scu4_Changes
Librerías
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## Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
## Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167##
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## Reliability analysis
## Call: alpha(x = scu[, -c(1:3)])
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## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.96 0.34 14 0.0057 3.6 0.92 0.32
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## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.93 0.94
## Duhachek 0.92 0.93 0.94
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## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc)
## Alumno_Soledad 0.93 0.93 0.95
## Alumno_Violencia 0.93 0.93 0.95
## Alumno_Drogas 0.93 0.93 0.95
## Alumno_Salud mental 0.93 0.93 0.95
## Alumno_Redes sociales 0.93 0.93 0.95
## Alumno_Paro juvenil 0.93 0.93 0.96
## Alumno_Acoso escolar 0.93 0.93 0.95
## Alumno_Ciberacoso 0.93 0.93 0.95
## Alumno_Transtornos alimnetarios 0.93 0.93 0.95
## Alumno_Abandono escolar 0.93 0.93 0.95
## Alumno_Presión compañeros 0.93 0.93 0.95
## Alumno_Presión sobre rendimiento académico 0.93 0.93 0.95
## Alumno_Inteligencia Artificial 0.93 0.93 0.96
## Otros_Cambio climático 0.93 0.93 0.95
## Otros_Soledad 0.93 0.93 0.95
## Otros_Violencia 0.93 0.93 0.95
## Otros_Drogas 0.93 0.93 0.95
## Otros_Salud mental 0.93 0.93 0.95
## Otros_Redes sociales 0.93 0.93 0.96
## Otros_Paro juvenil 0.93 0.93 0.95
## Otros_Acoso escolar 0.93 0.93 0.95
## Otros_Ciberacoso 0.93 0.93 0.95
## Otros_Transtornos alimnetarios 0.93 0.93 0.95
## Otros_Abandono escolar 0.93 0.93 0.95
## Otros_Presión compañeros 0.93 0.93 0.95
## Otros_Presión sobre rendimiento académico 0.93 0.93 0.95
## Otros_Inteligencia Artificial 0.93 0.93 0.95
## average_r S/N alpha se var.r med.r
## Alumno_Soledad 0.35 14 0.0059 0.019 0.32
## Alumno_Violencia 0.34 13 0.0060 0.020 0.32
## Alumno_Drogas 0.34 14 0.0059 0.020 0.33
## Alumno_Salud mental 0.34 13 0.0060 0.019 0.32
## Alumno_Redes sociales 0.34 14 0.0059 0.020 0.33
## Alumno_Paro juvenil 0.35 14 0.0058 0.020 0.33
## Alumno_Acoso escolar 0.34 13 0.0061 0.019 0.32
## Alumno_Ciberacoso 0.34 13 0.0061 0.019 0.32
## Alumno_Transtornos alimnetarios 0.34 13 0.0061 0.019 0.32
## Alumno_Abandono escolar 0.34 13 0.0060 0.020 0.32
## Alumno_Presión compañeros 0.34 13 0.0060 0.020 0.32
## Alumno_Presión sobre rendimiento académico 0.34 14 0.0059 0.020 0.33
## Alumno_Inteligencia Artificial 0.35 14 0.0057 0.018 0.34
## Otros_Cambio climático 0.34 14 0.0059 0.019 0.32
## Otros_Soledad 0.34 13 0.0061 0.020 0.32
## Otros_Violencia 0.34 13 0.0060 0.019 0.32
## Otros_Drogas 0.34 14 0.0059 0.020 0.32
## Otros_Salud mental 0.34 13 0.0061 0.019 0.32
## Otros_Redes sociales 0.35 14 0.0057 0.020 0.34
## Otros_Paro juvenil 0.34 14 