Parentalidad transformacional según convivencia
Hipótesis: Las percepciones de lxs adolescentes de la parentalidad de sus familias difieren si conviven o no con su madre/padre.
Estimaciones
Los p-valores se obtuvieron con el test de U de Mann-Whitney.
base <- data.frame(cbind(T1[c(1:3,154)],
LTm_ii=rowMeans(T1[c(88,92,96,100)]),
LTm_mi=rowMeans(T1[c(89,93,97,101)]),
LTm_ei=rowMeans(T1[c(90,94,98,102)]),
LTm_ci=rowMeans(T1[c(91,95,99,103)]),
LTm_LT=rowMeans(T1[88:103]),
LTp_ii=rowMeans(T1[c(104,108,112,116)]),
LTp_mi=rowMeans(T1[c(105,109,113,117)]),
LTp_ei=rowMeans(T1[c(106,110,114,118)]),
LTp_ci=rowMeans(T1[c(107,111,115,119)]),
LTp_LT=rowMeans(T1[104:119])))
base <- base[complete.cases(base),]
base$padre <- ifelse(base$familia_T1=="ambos"|base$familia_T1=="padre",1,0)
base$madre <- ifelse(base$familia_T1=="ambos"|base$familia_T1=="madre",1,0)
tabla <- data.frame(Variable=c("Influencia idealizada (madre)",
"Motivación inspiracional (madre)",
"Estimulación intelectual (madre)",
"Consideración individualizada (madre)",
"Parentalidad transformacional (madre)",
"Influencia idealizada (padre)",
"Motivación inspiracional (padre)",
"Estimulación intelectual (padre)",
"Consideración individualizada (padre)",
"Parentalidad transformacional (padre)"),
`Convive - No convive`=round(c(diff(as.data.frame(emmeans(lm(LTm_ii~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_mi~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_ei~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_ci~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_LT~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ii~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_mi~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ei~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ci~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_LT~padre, data=base), pairwise ~ padre)$emmeans)$emmean)),2),
p=round(c(wilcox.test(LTm_ii~madre, data=base)$p.value,
wilcox.test(LTm_mi~madre, data=base)$p.value,
wilcox.test(LTm_ei~madre, data=base)$p.value,
wilcox.test(LTm_ci~madre, data=base)$p.value,
wilcox.test(LTm_LT~madre, data=base)$p.value,
wilcox.test(LTp_ii~padre, data=base)$p.value,
wilcox.test(LTp_mi~padre, data=base)$p.value,
wilcox.test(LTp_ei~padre, data=base)$p.value,
wilcox.test(LTp_ci~padre, data=base)$p.value,
wilcox.test(LTp_LT~padre, data=base)$p.value),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("r","c","c"),
caption = "Diferencias en LT y sus dimensiones según convivencia") %>%
kable_styling(full_width = F,
position = "center", font_size = 12)%>%
row_spec(6:10, bold = T, color = "black", background = "darkorange")| Variable | Convive - No convive | p |
|---|---|---|
| Influencia idealizada (madre) | 0.03 | 0.645 |
| Motivación inspiracional (madre) | -0.09 | 0.354 |
| Estimulación intelectual (madre) | -0.01 | 0.938 |
| Consideración individualizada (madre) | 0.01 | 0.507 |
| Parentalidad transformacional (madre) | -0.01 | 0.938 |
| Influencia idealizada (padre) | 0.95 | 0.000 |
| Motivación inspiracional (padre) | 0.69 | 0.000 |
| Estimulación intelectual (padre) | 0.66 | 0.000 |
| Consideración individualizada (padre) | 0.88 | 0.000 |
| Parentalidad transformacional (padre) | 0.80 | 0.000 |
Estimaciones sin los casos de Padre en los que puntuaron todo 1
base2 <- data.frame(cbind(T1[rowSums(T1[104:119],na.rm=T)>16,c(1:3,154)],
LTm_ii=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,c(88,92,96,100)]),
LTm_mi=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,c(89,93,97,101)]),
LTm_ei=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,c(90,94,98,102)]),
LTm_ci=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,c(91,95,99,103)]),
LTm_LT=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,88:103]),
LTp_ii=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,c(104,108,112,116)]),
LTp_mi=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,c(105,109,113,117)]),
LTp_ei=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,c(106,110,114,118)]),
LTp_ci=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,c(107,111,115,119)]),
LTp_LT=rowMeans(T1[rowSums(T1[104:119],na.rm=T)>16,104:119])))
base2 <- base2[complete.cases(base2),]
base2$padre <- ifelse(base2$familia_T1=="ambos"|base2$familia_T1=="padre",1,0)
base2$madre <- ifelse(base2$familia_T1=="ambos"|base2$familia_T1=="madre",1,0)
tabla <- data.frame(Variable=c("Influencia idealizada (madre)",
"Motivación inspiracional (madre)",
"Estimulación intelectual (madre)",
"Consideración individualizada (madre)",
"Parentalidad transformacional (madre)",
"Influencia idealizada (padre)",
"Motivación inspiracional (padre)",
"Estimulación intelectual (padre)",
"Consideración individualizada (padre)",
"Parentalidad transformacional (padre)"),
`Convive - No convive`=round(c(diff(as.data.frame(emmeans(lm(LTm_ii~madre, data=base2), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_mi~madre, data=base2), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_ei~madre, data=base2), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_ci~madre, data=base2), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_LT~madre, data=base2), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ii~padre, data=base2), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_mi~padre, data=base2), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ei~padre, data=base2), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ci~padre, data=base2), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_LT~padre, data=base2), pairwise ~ padre)$emmeans)$emmean)),2),
p=round(c(wilcox.test(LTm_ii~madre, data=base2)$p.value,
wilcox.test(LTm_mi~madre, data=base2)$p.value,
wilcox.test(LTm_ei~madre, data=base2)$p.value,
wilcox.test(LTm_ci~madre, data=base2)$p.value,
wilcox.test(LTm_LT~madre, data=base2)$p.value,
wilcox.test(LTp_ii~padre, data=base2)$p.value,
wilcox.test(LTp_mi~padre, data=base2)$p.value,
wilcox.test(LTp_ei~padre, data=base2)$p.value,
wilcox.test(LTp_ci~padre, data=base2)$p.value,
wilcox.test(LTp_LT~padre, data=base2)$p.value),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("r","c","c"),
caption = "Diferencias en LT y sus dimensiones según convivencia") %>%
kable_styling(full_width = F,
position = "center", font_size = 12)%>%
row_spec(6:10, bold = T, color = "black", background = "darkorange")| Variable | Convive - No convive | p |
|---|---|---|
| Influencia idealizada (madre) | 0.04 | 0.660 |
| Motivación inspiracional (madre) | -0.09 | 0.363 |
| Estimulación intelectual (madre) | -0.01 | 0.934 |
| Consideración individualizada (madre) | 0.02 | 0.491 |
| Parentalidad transformacional (madre) | -0.01 | 0.951 |
| Influencia idealizada (padre) | 0.80 | 0.000 |
| Motivación inspiracional (padre) | 0.52 | 0.000 |
| Estimulación intelectual (padre) | 0.50 | 0.000 |
| Consideración individualizada (padre) | 0.72 | 0.000 |
| Parentalidad transformacional (padre) | 0.64 | 0.000 |
Gráficos
base_grafico <- data.frame("Variable"= c(rep("IIm",nrow(base)),
rep("MIm",nrow(base)),
rep("EIm",nrow(base)),
rep("CIm",nrow(base)),
rep("LTm",nrow(base))),
"Puntaje"=c(base$LTm_ii,
base$LTm_mi,
base$LTm_ei,
base$LTm_ci,
base$LTm_LT),
"Madre"=rep(base$madre,5))
ggplot(base_grafico, aes(x=Variable, y=Puntaje, color=as.factor(Madre)))+
geom_point(alpha=0.15, size=2.25,pch = 20, position = position_jitterdodge(jitter.width = .35))+
geom_boxplot(aes(fill = as.factor(Madre),color=as.factor(Madre)), alpha=0.5,outlier.shape = NA, lwd=1)+
theme_minimal()+
ylab("Puntaje")+
ggtitle("Maternidad transformacional según convivencia")+
scale_y_continuous(breaks = seq(0, 6, by = 1), limits = c(0,6))+
scale_fill_manual(name = "Convivencia", labels=c("No", "Sí"),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Convivencia", labels=c("No", "Sí"),values=c("#f08f56","#10b0bc"))
base_grafico <- data.frame("Variable"= c(rep("IIp",nrow(base)),
rep("MIp",nrow(base)),
rep("EIp",nrow(base)),
rep("CIp",nrow(base)),
rep("LTp",nrow(base))),
"Puntaje"=c(base$LTp_ii,
base$LTp_mi,
base$LTp_ei,
base$LTp_ci,
base$LTp_LT),
"Padre"=rep(base$padre,5))
ggplot(base_grafico, aes(x=Variable, y=Puntaje, color=as.factor(Padre)))+
geom_point(alpha=0.15, size=2.25,pch = 20, position = position_jitterdodge(jitter.width = .35))+
geom_boxplot(aes(fill = as.factor(Padre),color=as.factor(Padre)), alpha=0.5,outlier.shape = NA, lwd=1)+
theme_minimal()+
ylab("Puntaje")+
ggtitle("Paternidad transformacional según convivencia")+
scale_y_continuous(breaks = seq(0, 6, by = 1), limits = c(0,6))+
scale_fill_manual(name = "Convivencia", labels=c("No", "Sí"),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Convivencia", labels=c("No", "Sí"),values=c("#f08f56","#10b0bc"))
Gráfico de paternidad sin considerar los casos de todo 1
base_grafico <- data.frame("Variable"= c(rep("IIp",nrow(base2)),
rep("MIp",nrow(base2)),
rep("EIp",nrow(base2)),
rep("CIp",nrow(base2)),
rep("LTp",nrow(base2))),
"Puntaje"=c(base2$LTp_ii,
base2$LTp_mi,
base2$LTp_ei,
base2$LTp_ci,
base2$LTp_LT),
"Padre"=rep(base2$padre,5))
ggplot(base_grafico, aes(x=Variable, y=Puntaje, color=as.factor(Padre)))+
geom_point(alpha=0.15, size=2.25,pch = 20, position = position_jitterdodge(jitter.width = .35))+
geom_boxplot(aes(fill = as.factor(Padre),color=as.factor(Padre)), alpha=0.5,outlier.shape = NA, lwd=1)+
theme_minimal()+
ylab("Puntaje")+
ggtitle("Paternidad transformacional según convivencia")+
scale_y_continuous(breaks = seq(0, 6, by = 1), limits = c(0,6))+
scale_fill_manual(name = "Convivencia", labels=c("No", "Sí"),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Convivencia", labels=c("No", "Sí"),values=c("#f08f56","#10b0bc"))
Convivencia “cruzada”
Predicción del nivel de LT de la madre a partir de la convivencia con el padre y del LT del padre a partir de la convivencia con la madre
base <- data.frame(cbind(T1[c(1:3,154)],
LTm_ii=rowMeans(T1[c(88,92,96,100)]),
LTm_mi=rowMeans(T1[c(89,93,97,101)]),
LTm_ei=rowMeans(T1[c(90,94,98,102)]),
LTm_ci=rowMeans(T1[c(91,95,99,103)]),
LTm_LT=rowMeans(T1[88:103]),
LTp_ii=rowMeans(T1[c(104,108,112,116)]),
LTp_mi=rowMeans(T1[c(105,109,113,117)]),
LTp_ei=rowMeans(T1[c(106,110,114,118)]),
LTp_ci=rowMeans(T1[c(107,111,115,119)]),
LTp_LT=rowMeans(T1[104:119])))
base <- base[complete.cases(base),]
base$padre <- ifelse(base$familia_T1=="ambos"|base$familia_T1=="padre",1,0)
base$madre <- ifelse(base$familia_T1=="ambos"|base$familia_T1=="madre",1,0)
tabla <- data.frame(Variable=c("Influencia idealizada (madre)",
"Motivación inspiracional (madre)",
"Estimulación intelectual (madre)",
"Consideración individualizada (madre)",
"Parentalidad transformacional (madre)",
"Influencia idealizada (padre)",
"Motivación inspiracional (padre)",
"Estimulación intelectual (padre)",
"Consideración individualizada (padre)",
"Parentalidad transformacional (padre)"),
`Convive - No convive`=round(c(diff(as.data.frame(emmeans(lm(LTm_ii~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_mi~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_ei~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_ci~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTm_LT~padre, data=base), pairwise ~ padre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ii~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_mi~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ei~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_ci~madre, data=base), pairwise ~ madre)$emmeans)$emmean),
diff(as.data.frame(emmeans(lm(LTp_LT~madre, data=base), pairwise ~ madre)$emmeans)$emmean)),2),
p=round(c(wilcox.test(LTm_ii~padre, data=base)$p.value,
wilcox.test(LTm_mi~padre, data=base)$p.value,
wilcox.test(LTm_ei~padre, data=base)$p.value,
wilcox.test(LTm_ci~padre, data=base)$p.value,
wilcox.test(LTm_LT~padre, data=base)$p.value,
wilcox.test(LTp_ii~madre, data=base)$p.value,
wilcox.test(LTp_mi~madre, data=base)$p.value,
wilcox.test(LTp_ei~madre, data=base)$p.value,
wilcox.test(LTp_ci~madre, data=base)$p.value,
wilcox.test(LTp_LT~madre, data=base)$p.value),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("r","c","c"),
caption = "Diferencias en LT y sus dimensiones según convivencia con la otra fig. parental") %>%
kable_styling(full_width = F,
position = "center", font_size = 12)| Variable | Convive - No convive | p |
|---|---|---|
| Influencia idealizada (madre) | 0.15 | 0.221 |
| Motivación inspiracional (madre) | 0.15 | 0.257 |
| Estimulación intelectual (madre) | 0.16 | 0.285 |
| Consideración individualizada (madre) | 0.12 | 0.121 |
| Parentalidad transformacional (madre) | 0.14 | 0.261 |
| Influencia idealizada (padre) | -0.15 | 0.168 |
| Motivación inspiracional (padre) | -0.02 | 0.567 |
| Estimulación intelectual (padre) | 0.09 | 0.252 |
| Consideración individualizada (padre) | -0.07 | 0.816 |
| Parentalidad transformacional (padre) | -0.04 | 0.752 |
No hay diferencias en la parentalidad transformacional de una de las figuras parentales en función de la convivencia con la otra figura.
