Music and Candidate Support
2024-06-14
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readxl)
library(texreg)## Version: 1.39.3
## Date: 2023-11-09
## Author: Philip Leifeld (University of Essex)
##
## Consider submitting praise using the praise or praise_interactive functions.
## Please cite the JSS article in your publications -- see citation("texreg").library(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:texreg':
##
## extractlibrary(ggpubr)## Loading required package: ggplot2Survey <- read_excel("SurveyFinal.xlsx", sheet = "Sheet0")
head(Survey)## # A tibble: 6 × 21
## acepto edad genero pref_partidos candidatos voto competente_c_1 lider_c_1
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Acepto 20 Femenino No tengo nin… Jorge Álv… Xóch… 3 3
## 2 Acepto 20 Femenino No tengo nin… Jorge Álv… Xóch… 4 3
## 3 Acepto 23 Femenino No tengo nin… Jorge Álv… Anul… 4 3
## 4 Acepto 21 Masculino No tengo nin… Jorge Álv… Xóch… <NA> <NA>
## 5 Acepto 23 Femenino No tengo nin… Jorge Álv… Xóch… 3 4
## 6 Acepto 22 Femenino PAN Jorge Álv… Xóch… <NA> <NA>
## # ℹ 13 more variables: amigable_c_1 <chr>, honesto_c_1 <chr>,
## # empatico_c_1 <dbl>, sentimientos_c_1 <chr>, competente_t_1 <dbl>,
## # lider_t_1 <chr>, amigable_t_1 <chr>, honesto_t_1 <chr>, empatico_t_1 <chr>,
## # sentimientos_t_1 <chr>, maynezmaynez <chr>, ultima <chr>, Music <chr>Clean_Surv = Survey%>%
mutate(Music = as.factor(Music))%>%
mutate(edad = as.numeric(edad))%>%
mutate(competente_c = as.numeric(competente_c_1))%>%
mutate(lider_c = as.numeric(lider_c_1))%>%
mutate(amigable_c = as.numeric(amigable_c_1))%>%
mutate(honesto_c = as.numeric(honesto_c_1))%>%
mutate(empatico_c = as.numeric(empatico_c_1))%>%
mutate(sentimientos_c = as.numeric(sentimientos_c_1))%>%
mutate(competente_t = as.numeric(competente_t_1))%>%
mutate(lider_t = as.numeric(lider_t_1))%>%
mutate(amigable_t = as.numeric(amigable_t_1))%>%
mutate(honesto_t = as.numeric(honesto_t_1))%>%
mutate(empatico_t = as.numeric(empatico_t_1))%>%
mutate(sentimientos_t = as.numeric(sentimientos_t_1))%>%
mutate(candidatos = ifelse(candidatos == "Jorge Álvarez Máynez,Xóchitl Gálvez Ruiz,Claudia Sheinbaum Pardo", "Sí sabe", "No sabe"))%>%
mutate(pref_partido = ifelse(pref_partidos == "Movimiento Ciudadano", "Simpatizantes", "No simpatizantes"))%>%
mutate(maynezmaynez = as.factor(maynezmaynez))%>%
mutate(voto = ifelse(voto == "Jorge Álvarez Máynez", "Simpatizantes","No simpatizantes"))%>%
select(edad, genero, pref_partido, candidatos, voto, competente_c, lider_c, amigable_c, honesto_c, empatico_c, sentimientos_c, competente_t, lider_t, amigable_t, honesto_t, empatico_t, sentimientos_t, maynezmaynez, Music)age_mean = mean(Clean_Surv$edad)
age_mean## [1] 21.75269age_sd = sd(Clean_Surv$edad)
age_sd## [1] 2.509356conteo_genero <- table(Clean_Surv$genero)
proporcion_genero <- prop.table(conteo_genero)
print(proporcion_genero)##
## Femenino Masculino No binario/ tercer género
## 0.54838710 0.44086022 0.01075269conteo_grupos <- table(Clean_Surv$Music)
names(conteo_grupos) <- c("Control", "Treatment")
proporcion_grupos = prop.table(conteo_grupos)
print(proporcion_grupos)## Control Treatment
## 0.5376344 0.4623656#1st regression model
# Nueva base con columnas por grupos, preguntas y respuestas (6 filas por cada encuestado)
encuesta_long <- Clean_Surv %>%
pivot_longer(cols = c(competente_c, lider_c, amigable_c, honesto_c, empatico_c, sentimientos_c,
competente_t, lider_t, amigable_t, honesto_t, empatico_t, sentimientos_t),
names_to = c("pregunta", "grupo"),
names_sep = "_",