0.0059 0.019 0.33
## Otros_Acoso escolar 0.34 13 0.0060 0.019 0.32
## Otros_Ciberacoso 0.34 13 0.0060 0.019 0.32
## Otros_Transtornos alimnetarios 0.34 13 0.0060 0.019 0.32
## Otros_Abandono escolar 0.34 13 0.0060 0.019 0.32
## Otros_Presión compañeros 0.34 13 0.0060 0.019 0.32
## Otros_Presión sobre rendimiento académico 0.34 13 0.0059 0.019 0.32
## Otros_Inteligencia Artificial 0.35 14 0.0058 0.020 0.33
##
## Item statistics
## n raw.r std.r r.cor r.drop mean
## Alumno_Soledad 286 0.53 0.53 0.51 0.48 3.7
## Alumno_Violencia 286 0.67 0.66 0.65 0.63 4.4
## Alumno_Drogas 286 0.56 0.55 0.54 0.51 3.8
## Alumno_Salud mental 286 0.66 0.66 0.65 0.62 4.5
## Alumno_Redes sociales 286 0.54 0.54 0.51 0.50 3.2
## Alumno_Paro juvenil 286 0.49 0.49 0.46 0.44 3.6
## Alumno_Acoso escolar 286 0.68 0.68 0.67 0.65 4.1
## Alumno_Ciberacoso 286 0.71 0.71 0.71 0.68 3.8
## Alumno_Transtornos alimnetarios 286 0.70 0.69 0.69 0.66 3.8
## Alumno_Abandono escolar 286 0.64 0.63 0.62 0.60 3.5
## Alumno_Presión compañeros 286 0.65 0.64 0.63 0.61 3.5
## Alumno_Presión sobre rendimiento académico 286 0.54 0.54 0.52 0.50 3.9
## Alumno_Inteligencia Artificial 286 0.38 0.37 0.35 0.32 3.1
## Otros_Cambio climático 286 0.58 0.59 0.57 0.54 3.8
## Otros_Soledad 286 0.68 0.69 0.68 0.65 3.5
## Otros_Violencia 286 0.67 0.68 0.67 0.64 3.9
## Otros_Drogas 286 0.60 0.60 0.58 0.55 3.4
## Otros_Salud mental 286 0.68 0.69 0.68 0.65 3.8
## Otros_Redes sociales 286 0.44 0.44 0.40 0.38 3.4
## Otros_Paro juvenil 286 0.55 0.55 0.54 0.50 3.1
## Otros_Acoso escolar 286 0.68 0.68 0.68 0.64 3.7
## Otros_Ciberacoso 286 0.66 0.66 0.65 0.62 3.4
## Otros_Transtornos alimnetarios 286 0.66 0.66 0.65 0.62 3.4
## Otros_Abandono escolar 286 0.67 0.67 0.66 0.63 3.1
## Otros_Presión compañeros 286 0.63 0.64 0.63 0.60 3.3
## Otros_Presión sobre rendimiento académico 286 0.60 0.60 0.59 0.56 3.4
## Otros_Inteligencia Artificial 286 0.50 0.50 0.48 0.45 2.9
## sd
## Alumno_Soledad 1.5
## Alumno_Violencia 1.5
## Alumno_Drogas 1.7
## Alumno_Salud mental 1.5
## Alumno_Redes sociales 1.5
## Alumno_Paro juvenil 1.5
## Alumno_Acoso escolar 1.5
## Alumno_Ciberacoso 1.6
## Alumno_Transtornos alimnetarios 1.7
## Alumno_Abandono escolar 1.5
## Alumno_Presión compañeros 1.5
## Alumno_Presión sobre rendimiento académico 1.5
## Alumno_Inteligencia Artificial 1.6
## Otros_Cambio climático 1.4
## Otros_Soledad 1.5
## Otros_Violencia 1.5
## Otros_Drogas 1.6
## Otros_Salud mental 1.5
## Otros_Redes sociales 1.6
## Otros_Paro juvenil 1.4
## Otros_Acoso escolar 1.5
## Otros_Ciberacoso 1.5
## Otros_Transtornos alimnetarios 1.5
## Otros_Abandono escolar 1.5
## Otros_Presión compañeros 1.4
## Otros_Presión sobre rendimiento académico 1.5
## Otros_Inteligencia Artificial 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## Alumno_Soledad 0.10 0.14 0.18 0.27 0.19 0.12 0