Consideraciones de género
En padres, todos los modelos con interacción fueron significativos (y todos sus términos también). En madres, ninguno lo fue (i.e., convivir con la madre no predice la percepción de su parentalidad transformacional, sin importar el sexo, pero tampoco el sexo predice bien la variable).
modLT <- lm(LTp_LT~padre*sexo_T1, data=base)
base_grafico <- data.frame("LT"= c(sum(as.data.frame(summary(modLT)$coef)$Estimate[1:4]),
sum(as.data.frame(summary(modLT)$coef)$Estimate[1:2]),
sum(as.data.frame(summary(modLT)$coef)$Estimate[c(1,3)]),
sum(as.data.frame(summary(modLT)$coef)$Estimate[1])),
"Padre"= c("Convive","Convive","No convive","No convive"),
"Sexo"= rep(c("Masc.","Fem."),2),
"int"=c("p_m","p_f","np_m","np_f"))
ggplot(base_grafico, aes(x=Padre, y=LT, color=as.factor(Sexo), group=as.factor(Sexo)))+
geom_point(size=5)+
geom_line(lwd=2)+
theme_minimal()+
ylab("LTp")+
ggtitle("Paternidad transformacional según convivencia y sexo")+
scale_y_continuous(breaks = seq(0, 6, by = 1), limits = c(0,6))+
scale_fill_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))
mod1 <- lm(LTp_LT~sexo_T1, base[base$padre==1,])
mod2 <- lm(LTp_LT~sexo_T1, base[base$padre==0,])
mod3 <- lm(LTp_LT~padre, base[base$sexo_T1=="m",])
mod4 <- lm(LTp_LT~padre, base[base$sexo_T1=="f",])
tabla <- data.frame(Comparación=c("Sexo | convive",
"Sexo | no convive",
"Convivencia | masc.",
"Convivencia | fem."),
Estimación= c(round(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$estimate,2)),
p=round(c(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$p.value,
as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$p.value),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("l","c","c"),
caption = "Comparaciones de efectos simples en Paternidad transformacional") %>%
kable_styling(full_width = F,
position = "center", font_size = 12)| Comparación | Estimación | p |
|---|---|---|
| Sexo | convive | -0.19 | 0.000 |
| Sexo | no convive | -0.70 | 0.002 |
| Convivencia | masc. | -0.57 | 0.000 |
| Convivencia | fem. | -1.08 | 0.000 |
modII <- lm(LTp_ii~padre*sexo_T1, data=base)
base_grafico <- data.frame("II"= c(sum(as.data.frame(summary(modII)$coef)$Estimate[1:4]),
sum(as.data.frame(summary(modII)$coef)$Estimate[1:2]),
sum(as.data.frame(summary(modII)$coef)$Estimate[c(1,3)]),
sum(as.data.frame(summary(modII)$coef)$Estimate[1])),
"Padre"= c("Convive","Convive","No convive","No convive"),
"Sexo"= rep(c("Masc.","Fem."),2),
"int"=c("p_m","p_f","np_m","np_f"))
ggplot(base_grafico, aes(x=Padre, y=II, color=as.factor(Sexo), group=as.factor(Sexo)))+
geom_point(size=5)+
geom_line(lwd=2)+
theme_minimal()+
ylab("IIp")+
ggtitle("Influencia idealizada según convivencia y sexo")+
scale_y_continuous(breaks = seq(0, 6, by = 1), limits = c(0,6))+
scale_fill_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))
mod1 <- lm(LTp_ii~sexo_T1, base[base$padre==1,])
mod2 <- lm(LTp_ii~sexo_T1, base[base$padre==0,])
mod3 <- lm(LTp_ii~padre, base[base$sexo_T1=="m",])
mod4 <- lm(LTp_ii~padre, base[base$sexo_T1=="f",])
tabla <- data.frame(Comparación=c("Sexo | convive",
"Sexo | no convive",
"Convivencia | masc.",
"Convivencia | fem."),
Estimación= c(round(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$estimate,2)),
p=round(c(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$p.value,
as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$p.value),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("l","c","c"),
caption = "Comparaciones de efectos simples en Influencia idealizada (padre)") %>%
kable_styling(full_width = F,
position = "center", font_size = 12)| Comparación | Estimación | p |
|---|---|---|
| Sexo | convive | -0.31 | 0 |
| Sexo | no convive | -0.90 | 0 |
| Convivencia | masc. | -0.69 | 0 |
| Convivencia | fem. | -1.29 | 0 |
modMI <- lm(LTp_mi~padre*sexo_T1, data=base)
modEI <- lm(LTp_ei~padre*sexo_T1, data=base)
modCI <- lm(LTp_ci~padre*sexo_T1, data=base)
base_grafico <- data.frame("MI"= c(sum(as.data.frame(summary(modMI)$coef)$Estimate[1:4]),
sum(as.data.frame(summary(modMI)$coef)$Estimate[1:2]),
sum(as.data.frame(summary(modMI)$coef)$Estimate[c(1,3)]),
sum(as.data.frame(summary(modMI)$coef)$Estimate[1])),
"Padre"= c("Convive","Convive","No convive","No convive"),
"Sexo"= rep(c("Masc.","Fem."),2),
"int"=c("p_m","p_f","np_m","np_f"))
ggplot(base_grafico, aes(x=Padre, y=MI, color=as.factor(Sexo), group=as.factor(Sexo)))+
geom_point(size=5)+
geom_line(lwd=2)+
theme_minimal()+
ylab("MIp")+
ggtitle("Motivación inspiracional según convivencia y sexo")+
scale_y_continuous(breaks = seq(0, 6, by = 1), limits = c(0,6))+
scale_fill_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))
mod1 <- lm(LTp_mi~sexo_T1, base[base$padre==1,])
mod2 <- lm(LTp_mi~sexo_T1, base[base$padre==0,])
mod3 <- lm(LTp_mi~padre, base[base$sexo_T1=="m",])
mod4 <- lm(LTp_mi~padre, base[base$sexo_T1=="f",])
tabla <- data.frame(Comparación=c("Sexo | convive",
"Sexo | no convive",
"Convivencia | masc.",
"Convivencia | fem."),
Estimación= c(round(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$estimate,2)),
p=round(c(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$p.value,
as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$p.value),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("l","c","c"),
caption = "Comparaciones de efectos simples en Motivación inspiracional (padre)") %>%
kable_styling(full_width = F,
position = "center", font_size = 12)| Comparación | Estimación | p |
|---|---|---|
| Sexo | convive | -0.07 | 0.143 |
| Sexo | no convive | -0.57 | 0.017 |
| Convivencia | masc. | -0.46 | 0.000 |
| Convivencia | fem. | -0.96 | 0.000 |
modEI <- lm(LTp_ei~padre*sexo_T1, data=base)
modCI <- lm(LTp_ci~padre*sexo_T1, data=base)
base_grafico <- data.frame("EI"= c(sum(as.data.frame(summary(modEI)$coef)$Estimate[1:4]),
sum(as.data.frame(summary(modEI)$coef)$Estimate[1:2]),
sum(as.data.frame(summary(modEI)$coef)$Estimate[c(1,3)]),
sum(as.data.frame(summary(modEI)$coef)$Estimate[1])),
"Padre"= c("Convive","Convive","No convive","No convive"),
"Sexo"= rep(c("Masc.","Fem."),2),
"int"=c("p_m","p_f","np_m","np_f"))
ggplot(base_grafico, aes(x=Padre, y=EI, color=as.factor(Sexo), group=as.factor(Sexo)))+
geom_point(size=5)+
geom_line(lwd=2)+
theme_minimal()+
ylab("EIp")+
ggtitle("Estimulación intelectual según convivencia y sexo")+
scale_y_continuous(breaks = seq(0, 6, by = 1), limits = c(0,6))+
scale_fill_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))
mod1 <- lm(LTp_ei~sexo_T1, base[base$padre==1,])
mod2 <- lm(LTp_ei~sexo_T1, base[base$padre==0,])
mod3 <- lm(LTp_ei~padre, base[base$sexo_T1=="m",])
mod4 <- lm(LTp_ei~padre, base[base$sexo_T1=="f",])
tabla <- data.frame(Comparación=c("Sexo | convive",
"Sexo | no convive",
"Convivencia | masc.",
"Convivencia | fem."),
Estimación= c(round(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$estimate,2)),
p=round(c(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$p.value,
as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$p.value),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("l","c","c"),
caption = "Comparaciones de efectos simples en Estimulación intelectual (padre)") %>%
kable_styling(full_width = F,
position = "center", font_size = 12)| Comparación | Estimación | p |
|---|---|---|
| Sexo | convive | -0.19 | 0.001 |
| Sexo | no convive | -0.54 | 0.017 |
| Convivencia | masc. | -0.51 | 0.000 |
| Convivencia | fem. | -0.86 | 0.000 |
modCI <- lm(LTp_ci~padre*sexo_T1, data=base)
base_grafico <- data.frame("CI"= c(sum(as.data.frame(summary(modCI)$coef)$Estimate[1:4]),
sum(as.data.frame(summary(modCI)$coef)$Estimate[1:2]),
sum(as.data.frame(summary(modCI)$coef)$Estimate[c(1,3)]),
sum(as.data.frame(summary(modCI)$coef)$Estimate[1])),
"Padre"= c("Convive","Convive","No convive","No convive"),
"Sexo"= rep(c("Masc.","Fem."),2),
"int"=c("p_m","p_f","np_m","np_f"))
ggplot(base_grafico, aes(x=Padre, y=CI, color=as.factor(Sexo), group=as.factor(Sexo)))+
geom_point(size=5)+
geom_line(lwd=2)+
theme_minimal()+
ylab("CIp")+
ggtitle("Consideración individualizada según convivencia y sexo")+
scale_y_continuous(breaks = seq(0, 6, by = 1), limits = c(0,6))+
scale_fill_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Sexo", labels=c("Fem.", "Masc."),values=c("#f08f56","#10b0bc"))
mod1 <- lm(LTp_ci~sexo_T1, base[base$padre==1,])
mod2 <- lm(LTp_ci~sexo_T1, base[base$padre==0,])
mod3 <- lm(LTp_ci~padre, base[base$sexo_T1=="m",])
mod4 <- lm(LTp_ci~padre, base[base$sexo_T1=="f",])
tabla <- data.frame(Comparación=c("Sexo | convive",
"Sexo | no convive",
"Convivencia | masc.",
"Convivencia | fem."),
Estimación= c(round(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$estimate,2),
round(as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$estimate,2)),
p=round(c(as.data.frame(emmeans(mod1, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod2, pairwise ~ sexo_T1)$contrasts)$p.value,
as.data.frame(emmeans(mod3, pairwise ~ padre)$contrasts)$p.value,
as.data.frame(emmeans(mod4, pairwise ~ padre)$contrasts)$p.value),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("l","c","c"),
caption = "Comparaciones de efectos simples en Estimulación intelectual (padre)") %>%
kable_styling(full_width = F,
position = "center", font_size = 12)| Comparación | Estimación | p |
|---|---|---|
| Sexo | convive | -0.21 | 0.000 |
| Sexo | no convive | -0.79 | 0.001 |
| Convivencia | masc. | -0.61 | 0.000 |
| Convivencia | fem. | -1.20 | 0.000 |
Limitaciones
- Sólo 45 (3.98%) casos no conviven con la madre, mientras que 167 (14.78%) casos no conviven con el padre. Podría ser que falten casos de no convivencia con la madre para encontrar diferencias significativas.
