forma exploratoria y para ver como daba. No considerar para reportar en resultados.
factores_pmi<-'Integracion social =~ M11 + M18 + M8 + M2 + M30 + M19 + M16 + M4 + M13
Personal =~ M23 + M1 + M10 + M24 + M9 + M6
Superacion =~ M17 + M26 + M7 + M3 + M15 + M20 + M22
Sociodeportivo =~ M25 + M21 + M9 + M28 + M12 + M27 + M14 + M5'
AFC <- cfa(factores_pmi,orthogonal=FALSE, data=df_pmi, estimator="WLSMV",ordered =names(df_pmi))## Warning in lavaan::lavaan(model = factores_pmi, data = df_pmi, ordered = names(df_pmi), : lavaan WARNING:
## the optimizer (NLMINB) claimed the model converged, but not all
## elements of the gradient are (near) zero; the optimizer may not
## have found a local solution use check.gradient = FALSE to skip
## this check.## Warning in sqrt(A1[[g]]): Se han producido NaNs## Warning in lavaan::lavaan(model = factores_pmi, data = df_pmi, ordered =
## names(df_pmi), : lavaan WARNING: estimation of the baseline model failed.summary(AFC, fit.measures=TRUE)## Warning in lav_object_summary(object = object, header = header, fit.measures = fit.measures, : lavaan WARNING: fit measures not available if model did not converge## lavaan 0.6-12 did NOT end normally after 1 iterations
## ** WARNING ** Estimates below are most likely unreliable
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 87
##
## Number of observations 41
##
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Integracionsocial =~
## M11 1.000
## M18 0.993 NA
## M8 0.944 NA
## M2 0.686 NA
## M30 0.657 NA
## M19 0.663 NA
## M16 0.667 NA
## M4 0.661 NA
## M13 0.632 NA
## Personal =~
## M23 1.000
## M1 0.693 NA
## M10 0.684 NA
## M24 0.713 NA
## M9 0.607 NA
## M6 0.712 NA
## Superacion =~
## M17 1.000
## M26 0.702 NA
## M7 0.708 NA
## M3 0.676 NA
## M15 0.716 NA
## M20 0.696 NA
## M22 0.685 NA
## Sociodeportivo =~
## M25 1.000
## M21 0.648 NA
## M9 0.616 NA
## M28 0.647 NA
## M12 0.653 NA
## M27 0.623 NA
## M14 0.624 NA
## M5 0.640 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Integracionsocial ~~
## Personal 0.443 NA
## Superacion 0.609 NA
## Sociodeportivo 0.243 NA
## Personal ~~
## Superacion 0.301 NA
## Sociodeportivo -0.110 NA
## Superacion ~~
## Sociodeportivo 0.108 NA
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .M11 0.000
## .M18 0.000
## .M8 0.000
## .M2 0.000
## .M30 0.000
## .M19 0.000
## .M16 0.000
## .M4 0.000
## .M13 0.000
## .M23 0.000
## .M1 0.000
## .M10 0.000
## .M24 0.000
## .M9 0.000
## .M6 0.000
## .M17 0.000
## .M26 0.000
## .M7 0.000
## .M3 0.000
## .M15 0.000
## .M20 0.000
## .M22 0.000
## .M25 0.000
## .M21 0.000
## .M28 0.000
## .M12 0.000
## .M27 0.000
## .M14 0.000
## .M5 0.000
## Integracionscl 0.000
## Personal 0.000
## Superacion 0.000
## Sociodeportivo 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## M11|t1 -0.018 NA
## M11|t2 0.022 NA
## M18|t1 0.021 NA
## M18|t2 0.020 NA
## M8|t1 0.017 NA
## M8|t2 0.020 NA
## M2|t1 -0.021 NA
## M2|t2 0.024 NA
## M30|t1 -0.017 NA
## M30|t2 0.022 NA
## M19|t1 -0.017 NA
## M19|t2 0.018 NA
## M16|t1 -0.005 NA
## M16|t2 0.021 NA
## M4|t1 -0.017 NA
## M4|t2 0.017 NA
## M13|t1 -0.023 NA
## M13|t2 0.001 NA
## M23|t1 0.022 NA
## M1|t1 0.020 NA
## M10|t1 0.017 NA
## M10|t2 0.022 NA
## M24|t1 0.023 NA
## M9|t1 -0.015 NA
## M9|t2 -0.024 NA
## M6|t1 0.017 NA
## M6|t2 0.015 NA
## M17|t1 0.005 NA
## M17|t2 0.023 NA
## M26|t1 0.023 NA
## M7|t1 0.013 NA
## M7|t2 0.024 NA
## M3|t1 0.017 NA
## M3|t2 0.015 NA
## M15|t1 0.020 NA
## M20|t1 0.023 NA
## M22|t1 0.023 NA
## M25|t1 -0.015 NA
## M25|t2 -0.022 NA
## M21|t1 -0.011 NA
## M21|t2 0.021 NA
## M28|t1 0.020 NA
## M28|t2 0.022 NA
## M12|t1 -0.003 NA
## M12|t2 0.024 NA
## M27|t1 -0.024 NA
## M27|t2 -0.001 NA
## M14|t1 -0.020 NA
## M14|t2 0.018 NA
## M5|t1 -0.011 NA
## M5|t2 0.024 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .M11 -0.091
## .M18 -0.076
## .M8 0.028
## .M2 0.487
## .M30 0.529
## .M19 0.520
## .M16 0.514
## .M4 0.524
## .M13 0.564
## .M23 0.061
## .M1 0.549
## .M10 0.560
## .M24 0.523
## .M9 0.425
## .M6 0.524
## .M17 0.019
## .M26 0.517
## .M7 0.508
## .M3 0.552
## .M15 0.497
## .M20 0.524
## .M22 0.539
## .M25 0.181
## .M21 0.656
## .M28 0.658
## .M12 0.650
## .M27 0.682
## .M14 0.681
## .M5 0.665
## Integracionscl 1.091 NA
## Personal 0.939 NA
## Superacion 0.981 NA
## Sociodeportivo 0.819 NA
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|)
## M11 1.000
## M18 1.000
## M8 1.000
## M2 1.000
## M30 1.000
## M19 1.000
## M16 1.000
## M4 1.000
## M13 1.000
## M23 1.000
## M1 1.000
## M10 1.000
## M24 1.000
## M9 1.000
## M6 1.000
## M17 1.000
## M26 1.000
## M7 1.000
## M3 1.000
## M15 1.000
## M20 1.000
## M22 1.000
## M25 1.000
## M21 1.000
## M28 1.000
## M12 1.000
## M27 1.000
## M14 1.000
## M5 1.000semPaths(AFC,
intercepts = FALSE,
edge.label.cex=1.5,
optimizeLatRes = TRUE,
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt=1,
edge.color ="black",
esize = 1,
label.prop=1,
sizeLat = 6,
"std",
layout="tree2",
rotation = 2,
weighted = FALSE)