## Alumno_Violencia 0.06 0.07 0.12 0.22 0.24 0.30 0
## Alumno_Drogas 0.13 0.12 0.17 0.17 0.19 0.22 0
## Alumno_Salud mental 0.06 0.06 0.12 0.17 0.28 0.30 0
## Alumno_Redes sociales 0.17 0.19 0.23 0.23 0.10 0.08 0
## Alumno_Paro juvenil 0.11 0.13 0.24 0.22 0.19 0.11 0
## Alumno_Acoso escolar 0.08 0.09 0.15 0.20 0.26 0.22 0
## Alumno_Ciberacoso 0.12 0.12 0.13 0.21 0.27 0.15 0
## Alumno_Transtornos alimnetarios 0.15 0.12 0.15 0.17 0.19 0.22 0
## Alumno_Abandono escolar 0.13 0.14 0.19 0.28 0.14 0.12 0
## Alumno_Presión compañeros 0.13 0.16 0.22 0.20 0.17 0.11 0
## Alumno_Presión sobre rendimiento académico 0.07 0.11 0.17 0.23 0.27 0.14 0
## Alumno_Inteligencia Artificial 0.22 0.15 0.23 0.17 0.15 0.08 0
## Otros_Cambio climático 0.09 0.10 0.22 0.26 0.22 0.12 0
## Otros_Soledad 0.14 0.11 0.23 0.26 0.16 0.09 0
## Otros_Violencia 0.08 0.09 0.20 0.24 0.23 0.14 0
## Otros_Drogas 0.16 0.13 0.21 0.22 0.17 0.10 0
## Otros_Salud mental 0.10 0.10 0.18 0.27 0.23 0.12 0
## Otros_Redes sociales 0.17 0.18 0.18 0.19 0.16 0.12 0
## Otros_Paro juvenil 0.16 0.21 0.26 0.19 0.12 0.06 0
## Otros_Acoso escolar 0.09 0.13 0.22 0.21 0.24 0.10 0
## Otros_Ciberacoso 0.14 0.15 0.24 0.25 0.15 0.08 0
## Otros_Transtornos alimnetarios 0.17 0.12 0.23 0.21 0.20 0.07 0
## Otros_Abandono escolar 0.17 0.20 0.24 0.21 0.12 0.07 0
## Otros_Presión compañeros 0.13 0.18 0.22 0.23 0.18 0.06 0
## Otros_Presión sobre rendimiento académico 0.13 0.14 0.24 0.24 0.16 0.08 0
## Otros_Inteligencia Artificial 0.27 0.18 0.22 0.16 0.10 0.06 0Descriptivos
## tibble [286 × 30] (S3: tbl_df/tbl/data.frame)
## $ Género : chr [1:286] "Home" "Home" "Home" "Home" ...
## $ Curso : chr [1:286] "3r" "3r" "3r" "3r" ...
## $ Alumno_Cambio climático : num [1:286] 3 4 1 6 5 5 3 1 6 5 ...
## $ Alumno_Soledad : num [1:286] 3 3 1 5 6 3 2 1 2 3 ...
## $ Alumno_Violencia : num [1:286] 4 4 4 5 4 6 2 1 4 4 ...
## $ Alumno_Drogas : num [1:286] 3 5 3 4 3 6 2 1 6 1 ...
## $ Alumno_Salud mental : num [1:286] 3 2 4 5 5 5 4 2 3 6 ...
## $ Alumno_Redes sociales : num [1:286] 4 1 3 2 3 5 4 2 1 6 ...
## $ Alumno_Paro juvenil : num [1:286] 5 6 1 4 1 3 4 4 2 5 ...
## $ Alumno_Acoso escolar : num [1:286] 2 1 2 4 3 6 2 2 3 5 ...
## $ Alumno_Ciberacoso : num [1:286] 1 1 1 4 4 6 2 1 5 5 ...
## $ Alumno_Transtornos alimnetarios : num [1:286] 1 1 1 4 3 5 1 1 2 6 ...
## $ Alumno_Abandono escolar : num [1:286] 1 4 1 4 4 4 2 1 3 3 ...
## $ Alumno_Presión compañeros : num [1:286] 1 3 1 3 4 3 1 1 3 2 ...
## $ Alumno_Presión sobre rendimiento académico: num [1:286] 4 6 2 4 5 2 3 2 6 5 ...
## $ Alumno_Inteligencia Artificial : num [1:286] 1 6 1 2 6 1 5 1 3 2 ...
## $ Otros_Cambio climático : num [1:286] 2 3 1 6 6 5 4 3 3 4 ...
## $ Otros_Soledad : num [1:286] 2 4 1 5 6 3 2 1 3 4 ...
## $ Otros_Violencia : num [1:286] 4 5 3 5 5 6 3 4 4 5 ...
## $ Otros_Drogas : num [1:286] 4 3 3 4 6 6 3 1 2 2 ...
## $ Otros_Salud mental : num [1:286] 3 3 3 5 5 6 1 2 2 4 ...
## $ Otros_Redes sociales : num [1:286] 2 5 6 2 3 4 1 4 4 4 ...
## $ Otros_Paro juvenil : num [1:286] 3 3 1 4 2 5 2 1 5 5 ...
## $ Otros_Acoso escolar : num [1:286] 4 5 3 4 5 6 1 2 2 5 ...
## $ Otros_Ciberacoso : num [1:286] 3 5 3 4 5 4 3 4 3 5 ...
## $ Otros_Transtornos alimnetarios : num [1:286] 2 2 2 4 3 5 1 3 5 6 ...
## $ Otros_Abandono escolar : num [1:286] 3 2 1 4 3 6 1 1 4 4 ...
## $ Otros_Presión compañeros : num [1:286] 4 6 2 3 4 5 1 1 2 5 ...
## $ Otros_Presión sobre rendimiento académico : num [1:286] 3 4 2 4 3 6 3 1 4 5 ...
## $ Otros_Inteligencia Artificial : num [1:286] 2 4 1 2 6 5 2 1 2 3 ...| Name | scu |
| Number of rows | 286 |
| Number of columns | 30 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 28 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Género | 0 | 1 | 4 | 6 | 0 | 3 | 0 |
| Curso | 0 | 1 | 2 | 2 | 0 | 4 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Alumno_Cambio climático | 0 | 1 | 4.34 | 1.34 | 1 | 4.00 | 5 | 5 | 6 | ▂▃▆▇▅ |
| Alumno_Soledad | 0 | 1 | 3.66 | 1.50 | 1 | 3.00 | 4 | 5 | 6 | ▇▆▇▆▃ |
| Alumno_Violencia | 0 | 1 | 4.40 | 1.48 | 1 | 4.00 | 5 | 6 | 6 | ▃▃▆▆▇ |
| Alumno_Drogas | 0 | 1 | 3.84 | 1.69 | 1 | 3.00 | 4 | 5 | 6 | ▇▆▆▆▇ |
| Alumno_Salud mental | 0 | 1 | 4.47 | 1.47 | 1 | 4.00 | 5 | 6 | 6 | ▃▃▅▇▇ |
| Alumno_Redes sociales | 0 | 1 | 3.17 | 1.50 | 1 | 2.00 | 3 | 4 | 6 | ▇▅▅▂▂ |