- Los puntajes de 1 en Paternidad transformacional puede que sean por no conocer al padre, no porque realmente es 1.
LT y valores personales
Madre
Hipótesis: Las conductas de Maternidad trasformacional se asocian positivamente con valores de AT y AC y negativamente con AP y C.
AT sin relación. AP negativa. AC y C positiva
base <- data.frame(cbind(T1[c(1:3)],
LTm_ii=rowMeans(T1[c(88,92,96,100)]),
LTm_mi=rowMeans(T1[c(89,93,97,101)]),
LTm_ei=rowMeans(T1[c(90,94,98,102)]),
LTm_ci=rowMeans(T1[c(91,95,99,103)]),
LTm_LT=rowMeans(T1[88:103]),
LTp_ii=rowMeans(T1[c(104,108,112,116)]),
LTp_mi=rowMeans(T1[c(105,109,113,117)]),
LTp_ei=rowMeans(T1[c(106,110,114,118)]),
LTp_ci=rowMeans(T1[c(107,111,115,119)]),
LTp_LT=rowMeans(T1[104:119]),
LTc_ii=rowMeans(T1[c(73,76,83,87)]),
LTc_mi=rowMeans(T1[c(75,77,79,86)]),
LTc_ei=rowMeans(T1[c(74,78,81,84)]),
LTc_ci=rowMeans(T1[c(72,80,82,85)]),
LTc_LT=rowMeans(T1[72:87]),
AT=rowMeans(T1[c(44,49,53,59,60)]),
AC=rowMeans(T1[c(42,47,51,52,56,62)]),
AP=rowMeans(T1[c(43,45,54,58)]),
C=rowMeans(T1[c(46,48,50,55,57,61)]),
VP_media=rowMeans(T1[42:62]),
ATc=rowMeans(T1[c(44,49,53,59,60)])-rowMeans(T1[42:62]),
ACc=rowMeans(T1[c(42,47,51,52,56,62)])-rowMeans(T1[42:62]),
APc=rowMeans(T1[c(43,45,54,58)])-rowMeans(T1[42:62]),
Cc=rowMeans(T1[c(46,48,50,55,57,61)])-rowMeans(T1[42:62])))
base <- base[complete.cases(base),]
tabla <- data.frame(`Variable criterio`=c("Autotrascendencia", rep(NA,5),
"Apertura al cambio", rep(NA,5),
"Autopromoción", rep(NA,5),
"Conservación", rep(NA,5)),
`Variable predictora`=rep(c(NA,"Influencia idealizada (madre)",
"Motivación inspiracional (madre)",
"Estimulación intelectual (madre)",
"Consideración individualizada (madre)",
"Parentalidad transformacional (madre)"),4),
beta=round(c(NA,coefficients(lm(ATc~LTm_ii,base))[2],
coefficients(lm(ATc~LTm_mi,base))[2],
coefficients(lm(ATc~LTm_ei,base))[2],
coefficients(lm(ATc~LTm_ci,base))[2],
coefficients(lm(ATc~LTm_LT,base))[2],
NA,coefficients(lm(ACc~LTm_ii,base))[2],
coefficients(lm(ACc~LTm_mi,base))[2],
coefficients(lm(ACc~LTm_ei,base))[2],
coefficients(lm(ACc~LTm_ci,base))[2],
coefficients(lm(ACc~LTm_LT,base))[2],
NA,coefficients(lm(APc~LTm_ii,base))[2],
coefficients(lm(APc~LTm_mi,base))[2],
coefficients(lm(APc~LTm_ei,base))[2],
coefficients(lm(APc~LTm_ci,base))[2],
coefficients(lm(APc~LTm_LT,base))[2],
NA,coefficients(lm(Cc~LTm_ii,base))[2],
coefficients(lm(Cc~LTm_mi,base))[2],
coefficients(lm(Cc~LTm_ei,base))[2],
coefficients(lm(Cc~LTm_ci,base))[2],
coefficients(lm(Cc~LTm_LT,base))[2]),2),
p=round(c(NA,summary(lm(ATc~LTm_ii,base))$coefficients[2,4],
summary(lm(ATc~LTm_mi,base))$coefficients[2,4],
summary(lm(ATc~LTm_ei,base))$coefficients[2,4],
summary(lm(ATc~LTm_ci,base))$coefficients[2,4],
summary(lm(ATc~LTm_LT,base))$coefficients[2,4],
NA,summary(lm(ACc~LTm_ii,base))$coefficients[2,4],
summary(lm(ACc~LTm_mi,base))$coefficients[2,4],
summary(lm(ACc~LTm_ei,base))$coefficients[2,4],
summary(lm(ACc~LTm_ci,base))$coefficients[2,4],
summary(lm(ACc~LTm_LT,base))$coefficients[2,4],
NA,summary(lm(APc~LTm_ii,base))$coefficients[2,4],
summary(lm(APc~LTm_mi,base))$coefficients[2,4],
summary(lm(APc~LTm_ei,base))$coefficients[2,4],
summary(lm(APc~LTm_ci,base))$coefficients[2,4],
summary(lm(APc~LTm_LT,base))$coefficients[2,4],
NA,summary(lm(Cc~LTm_ii,base))$coefficients[2,4],
summary(lm(Cc~LTm_mi,base))$coefficients[2,4],
summary(lm(Cc~LTm_ei,base))$coefficients[2,4],
summary(lm(Cc~LTm_ci,base))$coefficients[2,4],
summary(lm(Cc~LTm_LT,base))$coefficients[2,4]),3),
`F`=round(c(NA,summary(lm(ATc~LTm_ii,base))$fstatistic[1],
summary(lm(ATc~LTm_mi,base))$fstatistic[1],
summary(lm(ATc~LTm_ei,base))$fstatistic[1],
summary(lm(ATc~LTm_ci,base))$fstatistic[1],
summary(lm(ATc~LTm_LT,base))$fstatistic[1],
NA,summary(lm(ACc~LTm_ii,base))$fstatistic[1],
summary(lm(ACc~LTm_mi,base))$fstatistic[1],
summary(lm(ACc~LTm_ei,base))$fstatistic[1],
summary(lm(ACc~LTm_ci,base))$fstatistic[1],
summary(lm(ACc~LTm_LT,base))$fstatistic[1],
NA,summary(lm(APc~LTm_ii,base))$fstatistic[1],
summary(lm(APc~LTm_mi,base))$fstatistic[1],
summary(lm(APc~LTm_ei,base))$fstatistic[1],
summary(lm(APc~LTm_ci,base))$fstatistic[1],
summary(lm(APc~LTm_LT,base))$fstatistic[1],
NA,summary(lm(Cc~LTm_ii,base))$fstatistic[1],
summary(lm(Cc~LTm_mi,base))$fstatistic[1],
summary(lm(Cc~LTm_ei,base))$fstatistic[1],
summary(lm(Cc~LTm_ci,base))$fstatistic[1],
summary(lm(Cc~LTm_LT,base))$fstatistic[1]),2),
`P`=round(c(NA,pf(summary(lm(ATc~LTm_ii,base))$fstatistic[1],summary(lm(ATc~LTm_ii,base))$fstatistic[2],summary(lm(ATc~LTm_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTm_mi,base))$fstatistic[1],summary(lm(ATc~LTm_mi,base))$fstatistic[2],summary(lm(ATc~LTm_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTm_ei,base))$fstatistic[1],summary(lm(ATc~LTm_ei,base))$fstatistic[2],summary(lm(ATc~LTm_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTm_ci,base))$fstatistic[1],summary(lm(ATc~LTm_ci,base))$fstatistic[2],summary(lm(ATc~LTm_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTm_LT,base))$fstatistic[1],summary(lm(ATc~LTm_LT,base))$fstatistic[2],summary(lm(ATc~LTm_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(ACc~LTm_ii,base))$fstatistic[1],summary(lm(ACc~LTm_ii,base))$fstatistic[2],summary(lm(ACc~LTm_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTm_mi,base))$fstatistic[1],summary(lm(ACc~LTm_mi,base))$fstatistic[2],summary(lm(ACc~LTm_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTm_ei,base))$fstatistic[1],summary(lm(ACc~LTm_ei,base))$fstatistic[2],summary(lm(ACc~LTm_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTm_ci,base))$fstatistic[1],summary(lm(ACc~LTm_ci,base))$fstatistic[2],summary(lm(ACc~LTm_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTm_LT,base))$fstatistic[1],summary(lm(ACc~LTm_LT,base))$fstatistic[2],summary(lm(ACc~LTm_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(APc~LTm_ii,base))$fstatistic[1],summary(lm(APc~LTm_ii,base))$fstatistic[2],summary(lm(APc~LTm_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTm_mi,base))$fstatistic[1],summary(lm(APc~LTm_mi,base))$fstatistic[2],summary(lm(APc~LTm_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTm_ei,base))$fstatistic[1],summary(lm(APc~LTm_ei,base))$fstatistic[2],summary(lm(APc~LTm_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTm_ci,base))$fstatistic[1],summary(lm(APc~LTm_ci,base))$fstatistic[2],summary(lm(APc~LTm_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTm_LT,base))$fstatistic[1],summary(lm(APc~LTm_LT,base))$fstatistic[2],summary(lm(APc~LTm_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(Cc~LTm_ii,base))$fstatistic[1],summary(lm(Cc~LTm_ii,base))$fstatistic[2],summary(lm(Cc~LTm_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTm_mi,base))$fstatistic[1],summary(lm(Cc~LTm_mi,base))$fstatistic[2],summary(lm(Cc~LTm_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTm_ei,base))$fstatistic[1],summary(lm(Cc~LTm_ei,base))$fstatistic[2],summary(lm(Cc~LTm_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTm_ci,base))$fstatistic[1],summary(lm(Cc~LTm_ci,base))$fstatistic[2],summary(lm(Cc~LTm_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTm_LT,base))$fstatistic[1],summary(lm(Cc~LTm_LT,base))$fstatistic[2],summary(lm(Cc~LTm_LT,base))$fstatistic[3],lower.tail = F)),3),
`R2`=round(c(NA,summary(lm(ATc~LTm_ii,base))$r.squared,
summary(lm(ATc~LTm_mi,base))$r.squared,
summary(lm(ATc~LTm_ei,base))$r.squared,
summary(lm(ATc~LTm_ci,base))$r.squared,
summary(lm(ATc~LTm_LT,base))$r.squared,
NA,summary(lm(ACc~LTm_ii,base))$r.squared,
summary(lm(ACc~LTm_mi,base))$r.squared,
summary(lm(ACc~LTm_ei,base))$r.squared,
summary(lm(ACc~LTm_ci,base))$r.squared,
summary(lm(ACc~LTm_LT,base))$r.squared,
NA,summary(lm(APc~LTm_ii,base))$r.squared,
summary(lm(APc~LTm_mi,base))$r.squared,
summary(lm(APc~LTm_ei,base))$r.squared,