values_to = "respuesta") %>%
mutate(grupo = ifelse(grupo == "t", "tratamiento", "control"),
grupo = as.factor(grupo))
# lista vacia para poner los modelos
modelos0 <- list()
for (preg in unique(encuesta_long$pregunta)) {
datos_pregunta <- encuesta_long %>% filter(pregunta == preg)
modelo0 <- lm(respuesta ~ grupo, data = datos_pregunta)
modelos0[[preg]] <- modelo0
cat("\nResumen del modelo para", preg, ":\n")
print(summary(modelo0))
screenreg(modelos0)
}##
## Resumen del modelo para competente :
##
## Call:
## lm(formula = respuesta ~ grupo, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5000 -0.5000 -0.1628 0.8372 1.8372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5000 0.1362 25.704 <2e-16 ***
## grupotratamiento -0.3372 0.2002 -1.684 0.0956 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9628 on 91 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.03022, Adjusted R-squared: 0.01956
## F-statistic: 2.836 on 1 and 91 DF, p-value: 0.09562
##
##
## Resumen del modelo para lider :
##
## Call:
## lm(formula = respuesta ~ grupo, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0000 -0.8605 0.0000 0.1395 2.1395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0000 0.1215 24.692 <2e-16 ***
## grupotratamiento -0.1395 0.1787 -0.781 0.437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8591 on 91 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.006657, Adjusted R-squared: -0.004259
## F-statistic: 0.6099 on 1 and 91 DF, p-value: 0.4369
##
##
## Resumen del modelo para amigable :
##
## Call:
## lm(formula = respuesta ~ grupo, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8372 -0.1600 0.1628 0.8400 1.1628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1600 0.1263 32.937 <2e-16 ***
## grupotratamiento -0.3228 0.1857 -1.738 0.0856 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8931 on 91 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.03212, Adjusted R-squared: 0.02149
## F-statistic: 3.02 on 1 and 91 DF, p-value: 0.08562
##
##
## Resumen del modelo para honesto :
##
## Call:
## lm(formula = respuesta ~ grupo, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.97959 0.02041 0.02041 0.16279 2.16279
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9796 0.1052 28.313 <2e-16 ***
## grupotratamiento -0.1424 0.1539 -0.925 0.357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7367 on 90 degrees of freedom
## (94 observations deleted due to missingness)
## Multiple R-squared: 0.009417, Adjusted R-squared: -0.00159
## F-statistic: 0.8556 on 1 and 90 DF, p-value: 0.3575
##
##
## Resumen del modelo para empatico :
##
## Call:
## lm(formula = respuesta ~ grupo, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4000 -0.4186 0.5814 0.6000 1.6000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4000 0.1227 27.717 <2e-16 ***
## grupotratamiento 0.0186 0.1804 0.103 0.918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8674 on 91 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.0001169, Adjusted R-squared: -0.01087
## F-statistic: 0.01064 on 1 and 91 DF, p-value: 0.9181
##
##
## Resumen del modelo para sentimientos :
##
## Call:
## lm(formula = respuesta ~ grupo, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.920 -1.814 0.186 1.186 4.186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.9200 0.3159 18.737 <2e-16 ***
## grupotratamiento -0.1060 0.4646 -0.228 0.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.234 on 91 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.0005721, Adjusted R-squared: -0.01041
## F-statistic: 0.05209 on 1 and 91 DF, p-value: 0.82#2nd regression model
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.4