| Alumno_Paro juvenil | 0 | 1 | 3.58 | 1.48 | 1 | 3.00 | 4 | 5 | 6 | ▇▇▇▆▃ |
| Alumno_Acoso escolar | 0 | 1 | 4.14 | 1.53 | 1 | 3.00 | 4 | 5 | 6 | ▅▅▆▇▇ |
| Alumno_Ciberacoso | 0 | 1 | 3.83 | 1.60 | 1 | 3.00 | 4 | 5 | 6 | ▇▃▆▇▅ |
| Alumno_Transtornos alimnetarios | 0 | 1 | 3.80 | 1.73 | 1 | 2.00 | 4 | 5 | 6 | ▇▅▅▆▆ |
| Alumno_Abandono escolar | 0 | 1 | 3.53 | 1.52 | 1 | 2.00 | 4 | 5 | 6 | ▇▅▇▃▃ |
| Alumno_Presión compañeros | 0 | 1 | 3.45 | 1.54 | 1 | 2.00 | 3 | 5 | 6 | ▇▆▆▅▃ |
| Alumno_Presión sobre rendimiento académico | 0 | 1 | 3.94 | 1.45 | 1 | 3.00 | 4 | 5 | 6 | ▆▅▇▇▅ |
| Alumno_Inteligencia Artificial | 0 | 1 | 3.12 | 1.59 | 1 | 2.00 | 3 | 4 | 6 | ▇▅▃▃▂ |
| Otros_Cambio climático | 0 | 1 | 3.75 | 1.44 | 1 | 3.00 | 4 | 5 | 6 | ▆▇▇▇▃ |
| Otros_Soledad | 0 | 1 | 3.48 | 1.49 | 1 | 2.25 | 4 | 5 | 6 | ▇▇▇▅▃ |
| Otros_Violencia | 0 | 1 | 3.88 | 1.46 | 1 | 3.00 | 4 | 5 | 6 | ▆▆▇▇▅ |
| Otros_Drogas | 0 | 1 | 3.39 | 1.56 | 1 | 2.00 | 3 | 5 | 6 | ▇▆▆▅▂ |
| Otros_Salud mental | 0 | 1 | 3.80 | 1.46 | 1 | 3.00 | 4 | 5 | 6 | ▆▅▇▇▃ |
| Otros_Redes sociales | 0 | 1 | 3.35 | 1.63 | 1 | 2.00 | 3 | 5 | 6 | ▇▅▅▃▃ |
| Otros_Paro juvenil | 0 | 1 | 3.05 | 1.43 | 1 | 2.00 | 3 | 4 | 6 | ▇▆▃▂▁ |
| Otros_Acoso escolar | 0 | 1 | 3.71 | 1.46 | 1 | 3.00 | 4 | 5 | 6 | ▇▇▇▇▃ |
| Otros_Ciberacoso | 0 | 1 | 3.35 | 1.46 | 1 | 2.00 | 3 | 4 | 6 | ▇▆▇▅▂ |
| Otros_Transtornos alimnetarios | 0 | 1 | 3.37 | 1.53 | 1 | 2.00 | 3 | 5 | 6 | ▇▆▆▆▂ |
| Otros_Abandono escolar | 0 | 1 | 3.10 | 1.47 | 1 | 2.00 | 3 | 4 | 6 | ▇▅▅▂▂ |
| Otros_Presión compañeros | 0 | 1 | 3.33 | 1.44 | 1 | 2.00 | 3 | 4 | 6 | ▇▆▆▅▂ |
| Otros_Presión sobre rendimiento académico | 0 | 1 | 3.40 | 1.45 | 1 | 2.00 | 3 | 4 | 6 | ▇▇▇▅▂ |
| Otros_Inteligencia Artificial | 0 | 1 | 2.85 | 1.54 | 1 | 1.00 | 3 | 4 | 6 | ▇▅▃▂▁ |
Diferencias yo vs otros
scu <- read_excel("scu4___change.xlsx",
sheet = "SCU2")
t_apa(t_test(scu$`Cambio climático` ~ scu$Preocupación, data = scu))## t(566.91) = 5.01, p < .001, d = 0.42##
## Welch Two Sample t-test
##
## data: scu$`Cambio climático` by scu$Preocupación
## t = 5.0147, df = 566.91, p-value = 7.115e-07
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.3552080 0.8126242
## sample estimates:
## mean in group Alumno mean in group Otros
## 4.335664 3.751748ggplot(data = scu, aes(x = scu$Preocupación, y= scu$`Cambio climático`)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Cambio climático') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Cambio climático` `` is discouraged.
## ℹ Use `Cambio climático` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Cambio climático` `` is discouraged.
## ℹ Use `Cambio climático` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Cambio climático` `` is discouraged.
## ℹ Use `Cambio climático` instead.
## t(569.99) = 1.48, p = .139, d = 0.12##
## Welch Two Sample t-test
##
## data: scu$Soledad by scu$Preocupación
## t = 1.4816, df = 569.99, p-value = 0.139
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## -0.06035213 0.43098150
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.664336 3.479021## t(569.89) = 4.24, p < .001, d = 0.35##
## Welch Two Sample t-test
##
## data: scu$Violencia by scu$Preocupación
## t = 4.2364, df = 569.89, p-value = 2.649e-05
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.2794343 0.7625238
## sample estimates:
## mean in group Alumno mean in group Otros
## 4.402098 3.881119ggplot(data = scu, aes(x = scu$Preocupación, y = scu$Violencia)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Violencia') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Violencia` is discouraged.
## ℹ Use `Violencia` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Violencia` is discouraged.
## ℹ Use `Violencia` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Violencia` is discouraged.
## ℹ Use `Violencia` instead.
## t(566.39) = 3.32, p < .001, d = 0.28##
## Welch Two Sample t-test
##
## data: scu$Drogas by scu$Preocupación
## t = 3.3249, df = 566.39, p-value = 0.0009418
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.1845927 0.7175052
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.842657 3.391608ggplot(data = scu, aes(x = scu$Preocupación, y = scu$Drogas)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Drogas') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Drogas` is discouraged.