summary(lm(APc~LTm_ci,base))$r.squared,
summary(lm(APc~LTm_LT,base))$r.squared,
NA,summary(lm(Cc~LTm_ii,base))$r.squared,
summary(lm(Cc~LTm_mi,base))$r.squared,
summary(lm(Cc~LTm_ei,base))$r.squared,
summary(lm(Cc~LTm_ci,base))$r.squared,
summary(lm(Cc~LTm_LT,base))$r.squared),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("r","l","c","c","c","c","c"),
caption = "Modelos de regresión simple") %>%
kable_styling(full_width = F,
position = "center", font_size = 12) %>%
row_spec(c(8:12,14:18,20,24), bold = T, color = "black", background = "darkorange")| Variable criterio | Variable predictora | beta | p | F | P | R2 |
|---|---|---|---|---|---|---|
| Autotrascendencia | ||||||
| Influencia idealizada (madre) | 0.00 | 0.906 | 0.01 | 0.906 | 0.000 | |
| Motivación inspiracional (madre) | -0.01 | 0.781 | 0.08 | 0.781 | 0.000 | |
| Estimulación intelectual (madre) | 0.02 | 0.448 | 0.58 | 0.448 | 0.001 | |
| Consideración individualizada (madre) | 0.02 | 0.474 | 0.51 | 0.474 | 0.000 | |
| Parentalidad transformacional (madre) | 0.01 | 0.716 | 0.13 | 0.716 | 0.000 | |
| Apertura al cambio | ||||||
| Influencia idealizada (madre) | 0.05 | 0.027 | 4.92 | 0.027 | 0.005 | |
| Motivación inspiracional (madre) | 0.04 | 0.042 | 4.15 | 0.042 | 0.004 | |
| Estimulación intelectual (madre) | 0.07 | 0.000 | 12.30 | 0.000 | 0.011 | |
| Consideración individualizada (madre) | 0.06 | 0.004 | 8.27 | 0.004 | 0.008 | |
| Parentalidad transformacional (madre) | 0.07 | 0.003 | 8.68 | 0.003 | 0.008 | |
| Autopromoción | ||||||
| Influencia idealizada (madre) | -0.16 | 0.000 | 22.15 | 0.000 | 0.020 | |
| Motivación inspiracional (madre) | -0.11 | 0.003 | 8.60 | 0.003 | 0.008 | |
| Estimulación intelectual (madre) | -0.18 | 0.000 | 28.29 | 0.000 | 0.026 | |
| Consideración individualizada (madre) | -0.17 | 0.000 | 19.55 | 0.000 | 0.018 | |
| Parentalidad transformacional (madre) | -0.19 | 0.000 | 23.24 | 0.000 | 0.021 | |
| Conservación | ||||||
| Influencia idealizada (madre) | 0.06 | 0.007 | 7.43 | 0.007 | 0.007 | |
| Motivación inspiracional (madre) | 0.03 | 0.159 | 1.98 | 0.159 | 0.002 | |
| Estimulación intelectual (madre) | 0.04 | 0.091 | 2.85 | 0.091 | 0.003 | |
| Consideración individualizada (madre) | 0.03 | 0.166 | 1.92 | 0.166 | 0.002 | |
| Parentalidad transformacional (madre) | 0.05 | 0.046 | 4.00 | 0.046 | 0.004 |
Modelos múltiples
Apertura al cambio
summary(lm(ACc~LTm_ii + LTm_mi + LTm_ei + LTm_ci, base))##
## Call:
## lm(formula = ACc ~ LTm_ii + LTm_mi + LTm_ei + LTm_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.57934 -0.34630 -0.01297 0.34401 1.32118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06757 0.12826 0.527 0.5984
## LTm_ii -0.02527 0.03660 -0.690 0.4901
## LTm_mi -0.03333 0.03732 -0.893 0.3719
## LTm_ei 0.08008 0.03363 2.381 0.0174 *
## LTm_ci 0.04481 0.04132 1.085 0.2783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5082 on 1056 degrees of freedom
## Multiple R-squared: 0.01323, Adjusted R-squared: 0.009489
## F-statistic: 3.539 on 4 and 1056 DF, p-value: 0.007064Autopromoción
summary(lm(APc~LTm_ii + LTm_mi + LTm_ei + LTm_ci, base))##
## Call:
## lm(formula = APc ~ LTm_ii + LTm_mi + LTm_ei + LTm_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.79376 -0.54188 0.00966 0.63477 2.50429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.40993 0.21995 1.864 0.0626 .
## LTm_ii -0.08832 0.06276 -1.407 0.1596
## LTm_mi 0.14810 0.06399 2.314 0.0208 *
## LTm_ei -0.15912 0.05767 -2.759 0.0059 **
## LTm_ci -0.08501 0.07085 -1.200 0.2305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8716 on 1056 degrees of freedom
## Multiple R-squared: 0.03196, Adjusted R-squared: 0.0283
## F-statistic: 8.717 on 4 and 1056 DF, p-value: 6.353e-07Conservación
summary(lm(Cc~LTm_ii + LTm_mi + LTm_ei + LTm_ci, base))##
## Call:
## lm(formula = Cc ~ LTm_ii + LTm_mi + LTm_ei + LTm_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7806 -0.3673 0.0360 0.4119 1.6485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.819367 0.144828 -5.658 1.98e-08 ***
## LTm_ii 0.100676 0.041323 2.436 0.015 *
## LTm_mi -0.019106 0.042136 -0.453 0.650
## LTm_ei -0.002512 0.037975 -0.066 0.947
## LTm_ci -0.030736 0.046653 -0.659 0.510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5739 on 1056 degrees of freedom
## Multiple R-squared: 0.008329, Adjusted R-squared: 0.004573
## F-statistic: 2.217 on 4 and 1056 DF, p-value: 0.0652Padre
Hipótesis: Las conductas de Paternidad trasformacional se asocian positivamente con valores de AT y AC y negativamente con AP y C.
AT y AC sin relación. AP negativa. C positiva
tabla <- data.frame(`Variable criterio`=c("Autotrascendencia", rep(NA,5),
"Apertura al cambio", rep(NA,5),
"Autopromoción", rep(NA,5),
"Conservación", rep(NA,5)),
`Variable predictora`=rep(c(NA,"Influencia idealizada (padre)",
"Motivación inspiracional (padre)",
"Estimulación intelectual (padre)",
"Consideración individualizada (padre)",
"Parentalidad transformacional (padre)"),4),
beta=round(c(NA,coefficients(lm(ATc~LTp_ii,base))[2],
coefficients(lm(ATc~LTp_mi,base))[2],
coefficients(lm(ATc~LTp_ei,base))[2],
coefficients(lm(ATc~LTp_ci,base))[2],
coefficients(lm(ATc~LTp_LT,base))[2],
NA,coefficients(lm(ACc~LTp_ii,base))[2],
coefficients(lm(ACc~LTp_mi,base))[2],
coefficients(lm(ACc~LTp_ei,base))[2],
coefficients(lm(ACc~LTp_ci,base))[2],
coefficients(lm(ACc~LTp_LT,base))[2],
NA,coefficients(lm(APc~LTp_ii,base))[2],
coefficients(lm(APc~LTp_mi,base))[2],
coefficients(lm(APc~LTp_ei,base))[2],
coefficients(lm(APc~LTp_ci,base))[2],
coefficients(lm(APc~LTp_LT,base))[2],
NA,coefficients(lm(Cc~LTp_ii,base))[2],
coefficients(lm(Cc~LTp_mi,base))[2],
coefficients(lm(Cc~LTp_ei,base))[2],
coefficients(lm(Cc~LTp_ci,base))[2],
coefficients(lm(Cc~LTp_LT,base))[2]),2),
p=round(c(NA,summary(lm(ATc~LTp_ii,base))$coefficients[2,4],
summary(lm(ATc~LTp_mi,base))$coefficients[2,4],
summary(lm(ATc~LTp_ei,base))$coefficients[2,4],
summary(lm(ATc~LTp_ci,base))$coefficients[2,4],
summary(lm(ATc~LTp_LT,base))$coefficients[2,4],
NA,summary(lm(ACc~LTp_ii,base))$coefficients[2,4],
summary(lm(ACc~LTp_mi,base))$coefficients[2,4],
summary(lm(ACc~LTp_ei,base))$coefficients[2,4],
summary(lm(ACc~LTp_ci,base))$coefficients[2,4],
summary(lm(ACc~LTp_LT,base))$coefficients[2,4],
NA,summary(lm(APc~LTp_ii,base))$coefficients[2,4],
summary(lm(APc~LTp_mi,base))$coefficients[2,4],
summary(lm(APc~LTp_ei,base))$coefficients[2,4],
summary(lm(APc~LTp_ci,base))$coefficients[2,4],
summary(lm(APc~LTp_LT,base))$coefficients[2,4],
NA,summary(lm(Cc~LTp_ii,base))$coefficients[2,4],
summary(lm(Cc~LTp_mi,base))$coefficients[2,4],
summary(lm(Cc~LTp_ei,base))$coefficients[2,4],
summary(lm(Cc~LTp_ci,base))$coefficients[2,4],
summary(lm(Cc~LTp_LT,base))$coefficients[2,4]),3),
`F`=round(c(NA,summary(lm(ATc~LTp_ii,base))$fstatistic[1],
summary(lm(ATc~LTp_mi,base))$fstatistic[1],
summary(lm(ATc~LTp_ei,base))$fstatistic[1],
summary(lm(ATc~LTp_ci,base))$fstatistic[1],
summary(lm(ATc~LTp_LT,base))$fstatistic[1],
NA,summary(lm(ACc~LTp_ii,base))$fstatistic[1],
summary(lm(ACc~LTp_mi,base))$fstatistic[1],
summary(lm(ACc~LTp_ei,base))$fstatistic[1],
summary(lm(ACc~LTp_ci,base))$fstatistic[1],
summary(lm(ACc~LTp_LT,base))$fstatistic[1],
NA,summary(lm(APc~LTp_ii,base))$fstatistic[1],
summary(lm(APc~LTp_mi,base))$fstatistic[1],
summary(lm(APc~LTp_ei,base))$fstatistic[1],
summary(lm(APc~LTp_ci,base))$fstatistic[1],
summary(lm(APc~LTp_LT,base))$fstatistic[1],
NA,summary(lm(Cc~LTp_ii,base))$fstatistic[1],
summary(lm(Cc~LTp_mi,base))$fstatistic[1],
summary(lm(Cc~LTp_ei,base))$fstatistic[1],
summary(lm(Cc~LTp_ci,base))$fstatistic[1],
summary(lm(Cc~LTp_LT,base))$fstatistic[1]),2),
