Analisis
Benjamin Lira
2022-08-02
Enlace Qualrics
Limpieza de datos
Comenzamos por leer los datos directamente de qualtrics.
Eliminar casos
df <-
df |>
# Consentimiento
filter(QID1 == "Sí acepto participar de esta investigación") |>
# Respuestas incompletas
filter(Finished == "True")Para facilitar el analisis, vamos a crear bases independientes para cada constructo y transformamos todo a numeros.
Algunas de las variables no tienen numeros, así que las estamos transformando de acuerdo a lo siguiente:
read_csv(".recode", col_names = F) |> slice(1:11) |> gt::gt()| X1 | X2 |
|---|---|
| Extremadamente | 5 |
| Bastante | 4 |
| Moderadamente | 3 |
| Un poco | 2 |
| Levemente o casi nada | 1 |
| Totalmente en desacuerdo | 1 |
| En desacuerdo | 2 |
| Ligeramente en desacuerdo | 3 |
| Ni de acuerdo ni en desacuerdo | 4 |
| Ligeramente de acuerdo | 5 |
| De acuerdo | 6 |
prelims <- df |> select(1:18)
demo <- df |> select(id,A1:A11) |> mutate(A6 = as.numeric(A6))
capacidad <- df |> select(id,B1_1:B1_36)|> mutate_at(2:37,parse_number)
panas <-
df |>
select(id, starts_with("C1_")) |>
pivot_longer(starts_with("C1_")) |>
recode_with_csv(old_column = value) |>
mutate(value = as.numeric(value)) |>
pivot_wider(names_from = "name", values_from = "value")
flour <-
df |>
select(id,starts_with("D1_")) |>
pivot_longer(starts_with("D1_")) |>
recode_with_csv(old_column = value) |>
mutate(value = as.numeric(value)) |>
pivot_wider(names_from = "name", values_from = "value")
familia <- df |> select(id,E1:E12) |> mutate_at(2:13,parse_number)Validez
Analisis Factorial Exploratorio
Empecemos por un analisis factorial exploratorio. De acuerdo con la estructura teórica de la prueba deberíamos extraer 5 factores: social, organización y adaptación, estabilidad emocional, liderazgo, evaluación y cognición.
Correlaciones entre items
Muchas veces es útil observar las correlaciones entre los ítems de la prubea.
library(gt)
capacidad |>
select(-id) |>
Ben::harcor() |>
gt::gt() |>
Ben::gt_apa()| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. B1_1 | ||||||||||||||||||||||||||||||||||||
| 2. B1_2 | .35*** | |||||||||||||||||||||||||||||||||||
| 3. B1_3 | .22*** | .28*** | ||||||||||||||||||||||||||||||||||
| 4. B1_4 | .24*** | .28*** | .24*** | |||||||||||||||||||||||||||||||||
| 5. B1_5 | .16* | .27*** | .37*** | .43*** | ||||||||||||||||||||||||||||||||
| 6. B1_6 | .19** | .29*** | .44*** | .23*** | .56*** | |||||||||||||||||||||||||||||||
| 7. B1_7 | .48*** | .19** | .23*** | .15* | .10 | .18** | ||||||||||||||||||||||||||||||
| 8. B1_8 | .28*** | .32*** | .12† | .22*** | .16* | .28*** | .28*** | |||||||||||||||||||||||||||||
| 9. B1_9 | .48*** | .28*** | .35*** | .33*** | .24*** | .32*** | .43*** | .39*** | ||||||||||||||||||||||||||||
| 10. B1_10 | .21** | .36*** | .45*** | .17** | .37*** | .45*** | .22*** | .34*** | .30*** | |||||||||||||||||||||||||||
| 11. B1_11 | .12† | .25*** | .15* | .38*** | .43*** | .34*** | .02 | .16* | .27*** | .21*** | ||||||||||||||||||||||||||
| 12. B1_12 | .25*** | .23*** | .17** | .24*** | .23*** | .31*** | .28*** | .36*** | .46*** | .27*** | .24*** | |||||||||||||||||||||||||
| 13. B1_13 | .20** | .32*** | .27*** | .21** | .42*** | .41*** | .22*** | .30*** | .34*** | .54*** | .37*** | .26*** | ||||||||||||||||||||||||
| 14. B1_14 | .08 | .25*** | .25*** | .31*** | .37*** | .36*** | .03 | .16* | .26*** | .42*** | .46*** | .17** | .46*** | |||||||||||||||||||||||
| 15. B1_15 | .41*** | .36*** | .33*** | .37*** | .23*** | .27*** | .33*** | .29*** | .53*** | .24*** | .11† | .40*** | .22*** | .15* | ||||||||||||||||||||||
| 16. B1_16 | .22*** | .26*** | .65*** | .26*** | .38*** | .41*** | .26*** | .13* | .42*** | .48*** | .24*** | .24*** | .29*** | .31*** | .36*** | |||||||||||||||||||||
| 17. B1_17 | .42*** | .37*** | .28*** | .31*** | .30*** | .35*** | .31*** | .27*** | .42*** | .27*** | .28*** | .33*** | .29*** | .17* | .46*** | .39*** | ||||||||||||||||||||
| 18. B1_18 | .14* | .31*** | .35*** | .31*** | .42*** | .46*** | .20** | .21** | .24*** | .53*** | .41*** | .17** | .51*** | .61*** | .20** | .38*** | .28*** | |||||||||||||||||||
| 19. B1_19 | .41*** | .26*** | .44*** | .19** | .26*** | .38*** | .39*** | .27*** | .37*** | .48*** | .21*** | .27*** | .35*** | .21** | .28*** | .46*** | .31*** | .38*** | ||||||||||||||||||
| 20. B1_20 | .37*** | .57*** | .27*** | .29*** | .32*** | .41*** | .34*** | .45*** | .36*** | .40*** | .22*** | .38*** | .35*** | .15* | .40*** | .29*** | .42*** | .34*** | .38*** | |||||||||||||||||
| 21. B1_21 | .50*** | .49*** | .21** | .27*** | .19** | .30*** | .34*** | .38*** | .37*** | .50*** | .19** | .28*** | .33*** | .29*** | .41*** | .30*** | .43*** | .36*** | .37*** | .45*** | ||||||||||||||||
| 22. B1_22 | .28*** | .33*** | .52*** | .28*** | .43*** | .52*** | .32*** | .24*** | .46*** | .49*** | .25*** | .30*** | .42*** | .29*** | .40*** | .52*** | .38*** | .35*** | .42*** | .39*** | .32*** | |||||||||||||||
| 23. B1_23 | .28*** | .35*** | .17* | .35*** | .20** | .26*** | .19** | .32*** | .34*** | .27*** | .45*** | .22*** | .29*** | .47*** | .26*** | .20** | .38*** | .48*** | .31*** | .28*** | .42*** | .25*** | ||||||||||||||
| 24. B1_24 | .57*** | .41*** | .29*** | .39*** | .24*** | .24*** | .32*** | .35*** | .60*** | .34*** | .21** | .38*** | .31*** | .16* | .53*** | .39*** | .51*** | .26*** | .39*** | .43*** | .51*** | .31*** | .40*** | |||||||||||||
| 25. B1_25 | .25*** | .32*** | .29*** | .13* | .40*** | .40*** | .28*** | .50*** | .40*** | .49*** | .32*** | .34*** | .45*** | .27*** | .24*** | .26*** | .26*** | .36*** | .40*** | .44*** | .29*** | .48*** | .33*** | .33*** | ||||||||||||
| 26. B1_26 | .25*** | .41*** | .15* | .29*** | .37*** | .35*** | .25*** | .37*** | .35*** | .40*** | .43*** | .24*** | .67*** | .32*** | .29*** | .22*** | .43*** | .43*** | .27*** | .40*** | .36*** | .35*** | .41*** | .43*** | .50*** | |||||||||||
| 27. B1_27 | .25*** | .46*** | .36*** | .26*** | .44*** | .49*** | .25*** | .31*** | .35*** | .48*** | .26*** | .28*** | .49*** | .37*** | .31*** | .35*** | .36*** | .47*** | .30*** | .46*** | .38*** | .53*** | .31*** | .35*** | .52*** | .50*** | ||||||||||
| 28. B1_28 | .25*** | .40*** | .44*** | .24*** | .39*** | .53*** | .22*** | .27*** | .35*** | .42*** | .29*** | .25*** | .39*** | .28*** | .39*** | .41*** | .44*** | .41*** | .33*** | .46*** | .35*** | .42*** | .34*** | .34*** | .43*** | .39*** | .48*** | |||||||||