## ℹ Use `Drogas` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Drogas` is discouraged.
## ℹ Use `Drogas` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Drogas` is discouraged.
## ℹ Use `Drogas` instead.
## t(569.97) = 5.41, p < .001, d = 0.45##
## Welch Two Sample t-test
##
## data: scu$`Salud mental` by scu$Preocupación
## t = 5.4107, df = 569.97, p-value = 9.256e-08
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.4231760 0.9054954
## sample estimates:
## mean in group Alumno mean in group Otros
## 4.465035 3.800699ggplot(data = scu, aes(x = scu$Preocupación, y = scu$`Salud mental`)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Salud mental') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Salud mental` `` is discouraged.
## ℹ Use `Salud mental` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Salud mental` `` is discouraged.
## ℹ Use `Salud mental` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Salud mental` `` is discouraged.
## ℹ Use `Salud mental` instead.
## t(565.91) = -1.39, p = .165, d = -0.12##
## Welch Two Sample t-test
##
## data: scu$`Redes sociales` by scu$Preocupación
## t = -1.3893, df = 565.91, p-value = 0.1653
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## -0.43886095 0.07522459
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.171329 3.353147## t(569.34) = 4.33, p < .001, d = 0.36##
## Welch Two Sample t-test
##
## data: scu$`Paro juvenil` by scu$Preocupación
## t = 4.3264, df = 569.34, p-value = 1.79e-05
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.2882810 0.7676631
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.580420 3.052448ggplot(data = scu, aes(x = scu$Preocupación, y = scu$`Paro juvenil`)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Paro juvenil') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Paro juvenil` `` is discouraged.
## ℹ Use `Paro juvenil` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Paro juvenil` `` is discouraged.
## ℹ Use `Paro juvenil` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Paro juvenil` `` is discouraged.
## ℹ Use `Paro juvenil` instead.
## t(568.90) = 3.50, p < .001, d = 0.29##
## Welch Two Sample t-test
##
## data: scu$`Acoso escolar` by scu$Preocupación
## t = 3.5008, df = 568.9, p-value = 0.0005002
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.1918482 0.6822777
## sample estimates:
## mean in group Alumno mean in group Otros
## 4.143357 3.706294ggplot(data = scu, aes(x = scu$Preocupación, y = scu$`Acoso escolar`)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Acoso escolar') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Acoso escolar` `` is discouraged.
## ℹ Use `Acoso escolar` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Acoso escolar` `` is discouraged.
## ℹ Use `Acoso escolar` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Acoso escolar` `` is discouraged.
## ℹ Use `Acoso escolar` instead.
## t(565.48) = 3.72, p < .001, d = 0.31##
## Welch Two Sample t-test
##
## data: scu$Ciberacoso by scu$Preocupación
## t = 3.7172, df = 565.48, p-value = 0.0002215
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.2242544 0.7267946
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.828671 3.353147ggplot(data = scu, aes(x = scu$Preocupación, y = scu$Ciberacoso)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Ciberacoso') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Ciberacoso` is discouraged.
## ℹ Use `Ciberacoso` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Ciberacoso` is discouraged.
## ℹ Use `Ciberacoso` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `scu$Ciberacoso` is discouraged.
## ℹ Use `Ciberacoso` instead.
## t(561.44) = 3.18, p = .002, d = 0.27##
## Welch Two Sample t-test
##
## data: scu$`Transtornos alimnetarios` by scu$Preocupación
## t = 3.1791, df = 561.44, p-value = 0.001559
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.1656851 0.7014478
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.800699 3.367133ggplot(data = scu, aes(x = scu$Preocupación, y = scu$`Transtornos alimnetarios`)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Transtornos alimentarios') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Transtornos alimnetarios` `` is discouraged.
## ℹ Use `Transtornos alimnetarios` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Transtornos alimnetarios` `` is discouraged.
## ℹ Use `Transtornos alimnetarios` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Transtornos alimnetarios` `` is discouraged.
## ℹ Use `Transtornos alimnetarios` instead.
## t(569.25) = 3.44, p < .001, d = 0.29##
## Welch Two Sample t-test
##
## data: scu$`Abandono escolar` by scu$Preocupación
## t = 3.4373, df = 569.25, p-value = 0.0006304
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.1843203 0.6758196
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.534965 3.104895ggplot(data = scu, aes(x = scu$Preocupación, y = scu$`Abandono escolar`)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Abandono escolar') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Abandono escolar` `` is discouraged.
## ℹ Use `Abandono escolar` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Abandono escolar` `` is discouraged.
## ℹ Use `Abandono escolar` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Abandono escolar` `` is discouraged.
## ℹ Use `Abandono escolar` instead.
## t(567.43) = 0.95, p = .341, d = 0.08##
## Welch Two Sample t-test
##
## data: scu$`Presión compañeros` by scu$Preocupación
## t = 0.95305, df = 567.43, p-value = 0.341
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## -0.1261233 0.3638855
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.451049 3.332168## t(570.00) = 4.40, p < .001, d = 0.37##
## Welch Two Sample t-test
##
## data: scu$`Presión sobre rendimiento académico` by scu$Preocupación
## t = 4.4024, df = 570, p-value = 1.279e-05
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.2962875 0.7736426
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.937063 3.402098ggplot(data = scu, aes(x = scu$Preocupación, y = scu$`Presión sobre rendimiento académico`)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Presión sobre rendimiento académico') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Presión sobre rendimiento académico` `` is discouraged.
