PLINES 2024
Humberto Perez Leon
2024-11-06
Importación de archivos y carga de librerías
library(readxl)
library(writexl)
library(TAM)
library(tidyr)
library(tibble)
plin2024 <- read_xlsx("Base PLINES 2024_CODIFICADOS.xlsx")
plin_resp <- plin2024[,c(1,15:19)]Explorar datos
En esta parte, se eliminan las filas donde la totalidad de respuestas es NA.
Análisis Rasch de créditos parciales con TAM
Se ha utilizado el modelo Rasch de Créditos Parciales, usando el procedimiento de máxima verosimilitud marginal (MML).
Análisis en Teoría Clásica
Se muestran las frecuencias de las categorías en los datos
| index | group | groupindex | itemno | item | N | Categ | AbsFreq | RelFreq | |
|---|---|---|---|---|---|---|---|---|---|
| 3 | 1 | 1 | 1 | 1 | ASPECTO 1 | 444 | 0 | 16 | 0.0360360 |
| 4 | 2 | 1 | 1 | 1 | ASPECTO 1 | 444 | 1 | 200 | 0.4504505 |
| 5 | 3 | 1 | 1 | 1 | ASPECTO 1 | 444 | 2 | 203 | 0.4572072 |
| 2 | 4 | 1 | 1 | 1 | ASPECTO 1 | 444 | 3 | 25 | 0.0563063 |
| 7 | 5 | 1 | 1 | 2 | ASPECTO 2 | 444 | 0 | 104 | 0.2342342 |
| 9 | 6 | 1 | 1 | 2 | ASPECTO 2 | 444 | 1 | 35 | 0.0788288 |
| 8 | 7 | 1 | 1 | 2 | ASPECTO 2 | 444 | 2 | 131 | 0.2950450 |
| 6 | 8 | 1 | 1 | 2 | ASPECTO 2 | 444 | 3 | 174 | 0.3918919 |
| 11 | 9 | 1 | 1 | 3 | ASPECTO 3 | 444 | 0 | 31 | 0.0698198 |
| 13 | 10 | 1 | 1 | 3 | ASPECTO 3 | 444 | 1 | 215 | 0.4842342 |
| 12 | 11 | 1 | 1 | 3 | ASPECTO 3 | 444 | 2 | 133 | 0.2995495 |
| 10 | 12 | 1 | 1 | 3 | ASPECTO 3 | 444 | 3 | 65 | 0.1463964 |
| 15 | 13 | 1 | 1 | 4 | ASPECTO 4 | 444 | 0 | 101 | 0.2274775 |
| 17 | 14 | 1 | 1 | 4 | ASPECTO 4 | 444 | 1 | 166 | 0.3738739 |
| 16 | 15 | 1 | 1 | 4 | ASPECTO 4 | 444 | 2 | 123 | 0.2770270 |
| 14 | 16 | 1 | 1 | 4 | ASPECTO 4 | 444 | 3 | 54 | 0.1216216 |
| 19 | 17 | 1 | 1 | 5 | ASPECTO 5 | 444 | 0 | 85 | 0.1914414 |
| 21 | 18 | 1 | 1 | 5 | ASPECTO 5 | 444 | 1 | 132 | 0.2972973 |
| 20 | 19 | 1 | 1 | 5 | ASPECTO 5 | 444 | 2 | 183 | 0.4121622 |
| 18 | 20 | 1 | 1 | 5 | ASPECTO 5 | 444 | 3 | 44 | 0.0990991 |
Se muestran las correlaciónes punto biseriales
| index | group | itemno | item | N | Categ | AbsFreq | RelFreq | rpb.WLE | M.WLE | SD.WLE |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | ASPECTO 1 | 444 | 0 | 0 | 0 | -0.3868228 | -2.1177777 | 1.4603873 |
| 2 | 1 | 1 | ASPECTO 1 | 444 | 1 | 0 | 0 | -0.3968118 | -0.4680346 | 0.7811499 |
| 3 | 1 | 1 | ASPECTO 1 | 444 | 2 | 0 | 0 | 0.3686848 | 0.4189473 | 0.6595157 |