`P`=round(c(NA,pf(summary(lm(ATc~LTp_ii,base))$fstatistic[1],summary(lm(ATc~LTp_ii,base))$fstatistic[2],summary(lm(ATc~LTp_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTp_mi,base))$fstatistic[1],summary(lm(ATc~LTp_mi,base))$fstatistic[2],summary(lm(ATc~LTp_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTp_ei,base))$fstatistic[1],summary(lm(ATc~LTp_ei,base))$fstatistic[2],summary(lm(ATc~LTp_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTp_ci,base))$fstatistic[1],summary(lm(ATc~LTp_ci,base))$fstatistic[2],summary(lm(ATc~LTp_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTp_LT,base))$fstatistic[1],summary(lm(ATc~LTp_LT,base))$fstatistic[2],summary(lm(ATc~LTp_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(ACc~LTp_ii,base))$fstatistic[1],summary(lm(ACc~LTp_ii,base))$fstatistic[2],summary(lm(ACc~LTp_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTp_mi,base))$fstatistic[1],summary(lm(ACc~LTp_mi,base))$fstatistic[2],summary(lm(ACc~LTp_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTp_ei,base))$fstatistic[1],summary(lm(ACc~LTp_ei,base))$fstatistic[2],summary(lm(ACc~LTp_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTp_ci,base))$fstatistic[1],summary(lm(ACc~LTp_ci,base))$fstatistic[2],summary(lm(ACc~LTp_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTp_LT,base))$fstatistic[1],summary(lm(ACc~LTp_LT,base))$fstatistic[2],summary(lm(ACc~LTp_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(APc~LTp_ii,base))$fstatistic[1],summary(lm(APc~LTp_ii,base))$fstatistic[2],summary(lm(APc~LTp_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTp_mi,base))$fstatistic[1],summary(lm(APc~LTp_mi,base))$fstatistic[2],summary(lm(APc~LTp_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTp_ei,base))$fstatistic[1],summary(lm(APc~LTp_ei,base))$fstatistic[2],summary(lm(APc~LTp_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTp_ci,base))$fstatistic[1],summary(lm(APc~LTp_ci,base))$fstatistic[2],summary(lm(APc~LTp_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTp_LT,base))$fstatistic[1],summary(lm(APc~LTp_LT,base))$fstatistic[2],summary(lm(APc~LTp_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(Cc~LTp_ii,base))$fstatistic[1],summary(lm(Cc~LTp_ii,base))$fstatistic[2],summary(lm(Cc~LTp_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTp_mi,base))$fstatistic[1],summary(lm(Cc~LTp_mi,base))$fstatistic[2],summary(lm(Cc~LTp_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTp_ei,base))$fstatistic[1],summary(lm(Cc~LTp_ei,base))$fstatistic[2],summary(lm(Cc~LTp_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTp_ci,base))$fstatistic[1],summary(lm(Cc~LTp_ci,base))$fstatistic[2],summary(lm(Cc~LTp_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTp_LT,base))$fstatistic[1],summary(lm(Cc~LTp_LT,base))$fstatistic[2],summary(lm(Cc~LTp_LT,base))$fstatistic[3],lower.tail = F)),3),
`R2`=round(c(NA,summary(lm(ATc~LTp_ii,base))$r.squared,
summary(lm(ATc~LTp_mi,base))$r.squared,
summary(lm(ATc~LTp_ei,base))$r.squared,
summary(lm(ATc~LTp_ci,base))$r.squared,
summary(lm(ATc~LTp_LT,base))$r.squared,
NA,summary(lm(ACc~LTp_ii,base))$r.squared,
summary(lm(ACc~LTp_mi,base))$r.squared,
summary(lm(ACc~LTp_ei,base))$r.squared,
summary(lm(ACc~LTp_ci,base))$r.squared,
summary(lm(ACc~LTp_LT,base))$r.squared,
NA,summary(lm(APc~LTp_ii,base))$r.squared,
summary(lm(APc~LTp_mi,base))$r.squared,
summary(lm(APc~LTp_ei,base))$r.squared,
summary(lm(APc~LTp_ci,base))$r.squared,
summary(lm(APc~LTp_LT,base))$r.squared,
NA,summary(lm(Cc~LTp_ii,base))$r.squared,
summary(lm(Cc~LTp_mi,base))$r.squared,
summary(lm(Cc~LTp_ei,base))$r.squared,
summary(lm(Cc~LTp_ci,base))$r.squared,
summary(lm(Cc~LTp_LT,base))$r.squared),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("r","l","c","c","c","c","c"),
caption = "Modelos de regresión simple") %>%
kable_styling(full_width = F,
position = "center", font_size = 12) %>%
row_spec(c(10,14:18,20,23,24), bold = T, color = "black", background = "darkorange")| Variable criterio | Variable predictora | beta | p | F | P | R2 |
|---|---|---|---|---|---|---|
| Autotrascendencia | ||||||
| Influencia idealizada (padre) | -0.01 | 0.562 | 0.34 | 0.562 | 0.000 | |
| Motivación inspiracional (padre) | 0.01 | 0.482 | 0.49 | 0.482 | 0.000 | |
| Estimulación intelectual (padre) | 0.02 | 0.322 | 0.98 | 0.322 | 0.001 | |
| Consideración individualizada (padre) | 0.02 | 0.324 | 0.97 | 0.324 | 0.001 | |
| Parentalidad transformacional (padre) | 0.01 | 0.582 | 0.30 | 0.582 | 0.000 | |
| Apertura al cambio | ||||||
| Influencia idealizada (padre) | 0.01 | 0.427 | 0.63 | 0.427 | 0.001 | |
| Motivación inspiracional (padre) | 0.02 | 0.266 | 1.24 | 0.266 | 0.001 | |
| Estimulación intelectual (padre) | 0.05 | 0.002 | 9.35 | 0.002 | 0.009 | |
| Consideración individualizada (padre) | 0.02 | 0.192 | 1.71 | 0.192 | 0.002 | |
| Parentalidad transformacional (padre) | 0.03 | 0.093 | 2.83 | 0.093 | 0.003 | |
| Autopromoción | ||||||
| Influencia idealizada (padre) | -0.09 | 0.000 | 12.32 | 0.000 | 0.012 | |
| Motivación inspiracional (padre) | -0.08 | 0.008 | 7.04 | 0.008 | 0.007 | |
| Estimulación intelectual (padre) | -0.14 | 0.000 | 27.59 | 0.000 | 0.025 | |
| Consideración individualizada (padre) | -0.12 | 0.000 | 18.85 | 0.000 | 0.017 | |
| Parentalidad transformacional (padre) | -0.12 | 0.000 | 18.10 | 0.000 | 0.017 | |
| Conservación | ||||||
| Influencia idealizada (padre) | 0.06 | 0.001 | 11.17 | 0.001 | 0.010 | |
| Motivación inspiracional (padre) | 0.02 | 0.241 | 1.37 | 0.241 | 0.001 | |
| Estimulación intelectual (padre) | 0.03 | 0.066 | 3.38 | 0.066 | 0.003 | |
| Consideración individualizada (padre) | 0.04 | 0.013 | 6.18 | 0.013 | 0.006 | |
| Parentalidad transformacional (padre) | 0.05 | 0.016 | 5.79 | 0.016 | 0.005 |
Modelos múltiples
Apertura al cambio
summary(lm(ACc~LTp_ii + LTp_mi + LTp_ei + LTp_ci, base))##
## Call:
## lm(formula = ACc ~ LTp_ii + LTp_mi + LTp_ei + LTp_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.72439 -0.33052 -0.01975 0.35018 1.33923
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27202 0.09479 2.870 0.00419 **
## LTp_ii -0.04339 0.03060 -1.418 0.15652
## LTp_mi -0.02592 0.03557 -0.729 0.46636
## LTp_ei 0.11717 0.02997 3.910 9.81e-05 ***
## LTp_ci -0.01677 0.03595 -0.466 0.64103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5075 on 1056 degrees of freedom
## Multiple R-squared: 0.0162, Adjusted R-squared: 0.01247
## F-statistic: 4.347 on 4 and 1056 DF, p-value: 0.001718Autopromoción
summary(lm(APc~LTp_ii + LTp_mi + LTp_ei + LTp_ci, base))##
## Call:
## lm(formula = APc ~ LTp_ii + LTp_mi + LTp_ei + LTp_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7888 -0.5593 0.0266 0.6233 2.5222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04163 0.16260 -0.256 0.797986
## LTp_ii 0.01818 0.05249 0.346 0.729202
## LTp_mi 0.17615 0.06102 2.887 0.003970 **
## LTp_ei -0.19307 0.05140 -3.756 0.000182 ***
## LTp_ci -0.11242 0.06166 -1.823 0.068576 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8705 on 1056 degrees of freedom
## Multiple R-squared: 0.03431, Adjusted R-squared: 0.03066
## F-statistic: 9.381 on 4 and 1056 DF, p-value: 1.882e-07Conservación
summary(lm(Cc~LTp_ii + LTp_mi + LTp_ei + LTp_ci, base))##
## Call:
## lm(formula = Cc ~ LTp_ii + LTp_mi + LTp_ei + LTp_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80750 -0.36596 0.04494 0.40473 1.63404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.70852 0.10666 -6.643 4.91e-11 ***
## LTp_ii 0.10905 0.03443 3.167 0.00158 **
## LTp_mi -0.10474 0.04002 -2.617 0.00899 **
## LTp_ei -0.01538 0.03372 -0.456 0.64844
## LTp_ci 0.04333 0.04045 1.071 0.28429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.571 on 1056 degrees of freedom
## Multiple R-squared: 0.01831, Adjusted R-squared: 0.01459
## F-statistic: 4.923 on 4 and 1056 DF, p-value: 0.0006181Coach
Hipótesis: Las conductas de Liderazgo trasformacional del/de la entrenador/a se asocian positivamente con valores de AT y AC y negativamente con AP y C.