## ℹ Use `Presión sobre rendimiento académico` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Presión sobre rendimiento académico` `` is discouraged.
## ℹ Use `Presión sobre rendimiento académico` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Presión sobre rendimiento académico` `` is discouraged.
## ℹ Use `Presión sobre rendimiento académico` instead.
## t(569.38) = 2.03, p = .043, d = 0.17##
## Welch Two Sample t-test
##
## data: scu$`Inteligencia Artificial` by scu$Preocupación
## t = 2.0263, df = 569.38, p-value = 0.0432
## alternative hypothesis: true difference in means between group Alumno and group Otros is not equal to 0
## 95 percent confidence interval:
## 0.00815654 0.52331199
## sample estimates:
## mean in group Alumno mean in group Otros
## 3.118881 2.853147ggplot(data = scu, aes(x = scu$Preocupación, y = scu$`Inteligencia Artificial`)) +
geom_jitter(size = 1, color = 'gray', alpha = 0.5) +
geom_violin(aes(fill =Preocupación), color = 'black', alpha = 0.8) +
geom_boxplot(color = 'black', alpha = 0.7) +
xlab('Preocupación') +
ylab('Inteligencia artificial') +
theme_minimal()## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Inteligencia Artificial` `` is discouraged.
## ℹ Use `Inteligencia Artificial` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Inteligencia Artificial` `` is discouraged.
## ℹ Use `Inteligencia Artificial` instead.## Warning: Use of `scu$Preocupación` is discouraged.
## ℹ Use `Preocupación` instead.## Warning: Use of `` scu$`Inteligencia Artificial` `` is discouraged.
## ℹ Use `Inteligencia Artificial` instead.
Correlaciones
scu <- read_excel("scu4___change.xlsx",
sheet = "SCU")
corre <- scu [, -c(1:3)]
apa.cor.table(corre, show.conf.interval = TRUE,
show.sig.stars = TRUE,landscape = TRUE)##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1
## 1. Alumno_Soledad 3.66 1.50
##
## 2. Alumno_Violencia 4.40 1.48 .49**
## [.40, .57]
##
## 3. Alumno_Drogas 3.84 1.69 .40**
## [.30, .50]
##
## 4. Alumno_Salud mental 4.47 1.47 .54**
## [.45, .62]
##
## 5. Alumno_Redes sociales 3.17 1.50 .37**
## [.27, .47]
##
## 6. Alumno_Paro juvenil 3.58 1.48 .36**
## [.25, .45]
##
## 7. Alumno_Acoso escolar 4.14 1.53 .45**
## [.35, .54]
##
## 8. Alumno_Ciberacoso 3.83 1.60 .48**
## [.38, .56]
##
## 9. Alumno_Transtornos alimnetarios 3.80 1.73 .46**
## [.36, .54]
##
## 10. Alumno_Abandono escolar 3.53 1.52 .37**
## [.27, .47]
##
## 11. Alumno_Presión compañeros 3.45 1.54 .42**
## [.32, .51]
##
## 12. Alumno_Presión sobre rendimiento académico 3.94 1.45 .41**
## [.31, .50]
##
## 13. Alumno_Inteligencia Artificial 3.12 1.59 .22**
## [.10, .32]
##
## 14. Otros_Cambio climático 3.75 1.44 .19**
## [.08, .30]
##
## 15. Otros_Soledad 3.48 1.49 .45**
## [.35, .53]
##
## 16. Otros_Violencia 3.88 1.46 .20**
## [.08, .31]
##
## 17. Otros_Drogas 3.39 1.56 .18**
## [.06, .29]
##
## 18. Otros_Salud mental 3.80 1.46 .27**
## [.16, .37]
##
## 19. Otros_Redes sociales 3.35 1.63 .21**
## [.09, .31]
##
## 20. Otros_Paro juvenil 3.05 1.43 .10
## [-.01, .21]
##
## 21. Otros_Acoso escolar 3.71 1.46 .18**
## [.07, .29]
##
## 22. Otros_Ciberacoso 3.35 1.46 .19**
## [.08, .30]
##
## 23. Otros_Transtornos alimnetarios 3.37 1.53 .21**
## [.09, .31]
##
## 24. Otros_Abandono escolar 3.10 1.47 .12*
## [.00, .23]
##
## 25. Otros_Presión compañeros 3.33 1.44 .17**
## [.06, .28]
##
## 26. Otros_Presión sobre rendimiento académico 3.40 1.45 .08
## [-.03, .20]
##
## 27. Otros_Inteligencia Artificial 2.85 1.54 .08
## [-.03, .20]
##
## 2 3 4 5 6 7 8
##
##
##
##
##
## .56**
## [.48, .64]
##
## .54** .45**
## [.45, .62] [.35, .54]
##
## .33** .25** .41**
## [.22, .43] [.14, .35] [.31, .50]
##
## .34** .25** .42** .35**
## [.24, .44] [.14, .35] [.32, .51] [.24, .44]
##
## .56** .45** .51** .41** .34**
## [.48, .64] [.35, .54] [.42, .59] [.31, .51] [.23, .44]
##
## .60** .45** .54** .44** .36** .82**
## [.52, .67] [.35, .54] [.46, .62] [.34, .53] [.25, .46] [.77, .85]
##
## .61** .42** .65** .40** .41** .69** .67**
## [.53, .68] [.32, .51] [.58, .71] [.30, .49] [.31, .50] [.62, .74] [.60, .73]
##
## .48** .40** .48** .30** .43** .50** .58**
## [.38, .56] [.30, .50] [.39, .56] [.19, .40] [.33, .52] [.41, .58] [.50, .65]
##