| 29. B1_29 | .46*** | .31*** | .38*** | .28*** | .26*** | .20** | .34*** | .28*** | .52*** | .28*** | .10 | .30*** | .25*** | .07 | .48*** | .44*** | .54*** | .16* | .33*** | .36*** | .40*** | .37*** | .28*** | .67*** | .25*** | .30*** | .31*** | .36*** | ||||||||
| 30. B1_30 | .18** | .35*** | .31*** | .43*** | .51*** | .39*** | .08 | .12† | .30*** | .28*** | .44*** | .18** | .29*** | .43*** | .23*** | .40*** | .40*** | .43*** | .28*** | .30*** | .23*** | .33*** | .34*** | .30*** | .26*** | .33*** | .35*** | .41*** | .28*** | |||||||
| 31. B1_31 | .35*** | .23*** | .44*** | .23*** | .33*** | .39*** | .50*** | .27*** | .36*** | .47*** | .14* | .30*** | .39*** | .24*** | .30*** | .49*** | .24*** | .32*** | .60*** | .36*** | .32*** | .52*** | .19** | .34*** | .33*** | .27*** | .36*** | .32*** | .38*** | .34*** | ||||||
| 32. B1_32 | .27*** | .43*** | .23*** | .33*** | .27*** | .41*** | .20** | .32*** | .39*** | .38*** | .36*** | .25*** | .38*** | .47*** | .34*** | .26*** | .37*** | .55*** | .28*** | .32*** | .48*** | .38*** | .65*** | .40*** | .32*** | .44*** | .42*** | .40*** | .34*** | .40*** | .27*** | |||||
| 33. B1_33 | .30*** | .24*** | .33*** | .16* | .29*** | .44*** | .31*** | .32*** | .41*** | .44*** | .31*** | .36*** | .39*** | .40*** | .29*** | .34*** | .35*** | .40*** | .43*** | .32*** | .30*** | .37*** | .45*** | .30*** | .44*** | .34*** | .36*** | .35*** | .21** | .32*** | .38*** | .43*** | ||||
| 34. B1_34 | .25*** | .36*** | .17* | .38*** | .56*** | .35*** | .04 | .24*** | .17** | .23*** | .49*** | .18** | .32*** | .31*** | .27*** | .17** | .36*** | .37*** | .19** | .42*** | .25*** | .27*** | .38*** | .29*** | .26*** | .45*** | .39*** | .35*** | .23*** | .39*** | .18** | .31*** | .21*** | |||
| 35. B1_35 | .30*** | .28*** | .48*** | .26*** | .32*** | .39*** | .25*** | .23*** | .41*** | .36*** | .15* | .35*** | .32*** | .27*** | .41*** | .44*** | .33*** | .17** | .41*** | .35*** | .35*** | .48*** | .20** | .36*** | .25*** | .24*** | .32*** | .37*** | .38*** | .27*** | .46*** | .24*** | .40*** | .20** | ||
| 36. B1_36 | .12† | .22*** | .32*** | .38*** | .47*** | .38*** | .05 | .24*** | .25*** | .34*** | .62*** | .19** | .45*** | .57*** | .17** | .29*** | .27*** | .50*** | .25*** | .20** | .18** | .34*** | .42*** | .22*** | .33*** | .41*** | .38*** | .26*** | .16* | .42*** | .28*** | .43*** | .38*** | .43*** | .25*** | |
| M | 3.56 | 4.54 | 4.60 | 5.15 | 4.74 | 4.44 | 4.03 | 4.18 | 4.51 | 4.29 | 4.84 | 4.70 | 4.00 | 4.30 | 4.69 | 4.31 | 4.40 | 4.47 | 4.18 | 4.55 | 3.91 | 4.54 | 4.39 | 4.26 | 4.35 | 4.14 | 4.59 | 4.62 | 3.98 | 4.68 | 4.26 | 4.19 | 4.65 | 4.65 | 4.44 | 4.66 |
| SD | 1.43 | 1.32 | 1.28 | 0.91 | 1.18 | 1.26 | 1.45 | 1.38 | 1.27 | 1.36 | 1.18 | 1.27 | 1.46 | 1.40 | 1.18 | 1.43 | 1.33 | 1.33 | 1.39 | 1.30 | 1.50 | 1.23 | 1.43 | 1.38 | 1.27 | 1.47 | 1.25 | 1.20 | 1.54 | 1.30 | 1.38 | 1.42 | 1.22 | 1.25 | 1.32 | 1.25 |
| n | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 |
O mejor aún, con un heatmap. Se ven correlaciones altas entre items, pero no se notan factores claramente discernibles.
capacidad |>
select(-id) |>
corrr::correlate() |>
corrr::rearrange(method = "PCA") |>
corrr::stretch() |>
mutate_at(1:2, fct_inorder) |>
ggplot(aes(x,y,fill=r)) +
geom_tile()+
theme(axis.text.x= element_text(angle = 90))+
scale_fill_viridis_c()
Análisis Paralelo
El análisis paralelo nos puede ayudar a determinar el numero optimo de factores. Vamos a hacerlo tanto en base a correlaciones policóricas (más adecuado), como en base a correlaciones pearson (más comun)
set.seed(124)
parallel <- capacidad |>
select(-id) |>
psych::fa.parallel(plot = F)## Parallel analysis suggests that the number of factors = 5 and the number of components = 4parallel |>
Ben::plot_parallel()
El análisis paralelo sugiere 5 factores, o 4 componentes.
Veamos si lo replicamos usando correlaciones policóricas.
set.seed(1252)
parallel_poly <-
capacidad |>
select(-id) |>
psych::fa.parallel(cor = 'poly',plot = F)## Parallel analysis suggests that the number of factors = 6 and the number of components = 4parallel_poly |>
Ben::plot_parallel()
El análisis paralelo, con correlaciones policóricas sugiere 6 factores, o 4 componentes.
Extracción
Un modelo de 5 factores parece acomodarse adecuadamente a los datos. Haga click en los tabs correspondientes.
# Settings
# Usar rotación promax
rotate = 'promax'
# Usar unweighted least squares
fm = 'uls'
# Usar correlaciones policóricas
cor = "poly" 5 factores
| Item | V1 | V2 | V3 | V4 | V5 |
|---|---|---|---|---|---|
| B1_1 | .78 | .03 | -.02 | -.03 | -.01 |
| B1_2 | .77 | -.01 | -.00 | .10 | .10 |
| B1_3 | .72 | .29 | -.17 | -.10 | .09 |
| B1_4 | .71 | .14 | .01 | -.13 | .15 |
| B1_5 | .62 | .22 | .02 | .09 | -.03 |
| B1_6 | .58 | .06 | -.02 | .09 | .20 |
| B1_7 | .53 | -.10 | .24 | .22 | -.08 |
| B1_8 | .51 | .17 | .19 | -.07 | -.28 |
| B1_9 | .35 | .07 | .24 | .02 | .02 |
| B1_10 | .06 | .93 | -.16 | -.12 | .12 |
| B1_11 | .13 | .84 | -.24 | .08 | .07 |
| B1_12 | .18 | .65 | .13 | -.00 | -.14 |
| B1_13 | .12 | .62 | .23 | -.13 | .07 |
| B1_14 | .29 | .53 | .02 | -.08 | .08 |
| B1_15 | .23 | .52 | .05 | .17 | -.17 |
| B1_16 | -.09 | .51 | .37 | .23 | -.17 |
| B1_17 | -.16 | .46 | .39 | .05 | .17 |
| B1_18 | -.01 | .07 | .76 | .05 | -.10 |
| B1_19 | -.03 | .18 | .64 | -.01 | .14 |
| B1_20 | .33 | -.05 | .64 | -.20 | .17 |
| B1_21 | .36 | -.23 | .57 | .02 | -.07 |
| B1_22 | .12 | -.16 | .56 | .22 | .16 |
| B1_23 | -.14 | .17 | .53 | .28 | .02 |
| B1_24 | .30 | -.09 | .41 | -.07 | .27 |
| B1_25 | .11 | .27 | .33 | .06 | .15 |
| B1_26 | .31 | -.27 | -.02 | .79 | .07 |
| B1_27 | -.23 | .12 | -.06 | .77 | .20 |
| B1_28 | .28 | -.12 | .05 | .65 | .03 |
| B1_29 | -.15 | .19 | .17 | .62 | .07 |
| B1_30 | -.15 | .14 | -.00 | .51 | .39 |
| B1_31 | .09 | .29 | .15 | .46 | -.15 |
| B1_32 | .03 | -.16 | .29 | .01 | .70 |
| B1_33 | .34 | -.00 | -.24 | .13 | .65 |
| B1_34 | -.22 | .37 | .29 | -.14 | .61 |
| B1_35 | -.09 | -.13 | .10 | .45 | .55 |
| B1_36 | .08 | .24 | -.19 | .27 | .52 |
| Summary statistics | |||||
| Eigenvalues | 15.7779849 | 3.022406 | 2.1464158 | 1.7207470 | 1.29406945 |
| Variance Explained | 0.1511684 | 0.140046 | 0.1241328 | 0.1088058 | 0.09049429 |
1 factor
| Item | V1 |
|---|---|
| B1_1 | .74 |
| B1_2 | .73 |
| B1_3 | .73 |
| B1_4 | .72 |
| B1_5 | .72 |
| B1_6 | .71 |