AC sin relación. AP negativa. AT y C positiva
tabla <- data.frame(`Variable criterio`=c("Autotrascendencia", rep(NA,5),
"Apertura al cambio", rep(NA,5),
"Autopromoción", rep(NA,5),
"Conservación", rep(NA,5)),
`Variable predictora`=rep(c(NA,"Influencia idealizada (coach)",
"Motivación inspiracional (coach)",
"Estimulación intelectual (coach)",
"Consideración individualizada (coach)",
"Liderazgo transformacional (coach)"),4),
beta=round(c(NA,coefficients(lm(ATc~LTc_ii,base))[2],
coefficients(lm(ATc~LTc_mi,base))[2],
coefficients(lm(ATc~LTc_ei,base))[2],
coefficients(lm(ATc~LTc_ci,base))[2],
coefficients(lm(ATc~LTc_LT,base))[2],
NA,coefficients(lm(ACc~LTc_ii,base))[2],
coefficients(lm(ACc~LTc_mi,base))[2],
coefficients(lm(ACc~LTc_ei,base))[2],
coefficients(lm(ACc~LTc_ci,base))[2],
coefficients(lm(ACc~LTc_LT,base))[2],
NA,coefficients(lm(APc~LTc_ii,base))[2],
coefficients(lm(APc~LTc_mi,base))[2],
coefficients(lm(APc~LTc_ei,base))[2],
coefficients(lm(APc~LTc_ci,base))[2],
coefficients(lm(APc~LTc_LT,base))[2],
NA,coefficients(lm(Cc~LTc_ii,base))[2],
coefficients(lm(Cc~LTc_mi,base))[2],
coefficients(lm(Cc~LTc_ei,base))[2],
coefficients(lm(Cc~LTc_ci,base))[2],
coefficients(lm(Cc~LTc_LT,base))[2]),2),
p=round(c(NA,summary(lm(ATc~LTc_ii,base))$coefficients[2,4],
summary(lm(ATc~LTc_mi,base))$coefficients[2,4],
summary(lm(ATc~LTc_ei,base))$coefficients[2,4],
summary(lm(ATc~LTc_ci,base))$coefficients[2,4],
summary(lm(ATc~LTc_LT,base))$coefficients[2,4],
NA,summary(lm(ACc~LTc_ii,base))$coefficients[2,4],
summary(lm(ACc~LTc_mi,base))$coefficients[2,4],
summary(lm(ACc~LTc_ei,base))$coefficients[2,4],
summary(lm(ACc~LTc_ci,base))$coefficients[2,4],
summary(lm(ACc~LTc_LT,base))$coefficients[2,4],
NA,summary(lm(APc~LTc_ii,base))$coefficients[2,4],
summary(lm(APc~LTc_mi,base))$coefficients[2,4],
summary(lm(APc~LTc_ei,base))$coefficients[2,4],
summary(lm(APc~LTc_ci,base))$coefficients[2,4],
summary(lm(APc~LTc_LT,base))$coefficients[2,4],
NA,summary(lm(Cc~LTc_ii,base))$coefficients[2,4],
summary(lm(Cc~LTc_mi,base))$coefficients[2,4],
summary(lm(Cc~LTc_ei,base))$coefficients[2,4],
summary(lm(Cc~LTc_ci,base))$coefficients[2,4],
summary(lm(Cc~LTc_LT,base))$coefficients[2,4]),3),
`F`=round(c(NA,summary(lm(ATc~LTc_ii,base))$fstatistic[1],
summary(lm(ATc~LTc_mi,base))$fstatistic[1],
summary(lm(ATc~LTc_ei,base))$fstatistic[1],
summary(lm(ATc~LTc_ci,base))$fstatistic[1],
summary(lm(ATc~LTc_LT,base))$fstatistic[1],
NA,summary(lm(ACc~LTc_ii,base))$fstatistic[1],
summary(lm(ACc~LTc_mi,base))$fstatistic[1],
summary(lm(ACc~LTc_ei,base))$fstatistic[1],
summary(lm(ACc~LTc_ci,base))$fstatistic[1],
summary(lm(ACc~LTc_LT,base))$fstatistic[1],
NA,summary(lm(APc~LTc_ii,base))$fstatistic[1],
summary(lm(APc~LTc_mi,base))$fstatistic[1],
summary(lm(APc~LTc_ei,base))$fstatistic[1],
summary(lm(APc~LTc_ci,base))$fstatistic[1],
summary(lm(APc~LTc_LT,base))$fstatistic[1],
NA,summary(lm(Cc~LTc_ii,base))$fstatistic[1],
summary(lm(Cc~LTc_mi,base))$fstatistic[1],
summary(lm(Cc~LTc_ei,base))$fstatistic[1],
summary(lm(Cc~LTc_ci,base))$fstatistic[1],
summary(lm(Cc~LTc_LT,base))$fstatistic[1]),2),
`P`=round(c(NA,pf(summary(lm(ATc~LTc_ii,base))$fstatistic[1],summary(lm(ATc~LTc_ii,base))$fstatistic[2],summary(lm(ATc~LTc_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTc_mi,base))$fstatistic[1],summary(lm(ATc~LTc_mi,base))$fstatistic[2],summary(lm(ATc~LTc_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTc_ei,base))$fstatistic[1],summary(lm(ATc~LTc_ei,base))$fstatistic[2],summary(lm(ATc~LTc_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTc_ci,base))$fstatistic[1],summary(lm(ATc~LTc_ci,base))$fstatistic[2],summary(lm(ATc~LTc_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ATc~LTc_LT,base))$fstatistic[1],summary(lm(ATc~LTc_LT,base))$fstatistic[2],summary(lm(ATc~LTc_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(ACc~LTc_ii,base))$fstatistic[1],summary(lm(ACc~LTc_ii,base))$fstatistic[2],summary(lm(ACc~LTc_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTc_mi,base))$fstatistic[1],summary(lm(ACc~LTc_mi,base))$fstatistic[2],summary(lm(ACc~LTc_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTc_ei,base))$fstatistic[1],summary(lm(ACc~LTc_ei,base))$fstatistic[2],summary(lm(ACc~LTc_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTc_ci,base))$fstatistic[1],summary(lm(ACc~LTc_ci,base))$fstatistic[2],summary(lm(ACc~LTc_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(ACc~LTc_LT,base))$fstatistic[1],summary(lm(ACc~LTc_LT,base))$fstatistic[2],summary(lm(ACc~LTc_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(APc~LTc_ii,base))$fstatistic[1],summary(lm(APc~LTc_ii,base))$fstatistic[2],summary(lm(APc~LTc_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTc_mi,base))$fstatistic[1],summary(lm(APc~LTc_mi,base))$fstatistic[2],summary(lm(APc~LTc_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTc_ei,base))$fstatistic[1],summary(lm(APc~LTc_ei,base))$fstatistic[2],summary(lm(APc~LTc_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTc_ci,base))$fstatistic[1],summary(lm(APc~LTc_ci,base))$fstatistic[2],summary(lm(APc~LTc_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(APc~LTc_LT,base))$fstatistic[1],summary(lm(APc~LTc_LT,base))$fstatistic[2],summary(lm(APc~LTc_LT,base))$fstatistic[3],lower.tail = F),
NA,pf(summary(lm(Cc~LTc_ii,base))$fstatistic[1],summary(lm(Cc~LTc_ii,base))$fstatistic[2],summary(lm(Cc~LTc_ii,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTc_mi,base))$fstatistic[1],summary(lm(Cc~LTc_mi,base))$fstatistic[2],summary(lm(Cc~LTc_mi,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTc_ei,base))$fstatistic[1],summary(lm(Cc~LTc_ei,base))$fstatistic[2],summary(lm(Cc~LTc_ei,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTc_ci,base))$fstatistic[1],summary(lm(Cc~LTc_ci,base))$fstatistic[2],summary(lm(Cc~LTc_ci,base))$fstatistic[3],lower.tail = F),
pf(summary(lm(Cc~LTc_LT,base))$fstatistic[1],summary(lm(Cc~LTc_LT,base))$fstatistic[2],summary(lm(Cc~LTc_LT,base))$fstatistic[3],lower.tail = F)),3),
`R2`=round(c(NA,summary(lm(ATc~LTc_ii,base))$r.squared,
summary(lm(ATc~LTc_mi,base))$r.squared,
summary(lm(ATc~LTc_ei,base))$r.squared,
summary(lm(ATc~LTc_ci,base))$r.squared,
summary(lm(ATc~LTc_LT,base))$r.squared,
NA,summary(lm(ACc~LTc_ii,base))$r.squared,
summary(lm(ACc~LTc_mi,base))$r.squared,
summary(lm(ACc~LTc_ei,base))$r.squared,
summary(lm(ACc~LTc_ci,base))$r.squared,
summary(lm(ACc~LTc_LT,base))$r.squared,
NA,summary(lm(APc~LTc_ii,base))$r.squared,
summary(lm(APc~LTc_mi,base))$r.squared,
summary(lm(APc~LTc_ei,base))$r.squared,
summary(lm(APc~LTc_ci,base))$r.squared,
summary(lm(APc~LTc_LT,base))$r.squared,
NA,summary(lm(Cc~LTc_ii,base))$r.squared,
summary(lm(Cc~LTc_mi,base))$r.squared,
summary(lm(Cc~LTc_ei,base))$r.squared,
summary(lm(Cc~LTc_ci,base))$r.squared,
summary(lm(Cc~LTc_LT,base))$r.squared),3),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("r","l","c","c","c","c","c"),
caption = "Modelos de regresión simple") %>%
kable_styling(full_width = F,
position = "center", font_size = 12) %>%
row_spec(c(2:6,10,14:18,20,21,23,24), bold = T, color = "black", background = "darkorange")| Variable criterio | Variable predictora | beta | p | F | P | R2 |
|---|---|---|---|---|---|---|
| Autotrascendencia | ||||||
| Influencia idealizada (coach) | 0.03 | 0.047 | 3.94 | 0.047 | 0.004 | |
| Motivación inspiracional (coach) | 0.05 | 0.002 | 9.28 | 0.002 | 0.009 | |
| Estimulación intelectual (coach) | 0.05 | 0.006 | 7.57 | 0.006 | 0.007 | |
| Consideración individualizada (coach) | 0.05 | 0.001 | 10.59 | 0.001 | 0.010 | |
| Liderazgo transformacional (coach) | 0.06 | 0.002 | 9.43 | 0.002 | 0.009 | |
| Apertura al cambio | ||||||
| Influencia idealizada (coach) | 0.01 | 0.591 | 0.29 | 0.591 | 0.000 | |
| Motivación inspiracional (coach) | 0.01 | 0.396 | 0.72 | 0.396 | 0.001 | |
| Estimulación intelectual (coach) | 0.04 | 0.014 | 6.09 | 0.014 | 0.006 | |
| Consideración individualizada (coach) | 0.01 | 0.452 | 0.57 | 0.452 | 0.001 | |
| Liderazgo transformacional (coach) | 0.02 | 0.208 | 1.59 | 0.208 | 0.001 | |
| Autopromoción | ||||||
| Influencia idealizada (coach) | -0.11 | 0.000 | 19.03 | 0.000 | 0.018 | |
| Motivación inspiracional (coach) | -0.14 | 0.000 | 27.28 | 0.000 | 0.025 | |
| Estimulación intelectual (coach) | -0.14 | 0.000 | 26.27 | 0.000 | 0.024 | |
| Consideración individualizada (coach) | -0.18 | 0.000 | 45.76 | 0.000 | 0.041 | |
| Liderazgo transformacional (coach) | -0.18 | 0.000 | 35.91 | 0.000 | 0.033 | |
| Conservación | ||||||
| Influencia idealizada (coach) | 0.04 | 0.016 | 5.79 | 0.016 | 0.005 | |
| Motivación inspiracional (coach) | 0.04 | 0.031 | 4.65 | 0.031 | 0.004 | |
| Estimulación intelectual (coach) | 0.02 | 0.394 | 0.73 | 0.394 | 0.001 | |
| Consideración individualizada (coach) | 0.06 | 0.000 | 12.97 | 0.000 | 0.012 | |
| Liderazgo transformacional (coach) | 0.05 | 0.012 | 6.40 | 0.012 | 0.006 |
Modelos múltiples
Autotrascendencia
summary(lm(ATc~LTc_ii + LTc_mi + LTc_ei + LTc_ci, base))##
## Call:
## lm(formula = ATc ~ LTc_ii + LTc_mi + LTc_ei + LTc_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8878 -0.3594 0.0162 0.3679 1.6587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.30107 0.09307 3.235 0.00126 **
## LTc_ii -0.04380 0.02872 -1.525 0.12753
## LTc_mi 0.02904 0.02990 0.971 0.33165
## LTc_ei 0.02599 0.02578 1.008 0.31362
## LTc_ci 0.05007 0.03126 1.602 0.10949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5391 on 1056 degrees of freedom
## Multiple R-squared: 0.01288, Adjusted R-squared: 0.009146
## F-statistic: 3.446 on 4 and 1056 DF, p-value: 0.00829Apertura al cambio
summary(lm(ACc~LTc_ii + LTc_mi + LTc_ei + LTc_ci, base))##
## Call:
## lm(formula = ACc ~ LTc_ii + LTc_mi + LTc_ei + LTc_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.63685 -0.33500 -0.01718 0.34881 1.32627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.269011 0.087956 3.058 0.00228 **
## LTc_ii -0.031130 0.027139 -1.147 0.25161
## LTc_mi -0.001573 0.028259 -0.056 0.95561
## LTc_ei 0.070179 0.024367 2.880 0.00406 **
## LTc_ci -0.006745 0.029542 -0.228 0.81945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5094 on 1056 degrees of freedom
## Multiple R-squared: 0.008545, Adjusted R-squared: 0.00479
## F-statistic: 2.275 on 4 and 1056 DF, p-value: 0.05934Autopromoción
summary(lm(APc~LTc_ii + LTc_mi + LTc_ei + LTc_ci, base))##
## Call:
## lm(formula = APc ~ LTc_ii + LTc_mi + LTc_ei + LTc_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.64226 -0.55418 0.01793 0.58714 2.28145
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.379644 0.149441 2.540 0.0112 *
## LTc_ii 0.083903 0.046110 1.820 0.0691 .
## LTc_mi -0.002956 0.048014 -0.062 0.9509
## LTc_ei -0.060639 0.041401 -1.465 0.1433
## LTc_ci -0.209203 0.050193 -4.168 3.33e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8656 on 1056 degrees of freedom
## Multiple R-squared: 0.04527, Adjusted R-squared: 0.04165
## F-statistic: 12.52 on 4 and 1056 DF, p-value: 5.93e-10Conservación
summary(lm(Cc~LTc_ii + LTc_mi + LTc_ei + LTc_ci, base))##
## Call:
## lm(formula = Cc ~ LTc_ii + LTc_mi + LTc_ei + LTc_ci, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.74958 -0.37910 0.04003 0.41606 1.69677
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.77300 0.09866 -7.835 1.14e-14 ***
## LTc_ii 0.01169 0.03044 0.384 0.70098
## LTc_mi -0.02066 0.03170 -0.652 0.51473
## LTc_ei -0.05142 0.02733 -1.881 0.06024 .
## LTc_ci 0.10448 0.03314 3.153 0.00166 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5714 on 1056 degrees of freedom
## Multiple R-squared: 0.01675, Adjusted R-squared: 0.01303
## F-statistic: 4.497 on 4 and 1056 DF, p-value: 0.001318Valores según deporte
Hipótesis: Cada deporte se asocia con un sistema de valores particular.
Considerando la alta relación de valores con edad y sexo y que hay deportes en los que sólo contamos con una de las ramas, en todos los modelos se incluyen las variables edad y sexo como control.