## .49** .43** .50** .42** .32** .54** .55**
## [.39, .57] [.33, .52] [.40, .58] [.32, .51] [.21, .42] [.45, .62] [.47, .63]
##
## .43** .36** .42** .26** .40** .44** .44**
## [.33, .52] [.25, .45] [.32, .52] [.14, .36] [.30, .50] [.34, .53] [.35, .53]
##
## .25** .22** .26** .26** .18** .18** .25**
## [.14, .35] [.10, .32] [.15, .37] [.15, .37] [.07, .29] [.06, .29] [.14, .36]
##
## .26** .17** .26** .26** .22** .22** .22**
## [.15, .36] [.06, .28] [.14, .36] [.15, .36] [.11, .33] [.11, .33] [.11, .33]
##
## .33** .25** .40** .31** .19** .32** .36**
## [.23, .43] [.14, .36] [.30, .49] [.21, .42] [.08, .30] [.21, .42] [.25, .46]
##
## .45** .27** .29** .27** .17** .37** .41**
## [.35, .54] [.16, .37] [.18, .40] [.16, .38] [.05, .28] [.26, .46] [.31, .50]
##
## .36** .48** .22** .19** .09 .33** .32**
## [.26, .46] [.39, .57] [.11, .33] [.08, .30] [-.03, .20] [.22, .43] [.21, .42]
##
## .38** .29** .42** .26** .21** .36** .38**
## [.28, .48] [.18, .39] [.32, .51] [.15, .37] [.09, .32] [.25, .45] [.28, .48]
##
## .18** .16** .15* .35** .18** .16** .20**
## [.07, .29] [.05, .27] [.03, .26] [.24, .45] [.07, .29] [.05, .27] [.09, .31]
##
## .26** .14* .22** .18** .33** .19** .24**
## [.15, .37] [.03, .25] [.11, .33] [.06, .29] [.22, .43] [.08, .30] [.12, .34]
##
## .30** .30** .31** .30** .20** .41** .39**
## [.19, .40] [.19, .40] [.20, .41] [.19, .40] [.08, .31] [.31, .50] [.29, .48]
##
## .30** .24** .32** .27** .21** .35** .44**
## [.19, .40] [.13, .35] [.21, .42] [.16, .38] [.10, .32] [.24, .45] [.34, .53]
##
## .33** .23** .40** .28** .22** .37** .38**
## [.23, .43] [.11, .33] [.30, .49] [.17, .39] [.11, .33] [.27, .47] [.27, .47]
##
## .28** .27** .26** .29** .22** .36** .39**
## [.17, .39] [.16, .37] [.14, .36] [.18, .39] [.10, .32] [.26, .46] [.29, .48]
##
## .28** .25** .30** .24** .23** .27** .23**
## [.17, .38] [.13, .35] [.19, .40] [.13, .35] [.12, .34] [.16, .38] [.12, .34]
##
## .24** .23** .26** .19** .21** .27** .23**
## [.13, .35] [.11, .33] [.15, .37] [.08, .30] [.10, .32] [.16, .37] [.11, .33]
##
## .22** .15* .19** .22** .13* .17** .19**
## [.11, .33] [.03, .26] [.08, .30] [.10, .33] [.01, .24] [.06, .28] [.07, .30]
##
## 9 10 11 12 13 14 15
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .58**
## [.49, .65]
##
## .61** .57**
## [.53, .68] [.48, .64]
##
## .46** .46** .54**
## [.37, .55] [.36, .54] [.45, .62]
##
## .21** .32** .33** .25**
## [.10, .32] [.21, .42] [.22, .43] [.14, .36]
##
## .22** .20** .21** .15* .21**
## [.10, .32] [.08, .31] [.10, .32] [.03, .26] [.09, .32]
##
## .35** .32** .34** .24** .19** .58**
## [.25, .45] [.21, .42] [.23, .44] [.13, .34] [.07, .29] [.50, .65]
##
## .31** .29** .25** .23** .07 .56** .54**
## [.20, .41] [.18, .40] [.14, .36] [.12, .34] [-.04, .19] [.47, .63] [.46, .62]
##
## .26** .26** .24** .23** .13* .40** .44**
## [.15, .36] [.14, .36] [.13, .35] [.11, .33] [.02, .24] [.30, .49] [.34, .52]
##
## .34** .24** .28** .22** .10 .54** .56**
## [.24, .44] [.13, .35] [.17, .38] [.11, .33] [-.02, .21] [.45, .62] [.47, .63]
##
## .13* .24** .21** .17** .28** .27** .28**
## [.02, .24] [.13, .35] [.09, .32] [.06, .28] [.17, .38] [.16, .37] [.17, .39]
##
## .30** .25** .22** .24** .05 .41** .43**
## [.19, .40] [.14, .36] [.10, .33] [.13, .35] [-.07, .16] [.31, .51] [.33, .52]
##
## .36** .36** .27** .21** .10 .43** .51**
## [.25, .45] [.25, .46] [.16, .38] [.09, .32] [-.01, .22] [.33, .52] [.42, .59]
##
## .32** .32** .27** .19** .15* .44** .47**
## [.21, .42] [.22, .42] [.15, .37] [.08, .30] [.04, .26] [.34, .53] [.37, .55]
##
## .53** .33** .33** .17** .06 .38** .49**
## [.45, .61] [.22, .43] [.22, .43] [.06, .28] [-.06, .18] [.28, .48] [.40, .58]
##
## .34** .39** .34** .18** .10 .45** .51**
## [.24, .44] [.29, .48] [.23, .43] [.06, .29] [-.01, .22] [.35, .54] [.42, .59]
##
## .27** .24** .33** .27** .11 .46** .53**
## [.16, .38] [.13, .35] [.22, .43] [.16, .37] [-.00, .23] [.36, .55] [.44, .61]
##
## .24** .21** .27** .28** .16** .49** .49**
## [.13, .35] [.09, .32] [.16, .38] [.17, .38] [.05, .28] [.39, .57] [.39, .57]
##
## .20** .22** .23** .13* .47** .39** .34**
## [.08, .30] [.11, .33] [.12, .34] [.01, .24] [.37, .56] [.29, .48] [.23, .44]
##
## 16 17 18 19 20 21 22
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .62**