| B1_7 | .70 |
| B1_8 | .70 |
| B1_9 | .70 |
| B1_10 | .70 |
| B1_11 | .69 |
| B1_12 | .68 |
| B1_13 | .68 |
| B1_14 | .67 |
| B1_15 | .67 |
| B1_16 | .66 |
| B1_17 | .66 |
| B1_18 | .66 |
| B1_19 | .66 |
| B1_20 | .66 |
| B1_21 | .65 |
| B1_22 | .64 |
| B1_23 | .64 |
| B1_24 | .63 |
| B1_25 | .62 |
| B1_26 | .62 |
| B1_27 | .62 |
| B1_28 | .62 |
| B1_29 | .59 |
| B1_30 | .58 |
| B1_31 | .58 |
| B1_32 | .56 |
| B1_33 | .55 |
| B1_34 | .55 |
| B1_35 | .53 |
| B1_36 | .46 |
| Summary statistics | |
| Eigenvalues | 15.7779849 |
| Variance Explained | 0.4226471 |
2 factores
| Item | V1 | V2 |
|---|---|---|
| B1_1 | .88 | -.27 |
| B1_2 | .87 | -.20 |
| B1_3 | .85 | -.14 |
| B1_4 | .82 | -.32 |
| B1_5 | .82 | -.05 |
| B1_6 | .81 | -.04 |
| B1_7 | .64 | .06 |
| B1_8 | .61 | .12 |
| B1_9 | .61 | .12 |
| B1_10 | .59 | .17 |
| B1_11 | .59 | .12 |
| B1_12 | .57 | .15 |
| B1_13 | .54 | .26 |
| B1_14 | .53 | .04 |
| B1_15 | .53 | .07 |
| B1_16 | .44 | .28 |
| B1_17 | .44 | .25 |
| B1_18 | .41 | .26 |
| B1_19 | .36 | .36 |
| B1_20 | -.33 | .98 |
| B1_21 | -.29 | .94 |
| B1_22 | -.24 | .94 |
| B1_23 | -.07 | .83 |
| B1_24 | -.04 | .74 |
| B1_25 | -.07 | .71 |
| B1_26 | -.01 | .69 |
| B1_27 | .13 | .64 |
| B1_28 | .18 | .61 |
| B1_29 | .13 | .57 |
| B1_30 | .23 | .53 |
| B1_31 | .21 | .53 |
| B1_32 | .31 | .50 |
| B1_33 | .13 | .47 |
| B1_34 | .33 | .45 |
| B1_35 | .31 | .43 |
| B1_36 | .36 | .43 |
| Summary statistics | ||
| Eigenvalues | 15.7779849 | 3.0224061 |
| Variance Explained | 0.2539179 | 0.2417491 |
3 factores
| Item | V1 | V2 | V3 |
|---|---|---|---|
| B1_1 | .95 | -.11 | -.16 |
| B1_2 | .86 | -.28 | .10 |
| B1_3 | .82 | -.22 | .13 |
| B1_4 | .72 | .06 | -.11 |
| B1_5 | .70 | -.12 | .24 |
| B1_6 | .70 | .35 | -.30 |
| B1_7 | .58 | .34 | -.13 |
| B1_8 | .58 | -.04 | .17 |
| B1_9 | .56 | .31 | -.05 |
| B1_10 | .54 | -.22 | .43 |
| B1_11 | .50 | .22 | -.09 |
| B1_12 | .50 | .01 | .31 |
| B1_13 | .37 | .15 | .34 |
| B1_14 | .27 | .24 | .26 |
| B1_15 | -.18 | .86 | -.03 |
| B1_16 | .05 | .85 | -.07 |
| B1_17 | -.10 | .76 | .11 |
| B1_18 | -.20 | .70 | .22 |
| B1_19 | .15 | .69 | -.09 |
| B1_20 | -.05 | .68 | .20 |
| B1_21 | -.32 | .64 | .22 |
| B1_22 | .16 | .61 | .00 |
| B1_23 | .13 | .59 | -.11 |
| B1_24 | .18 | .54 | .09 |
| B1_25 | .08 | .46 | .11 |
| B1_26 | .30 | .44 | -.02 |
| B1_27 | -.10 | -.07 | .92 |
| B1_28 | -.01 | -.00 | .80 |
| B1_29 | -.13 | .19 | .73 |
| B1_30 | .00 | .14 | .72 |
| B1_31 | .21 | -.00 | .63 |
| B1_32 | .37 | -.11 | .58 |
| B1_33 | -.04 | .24 | .56 |
| B1_34 | -.06 | .27 | .55 |
| B1_35 | .30 | .19 | .36 |
| B1_36 | .31 | .15 | .34 |
| Summary statistics | |||
| Eigenvalues | 15.7779849 | 3.0224061 | 2.1464158 |
| Variance Explained | 0.1958804 | 0.1872679 | 0.1629063 |
4 factores
| Item | V1 | V2 | V3 | V4 |
|---|---|---|---|---|
| B1_1 | .81 | -.05 | -.06 | .01 |
| B1_2 | .80 | -.00 | .12 | -.02 |
| B1_3 | .76 | -.13 | .06 | .16 |
| B1_4 | .74 | -.22 | .01 | .27 |
| B1_5 | .63 | .09 | -.02 | .18 |
| B1_6 | .61 | -.04 | .23 | .05 |
| B1_7 | .56 | .34 | .00 | -.13 |
| B1_8 | .53 | .23 | -.36 | .14 |
| B1_9 | .44 | .35 | .04 | .03 |
| B1_10 | .39 | .21 | .01 | .07 |
| B1_11 | .38 | .20 | .21 | -.03 |
| B1_12 | .06 | .74 | -.09 | .07 |
| B1_13 | -.11 | .67 | .15 | .14 |
| B1_14 | -.11 | .62 | -.07 | .44 |
| B1_15 | -.19 | .55 | .38 | .12 |
| B1_16 | .19 | .55 | .26 | -.14 |
| B1_17 | .06 | .53 | .12 | .20 |
| B1_18 | .06 | .50 | .07 | .21 |
| B1_19 | .44 | .49 | -.10 | -.20 |
| B1_20 | .25 | .41 | .35 | -.17 |
| B1_21 | .15 | .31 | .17 | .28 |
| B1_22 | -.07 | .17 | .80 | -.12 |
| B1_23 | .37 | -.37 | .73 | .04 |
| B1_24 | .11 | .03 | .69 | -.05 |
| B1_25 | .07 | -.14 | .68 | .24 |
| B1_26 | -.18 | .22 | .67 | .11 |
| B1_27 | -.29 | .39 | .60 | .04 |
| B1_28 | -.13 | .05 | .53 | .41 |
| B1_29 | .26 | .39 | .45 | -.31 |
| B1_30 | .03 | -.12 | .06 | .89 |
| B1_31 | .08 | -.06 | .11 | .76 |
| B1_32 | .15 | .19 | -.01 | .61 |
| B1_33 | .16 | .26 | -.16 | .59 |
| B1_34 | .30 | .02 | .03 | .51 |
| B1_35 | -.12 | .39 | .20 | .45 |
| B1_36 | .20 | .28 | -.10 | .44 |
| Summary statistics | ||||
| Eigenvalues | 15.7779849 | 3.0224061 | 2.1464158 | 1.7207470 |
| Variance Explained | 0.1646409 | 0.1557498 | 0.1366923 | 0.1286027 |
6 factores
| Item | V1 | V2 | V3 | V4 | V5 | V6 |
|---|---|---|---|---|---|---|
| B1_1 | .99 | .08 | -.12 | .07 | -.25 | .00 |
| B1_2 | .90 | .14 | .08 | .03 | -.30 | -.04 |
| B1_3 | .65 | .20 | -.01 | -.12 | .18 | -.09 |
| B1_4 | .62 | -.15 | .28 | -.24 | .19 | .19 |
| B1_5 | .62 | .15 | -.16 | .09 | .22 | -.00 |
| B1_6 | .56 | .21 | .19 | -.18 | .04 | -.01 |
| B1_7 | .52 | .30 | -.09 | .09 | .05 | -.02 |
| B1_8 | .48 | -.14 | .02 | .19 | .28 | .09 |
| B1_9 | .34 | .06 | .08 | .10 | .16 | .25 |
| B1_10 | .03 | .69 | .02 | -.06 | -.02 | .16 |
| B1_11 | .10 | .68 | .03 | .09 | .29 | -.26 |
| B1_12 | -.01 | .68 | .14 | .07 | -.01 | .17 |
| B1_13 | .29 | .67 | -.06 | .05 | -.15 | .08 |
| B1_14 | .12 | .66 | -.12 | .13 | .04 | .11 |
| B1_15 | .07 | .51 | .11 | .18 | -.06 | .15 |
| B1_16 | .12 | .50 | -.05 | -.25 | .33 | -.07 |
| B1_17 | -.26 | .23 | .78 | .12 | -.00 | .06 |
| B1_18 | .12 | -.21 | .69 | .31 | -.03 | -.13 |
| B1_19 | -.08 | .20 | .66 | .05 | .01 | .11 |
| B1_20 | .27 | -.20 | .62 | .07 | .04 | .11 |
| B1_21 | .25 | .12 | .40 | -.03 | .31 | -.21 |
| B1_22 | -.21 | -.04 | .32 | .72 | .19 | -.11 |
| B1_23 | -.03 | .35 | .06 | .68 | -.22 | .02 |
| B1_24 | -.14 | .00 | -.03 | .68 | .11 | .28 |
| B1_25 | .37 | -.16 | -.23 | .63 | .18 | .09 |
| B1_26 | .08 | -.08 | .38 | .55 | .12 | -.22 |
| B1_27 | .27 | .08 | .22 | .53 | -.26 | .05 |
| B1_28 | .03 | -.02 | .01 | -.03 | .79 | .07 |
| B1_29 | -.29 | .32 | .01 | -.03 | .63 | .12 |
| B1_30 | .19 | -.15 | .25 | .06 | .45 | .10 |
| B1_31 | -.15 | .07 | .22 | .18 | .44 | .24 |
| B1_32 | .23 | -.07 | .01 | .09 | .44 | .31 |
| B1_33 | -.02 | .37 | -.04 | .12 | .41 | -.10 |
| B1_34 | .01 | .16 | -.00 | .12 | .08 | .60 |
| B1_35 | .01 | .22 | -.14 | .06 | .40 | .49 |
| B1_36 | -.01 | .38 | .37 | -.23 | -.00 | .46 |
| Summary statistics | ||||||
| Eigenvalues | 15.7779849 | 3.0224061 | 2.1464158 | 1.7207470 | 1.29406945 | 1.17384969 |
| Variance Explained | 0.1484773 | 0.1321321 | 0.1006723 | 0.1004661 | 0.09957405 | 0.05885717 |
Analisis Factorial Confirmatorio
De acuerdo a los análisis preliminares, podemos realizar un análisis factorial confirmatorio.