## ✔ lubridate 1.9.3 ✔ stringr 1.5.0
## ✔ purrr 1.0.2 ✔ tibble 3.2.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks texreg::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Nueva base con columnas por grupos, preguntas y respuestas (6 filas por cada encuestado)
encuesta_long <- Clean_Surv %>%
pivot_longer(cols = c(competente_c, lider_c, amigable_c, honesto_c, empatico_c, sentimientos_c,
competente_t, lider_t, amigable_t, honesto_t, empatico_t, sentimientos_t),
names_to = c("pregunta", "grupo"),
names_sep = "_",
values_to = "respuesta") %>%
mutate(grupo = ifelse(grupo == "t", "tratamiento", "control"),
grupo = as.factor(grupo))
# lista vacia para poner los modelos
modelos <- list()
for (preg in unique(encuesta_long$pregunta)) {
datos_pregunta <- encuesta_long %>% filter(pregunta == preg)
modelo <- lm(respuesta ~ grupo*voto, data = datos_pregunta)
modelos[[preg]] <- modelo
cat("\nResumen del modelo para", preg, ":\n")
print(summary(modelo))
}##
## Resumen del modelo para competente :
##
## Call:
## lm(formula = respuesta ~ grupo * voto, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.30000 -0.30000 -0.08108 0.70000 1.91892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3000 0.1447 22.798 < 2e-16 ***
## grupotratamiento -0.2189 0.2088 -1.048 0.29730
## votoSimpatizantes 1.0000 0.3237 3.090 0.00267 **
## grupotratamiento:votoSimpatizantes -0.4144 0.5168 -0.802 0.42477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9155 on 89 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.1425, Adjusted R-squared: 0.1136
## F-statistic: 4.932 on 3 and 89 DF, p-value: 0.003248
##
##
## Resumen del modelo para lider :
##
## Call:
## lm(formula = respuesta ~ grupo * voto, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8500 -0.8108 0.1500 0.1892 2.1892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.85000 0.13198 21.594 <2e-16 ***
## grupotratamiento -0.03919 0.19039 -0.206 0.8374
## votoSimpatizantes 0.75000 0.29511 2.541 0.0128 *
## grupotratamiento:votoSimpatizantes -0.39414 0.47122 -0.836 0.4051
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8347 on 89 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.08288, Adjusted R-squared: 0.05197
## F-statistic: 2.681 on 3 and 89 DF, p-value: 0.05163
##
##
## Resumen del modelo para amigable :
##
## Call:
## lm(formula = respuesta ~ grupo * voto, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7838 -0.1667 0.2162 0.5000 1.2162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.07500 0.14060 28.982 <2e-16 ***
## grupotratamiento -0.29122 0.20283 -1.436 0.155
## votoSimpatizantes 0.42500 0.31440 1.352 0.180
## grupotratamiento:votoSimpatizantes -0.04212 0.50201 -0.084 0.933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8893 on 89 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.06148, Adjusted R-squared: 0.02985
## F-statistic: 1.944 on 3 and 89 DF, p-value: 0.1283
##
##
## Resumen del modelo para honesto :
##
## Call:
## lm(formula = respuesta ~ grupo * voto, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7949 -0.7000 0.2051 0.2162 2.2162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.79487 0.11005 25.397 < 2e-16 ***
## grupotratamiento -0.01109 0.15772 -0.070 0.944112
## votoSimpatizantes 0.90513 0.24360 3.716 0.000355 ***
## grupotratamiento:votoSimpatizantes -0.52225 0.38836 -1.345 0.182162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6872 on 