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pairwise.t_per_question_per_year.txt
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pairwise.t_per_question_per_year.txt
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$`Conocía OneNote antes`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 40.7 13.57 5.321 0.00147 **
Residuals 222 566.2 2.55
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`OneNote es adecuado`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 21.3 7.100 5.698 0.000894 ***
Residuals 222 276.6 1.246
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Suficiente información previa`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 11.82 3.940 3.125 0.0267 *
Residuals 222 279.90 1.261
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Prefiero otra aplicación`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 56.7 18.903 11.44 5.29e-07 ***
Residuals 222 366.9 1.653
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Estructura del cuaderno`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 3.77 1.257 0.892 0.446
Residuals 222 312.77 1.409
$`Mantendría OneNote`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 33.28 11.09 8.037 4.16e-05 ***
Residuals 222 306.45 1.38
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Ahorro de tiempo`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 11.32 3.775 2.714 0.0457 *
Residuals 221 307.40 1.391
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Ayuda para entrega final`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 16.9 5.646 3.909 0.00952 **
Residuals 221 319.3 1.445
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Trabajo en grupo`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 27.07 9.022 7.703 6.44e-05 ***
Residuals 221 258.86 1.171
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Extensible a otras prácticas`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 13.37 4.458 3.979 0.00867 **
Residuals 221 247.59 1.120
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Problemas sincronización`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 14.9 4.95 2.828 0.0394 *
Residuals 220 385.0 1.75
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Conflictos de versiones`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 15.3 5.113 2.526 0.0584 .
Residuals 220 445.2 2.024
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Lentitud
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 17.5 5.833 3.245 0.0228 *
Residuals 220 395.5 1.798
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Formato demasiado simple`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 4.8 1.587 0.814 0.488
Residuals 220 429.2 1.951
$`Mal funcionamiento`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 16.1 5.351 3.469 0.017 *
Residuals 220 339.4 1.543
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`La plantilla nos ayudó a entrega final`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 29.17 9.724 8.82 1.52e-05 ***
Residuals 218 240.32 1.102
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`La plantilla es coherente con rúbrica`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 18 5.999 7.349 0.000103 ***
Residuals 218 178 0.816
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`La plantilla nos ayudó a trabajar`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 7.58 2.528 2.039 0.109
Residuals 218 270.29 1.240
$`Limite de palabras adecuado`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 0.7 0.2343 0.267 0.849
Residuals 218 191.3 0.8774
$`Tuvimos que modificar plantilla`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 56.7 18.89 12.68 1.14e-07 ***
Residuals 218 324.9 1.49
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`La plantilla facilita evaluación homogénea`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 21.73 7.242 7.944 4.73e-05 ***
Residuals 218 198.73 0.912
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`Será de ayuda en TFG, TFM…`
Df Sum Sq Mean Sq F value Pr(>F)
data.split[[i]]$Curso 3 7.01 2.335 1.96 0.121
Residuals 218 259.73 1.191
$`Conocía OneNote antes`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 1.0000 - -
2021-22 1.0000 1.0000 -
2022-23 0.0011 0.0167 0.0167
P value adjustment method: holm
$`OneNote es adecuado`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.01945 - -
2021-22 0.42903 0.42903 -
2022-23 0.42903 0.00093 0.05334
P value adjustment method: holm
$`Suficiente información previa`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.903 - -
2021-22 0.265 0.085 -
2022-23 0.265 0.082 0.903
P value adjustment method: holm
$`Prefiero otra aplicación`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.35009 - -
2021-22 0.28570 0.78066 -
2022-23 0.00069 7.1e-06 1.6e-06
P value adjustment method: holm
$`Estructura del cuaderno`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 1.00 - -
2021-22 1.00 1.00 -
2022-23 1.00 0.94 0.94
P value adjustment method: holm
$`Mantendría OneNote`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.0022 - -
2021-22 0.1729 0.1962 -
2022-23 0.2286 7e-05 0.0137
P value adjustment method: holm
$`Ahorro de tiempo`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.305 - -
2021-22 0.716 0.542 -
2022-23 0.556 0.033 0.542
P value adjustment method: holm
$`Ayuda para entrega final`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.170 - -
2021-22 0.170 0.956 -
2022-23 0.706 0.037 0.033
P value adjustment method: holm
$`Trabajo en grupo`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.012 - -
2021-22 0.510 0.054 -
2022-23 0.107 2.4e-05 0.050
P value adjustment method: holm
$`Extensible a otras prácticas`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.0969 - -
2021-22 0.5593 0.5982 -
2022-23 0.5982 0.0099 0.1035
P value adjustment method: holm
$`Problemas sincronización`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 1.000 - -
2021-22 1.000 1.000 -
2022-23 0.065 0.232 0.065
P value adjustment method: holm
$`Conflictos de versiones`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.746 - -
2021-22 0.746 0.971 -
2022-23 0.039 0.520 0.520
P value adjustment method: holm
$Lentitud
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.233 - -
2021-22 0.789 0.021 -
2022-23 1.000 0.103 1.000
P value adjustment method: holm
$`Formato demasiado simple`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 1 - -
2021-22 1 1 -
2022-23 1 1 1
P value adjustment method: holm
$`Mal funcionamiento`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 1.000 - -
2021-22 0.115 0.196 -
2022-23 0.055 0.115 1.000
P value adjustment method: holm
$`La plantilla nos ayudó a entrega final`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.7618 - -
2021-22 1.7e-05 0.0004 -
2022-23 0.3999 0.7618 0.0116
P value adjustment method: holm
$`La plantilla es coherente con rúbrica`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 1.00000 - -
2021-22 0.00020 0.00097 -
2022-23 1.00000 1.00000 0.00671
P value adjustment method: holm
$`La plantilla nos ayudó a trabajar`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.77 - -
2021-22 0.25 0.94 -
2022-23 0.94 0.77 0.25
P value adjustment method: holm
$`Limite de palabras adecuado`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 1 - -
2021-22 1 1 -
2022-23 1 1 1
P value adjustment method: holm
$`Tuvimos que modificar plantilla`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 3.2e-06 - -
2021-22 1.1e-06 0.8426 -
2022-23 0.0019 0.3594 0.3594
P value adjustment method: holm
$`La plantilla facilita evaluación homogénea`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.9980 - -
2021-22 6.7e-05 0.0035 -
2022-23 1.0000 1.0000 0.0012
P value adjustment method: holm
$`Será de ayuda en TFG, TFM…`
Pairwise comparisons using t tests with pooled SD
data: data.split[[i]]$value and data.split[[i]]$Curso
2019-20 2020-21 2021-22
2020-21 0.43 - -
2021-22 0.42 1.00 -
2022-23 1.00 0.42 0.33
P value adjustment method: holm