Indoor vs. outdoor
base <- data.frame(cbind(T1[c(1:4)],
AT=rowMeans(T1[c(44,49,53,59,60)]),
AC=rowMeans(T1[c(42,47,51,52,56,62)]),
AP=rowMeans(T1[c(43,45,54,58)]),
C=rowMeans(T1[c(46,48,50,55,57,61)]),
VP_media=rowMeans(T1[42:62]),
ATc=rowMeans(T1[c(44,49,53,59,60)])-rowMeans(T1[42:62]),
ACc=rowMeans(T1[c(42,47,51,52,56,62)])-rowMeans(T1[42:62]),
APc=rowMeans(T1[c(43,45,54,58)])-rowMeans(T1[42:62]),
Cc=rowMeans(T1[c(46,48,50,55,57,61)])-rowMeans(T1[42:62])))
base <- base[complete.cases(base),]
base$deporte_dic <- ifelse(base$deporte_T1%in%c("basquet","handball","voley"),"indoor","outdoor")
tabla <- data.frame(`Variable criterio`=c("Autotrascendencia","Apertura al cambio", "Autopromoción","Conservación"),
rbind(as.data.frame(emmeans(lm(ATc~deporte_dic+sexo_T1+edad_T1,base), pairwise~deporte_dic)$contrasts),
as.data.frame(emmeans(lm(ACc~deporte_dic+sexo_T1+edad_T1,base), pairwise~deporte_dic)$contrasts),
as.data.frame(emmeans(lm(APc~deporte_dic+sexo_T1+edad_T1,base), pairwise~deporte_dic)$contrasts),
as.data.frame(emmeans(lm(Cc~deporte_dic+sexo_T1+edad_T1,base), pairwise~deporte_dic)$contrasts)),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("l","c","c","c","c","c"),
caption = "Modelos de regresión simple") %>%
kable_styling(full_width = F,
position = "center", font_size = 12) %>%
row_spec(c(1), bold = T, color = "black", background = "darkorange")| Variable criterio | contrast | estimate | SE | df | t.ratio | p.value |
|---|---|---|---|---|---|---|
| Autotrascendencia | indoor - outdoor | 0.0804888 | 0.0320646 | 1146 | 2.5102062 | 0.0122030 |
| Apertura al cambio | indoor - outdoor | -0.0277989 | 0.0300713 | 1146 | -0.9244341 | 0.3554549 |
| Autopromoción | indoor - outdoor | 0.0142907 | 0.0519600 | 1146 | 0.2750333 | 0.7833402 |
| Conservación | indoor - outdoor | -0.0488022 | 0.0334555 | 1146 | -1.4587207 | 0.1449160 |
base2 <- as.data.frame(emmeans(lm(ATc~deporte_dic+sexo_T1+edad_T1,base), pairwise~deporte_dic)$emmeans)
base2deporte_dic emmean SE df lower.CL upper.CL indoor 0.6477009 0.02310356 1146 0.6023709 0.6930310 outdoor 0.5672121 0.02217533 1146 0.5237033 0.6107209
Results are averaged over the levels of: sexo_T1 Confidence level used: 0.95
ggplot(base2, aes(x=deporte_dic, y=emmean, color=deporte_dic)) +
geom_point(size=4)+
geom_errorbar(aes(ymin=lower.CL, ymax=upper.CL), width=.5,
position=position_dodge(0.05), lwd=1)+
geom_point(data = base,aes(x=deporte_dic, y=ATc, color=deporte_dic),
alpha=.15, position = position_jitterdodge())+
theme_minimal()+
theme(legend.position = "none")+
xlab("Deporte")+ylab("Autotrascendencia")+
scale_fill_manual(name = "Deporte", labels=c("Indoor", "Outdoor"),values=c("#f08f56","#10b0bc"))+
scale_color_manual(name = "Deporte", labels=c("Indoor", "Outdoor"),values=c("#f08f56","#10b0bc"))
Diferencias en indoor
tabla <- data.frame(`Variable criterio`=c("Autotrascendencia",NA,NA,"Apertura al cambio",NA,NA, "Autopromoción",NA,NA,"Conservación",NA,NA),
rbind(as.data.frame(emmeans(lm(ATc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="indoor",]), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(ACc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="indoor",]), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(APc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="indoor",]), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(Cc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="indoor",]), pairwise~deporte_T1)$contrasts)),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("l","l","c","c","c","c","c"),
caption = "Comparación de medias") %>%
kable_styling(full_width = F,
position = "center", font_size = 12) | Variable criterio | contrast | estimate | SE | df | t.ratio | p.value |
|---|---|---|---|---|---|---|
| Autotrascendencia | basquet - handball | 0.0449877 | 0.0625693 | 544 | 0.7190052 | 0.7523448 |
| basquet - voley | -0.0001035 | 0.0537362 | 544 | -0.0019266 | 0.9999980 | |
| handball - voley | -0.0450912 | 0.0624388 | 544 | -0.7221661 | 0.7504637 | |
| Apertura al cambio | basquet - handball | 0.0493506 | 0.0619054 | 544 | 0.7971931 | 0.7049094 |
| basquet - voley | 0.1176163 | 0.0531660 | 544 | 2.2122462 | 0.0700060 | |
| handball - voley | 0.0682657 | 0.0617763 | 544 | 1.1050478 | 0.5113484 | |
| Autopromoción | basquet - handball | -0.0912439 | 0.1116608 | 544 | -0.8171520 | 0.6925476 |
| basquet - voley | -0.1548292 | 0.0958973 | 544 | -1.6145316 | 0.2403237 | |
| handball - voley | -0.0635853 | 0.1114279 | 544 | -0.5706410 | 0.8358155 | |
| Conservación | basquet - handball | -0.0260110 | 0.0721738 | 544 | -0.3603945 | 0.9309220 |
| basquet - voley | -0.0143105 | 0.0619848 | 544 | -0.2308721 | 0.9710455 | |
| handball - voley | 0.0117005 | 0.0720232 | 544 | 0.1624543 | 0.9855562 |
Diferencias en outdoor
tabla <- data.frame(`Variable criterio`=c("Autotrascendencia",NA,NA,"Apertura al cambio",NA,NA, "Autopromoción",NA,NA,"Conservación",NA,NA),
rbind(as.data.frame(emmeans(lm(ATc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="outdoor",]), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(ACc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="outdoor",]), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(APc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="outdoor",]), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(Cc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="outdoor",]), pairwise~deporte_T1)$contrasts)),
check.names=FALSE)
kable(tabla,
"html",
booktabs = T,
align = c("l","l","c","c","c","c","c"),
caption = "Comparación de medias") %>%
kable_styling(full_width = F,
position = "center", font_size = 12) %>%
row_spec(c(5), bold = T, color = "black", background = "darkorange")| Variable criterio | contrast | estimate | SE | df | t.ratio | p.value |
|---|---|---|---|---|---|---|
| Autotrascendencia | futbol - hockey | -0.1017853 | 0.1202310 | 596 | -0.8465807 | 0.6741643 |
| futbol - rugby | 0.1470452 | 0.0746712 | 596 | 1.9692347 | 0.1207685 | |
| hockey - rugby | 0.2488304 | 0.1421904 | 596 | 1.7499808 | 0.1876415 | |
| Apertura al cambio | futbol - hockey | 0.1091758 | 0.1080081 | 596 | 1.0108114 | 0.5703058 |
| futbol - rugby | -0.1872803 | 0.0670800 | 596 | -2.7918944 | 0.0149271 | |
| hockey - rugby | -0.2964561 | 0.1277350 | 596 | -2.3208679 | 0.0537172 | |
| Autopromoción | futbol - hockey | -0.2951700 | 0.1799033 | 596 | -1.6407143 | 0.2293835 |
| futbol - rugby | -0.0499446 | 0.1117316 | 596 | -0.4470052 | 0.8957480 | |
| hockey - rugby | 0.2452254 | 0.2127614 | 596 | 1.1525838 | 0.4821523 | |
| Conservación | futbol - hockey | 0.1724252 | 0.1161610 | 596 | 1.4843643 | 0.2991466 |
| futbol - rugby | 0.0980391 | 0.0721435 | 596 | 1.3589457 | 0.3632898 | |
| hockey - rugby | -0.0743862 | 0.1373770 | 596 | -0.5414747 | 0.8508640 |
base2 <- as.data.frame(emmeans(lm(ACc~deporte_T1+sexo_T1+edad_T1,base[base$deporte_dic=="outdoor",]), pairwise~deporte_T1)$emmean)
ggplot(base2, aes(x=deporte_T1, y=emmean, color=deporte_T1)) +
geom_point(size=4)+
geom_errorbar(aes(ymin=lower.CL, ymax=upper.CL), width=.5,
position=position_dodge(0.05), lwd=1)+
geom_point(data = base[base$deporte_T1%in%c("futbol","hockey","rugby"),],aes(x=deporte_T1, y=ACc, color=deporte_T1),
alpha=.25, position = position_jitterdodge())+
theme_minimal()+
theme(legend.position = "none")+
xlab("Deporte")+ylab("Apertura al cambio")+
scale_fill_manual(name = "Deporte",values=c("#f08f56","#10b0bc","#2ec417"))+
scale_color_manual(name = "Deporte",values=c("#f08f56","#10b0bc","#2ec417"))+
annotate("text",
x=1:3,
y=-1,
label=c("A","AB","B"))
Diferencias entre todos
tabla <- data.frame(`Variable criterio`=c("Autotrascendencia",rep(NA,14),"Apertura al cambio",rep(NA,14), "Autopromoción",rep(NA,14),"Conservación",rep(NA,14)),
rbind(as.data.frame(emmeans(lm(ATc~deporte_T1+sexo_T1+edad_T1,base), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(ACc~deporte_T1+sexo_T1+edad_T1,base), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(APc~deporte_T1+sexo_T1+edad_T1,base), pairwise~deporte_T1)$contrasts),
as.data.frame(emmeans(lm(Cc~deporte_T1+sexo_T1+edad_T1,base), pairwise~deporte_T1)$contrasts)),
check.names=FALSE)
tabla[3:7] <- round(tabla[3:7],3)
kable(tabla,
"html",
booktabs = T,
align = c("l","l","c","c","c","c","c"),
caption = "Comparación de medias") %>%
kable_styling(full_width = F,
position = "center", font_size = 12) %>%
row_spec(c(4,15,28,30), bold = T, color = "black", background = "darkorange")| Variable criterio | contrast | estimate | SE | df | t.ratio | p.value |
|---|---|---|---|---|---|---|
| Autotrascendencia | basquet - futbol | 0.090 | 0.053 | 1142 | 1.699 | 0.533 |
| basquet - handball | 0.061 | 0.067 | 1142 | 0.908 | 0.945 | |
| basquet - hockey | 0.040 | 0.060 | 1142 | 0.672 | 0.985 | |
| basquet - rugby | 0.235 | 0.068 | 1142 | 3.468 | 0.007 | |
| basquet - voley | -0.004 | 0.057 | 1142 | -0.064 | 1.000 | |
| futbol - handball | -0.029 | 0.069 | 1142 | -0.425 | 0.998 | |
| futbol - hockey | -0.050 | 0.065 | 1142 | -0.767 | 0.973 | |
| futbol - rugby | 0.145 | 0.069 | 1142 | 2.107 | 0.284 | |
| futbol - voley | -0.094 | 0.061 | 1142 | -1.532 | 0.643 | |
| handball - hockey | -0.020 | 0.067 | 1142 | -0.302 | 1.000 | |
| handball - rugby | 0.174 | 0.082 | 1142 | 2.114 | 0.281 | |
| handball - voley | -0.064 | 0.067 | 1142 | -0.961 | 0.930 | |