| 4 | 1 | 1 | ASPECTO 1 | 444 | 3 | 0 | 0 | 0.3724712 | 1.6049037 | 1.2613658 |
| 5 | 1 | 2 | ASPECTO 2 | 444 | 0 | 0 | 0 | -0.5773211 | -1.1074618 | 0.9081704 |
| 6 | 1 | 2 | ASPECTO 2 | 444 | 1 | 0 | 0 | -0.1860800 | -0.6769068 | 0.5272006 |
| 7 | 1 | 2 | ASPECTO 2 | 444 | 2 | 0 | 0 | 0.0124929 | 0.0151598 | 0.5719070 |
| 8 | 1 | 2 | ASPECTO 2 | 444 | 3 | 0 | 0 | 0.5919058 | 0.7733296 | 0.7990603 |
| 9 | 1 | 3 | ASPECTO 3 | 444 | 0 | 0 | 0 | -0.3465398 | -1.3408412 | 1.3755354 |
| 10 | 1 | 3 | ASPECTO 3 | 444 | 1 | 0 | 0 | -0.3590091 | -0.3964646 | 0.7135018 |
| 11 | 1 | 3 | ASPECTO 3 | 444 | 2 | 0 | 0 | 0.2931682 | 0.4681424 | 0.7200650 |
| 12 | 1 | 3 | ASPECTO 3 | 444 | 3 | 0 | 0 | 0.3774782 | 0.9572393 | 1.1365832 |
| 13 | 1 | 4 | ASPECTO 4 | 444 | 0 | 0 | 0 | -0.4415686 | -0.8644759 | 1.0086091 |
| 14 | 1 | 4 | ASPECTO 4 | 444 | 1 | 0 | 0 | -0.2177268 | -0.3027485 | 0.6949469 |
| 15 | 1 | 4 | ASPECTO 4 | 444 | 2 | 0 | 0 | 0.2974814 | 0.5022179 | 0.6362244 |
| 16 | 1 | 4 | ASPECTO 4 | 444 | 3 | 0 | 0 | 0.4813187 | 1.3606117 | 0.8962753 |
| 17 | 1 | 5 | ASPECTO 5 | 444 | 0 | 0 | 0 | -0.4434389 | -0.9675165 | 1.0908570 |
| 18 | 1 | 5 | ASPECTO 5 | 444 | 1 | 0 | 0 | -0.0463133 | -0.0804153 | 0.8138760 |
| 19 | 1 | 5 | ASPECTO 5 | 444 | 2 | 0 | 0 | 0.1687156 | 0.2075255 | 0.7365627 |
| 20 | 1 | 5 | ASPECTO 5 | 444 | 3 | 0 | 0 | 0.3768039 | 1.1944109 | 1.1665572 |
Análisis Rasch
Estimaciones de ítems
Estos datos son los que deben usarse para establecer puntos de corte con método Bookmark
| rowname | xsi | se.xsi |
|---|---|---|
| ASPECTO 1_Cat1 | -3.0741955 | 0.2619638 |
| ASPECTO 1_Cat2 | -0.0316652 | 0.1030400 |
| ASPECTO 1_Cat3 | 2.6022508 | 0.2138376 |
| ASPECTO 2_Cat1 | 0.5487847 | 0.1338352 |
| ASPECTO 2_Cat2 | -1.4693027 | 0.1240412 |
| ASPECTO 2_Cat3 | -0.0261915 | 0.1109484 |
| ASPECTO 3_Cat1 | -2.4369759 | 0.1946119 |
| ASPECTO 3_Cat2 | 0.4729771 | 0.1062716 |
| ASPECTO 3_Cat3 | 1.1868265 | 0.1463312 |
| ASPECTO 4_Cat1 | -0.8555265 | 0.1245615 |
| ASPECTO 4_Cat2 | 0.3967327 | 0.1100256 |
| ASPECTO 4_Cat3 | 1.3696634 | 0.1582037 |
| ASPECTO 5_Cat1 | -0.8654573 | 0.1332062 |
| ASPECTO 5_Cat2 | -0.2900631 | 0.1070873 |
| ASPECTO 5_Cat3 | 1.9290226 | 0.1688316 |
Confiabilidad
La confiabilidad WLE es 0.6382233
Umbrales de Thustone
Con estos datos, se pueden establecer cortes considerando la mediana de cada categoría en todos los ítems (más el delta de probabilidad).