library(lavaan)
model <- c(paste("f1 =~ ",paste0("B1_",1:9, collapse = '+')),
paste("f2 =~ ",paste0("B1_",10:17, collapse = '+')),
paste("f3 =~ ",paste0("B1_",18:25, collapse = '+')),
paste("f4 =~ ",paste0("B1_",26:31, collapse = '+')),
paste("f5 =~ ",paste0("B1_",32:36, collapse = '+')))fit <- cfa(model,data = capacidad, ordered = T)
summary(fit, rsquare = T, fit.measures = T, standardized = T)## lavaan 0.6-11 ended normally after 65 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 190
##
## Number of observations 234
##
## Model Test User Model:
## Standard Robust
## Test Statistic 2948.412 2092.883
## Degrees of freedom 584 584
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.746
## Shift parameter 404.141
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 52315.775 8426.271
## Degrees of freedom 630 630
## P-value 0.000 0.000
## Scaling correction factor 6.630
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.954 0.806
## Tucker-Lewis Index (TLI) 0.951 0.791
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.132 0.105
## 90 Percent confidence interval - lower 0.127 0.100
## 90 Percent confidence interval - upper 0.137 0.110
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.105 0.105
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f1 =~
## B1_1 1.000 0.599 0.599
## B1_2 1.051 0.072 14.508 0.000 0.629 0.629
## B1_3 1.140 0.070 16.338 0.000 0.683 0.683
## B1_4 0.948 0.086 11.084 0.000 0.568 0.568
## B1_5 1.129 0.083 13.666 0.000 0.676 0.676
## B1_6 1.235 0.081 15.240 0.000 0.739 0.739
## B1_7 0.806 0.069 11.735 0.000 0.482 0.482
## B1_8 0.911 0.082 11.151 0.000 0.545 0.545
## B1_9 1.205 0.080 15.024 0.000 0.722 0.722
## f2 =~
## B1_10 1.000 0.727 0.727
## B1_11 0.853 0.053 16.108 0.000 0.620 0.620
## B1_12 0.747 0.061 12.214 0.000 0.543 0.543
## B1_13 0.994 0.041 23.968 0.000 0.723 0.723
## B1_14 0.855 0.045 19.034 0.000 0.622 0.622
## B1_15 0.913 0.051 18.048 0.000 0.664 0.664
## B1_16 0.947 0.038 25.109 0.000 0.688 0.688
## B1_17 0.916 0.052 17.598 0.000 0.666 0.666
## f3 =~
## B1_18 1.000 0.716 0.716
## B1_19 0.931 0.049 18.891 0.000 0.667 0.667
## B1_20 0.966 0.044 21.728 0.000 0.692 0.692
## B1_21 0.911 0.050 18.357 0.000 0.652 0.652
## B1_22 1.029 0.052 19.896 0.000 0.737 0.737
## B1_23 0.924 0.044 21.009 0.000 0.661 0.661
## B1_24 1.033 0.048 21.328 0.000 0.740 0.740
## B1_25 0.929 0.048 19.473 0.000 0.665 0.665
## f4 =~
## B1_26 1.000 0.717 0.717
## B1_27 1.023 0.047 21.710 0.000 0.734 0.734
## B1_28 0.981 0.050 19.440 0.000 0.704 0.704
## B1_29 0.921 0.053 17.402 0.000 0.661 0.661
## B1_30 0.862 0.053 16.416 0.000 0.619 0.619
## B1_31 0.956 0.045 21.266 0.000 0.686 0.686
## f5 =~
## B1_32 1.000 0.700 0.700
## B1_33 0.971 0.051 18.970 0.000 0.680 0.680
## B1_34 0.866 0.057 15.120 0.000 0.606 0.606
## B1_35 0.947 0.053 17.771 0.000 0.663 0.663
## B1_36 0.946 0.053 17.806 0.000 0.662 0.662
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## f1 ~~
## f2 0.425 0.036 11.745 0.000 0.977 0.977
## f3 0.429 0.034 12.665 0.000 1.001 1.001
## f4 0.430 0.035 12.157 0.000 1.002 1.002
## f5 0.406 0.035 11.612 0.000 0.969 0.969
## f2 ~~
## f3 0.533 0.034 15.773 0.000 1.024 1.024
## f4 0.541 0.034 15.883 0.000 1.038 1.038
## f5 0.532 0.034 15.632 0.000 1.045 1.045
## f3 ~~
## f4 0.539 0.033 16.259 0.000 1.049 1.049
## f5 0.502 0.034 14.954 0.000 1.001 1.001
## f4 ~~
## f5 0.496 0.033 14.906 0.000 0.987 0.987
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B1_1 0.000 0.000 0.000
## .B1_2 0.000 0.000 0.000
## .B1_3 0.000 0.000 0.000
## .B1_4 0.000 0.000 0.000
## .B1_5 0.000 0.000 0.000
## .B1_6 0.000 0.000 0.000
## .B1_7 0.000 0.000 0.000
## .B1_8 0.000 0.000 0.000
## .B1_9 0.000 0.000 0.000
## .B1_10 0.000 0.000 0.000
## .B1_11 0.000 0.000 0.000
## .B1_12 0.000 0.000 0.000
## .B1_13 0.000 0.000 0.000
## .B1_14 0.000 0.000 0.000
## .B1_15 0.000 0.000 0.000
## .B1_16 0.000 0.000 0.000
## .B1_17 0.000 0.000 0.000
## .B1_18 0.000 0.000 0.000
## .B1_19 0.000 0.000 0.000
## .B1_20 0.000 0.000 0.000
## .B1_21 0.000 0.000 0.000
## .B1_22 0.000 0.000 0.000
## .B1_23 0.000 0.000 0.000
## .B1_24 0.000 0.000 0.000
## .B1_25 0.000 0.000 0.000
## .B1_26 0.000 0.000 0.000
## .B1_27 0.000 0.000 0.000
## .B1_28 0.000 0.000 0.000
## .B1_29 0.000 0.000 0.000
## .B1_30 0.000 0.000 0.000
## .B1_31 0.000 0.000 0.000
## .B1_32 0.000 0.000 0.000
## .B1_33 0.000 0.000 0.000
## .B1_34 0.000 0.000 0.000
## .B1_35 0.000 0.000 0.000
## .B1_36 0.000 0.000 0.000
## f1 0.000 0.000 0.000
## f2 0.000 0.000 0.000
## f3 0.000 0.000 0.000
## f4 0.000 0.000 0.000
## f5 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## B1_1|t1 -1.882 0.164 -11.448 0.000 -1.882 -1.882
## B1_1|t2 -0.577 0.087 -6.609 0.000 -0.577 -0.577
## B1_1|t3 0.260 0.083 3.128 0.002 0.260 0.260
## B1_1|t4 1.397 0.119 11.738 0.000 1.397 1.397
## B1_2|t1 -2.026 0.185 -10.953 0.000 -2.026 -2.026
## B1_2|t2 -1.267 0.111 -11.396 0.000 -1.267 -1.267
## B1_2|t3 -0.615 0.088 -6.989 0.000 -0.615 -0.615
## B1_2|t4 0.839 0.094 8.968 0.000 0.839 0.839
## B1_3|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_3|t2 -1.221 0.109 -11.229 0.000 -1.221 -1.221
## B1_3|t3 -0.709 0.090 -7.868 0.000 -0.709 -0.709
## B1_3|t4 0.839 0.094 8.968 0.000 0.839 0.839
## B1_4|t1 -2.630 0.340 -7.734 0.000 -2.630 -2.630
## B1_4|t2 -2.118 0.200 -10.564 0.000 -2.118 -2.118
## B1_4|t3 -1.316 0.114 -11.548 0.000 -1.316 -1.316
## B1_4|t4 0.350 0.084 4.166 0.000 0.350 0.350
## B1_5|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_5|t2 -1.882 0.164 -11.448 0.000 -1.882 -1.882
## B1_5|t3 -0.709 0.090 -7.868 0.000 -0.709 -0.709
## B1_5|t4 0.641 0.089 7.242 0.000 0.641 0.641
## B1_6|t1 -2.118 0.200 -10.564 0.000 -2.118 -2.118
## B1_6|t2 -1.675 0.141 -11.856 0.000 -1.675 -1.675
## B1_6|t3 -0.419 0.085 -4.942 0.000 -0.419 -0.419
## B1_6|t4 0.917 0.096 9.557 0.000 0.917 0.917
## B1_7|t1 -1.823 0.157 -11.604 0.000 -1.823 -1.823
## B1_7|t2 -0.901 0.095 -9.441 0.000 -0.901 -0.901
## B1_7|t3 -0.161 0.082 -1.956 0.050 -0.161 -0.161
## B1_7|t4 1.156 0.105 10.954 0.000 1.156 1.156
## B1_8|t1 -1.949 0.173 -11.237 0.000 -1.949 -1.949
## B1_8|t2 -1.057 0.101 -10.448 0.000 -1.057 -1.057
## B1_8|t3 -0.282 0.083 -3.388 0.001 -0.282 -0.282
## B1_8|t4 1.156 0.105 10.954 0.000 1.156 1.156
## B1_9|t1 -2.118 0.200 -10.564 0.000 -2.118 -2.118
## B1_9|t2 -1.397 0.119 -11.738 0.000 -1.397 -1.397
## B1_9|t3 -0.564 0.087 -6.482 0.000 -0.564 -0.564
## B1_9|t4 0.917 0.096 9.557 0.000 0.917 0.917
## B1_10|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_10|t2 -1.291 0.113 -11.474 0.000 -1.291 -1.291
## B1_10|t3 -0.271 