## [.54, .68]
##
## .63** .54**
## [.55, .69] [.46, .62]
##
## .29** .27** .32**
## [.18, .39] [.16, .38] [.21, .42]
##
## .40** .28** .46** .20**
## [.30, .49] [.16, .38] [.37, .55] [.09, .31]
##
## .61** .44** .56** .23** .47**
## [.53, .67] [.34, .53] [.47, .63] [.11, .33] [.37, .56]
##
## .58** .44** .56** .22** .45** .71**
## [.50, .65] [.34, .53] [.48, .64] [.11, .33] [.36, .54] [.65, .76]
##
## .51** .35** .56** .21** .46** .55** .57**
## [.42, .59] [.24, .45] [.47, .63] [.09, .32] [.36, .54] [.47, .63] [.48, .64]
##
## .47** .45** .49** .22** .56** .61** .56**
## [.38, .56] [.35, .54] [.39, .57] [.11, .33] [.48, .64] [.54, .68] [.48, .64]
##
## .45** .42** .47** .28** .46** .53** .47**
## [.36, .54] [.32, .51] [.37, .56] [.17, .39] [.37, .55] [.44, .61] [.38, .56]
##
## .45** .42** .47** .27** .46** .50** .46**
## [.36, .54] [.32, .51] [.38, .56] [.16, .37] [.36, .55] [.41, .58] [.37, .55]
##
## .35** .40** .31** .43** .29** .29** .32**
## [.24, .45] [.29, .49] [.20, .41] [.33, .52] [.18, .39] [.18, .40] [.21, .42]
##
## 23 24 25 26
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .57**
## [.49, .65]
##
## .49** .60**
## [.39, .57] [.52, .67]
##
## .42** .53** .69**
## [.32, .51] [.45, .61] [.62, .75]
##
## .32** .37** .39** .34**
## [.21, .42] [.26, .47] [.29, .49] [.24, .44]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## Diferencias por cursos
## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 6.8 2.281 1.274 0.283
## Residuals 282 504.9 1.790## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 8.4 2.792 1.247 0.293
## Residuals 282 631.4 2.239## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 2.7 0.8969 0.407 0.748
## Residuals 282 622.1 2.2059## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 12.7 4.232 1.497 0.216
## Residuals 282 797.2 2.827## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 13.1 4.379 2.038 0.109
## Residuals 282 606.0 2.149## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 17.3 5.757 2.613 0.0516 .
## Residuals 282 621.3 2.203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 14.4 4.787 2.201 0.0882 .
## Residuals 282 613.3 2.175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 4.3 1.425 0.61 0.609
## Residuals 282 658.8 2.336## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 2.0 0.6828 0.227 0.878
## Residuals 282 849.6 3.0127## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 7.6 2.527 1.091 0.353
## Residuals 282 653.6 2.318## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 2.0 0.652 0.272 0.845
## Residuals 282 674.9 2.393## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 3.9 1.312 0.618 0.604
## Residuals 282 598.9 2.124## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 3.8 1.273 0.498 0.684
## Residuals 282 720.1 2.554## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 17.9 5.963 2.922 0.0344 *
## Residuals 282 575.5 2.041
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 14.9 4.962 2.255 0.0822 .
## Residuals 282 620.5 2.200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 2.0 0.6749 0.314 0.815
## Residuals 282 605.9 2.1487## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 20.0 6.680 2.811 0.0398 *
## Residuals 282 670.1 2.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 0.7 0.2369 0.11 0.954
## Residuals 282 608.9 2.1593## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 16.8 5.615 2.138 0.0956 .
## Residuals 282 740.5 2.626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 16.1 5.355 2.649 0.0492 *
## Residuals 282 570.1 2.022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 7.8 2.588 1.217 0.304
## Residuals 282 599.6 2.126## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 8.0 2.664 1.253 0.291
## Residuals 282 599.3 2.125## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 8.6 2.870 1.234 0.298
## Residuals 282 655.8 2.326## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 15.5 5.171 2.433 0.0652 .
## Residuals 282 599.3 2.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 4.5 1.485 0.714 0.545
## Residuals 282 587.0 2.082## Df Sum Sq Mean Sq F value Pr(>F)
## scu$Curso 3 9.1 3.022 1.274 0.283
## Residuals 282 668.8 2.372