88 degrees of freedom
## (94 observations deleted due to missingness)
## Multiple R-squared: 0.157, Adjusted R-squared: 0.1283
## F-statistic: 5.464 on 3 and 88 DF, p-value: 0.001721
##
##
## Resumen del modelo para empatico :
##
## Call:
## lm(formula = respuesta ~ grupo * voto, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2500 -0.3243 0.0000 0.6757 1.7500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.25000 0.13155 24.705 <2e-16 ***
## grupotratamiento 0.07432 0.18977 0.392 0.6963
## votoSimpatizantes 0.75000 0.29416 2.550 0.0125 *
## grupotratamiento:votoSimpatizantes -0.07432 0.46969 -0.158 0.8746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.832 on 89 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.1003, Adjusted R-squared: 0.06993
## F-statistic: 3.306 on 3 and 89 DF, p-value: 0.02382
##
##
## Resumen del modelo para sentimientos :
##
## Call:
## lm(formula = respuesta ~ grupo * voto, data = datos_pregunta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5405 -1.5405 0.4595 1.4595 4.4595
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.47500 0.33300 16.441 < 2e-16 ***
## grupotratamiento 0.06554 0.48038 0.136 0.89179
## votoSimpatizantes 2.22500 0.74461 2.988 0.00363 **
## grupotratamiento:votoSimpatizantes -0.26554 1.18894 -0.223 0.82378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.106 on 89 degrees of freedom
## (93 observations deleted due to missingness)
## Multiple R-squared: 0.1313, Adjusted R-squared: 0.1021
## F-statistic: 4.486 on 3 and 89 DF, p-value: 0.005585screenreg(modelos)##
## ========================================================================================================
## competente lider amigable honesto empatico sentimientos
## --------------------------------------------------------------------------------------------------------
## (Intercept) 3.30 *** 2.85 *** 4.07 *** 2.79 *** 3.25 *** 5.48 ***
## (0.14) (0.13) (0.14) (0.11) (0.13) (0.33)
## grupotratamiento -0.22 -0.04 -0.29 -0.01 0.07 0.07
## (0.21) (0.19) (0.20) (0.16) (0.19) (0.48)
## votoSimpatizantes 1.00 ** 0.75 * 0.43 0.91 *** 0.75 * 2.23 **
## (0.32) (0.30) (0.31) (0.24) (0.29) (0.74)
## grupotratamiento:votoSimpatizantes -0.41 -0.39 -0.04 -0.52 -0.07 -0.27
## (0.52) (0.47) (0.50) (0.39) (0.47) (1.19)
## --------------------------------------------------------------------------------------------------------
## R^2 0.14 0.08 0.06 0.16 0.10 0.13
## Adj. R^2 0.11 0.05 0.03 0.13 0.07 0.10
## Num. obs. 93 93 93 92 93 93
## ========================================================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05library(ggplot2)
coeficientes <- data.frame()
for (preg in names(modelos)) {
modelo <- modelos[[preg]]
coefs <- summary(modelo)$coefficients
coefs <- data.frame(Variable = rownames(coefs), Estimate = coefs[, "Estimate"])
coefs$Pregunta <- preg
coeficientes <- rbind(coeficientes, coefs)
}
preguntas <- unique(coeficientes$Pregunta)
resultados <- data.frame()
for (preg in preguntas) {
intercepto <- coeficientes %>% filter(Pregunta == preg & Variable == "(Intercept)") %>% pull(Estimate)
tratamiento <- coeficientes %>% filter(Pregunta == preg & Variable == "grupotratamiento") %>% pull(Estimate)
simpatizantes <- coeficientes %>% filter(Pregunta == preg & Variable == "votoSimpatizantes") %>% pull(Estimate)
interaccion <- coeficientes %>% filter(Pregunta == preg & Variable == "grupotratamiento:votoSimpatizantes") %>% pull(Estimate)
simpatizantes_control <- intercepto + simpatizantes