0.083 -3.258 0.001 -0.271 -0.271
## B1_10|t4 0.885 0.095 9.324 0.000 0.885 0.885
## B1_11|t1 -2.385 0.260 -9.186 0.000 -2.385 -2.385
## B1_11|t2 -1.769 0.151 -11.719 0.000 -1.769 -1.769
## B1_11|t3 -0.794 0.092 -8.606 0.000 -0.794 -0.794
## B1_11|t4 0.490 0.086 5.715 0.000 0.490 0.490
## B1_12|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_12|t2 -1.369 0.117 -11.681 0.000 -1.369 -1.369
## B1_12|t3 -0.736 0.091 -8.115 0.000 -0.736 -0.736
## B1_12|t4 0.654 0.089 7.368 0.000 0.654 0.654
## B1_13|t1 -1.823 0.157 -11.604 0.000 -1.823 -1.823
## B1_13|t2 -1.038 0.100 -10.341 0.000 -1.038 -1.038
## B1_13|t3 -0.021 0.082 -0.261 0.794 -0.021 -0.021
## B1_13|t4 0.950 0.097 9.787 0.000 0.950 0.950
## B1_14|t1 -1.949 0.173 -11.237 0.000 -1.949 -1.949
## B1_14|t2 -1.244 0.110 -11.314 0.000 -1.244 -1.244
## B1_14|t3 -0.293 0.083 -3.518 0.000 -0.293 -0.293
## B1_14|t4 0.839 0.094 8.968 0.000 0.839 0.839
## B1_15|t1 -2.385 0.260 -9.186 0.000 -2.385 -2.385
## B1_15|t2 -1.426 0.121 -11.789 0.000 -1.426 -1.426
## B1_15|t3 -0.808 0.093 -8.727 0.000 -0.808 -0.808
## B1_15|t4 0.854 0.094 9.087 0.000 0.854 0.854
## B1_16|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_16|t2 -1.002 0.099 -10.123 0.000 -1.002 -1.002
## B1_16|t3 -0.361 0.084 -4.296 0.000 -0.361 -0.361
## B1_16|t4 0.854 0.094 9.087 0.000 0.854 0.854
## B1_17|t1 -2.026 0.185 -10.953 0.000 -2.026 -2.026
## B1_17|t2 -1.291 0.113 -11.474 0.000 -1.291 -1.291
## B1_17|t3 -0.443 0.085 -5.200 0.000 -0.443 -0.443
## B1_17|t4 0.934 0.097 9.672 0.000 0.934 0.934
## B1_18|t1 -1.949 0.173 -11.237 0.000 -1.949 -1.949
## B1_18|t2 -1.316 0.114 -11.548 0.000 -1.316 -1.316
## B1_18|t3 -0.515 0.086 -5.971 0.000 -0.515 -0.515
## B1_18|t4 0.869 0.094 9.206 0.000 0.869 0.869
## B1_19|t1 -1.949 0.173 -11.237 0.000 -1.949 -1.949
## B1_19|t2 -1.076 0.102 -10.553 0.000 -1.076 -1.076
## B1_19|t3 -0.271 0.083 -3.258 0.001 -0.271 -0.271
## B1_19|t4 1.115 0.104 10.757 0.000 1.115 1.115
## B1_20|t1 -2.385 0.260 -9.186 0.000 -2.385 -2.385
## B1_20|t2 -1.342 0.116 -11.617 0.000 -1.342 -1.342
## B1_20|t3 -0.564 0.087 -6.482 0.000 -0.564 -0.564
## B1_20|t4 0.765 0.091 8.361 0.000 0.765 0.765
## B1_21|t1 -1.675 0.141 -11.856 0.000 -1.675 -1.675
## B1_21|t2 -0.823 0.093 -8.848 0.000 -0.823 -0.823
## B1_21|t3 -0.032 0.082 -0.391 0.696 -0.032 -0.032
## B1_21|t4 1.095 0.103 10.656 0.000 1.095 1.095
## B1_22|t1 -2.385 0.260 -9.186 0.000 -2.385 -2.385
## B1_22|t2 -1.556 0.131 -11.905 0.000 -1.556 -1.556
## B1_22|t3 -0.552 0.087 -6.354 0.000 -0.552 -0.552
## B1_22|t4 0.869 0.094 9.206 0.000 0.869 0.869
## B1_23|t1 -1.720 0.146 -11.801 0.000 -1.720 -1.720
## B1_23|t2 -1.135 0.105 -10.857 0.000 -1.135 -1.135
## B1_23|t3 -0.478 0.086 -5.586 0.000 -0.478 -0.478
## B1_23|t4 0.869 0.094 9.206 0.000 0.869 0.869
## B1_24|t1 -2.118 0.200 -10.564 0.000 -2.118 -2.118
## B1_24|t2 -1.076 0.102 -10.553 0.000 -1.076 -1.076
## B1_24|t3 -0.350 0.084 -4.166 0.000 -0.350 -0.350
## B1_24|t4 1.038 0.100 10.341 0.000 1.038 1.038
## B1_25|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_25|t2 -1.521 0.128 -11.891 0.000 -1.521 -1.521
## B1_25|t3 -0.350 0.084 -4.166 0.000 -0.350 -0.350
## B1_25|t4 1.002 0.099 10.123 0.000 1.002 1.002
## B1_26|t1 -1.882 0.164 -11.448 0.000 -1.882 -1.882
## B1_26|t2 -1.002 0.099 -10.123 0.000 -1.002 -1.002
## B1_26|t3 -0.183 0.083 -2.217 0.027 -0.183 -0.183
## B1_26|t4 0.901 0.095 9.441 0.000 0.901 0.901
## B1_27|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_27|t2 -1.593 0.134 -11.905 0.000 -1.593 -1.593
## B1_27|t3 -0.577 0.087 -6.609 0.000 -0.577 -0.577
## B1_27|t4 0.765 0.091 8.361 0.000 0.765 0.765
## B1_28|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_28|t2 -1.633 0.137 -11.889 0.000 -1.633 -1.633
## B1_28|t3 -0.654 0.089 -7.368 0.000 -0.654 -0.654
## B1_28|t4 0.839 0.094 8.968 0.000 0.839 0.839
## B1_29|t1 -1.675 0.141 -11.856 0.000 -1.675 -1.675
## B1_29|t2 -0.750 0.091 -8.239 0.000 -0.750 -0.750
## B1_29|t3 -0.129 0.082 -1.565 0.118 -0.129 -0.129
## B1_29|t4 1.038 0.100 10.341 0.000 1.038 1.038
## B1_30|t1 -2.118 0.200 -10.564 0.000 -2.118 -2.118
## B1_30|t2 -1.593 0.134 -11.905 0.000 -1.593 -1.593
## B1_30|t3 -0.602 0.088 -6.863 0.000 -0.602 -0.602
## B1_30|t4 0.539 0.087 6.227 0.000 0.539 0.539
## B1_31|t1 -2.118 0.200 -10.564 0.000 -2.118 -2.118
## B1_31|t2 -1.342 0.116 -11.617 0.000 -1.342 -1.342
## B1_31|t3 -0.205 0.083 -2.477 0.013 -0.205 -0.205
## B1_31|t4 0.823 0.093 8.848 0.000 0.823 0.823
## B1_32|t1 -1.823 0.157 -11.604 0.000 -1.823 -1.823
## B1_32|t2 -1.038 0.100 -10.341 0.000 -1.038 -1.038
## B1_32|t3 -0.293 0.083 -3.518 0.000 -0.293 -0.293
## B1_32|t4 1.076 0.102 10.553 0.000 1.076 1.076
## B1_33|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_33|t2 -1.633 0.137 -11.889 0.000 -1.633 -1.633
## B1_33|t3 -0.641 0.089 -7.242 0.000 -0.641 -0.641
## B1_33|t4 0.736 0.091 8.115 0.000 0.736 0.736
## B1_34|t1 -2.118 0.200 -10.564 0.000 -2.118 -2.118
## B1_34|t2 -1.521 0.128 -11.891 0.000 -1.521 -1.521
## B1_34|t3 -0.668 0.089 -7.493 0.000 -0.668 -0.668
## B1_34|t4 0.736 0.091 8.115 0.000 0.736 0.736
## B1_35|t1 -2.385 0.260 -9.186 0.000 -2.385 -2.385
## B1_35|t2 -1.556 0.131 -11.905 0.000 -1.556 -1.556
## B1_35|t3 -0.350 0.084 -4.166 0.000 -0.350 -0.350
## B1_35|t4 0.709 0.090 7.868 0.000 0.709 0.709
## B1_36|t1 -2.232 0.223 -10.015 0.000 -2.232 -2.232
## B1_36|t2 -1.633 0.137 -11.889 0.000 -1.633 -1.633
## B1_36|t3 -0.615 0.088 -6.989 0.000 -0.615 -0.615
## B1_36|t4 0.641 0.089 7.242 0.000 0.641 0.641
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B1_1 0.642 0.642 0.642
## .B1_2 0.604 0.604 0.604
## .B1_3 0.534 0.534 0.534
## .B1_4 0.678 0.678 0.678
## .B1_5 0.543 0.543 0.543
## .B1_6 0.453 0.453 0.453
## .B1_7 0.767 0.767 0.767
## .B1_8 0.702 0.702 0.702
## .B1_9 0.479 0.479 0.479
## .B1_10 0.472 0.472 0.472
## .B1_11 0.615 0.615 0.615
## .B1_12 0.705 0.705 0.705
## .B1_13 0.478 0.478 0.478
## .B1_14 0.614 0.614 0.614
## .B1_15 0.560 0.560 0.560
## .B1_16 0.527 0.527 0.527
## .B1_17 0.557 0.557 0.557
## .B1_18 0.487 0.487 0.487
## .B1_19 0.555 0.555 0.555
## .B1_20 0.521 0.521 0.521
## .B1_21 0.574 0.574 0.574
## .B1_22 0.457 0.457 0.457
## .B1_23 0.563 0.563 0.563
## .B1_24 0.453 0.453 0.453
## .B1_25 0.558 0.558 0.558
## .B1_26 0.486 0.486 0.486
## .B1_27 0.462 0.462 0.462
## .B1_28 0.505 0.505 0.505
## .B1_29 0.563 0.563 0.563
## .B1_30 0.617 0.617 0.617
## .B1_31 0.530 0.530 0.530
## .B1_32 0.510 0.510 0.510
## .B1_33 0.537 0.537 0.537
## .B1_34 0.632 0.632 0.632
## .B1_35 0.560 0.560 0.560
## .B1_36 0.562 0.562 0.562
## f1 0.358 0.046 7.844 0.000 1.000 1.000
## f2 