| Cat1 | Cat2 | Cat3 | |
|---|---|---|---|
| ASPECTO 1 | -3.1189270 | -0.0520935 | 2.6675720 |
| ASPECTO 2 | -0.8000793 | -0.5490417 | 0.3016663 |
| ASPECTO 3 | -2.4886780 | 0.2189026 | 1.4984436 |
| ASPECTO 4 | -1.0776672 | 0.3505554 | 1.6410828 |
| ASPECTO 5 | -1.2124329 | -0.0522766 | 2.0279846 |
Curvas características de los ítems
CCIs de ítems
## Iteration in WLE/MLE estimation 1 | Maximal change 0.993
## Iteration in WLE/MLE estimation 2 | Maximal change 0.3927
## Iteration in WLE/MLE estimation 3 | Maximal change 0.0303
## Iteration in WLE/MLE estimation 4 | Maximal change 0.0082
## Iteration in WLE/MLE estimation 5 | Maximal change 0.0021
## Iteration in WLE/MLE estimation 6 | Maximal change 6e-04
## Iteration in WLE/MLE estimation 7 | Maximal change 1e-04
## Iteration in WLE/MLE estimation 8 | Maximal change 0
## ----
## WLE Reliability= 0.638
## ....................................................
## Plots exported in png format into folder:
## C:/Users/hperezleon_innovasch/Documents/PLINES 2024/PlotsCCIs de categorías
## Iteration in WLE/MLE estimation 1 | Maximal change 0.993
## Iteration in WLE/MLE estimation 2 | Maximal change 0.3927
## Iteration in WLE/MLE estimation 3 | Maximal change 0.0303
## Iteration in WLE/MLE estimation 4 | Maximal change 0.0082
## Iteration in WLE/MLE estimation 5 | Maximal change 0.0021
## Iteration in WLE/MLE estimation 6 | Maximal change 6e-04
## Iteration in WLE/MLE estimation 7 | Maximal change 1e-04
## Iteration in WLE/MLE estimation 8 | Maximal change 0
## ----
## WLE Reliability= 0.638
## ....................................................
## Plots exported in png format into folder:
## C:/Users/hperezleon_innovasch/Documents/PLINES 2024/PlotsMapa de Wright
## Iteration in WLE/MLE estimation 1 | Maximal change 0.993
## Iteration in WLE/MLE estimation 2 | Maximal change 0.3927
## Iteration in WLE/MLE estimation 3 | Maximal change 0.0303
## Iteration in WLE/MLE estimation 4 | Maximal change 0.0082
## Iteration in WLE/MLE estimation 5 | Maximal change 0.0021
## Iteration in WLE/MLE estimation 6 | Maximal change 6e-04
## Iteration in WLE/MLE estimation 7 | Maximal change 1e-04
## Iteration in WLE/MLE estimation 8 | Maximal change 0
## ----
## WLE Reliability= 0.638
Ajuste al modelo
Item fit calculation based on 40 simulations |**********| |———-|