| hockey - rugby | 0.194 | 0.080 | 1142 | 2.421 | 0.150 | |
| hockey - voley | -0.044 | 0.049 | 1142 | -0.898 | 0.947 | |
| rugby - voley | -0.238 | 0.076 | 1142 | -3.120 | 0.023 | |
| Apertura al cambio | basquet - futbol | 0.045 | 0.050 | 1142 | 0.918 | 0.942 |
| basquet - handball | 0.062 | 0.062 | 1142 | 1.000 | 0.918 | |
| basquet - hockey | 0.087 | 0.056 | 1142 | 1.554 | 0.629 | |
| basquet - rugby | -0.129 | 0.063 | 1142 | -2.037 | 0.322 | |
| basquet - voley | 0.127 | 0.053 | 1142 | 2.386 | 0.162 | |
| futbol - handball | 0.017 | 0.065 | 1142 | 0.262 | 1.000 | |
| futbol - hockey | 0.042 | 0.060 | 1142 | 0.691 | 0.983 | |
| futbol - rugby | -0.174 | 0.064 | 1142 | -2.713 | 0.073 | |
| futbol - voley | 0.082 | 0.057 | 1142 | 1.434 | 0.706 | |
| handball - hockey | 0.025 | 0.063 | 1142 | 0.396 | 0.999 | |
| handball - rugby | -0.191 | 0.077 | 1142 | -2.484 | 0.130 | |
| handball - voley | 0.065 | 0.062 | 1142 | 1.039 | 0.905 | |
| hockey - rugby | -0.216 | 0.075 | 1142 | -2.878 | 0.047 | |
| hockey - voley | 0.040 | 0.046 | 1142 | 0.875 | 0.952 | |
| rugby - voley | 0.256 | 0.071 | 1142 | 3.586 | 0.005 | |
| Autopromoción | basquet - futbol | 0.001 | 0.086 | 1142 | 0.017 | 1.000 |
| basquet - handball | -0.141 | 0.108 | 1142 | -1.303 | 0.783 | |
| basquet - hockey | -0.150 | 0.097 | 1142 | -1.540 | 0.638 | |
| basquet - rugby | -0.083 | 0.110 | 1142 | -0.753 | 0.975 | |
| basquet - voley | -0.179 | 0.093 | 1142 | -1.934 | 0.382 | |
| futbol - handball | -0.142 | 0.112 | 1142 | -1.271 | 0.801 | |
| futbol - hockey | -0.151 | 0.105 | 1142 | -1.443 | 0.701 | |
| futbol - rugby | -0.084 | 0.111 | 1142 | -0.754 | 0.975 | |
| futbol - voley | -0.180 | 0.099 | 1142 | -1.822 | 0.452 | |
| handball - hockey | -0.009 | 0.109 | 1142 | -0.081 | 1.000 | |
| handball - rugby | 0.058 | 0.134 | 1142 | 0.437 | 0.998 | |
| handball - voley | -0.038 | 0.108 | 1142 | -0.350 | 0.999 | |
| hockey - rugby | 0.067 | 0.130 | 1142 | 0.516 | 0.996 | |
| hockey - voley | -0.029 | 0.079 | 1142 | -0.366 | 0.999 | |
| rugby - voley | -0.096 | 0.124 | 1142 | -0.777 | 0.971 | |
| Conservación | basquet - futbol | -0.121 | 0.055 | 1142 | -2.193 | 0.242 |
| basquet - handball | -0.019 | 0.070 | 1142 | -0.270 | 1.000 | |
| basquet - hockey | -0.021 | 0.063 | 1142 | -0.334 | 0.999 | |
| basquet - rugby | -0.012 | 0.071 | 1142 | -0.163 | 1.000 | |
| basquet - voley | -0.005 | 0.060 | 1142 | -0.084 | 1.000 | |
| futbol - handball | 0.102 | 0.072 | 1142 | 1.419 | 0.716 | |
| futbol - hockey | 0.100 | 0.068 | 1142 | 1.486 | 0.673 | |
| futbol - rugby | 0.110 | 0.072 | 1142 | 1.529 | 0.645 | |
| futbol - voley | 0.116 | 0.064 | 1142 | 1.823 | 0.451 | |
| handball - hockey | -0.002 | 0.070 | 1142 | -0.030 | 1.000 | |
| handball - rugby | 0.007 | 0.086 | 1142 | 0.085 | 1.000 | |
| handball - voley | 0.014 | 0.070 | 1142 | 0.198 | 1.000 | |
| hockey - rugby | 0.009 | 0.084 | 1142 | 0.112 | 1.000 | |
| hockey - voley | 0.016 | 0.051 | 1142 | 0.311 | 1.000 | |
| rugby - voley | 0.007 | 0.080 | 1142 | 0.082 | 1.000 |
base2 <- as.data.frame(emmeans(lm(ATc~deporte_T1+sexo_T1+edad_T1,base), pairwise~deporte_T1)$emmeans)
ggplot(base2, aes(x=deporte_T1, y=emmean, color=deporte_T1)) +
geom_point(size=4)+
geom_errorbar(aes(ymin=lower.CL, ymax=upper.CL), width=.5,
position=position_dodge(0.05), lwd=1)+
geom_point(data = base,aes(x=deporte_T1, y=ATc, color=deporte_T1),
alpha=.25, position = position_jitterdodge())+
theme_minimal()+
theme(legend.position = "none")+
xlab("Deporte")+ylab("Autotrascendencia")+
scale_fill_manual(name = "Deporte", values=c("#e47777","#f08f56", "#d2c31e",
"#2ec417","#10b0bc", "#b623f6"))+
scale_color_manual(name = "Deporte", values=c("#e47777","#f08f56", "#d2c31e",
"#2ec417","#10b0bc", "#b623f6"))+
annotate("text",
x=1:6,
y=-1,
label=c("A","AB","AB","AB","B","A"))
base2 <- as.data.frame(emmeans(lm(ACc~deporte_T1+sexo_T1+edad_T1,base), pairwise~deporte_T1)$emmeans)
ggplot(base2, aes(x=deporte_T1, y=emmean, color=deporte_T1)) +
geom_point(size=4)+
geom_errorbar(aes(ymin=lower.CL, ymax=upper.CL), width=.5,
position=position_dodge(0.05), lwd=1)+
geom_point(data = base,aes(x=deporte_T1, y=ACc, color=deporte_T1),
alpha=.25, position = position_jitterdodge())+
theme_minimal()+
theme(legend.position = "none")+
xlab("Deporte")+ylab("Apertura al cambio")+
scale_fill_manual(name = "Deporte",values=c("#e47777","#f08f56", "#d2c31e",
"#2ec417","#10b0bc", "#b623f6"))+
scale_color_manual(name = "Deporte",values=c("#e47777","#f08f56", "#d2c31e",
"#2ec417","#10b0bc", "#b623f6"))+
annotate("text",
x=1:6,
y=-1,
label=c("AB","AB","AB","A","B","A"))
Consumo según valores
Hipótesis: El cambio en el consumo de alcohol entre T1 y T2 se puede predecir a partir del nivel de valores personales (AT, AC, AP, C).
De acuerdo con las sugerencias de Schwartz, se usaron los puntajes centrados de valores. Ninguno de los modelos fue significativo. Se probó la interacción con el sexo y tampoco fueron significativos.
Hipótesis: Los valores personales permiten predecir los niveles de consumo (no el cambio en los mismos)
Los valores de Autotrascendencia y Autopromoción no fueron significativos. La Apertura al cambio (T1) y Conservación (T1) permiten predecir el consumo de alcohol tanto en T1 como en T2.
Apertura al cambio
base <- data.frame(cbind(longi[c(1:3)],
AT=rowMeans(longi[c(82,87,91,97,98)]),
AC=rowMeans(longi[c(80,85,89,90,94,100)]),
AP=rowMeans(longi[c(81,83,92,96)]),
C=rowMeans(longi[c(84,86,88,93,95,99)]),
VP_media=rowMeans(longi[80:100]),
ATc=rowMeans(longi[c(82,87,91,97,98)])-rowMeans(longi[80:100]),
ACc=rowMeans(longi[c(80,85,89,90,94,100)])-rowMeans(longi[80:100]),
APc=rowMeans(longi[c(81,83,92,96)])-rowMeans(longi[80:100]),
Cc=rowMeans(longi[c(84,86,88,93,95,99)])-rowMeans(longi[80:100]),
delta_alcohol=rowSums(longi[50:59])-rowSums(longi[40:49]),
alcoholT1=rowSums(longi[40:49]),
alcoholT2=rowSums(longi[50:59])))
base <- base[complete.cases(base),]
mod1 <- lm(alcoholT1~ACc, data=base)
plotT1 <- ggplot(base, aes(y=alcoholT1,x=ACc)) +
geom_point(size=2, alpha=.5, color="#f08f56")+
geom_abline(intercept=coefficients(mod1)[1],
slope = coefficients(mod1)[2],
color="#10b0bc",
lwd=2)+
theme_minimal()+
ylab("Alcohol en T1")+
ggtitle("Consumo de Alcohol en T1\nsegun Apertura al cambio (T1)")
mod2 <- lm(alcoholT2~ACc, data=base)
plotT2 <- ggplot(base, aes(y=alcoholT2,x=ACc)) +
geom_point(size=2, alpha=.5, color="#f08f56")+
geom_abline(intercept=coefficients(mod2)[1],
slope = coefficients(mod2)[2],
color="#10b0bc",
lwd=2)+
theme_minimal()+
ylab("Alcohol en T2")+
ggtitle("Consumo de Alcohol en T2\nsegun Apertura al cambio (T1)")
grid.arrange(plotT1,plotT2,
nrow=1)
summary(mod1)##
## Call:
## lm(formula = alcoholT1 ~ ACc, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.939 -2.982 -1.811 1.796 27.625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7424 0.2097 13.08 <2e-16 ***
## ACc 0.7179 0.3163 2.27 0.0235 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.244 on 671 degrees of freedom
## Multiple R-squared: 0.007621, Adjusted R-squared: 0.006142
## F-statistic: 5.153 on 1 and 671 DF, p-value: 0.02352summary(mod2)##
## Call:
## lm(formula = alcoholT2 ~ ACc, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.032 -2.876 -1.339 1.970 22.124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6838 0.1879 14.286 < 2e-16 ***
## ACc 0.8091 0.2834 2.855 0.00443 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.802 on 671 degrees of freedom
## Multiple R-squared: 0.012, Adjusted R-squared: 0.01053
## F-statistic: 8.153 on 1 and 671 DF, p-value: 0.004431Conservación
mod1 <- lm(alcoholT1~Cc, data=base)
plotT1 <- ggplot(base, aes(y=alcoholT1,x=Cc)) +
geom_point(size=2, alpha=.5, color="#f08f56")+
geom_abline(intercept=coefficients(mod1)[1],
slope = coefficients(mod1)[2],
color="#10b0bc",
lwd=2)+
theme_minimal()+
ylab("Alcohol en T1")+
ggtitle("Consumo de Alcohol en T1\nsegun Conservación (T1)")
mod2 <- lm(alcoholT2~Cc, data=base)
plotT2 <- ggplot(base, aes(y=alcoholT2,x=Cc)) +
geom_point(size=2, alpha=.5, color="#f08f56")+
geom_abline(intercept=coefficients(mod2)[1],
slope = coefficients(mod2)[2],
color="#10b0bc",
lwd=2)+
theme_minimal()+
ylab("Alcohol en T2")+
ggtitle("Consumo de Alcohol en T2\nsegun Conservación (T1)")
grid.arrange(plotT1,plotT2,
nrow=1)
summary(mod1)##
## Call:
## lm(formula = alcoholT1 ~ Cc, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.953 -2.917 -1.755 1.732 27.489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6886 0.2273 11.829 <2e-16 ***
## Cc -0.6398 0.2872 -2.228 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.244 on 671 degrees of freedom
## Multiple R-squared: 0.007341, Adjusted R-squared: 0.005862
## F-statistic: 4.962 on 1 and 671 DF, p-value: 0.02623summary(mod2)##
## Call:
## lm(formula = alcoholT2 ~ Cc, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.147 -2.811 -1.488 1.850 22.603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5850 0.2034 12.707 < 2e-16 ***
## Cc -0.7905 0.2571 -3.075 0.00219 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.799 on 671 degrees of freedom
## Multiple R-squared: 0.0139, Adjusted R-squared: 0.01243
## F-statistic: 9.456 on 1 and 671 DF, p-value: 0.00219Limitaciones
- Los \(R^2\) son un chiste
- La distribución de consumo de alcohol es muuuuy asimétrica, habría que probar otro tipo de modelos (e.g., gamma), pero no sé si amerita para un congreso