0.528 0.043 12.202 0.000 1.000 1.000
## f3 0.513 0.040 12.719 0.000 1.000 1.000
## f4 0.514 0.042 12.289 0.000 1.000 1.000
## f5 0.490 0.044 11.084 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## B1_1 1.000 1.000 1.000
## B1_2 1.000 1.000 1.000
## B1_3 1.000 1.000 1.000
## B1_4 1.000 1.000 1.000
## B1_5 1.000 1.000 1.000
## B1_6 1.000 1.000 1.000
## B1_7 1.000 1.000 1.000
## B1_8 1.000 1.000 1.000
## B1_9 1.000 1.000 1.000
## B1_10 1.000 1.000 1.000
## B1_11 1.000 1.000 1.000
## B1_12 1.000 1.000 1.000
## B1_13 1.000 1.000 1.000
## B1_14 1.000 1.000 1.000
## B1_15 1.000 1.000 1.000
## B1_16 1.000 1.000 1.000
## B1_17 1.000 1.000 1.000
## B1_18 1.000 1.000 1.000
## B1_19 1.000 1.000 1.000
## B1_20 1.000 1.000 1.000
## B1_21 1.000 1.000 1.000
## B1_22 1.000 1.000 1.000
## B1_23 1.000 1.000 1.000
## B1_24 1.000 1.000 1.000
## B1_25 1.000 1.000 1.000
## B1_26 1.000 1.000 1.000
## B1_27 1.000 1.000 1.000
## B1_28 1.000 1.000 1.000
## B1_29 1.000 1.000 1.000
## B1_30 1.000 1.000 1.000
## B1_31 1.000 1.000 1.000
## B1_32 1.000 1.000 1.000
## B1_33 1.000 1.000 1.000
## B1_34 1.000 1.000 1.000
## B1_35 1.000 1.000 1.000
## B1_36 1.000 1.000 1.000
##
## R-Square:
## Estimate
## B1_1 0.358
## B1_2 0.396
## B1_3 0.466
## B1_4 0.322
## B1_5 0.457
## B1_6 0.547
## B1_7 0.233
## B1_8 0.298
## B1_9 0.521
## B1_10 0.528
## B1_11 0.385
## B1_12 0.295
## B1_13 0.522
## B1_14 0.386
## B1_15 0.440
## B1_16 0.473
## B1_17 0.443
## B1_18 0.513
## B1_19 0.445
## B1_20 0.479
## B1_21 0.426
## B1_22 0.543
## B1_23 0.437
## B1_24 0.547
## B1_25 0.442
## B1_26 0.514
## B1_27 0.538
## B1_28 0.495
## B1_29 0.437
## B1_30 0.383
## B1_31 0.470
## B1_32 0.490
## B1_33 0.463
## B1_34 0.368
## B1_35 0.440
## B1_36 0.438El modelo tiene un fit bastante adecuado. \(\chi^2\) = 2092.88 [df = 584,N = 234], p <.001***, CFI = .95, TLI = .95, RMSEA = .13 [95% CI = .13 - .14], SRMR = .10
Confiabilidad
get_alpha <- function(i){capacidad |>
select(model[i] |> str_sub(8) |> str_split("\\+",simplify = T) |> as.character()) |>
psych::alpha()}
alfas =
enframe(1:5) |>
select(1) |>
mutate(alpha = map(name, get_alpha)) |>
mutate(
result = map(alpha, "total"),
scale_alfa = map_dbl(result, "std.alpha"),
scale_mean = map_dbl(result, "mean"),
scale_sd = map_dbl(result, "sd"),
itemstats = map(alpha, "item.stats"),
scores = map(alpha, "scores")
) |>
unnest(itemstats) |>
select(1, scale_alfa:sd,scores)
alfas |>
ggplot(aes(name,raw.r))+
geom_col(aes(y = scale_alfa),data = alfas |> select(name, scale_alfa) |> unique(), alpha = .4)+
geom_point(position = position_jitter(width = .3))+
geom_hline(yintercept = .6)+
labs(title = "Confiabilidad",
y = "Correlación item total (puntos)\nAlfa (barras)",
x = "Factor")
capacidad <-
alfas |>
select(name,scores) |>
unique() |>
spread(name,scores) |>
unnest(everything()) |>
rename_all(function(x)paste0("cap",x)) |>
cbind(capacidad) |>
select(colnames(capacidad), cap1:cap5) |>
as_tibble()Correlaciones con otras variables
Validez y confiabilidad de las otras variables
PANAS
panas |>
select(-1) |>
psych::fa(nfactors = 2, rotate = 'varimax') |>
Ben::gt_fatable(sort = F)| Item | V1 | V2 |
|---|---|---|
| C1_1 | .62 | -.06 |
| C1_2 | -.12 | .60 |
| C1_3 | .70 | -.11 |
| C1_4 | -.13 | .64 |
| C1_5 | -.12 | .68 |
| C1_6 | .70 | -.03 |
| C1_7 | .62 | -.14 |
| C1_8 | -.09 | .74 |
| C1_9 | -.09 | .61 |
| C1_10 | .80 | -.11 |
| C1_11 | -.00 | .70 |
| C1_12 | .82 | -.05 |
| C1_13 | .72 | -.21 |
| C1_14 | .60 | -.21 |
| C1_15 | -.08 | .72 |
| C1_16 | .74 | -.22 |
| C1_17 | -.12 | .75 |
| C1_18 | -.16 | .60 |
| C1_19 | .56 | .07 |
| C1_20 | -.07 | .53 |
| Summary statistics | ||
| Eigenvalues | 6.6747799 | 3.8605972 |
| Variance Explained | 0.2468101 | 0.2295664 |
panas_keys <- panas |>
select(-1) |>
psych::fa(nfactors = 2, rotate = 'varimax') |>
Ben::fa_tibble(sort = F) |>
arrange(V2) |>
mutate(factor=ifelse(V1> .30, "f1",'f2')) |>
select(Item,factor)
panas |>
select(panas_keys |> filter(factor == "f1") |> pull(Item)) |>
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = select(panas, pull(filter(panas_keys, factor ==
## "f1"), Item)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.91 0.49 9.4 0.0092 3 0.83 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.9 0.92
## Duhachek 0.89 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## C1_16 0.89 0.89 0.89 0.47 8.1 0.0106 0.0100 0.48
## C1_14 0.90 0.90 0.90 0.50 8.8 0.0098 0.0101 0.50
## C1_13 0.89 0.89 0.89 0.48 8.2 0.0104 0.0094 0.48
## C1_7 0.90 0.90 0.90 0.50 8.8 0.0098 0.0093 0.50
## C1_10 0.89 0.89 0.89 0.47 7.9 0.0109 0.0088 0.46
## C1_3 0.89 0.89 0.89 0.48 8.4 0.0102 0.0095 0.48
## C1_1 0.90 0.90 0.90 0.50 8.9 0.0098 0.0084 0.50
## C1_12 0.89 0.89 0.89 0.47 7.9 0.0110 0.0089 0.46
## C1_6 0.89 0.89 0.89 0.48 8.5 0.0102 0.0108 0.48
## C1_19 0.90 0.90 0.90 0.51 9.4 0.0093 0.0064 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## C1_16 234 0.79 0.79 0.77 0.73 3.1 1.1
## C1_14 234 0.67 0.68 0.63 0.59 3.3 1.1
## C1_13 234 0.77 0.77 0.75 0.70 3.3 1.1
## C1_7 234 0.69 0.68 0.63 0.60 2.8 1.2
## C1_10 234 0.82 0.82 0.81 0.77 3.0 1.2
## C1_3 234 0.74 0.74 0.71 0.67 3.0 1.1
## C1_1 234 0.68 0.67 0.63 0.59 3.1 1.1
## C1_12 234 0.83 0.82 0.81 0.77 2.9 1.2
## C1_6 234 0.73 0.74 0.70 0.66 2.9 1.0
## C1_19 234 0.60 0.60 0.54 0.50 3.1 1.1
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## C1_16 0.08 0.19 0.35 0.28 0.10 0
## C1_14 0.07 0.11 0.36 0.34 0.12 0
## C1_13 0.07 0.16 0.32 0.30 0.14 0
## C1_7 0.18 0.22 0.27 0.23 0.09 0
## C1_10 0.15 0.17 0.28 0.31 0.09 0
## C1_3 0.14 0.14 0.35 0.30 0.07 0
## C1_1 0.12 0.16 0.35 0.30 0.08 0
## C1_12 0.17 0.20 0.31 0.24 0.09 0
## C1_6 0.10 0.26 0.35 0.23 0.05 0
## C1_19 0.07 0.22 0.37 0.25 0.09 0panas |>
select(panas_keys |> filter(factor == "f2") |> pull(Item)) |>
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = select(panas, pull(filter(panas_keys, factor ==
## "f2"), Item)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.91 0.44 8 0.011 2.6 0.87 0.43
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.87 0.89 0.91