simpatizantes_tratamiento <- intercepto + tratamiento + simpatizantes + interaccion
no_simpatizantes_control <- intercepto
no_simpatizantes_tratamiento <- intercepto + tratamiento
df <- data.frame(
Pregunta = preg,
Group = c("Supporters No Music", "Supporters Music", "Not Supporters No Music", "Not Supporters Music"),
Coeficiente = c(simpatizantes_control, simpatizantes_tratamiento, no_simpatizantes_control, no_simpatizantes_tratamiento)
)
resultados <- rbind(resultados, df)
}
colores <- c("Supporters No Music" = "orange",
"Supporters Music" = "darkorange",
"Not Supporters No Music" = "blue",
"Not Supporters Music" = "darkblue")
graficos <- list()
for (preg in preguntas) {
datos_pregunta <- resultados %>% filter(Pregunta == preg)
p <- ggplot(datos_pregunta, aes(x = Group, y = Coeficiente, fill = Group)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = paste(preg),
x = "Group",
y = "Coefficient") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_manual(values = colores)
graficos[[preg]] <- p
}
# Organizar los gráficos usando ggarrange
ggarrange(plotlist = graficos, ncol = 2, nrow = ceiling(length(graficos)/2))
Mean and Standard deviation
datos_Control <- Clean_Surv %>%
filter(Music == 0)
datos_Tratamiento <- Clean_Surv %>%
filter(Music == 1)
preguntas_control <- c("competente_c", "lider_c", "amigable_c", "honesto_c", "empatico_c", "sentimientos_c")
preguntas_tratamiento <- c("competente_t", "lider_t", "amigable_t", "honesto_t", "empatico_t", "sentimientos_t")
varianzas_control <- list()
varianzas_tratamiento <- list()
medias_control <- list()
medias_tratamiento <- list()
for (pregc in preguntas_control) {
varianza <- var(datos_Control[[pregc]], na.rm = TRUE)
media <- mean(datos_Control[[pregc]], na.rm = TRUE)
varianzas_control[[pregc]] <- varianza
medias_control[[pregc]] <- media
}
for (pregt in preguntas_tratamiento) {
varianza <- var(datos_Tratamiento[[pregt]], na.rm = TRUE)
media <- mean(datos_Tratamiento[[pregt]], na.rm = TRUE)
varianzas_tratamiento[[pregt]] <- varianza
medias_tratamiento[[pregt]] <- media
}
df_varianzas_control <- data.frame(Variable = preguntas_control, Varianza_Control = unlist(varianzas_control))
df_varianzas_tratamiento <- data.frame(Variable = preguntas_tratamiento, Varianza_Tratamiento = unlist(varianzas_tratamiento))
df_medias_control <- data.frame(Variable = preguntas_control, Media_Control = unlist(medias_control))
df_medias_tratamiento <- data.frame(Variable = preguntas_tratamiento, Media_Tratamiento = unlist(medias_tratamiento))
print(df_varianzas_control)## Variable Varianza_Control
## competente_c competente_c 0.9897959
## lider_c lider_c 0.8163265
## amigable_c amigable_c 0.7085714
## honesto_c honesto_c 0.6037415
## empatico_c empatico_c 0.8979592
## sentimientos_c sentimientos_c 5.7893878print(df_varianzas_tratamiento)## Variable Varianza_Tratamiento
## competente_t competente_t 0.8538206
## lider_t lider_t 0.6467331
## amigable_t amigable_t 0.9014396
## honesto_t honesto_t 0.4728682
## empatico_t empatico_t 0.5825028
## sentimientos_t sentimientos_t 4.0598007print(df_medias_control)## Variable Media_Control
## competente_c competente_c 3.500000
## lider_c lider_c 3.000000
## amigable_c amigable_c 4.160000
## honesto_c honesto_c 2.979592
## empatico_c empatico_c 3.400000
## sentimientos_c sentimientos_c 5.920000print(df_medias_tratamiento)## Variable Media_Tratamiento
## competente_t competente_t 3.162791
## lider_t lider_t 2.860465
## amigable_t amigable_t 3.837209
## honesto_t honesto_t 2.837209
## empatico_t empatico_t 3.418605
## sentimientos_t sentimientos_t 5.813953