| parameter | Outfit | Outfit_t | Outfit_p | Outfit_pholm | Infit | Infit_t | Infit_p | Infit_pholm |
|---|---|---|---|---|---|---|---|---|
| ASPECTO 1_Cat1 | 0.5981395 | -2.1315882 | 0.0330407 | 0.3304072 | 0.9241677 | -0.2852812 | 0.7754287 | 1.0000000 |
| ASPECTO 1_Cat2 | 0.9201738 | -2.3471622 | 0.0189170 | 0.2080872 | 0.9203150 | -2.3281266 | 0.0199054 | 0.2786754 |
| ASPECTO 1_Cat3 | 0.7772219 | -1.4135687 | 0.1574886 | 1.0000000 | 0.9605641 | -0.1843518 | 0.8537375 | 1.0000000 |
| ASPECTO 2_Cat1 | 1.1003984 | 0.9108618 | 0.3623682 | 1.0000000 | 1.0276326 | 0.3998177 | 0.6892908 | 1.0000000 |
| ASPECTO 2_Cat2 | 0.9435663 | -1.0608482 | 0.2887589 | 1.0000000 | 0.9630019 | -0.6162622 | 0.5377215 | 1.0000000 |
| ASPECTO 2_Cat3 | 0.8736826 | -2.9768813 | 0.0029120 | 0.0378556 | 0.9358599 | -1.4642916 | 0.1431143 | 1.0000000 |
| ASPECTO 3_Cat1 | 1.0948439 | 0.5247711 | 0.5997423 | 1.0000000 | 0.9986949 | 0.0355164 | 0.9716679 | 1.0000000 |
| ASPECTO 3_Cat2 | 0.9693143 | -0.7808558 | 0.4348873 | 1.0000000 | 0.9702338 | -0.7358689 | 0.4618105 | 1.0000000 |
| ASPECTO 3_Cat3 | 1.3643393 | 3.5562393 | 0.0003762 | 0.0052668 | 1.0646047 | 0.7343603 | 0.4627292 | 1.0000000 |
| ASPECTO 4_Cat1 | 1.0542156 | 0.8154240 | 0.4148296 | 1.0000000 | 1.0571723 | 0.9013848 | 0.3673837 | 1.0000000 |
| ASPECTO 4_Cat2 | 0.9096507 | -2.0350878 | 0.0418420 | 0.3765783 | 0.9455737 | -1.2062754 | 0.2277113 | 1.0000000 |
| ASPECTO 4_Cat3 | 0.9148283 | -1.2510813 | 0.2109048 | 1.0000000 | 0.9731080 | -0.2499043 | 0.8026614 | 1.0000000 |
| ASPECTO 5_Cat1 | 1.1965486 | 2.4827290 | 0.0130380 | 0.1564563 | 1.0408041 | 0.5666538 | 0.5709494 | 1.0000000 |
| ASPECTO 5_Cat2 | 1.2420212 | 5.3775363 | 0.0000001 | 0.0000011 | 1.1669386 | 3.8073072 | 0.0001405 | 0.0021073 |
| ASPECTO 5_Cat3 | 1.1041662 | 0.8432109 | 0.3991105 | 1.0000000 | 1.0108824 | 0.1246249 | 0.9008205 | 1.0000000 |
Dimensionalidad
##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha| PC1 | PC2 | PC3 | PC4 | PC5 | |
|---|---|---|---|---|---|
| SS loadings | 1.4469413 | 1.3979786 | 1.1434696 | 0.9723831 | 0.0392275 |
| Proportion Var | 0.2893883 | 0.2795957 | 0.2286939 | 0.1944766 | 0.0078455 |
| Cumulative Var | 0.2893883 | 0.5689840 | 0.7976779 | 0.9921545 | 1.0000000 |
| Proportion Explained | 0.2893883 | 0.2795957 | 0.2286939 | 0.1944766 | 0.0078455 |
| Cumulative Proportion | 0.2893883 | 0.5689840 | 0.7976779 | 0.9921545 | 1.0000000 |