## Duhachek 0.87 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## C1_20 0.88 0.89 0.90 0.46 7.7 0.011 0.013 0.43
## C1_18 0.88 0.88 0.90 0.45 7.4 0.012 0.017 0.43
## C1_2 0.88 0.88 0.89 0.45 7.4 0.012 0.015 0.43
## C1_9 0.88 0.88 0.90 0.45 7.4 0.012 0.016 0.43
## C1_4 0.88 0.88 0.89 0.45 7.2 0.012 0.016 0.43
## C1_5 0.88 0.88 0.89 0.44 7.0 0.012 0.016 0.41
## C1_11 0.88 0.88 0.89 0.44 7.1 0.012 0.015 0.43
## C1_15 0.87 0.88 0.89 0.44 7.0 0.012 0.011 0.43
## C1_8 0.87 0.87 0.89 0.43 6.9 0.013 0.013 0.43
## C1_17 0.87 0.87 0.89 0.43 6.8 0.013 0.012 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## C1_20 234 0.60 0.62 0.56 0.51 1.9 1.1
## C1_18 234 0.69 0.68 0.62 0.59 2.5 1.4
## C1_2 234 0.67 0.68 0.64 0.58 2.8 1.1
## C1_9 234 0.68 0.67 0.62 0.59 2.2 1.2
## C1_4 234 0.70 0.70 0.66 0.61 3.1 1.2
## C1_5 234 0.73 0.74 0.71 0.66 2.5 1.2
## C1_11 234 0.72 0.72 0.68 0.65 3.1 1.2
## C1_15 234 0.74 0.74 0.72 0.66 2.5 1.2
## C1_8 234 0.76 0.76 0.74 0.69 2.7 1.3
## C1_17 234 0.78 0.77 0.76 0.71 2.4 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## C1_20 0.53 0.20 0.17 0.07 0.03 0
## C1_18 0.36 0.21 0.14 0.18 0.12 0
## C1_2 0.14 0.28 0.25 0.27 0.05 0
## C1_9 0.37 0.26 0.16 0.16 0.05 0
## C1_4 0.11 0.20 0.26 0.29 0.14 0
## C1_5 0.24 0.27 0.25 0.18 0.06 0
## C1_11 0.12 0.19 0.28 0.27 0.15 0
## C1_15 0.24 0.33 0.18 0.19 0.06 0
## C1_8 0.23 0.26 0.21 0.22 0.08 0
## C1_17 0.30 0.29 0.15 0.19 0.06 0panas <- panas |>
Ben::create_composite(selection = c(C1_16, C1_14, C1_13,C1_7,C1_10,C1_3,C1_1,C1_12,C1_6,C1_19), name = positive) |>
Ben::create_composite(selection = c(C1_20, C1_18, C1_2,C1_9,C1_4,C1_5,C1_11,C1_15,C1_8,C1_17), name = negative)Familia
familia |> select(-id) |> psych::fa.parallel(plot= F, cor = 'poly')## Parallel analysis suggests that the number of factors = 4 and the number of components = 1familia |> select(-id) |> psych::fa()## Factor Analysis using method = minres
## Call: psych::fa(r = select(familia, -id))
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## E1 -0.02 0.0006 1.00 1
## E2 -0.69 0.4817 0.52 1
## E3 -0.72 0.5204 0.48 1
## E4 0.45 0.2005 0.80 1
## E5 0.66 0.4368 0.56 1
## E6 0.59 0.3503 0.65 1
## E7 0.67 0.4513 0.55 1
## E8 -0.60 0.3641 0.64 1
## E9 0.81 0.6564 0.34 1
## E10 -0.64 0.4062 0.59 1
## E11 0.64 0.4099 0.59 1
## E12 -0.50 0.2495 0.75 1
##
## MR1
## SS loadings 4.53
## Proportion Var 0.38
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 66 and the objective function was 4.39 with Chi Square of 1001.78
## The degrees of freedom for the model are 54 and the objective function was 0.58
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 234 with the empirical chi square 122.32 with prob < 3.3e-07
## The total number of observations was 234 with Likelihood Chi Square = 131.06 with prob < 2.4e-08
##
## Tucker Lewis Index of factoring reliability = 0.899
## RMSEA index = 0.078 and the 90 % confidence intervals are 0.061 0.095
## BIC = -163.53
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.95
## Multiple R square of scores with factors 0.90
## Minimum correlation of possible factor scores 0.79familia |> mutate_at(c(2,3,4,9,11,13),function(x)8-x) |> select(-1) |> psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = select(mutate_at(familia, c(2, 3, 4, 9, 11,
## 13), function(x) 8 - x), -1))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.87 0.34 6.1 0.013 4.2 1.1 0.38
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.86 0.89
## Duhachek 0.84 0.86 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## E1 0.88 0.88 0.88 0.40 7.4 0.012 0.010 0.40
## E2 0.84 0.84 0.85 0.32 5.2 0.015 0.035 0.36
## E3 0.84 0.84 0.85 0.32 5.2 0.015 0.033 0.36
## E4 0.86 0.86 0.87 0.35 6.0 0.013 0.032 0.40
## E5 0.84 0.84 0.86 0.33 5.3 0.014 0.034 0.37
## E6 0.85 0.85 0.86 0.34 5.6 0.014 0.033 0.38
## E7 0.84 0.84 0.86 0.33 5.3 0.014 0.033 0.36
## E8 0.85 0.85 0.86 0.33 5.5 0.014 0.035 0.37
## E9 0.83 0.83 0.85 0.31 5.0 0.015 0.030 0.36
## E10 0.85 0.84 0.86 0.33 5.4 0.014 0.033 0.38
## E11 0.85 0.84 0.86 0.33 5.4 0.014 0.033 0.38
## E12 0.86 0.85 0.87 0.35 5.8 0.013 0.034 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## E1 234 0.12 0.14 0.029 0.0076 5.3 1.5
## E2 234 0.73 0.74 0.716 0.6629 4.7 1.7
## E3 234 0.75 0.75 0.730 0.6783 4.4 1.8
## E4 234 0.51 0.50 0.433 0.3978 4.6 1.9
## E5 234 0.70 0.70 0.667 0.6228 3.9 1.7
## E6 234 0.63 0.63 0.585 0.5360 3.3 1.8
## E7 234 0.70 0.70 0.668 0.6200 3.7 1.9
## E8 234 0.66 0.65 0.610 0.5632 4.4 2.0
## E9 234 0.81 0.80 0.803 0.7499 4.2 1.8
## E10 234 0.66 0.67 0.637 0.5875 4.1 1.4
## E11 234 0.67 0.67 0.634 0.5882 4.0 1.8
## E12 234 0.56 0.56 0.494 0.4570 3.7 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## E1 0.02 0.04 0.06 0.17 0.21 0.24 0.27 0
## E2 0.06 0.04 0.10 0.19 0.24 0.21 0.15 0
## E3 0.09 0.07 0.15 0.19 0.21 0.15 0.15 0
## E4 0.07 0.12 0.10 0.15 0.18 0.21 0.18 0
## E5 0.14 0.10 0.16 0.17 0.26 0.13 0.04 0
## E6 0.24 0.14 0.13 0.22 0.16 0.07 0.05 0
## E7 0.23 0.05 0.15 0.16 0.23 0.12 0.06 0
## E8 0.10 0.09 0.15 0.17 0.12 0.17 0.20 0
## E9 0.11 0.09 0.15 0.20 0.20 0.12 0.14 0
## E10 0.06 0.06 0.18 0.31 0.21 0.14 0.04 0
## E11 0.11 0.12 0.19 0.18 0.21 0.09 0.11 0
## E12 0.13 0.15 0.26 0.16 0.10 0.12 0.09 0familia <-
familia |>
#invert items
mutate_at(c(2,3,4,9,11,13),function(x)8-x) |>
Ben::create_composite(E1:E12,name = familia)Flourishing
flour |> select(-1) |> psych::fa.parallel(plot = F, cor = 'poly')## Parallel analysis suggests that the number of factors = 1 and the number of components = 1flour |> select(-1) |> psych::fa()## Factor Analysis using method = minres
## Call: psych::fa(r = select(flour, -1))
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## D1_1 0.78 0.60 0.40 1
## D1_2 0.69 0.48 0.52 1
## D1_3 0.78 0.60 0.40 1
## D1_4 0.61 0.37 0.63 1
## D1_5 0.72 0.51 0.49 1
## D1_6 0.81 0.65 0.35 1
## D1_7 0.74 0.55 0.45 1
## D1_8 0.65 0.42 0.58 1
##
## MR1
## SS loadings 4.20
## Proportion Var 0.52
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 28 and the objective function was 4.12 with Chi Square of 946.62
## The degrees of freedom for the model are 20 and the objective function was 0.37
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.06
##
## The harmonic number of observations is 234 with the empirical chi square 38.24 with prob < 0.0083
## The total number of observations was 234 with Likelihood Chi Square = 84.62 with prob < 6.4e-10
##
## Tucker Lewis Index of factoring reliability = 0.901
## RMSEA index = 0.117 and the 90 % confidence intervals are 0.093 0.144
## BIC = -24.49
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.95
## Multiple R square of scores with factors 0.90
## Minimum correlation of possible factor scores 0.81flour |> select(-1) |> psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = select(flour, -1))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.9 0.52 8.7 0.01 4.9 1.1 0.53
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.87 0.9 0.92
## Duhachek 0.88 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## D1_1 0.88 0.88 0.87 0.51 7.3 0.012 0.0052 0.52
## D1_2 0.88 0.89 0.88 0.53 7.7 0.011 0.0081 0.53
## D1_3 0.88 0.88 0.87 0.51 7.3 0.012 0.0051 0.52
## D1_4 0.89 0.89 0.89 0.54 8.3 0.011 0.0061 0.55
## D1_5 0.88 0.88 0.88 0.52 7.6 0.011 0.0074 0.54
## D1_6 0.88 0.88 0.87 0.50 7.1 0.012 0.0067 0.49
## D1_7 0.88 0.88 0.88 0.52 7.5 0.012 0.0067 0.52
## D1_8 0.89 0.89 0.88 0.53 8.0 0.011 0.0072 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## D1_1 234 0.81 0.80 0.77 0.73 4.6 1.7
## D1_2 234 0.74 0.74 0.69 0.66 4.8 1.5
## D1_3 234 0.81 0.80 0.78 0.73 4.6 1.5
## D1_4 234 0.67 0.69 0.62 0.58 4.9 1.4
## D1_5 234 0.74 0.76 0.71 0.67 5.3 1.3
## D1_6 234 0.83 0.82 0.80 0.76 5.0 1.5
## D1_7 234 0.78 0.77 0.74 0.70 4.8 1.7
## D1_8 234 0.70 0.71 0.66 0.61 5.2 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## D1_1 0.06 0.11 0.09 0.13 0.25 0.25 0.11 0
## D1_2 0.03 0.07 0.08 0.14 0.28 0.32 0.08 0
## D1_3 0.03 0.07 0.13 0.16 0.29 0.22 0.09 0
## D1_4 0.03 0.03 0.07 0.18 0.31 0.31 0.07 0
## D1_5 0.01 0.03 0.06 0.10 0.27 0.39 0.14 0
## D1_6 0.03 0.06 0.06 0.17 0.19 0.38 0.12 0
## D1_7 0.04 0.11 0.06 0.14 0.23 0.31 0.11 0
## D1_8 0.01 0.04 0.02 0.17 0.25 0.41 0.09 0flour <- flour |> Ben::create_composite(D1_1:D1_8,name = flour)Merge
base_limpia <- left_join(demo,capacidad) |>
left_join(panas) |>
left_join(flour) |>
left_join(familia)
base_totales <- left_join(demo,capacidad |> select(id,cap1:cap5)) |>
left_join(panas |> select(id, positive, negative)) |>
left_join(flour |> select(id, flour)) |>
left_join(familia |> select(id,familia))Evidencias de validez convergente y discriminante
Entre las variables
base_totales |>
select(-id,-A3) |>
fastDummies::dummy_cols(remove_selected_columns = T,remove_first_dummy = F) |>
select(cap1:familia) |>
Ben::harcor() |>
gt::gt()| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|
| 1. cap1 | |||||||||
| 2. cap2 | .77*** | ||||||||
| 3. cap3 | .83*** | .81*** | |||||||
| 4. cap4 | .80*** | .80*** | .81*** | ||||||
| 5. cap5 | .72*** | .79*** | .76*** | .73*** | |||||
| 6. positive | .51*** | .50*** | .55*** | .44*** | .47*** | ||||
| 7. negative | -.43*** | -.39*** | -.44*** | -.42*** | -.39*** | -.27*** | |||
| 8. flour | .55*** | .46*** | .56*** | .46*** | .48*** | .36*** | -.42*** | ||
| 9. familia | .11† | .10 | .16* | .15* | .09 | .09 | -.15* | .20** | |
| M | 4.42 | 4.44 | 4.33 | 4.38 | 4.52 | 2.86 | 2.44 | 3.44 | 3.52 |
| SD | 0.78 | 0.84 | 0.91 | 0.92 | 0.88 | 0.75 | 0.79 | 0.77 | 0.81 |
| n | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 | 234 |
Entre las capacidades y los demograficos
base_totales |>
select(-id,-A3) |>
fastDummies::dummy_cols(remove_selected_columns = T,remove_first_dummy = F) |>
select(-flour,-positive,-negative,-familia) |>
select(cap1:cap5,A6,everything()) |>
Ben::harcor() |>
slice(6:77) |>
select(1:6) |>
gt::gt()| 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|
| 6. A6 | .00 | .09 | .07 | .00 | .05 |
| 7. A1_No | .00 | .00 | -.01 | -.08 | .01 |
| 8. A1_Sí | .00 | .00 | .01 | .08 | -.01 |
| 9. A2_Facultad de Arquitectura y Urbanismo | -.09 | -.15* | -.10 | -.13* | -.10 |
| 10. A2_Facultad de Arte y Diseño | .05 | .06 | .05 | -.01 | .02 |
| 11. A2_Facultad de Artes Escénicas | .02 | .06 | .02 | .09 | .08 |
| 12. A2_Facultad de Ciencias Contables | .07 | .14* | .08 | .11† | .09 |
| 13. A2_Facultad de Ciencias e Ingeniería | -.05 | -.05 | -.02 | -.07 | -.05 |
| 14. A2_Facultad de Ciencias Sociales | .05 | -.03 | .02 | .03 | .00 |
| 15. A2_Facultad de Ciencias y Artes de la Comunicación | .06 | .10 | .10 | .07 | .07 |
| 16. A2_Facultad de Derecho | -.05 | -.04 | .00 | .03 | -.03 |
| 17. A2_Facultad de Educación | -.01 | .09 | .01 | .05 | .07 |
| 18. A2_Facultad de Estudios Interdisciplinarios | .09 | .05 | .05 | .03 | .07 |
| 19. A2_Facultad de Gestión y Alta Dirección | .01 | .03 | -.04 | .02 | -.03 |
| 20. A2_Facultad de Letras y Ciencias Humanas | -.05 | -.06 | -.16* | -.08 | -.04 |
| 21. A2_Facultad de Psicología | .00 | .00 | .03 | -.01 | .04 |
| 22. A4_Cuarto | -.01 | -.04 | -.02 | -.03 | .00 |
| 23. A4_Décimo | .11† | .14* | .12† | .14* | .10 |
| 24. A4_Noveno | -.08 | -.02 | -.06 | -.05 | -.02 |
| 25. A4_Octavo | .09 | .15* | .12† | .16* | .16* |
| 26. A4_Quinto | -.03 | -.09 | -.06 | -.02 | -.07 |
| 27. A4_Segundo | .00 | -.01 | .02 | -.05 | -.01 |
| 28. A4_Séptimo | .02 | .03 | -.01 | -.02 | -.02 |
| 29. A4_Sexto | -.06 | -.07 | -.04 | -.04 | -.04 |
| 30. A4_Tercer | -.01 | -.06 | -.03 | -.05 | -.07 |
| 31. A5_Femenino | .04 | .06 | .03 | .03 | .13* |
| 32. A5_Masculino | -.02 | -.03 | .01 | -.01 | -.10 |
| 33. A5_No binario | .01 | -.03 | -.05 | -.04 | -.02 |
| 34. A5_Prefiero no decir | -.09 | -.11† | -.09 | -.07 | -.09 |
| 35. A7_No | -.14* | -.20** | -.16* | -.17** | -.11 |
| 36. A7_Sí | .14* | .20** | .16* | .17** | .11 |
| 37. A8_No | -.15* | -.20** | -.20** | -.15* | -.15* |
| 38. A8_Sí | .15* | .20** | .20** | .15* | .15* |
| 39. A9_19 | -.02 | -.01 | -.11 | -.17 | -.03 |
| 40. A9_2006 | .02 | -.05 | .01 | -.11 | -.06 |
| 41. A9_2007 | .27† | .22 | .26† | .23 | .20 |
| 42. A9_2010 | .05 | -.25† | -.11 | -.06 | -.29† |
| 43. A9_2015 | .00 | .20 | .14 | .15 | .04 |
| 44. A9_2016 | -.11 | -.20 | -.16 | -.23 | -.11 |
| 45. A9_2017 | .05 | .09 | .07 | .02 | .03 |
| 46. A9_2019 | -.10 | -.13 | -.04 | -.04 | -.01 |
| 47. A9_2020 | -.18 | -.15 | -.13 | -.12 | -.11 |
| 48. A9_2021 | .17 | .27† | .26† | .23 | .30* |
| 49. A9_2022 | -.01 | -.09 | -.13 | -.01 | -.06 |
| 50. A9_2022-1 | -.09 | .04 | -.14 | -.03 | -.26† |
| 51. A9_Primero | -.06 | -.03 | .01 | -.09 | .04 |
| 52. A9_NA | -.15* | -.20** | -.20** | -.15* | -.15* |
| 53. A10_Por horas | -.24 | -.12 | -.27† | -.18 | -.14 |
| 54. A10_Tiempo completo (40hr) | .30* | .15 | .30* | .19 | .10 |
| 55. A10_Tiempo parcial (20hr) | -.01 | -.01 | .02 | .03 | .05 |
| 56. A10_NA | -.15* | -.20** | -.20** | -.15* | -.15* |
| 57. A11_4 | -.62** | -.42† | -.15 | -.28 | -.15 |
| 58. A11_6 | .43† | .51* | .71** | .43† | .52* |
| 59. A11_8 | .07 | .04 | -.15 | -.28 | .00 |
| 60. A11_8h | .17 | .04 | -.21 | .25 | .07 |
| 61. A11_10 | .07 | -.42† | -.09 | -.39 | -.38 |
| 62. A11_10-24 | -.09 | .16 | -.21 | .02 | -.52* |
| 63. A11_12 | -.15 | -.07 | -.21 | -.04 | .00 |
| 64. A11_15 | .02 | .02 | .05 | -.06 | .26 |
| 65. A11_16 | .49* | -.07 | .03 | .31 | .00 |
| 66. A11_20 | -.20 | -.19 | -.21 | -.10 | .14 |
| 67. A11_30 | -.06 | .26 | .38 | .33 | .03 |
| 68. A11_36 | -.04 | .04 | .03 | -.28 | .07 |
| 69. A11_55 | -.04 | -.13 | -.27 | -.16 | -.15 |
| 70. A11_NA | .00 | -.07 | -.03 | -.02 | -.03 |
| M | 4.42 | 4.44 | 4.33 | 4.38 | 4.52 |
| SD | 0.78 | 0.84 | 0.91 | 0.92 | 0.88 |
| n | 234 | 234 | 234 | 234 | 234 |