yazmin <-
read_csv("data/yazmin.csv")
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────
cols(
.default = col_double(),
genero = col_character()
)
ℹ Use `spec()` for the full column specifications.mirar el modelo
YazminModel <-
"c0 =~ i1 + i2 +i3 + i4 + i5 + i6 + i7 + i8 + i9 + i10 + i11
c1 =~ i12 + i13 +i14 + i15 + i16 + i17 + i18 + i19 + i20 + i21 + i22
c2 =~ i23 + i24 + i25 + i26 + i27 + i28 + i29 + i30 + i31 + i32 + i33
c3 =~ i34 + i35 +i36 + i37 + i38 + i39 + i40 + i41 + i42 + i43 + i44
rend_ac =~ i45 + i46 + i47 + i48
rend_ac =~ c0 + c1 + c2 + c3
"Necesitamos confirmar si las variables manifiestas construyen las variables latentes.
Por medio del Cronbach Alpha.
RcmdrMisc::reliability(cov(elvia %>%
select(i1, i2, i3, i4, i5, i6, i7, i8 , i9, i10, i11),
use ="complete.obs"))
Alpha reliability = 0.8555
Standardized alpha = 0.856
Reliability deleting each item in turn:
Alpha Std.Alpha r(item, total)
i1 0.8402 0.8413 0.5848
i2 0.8419 0.8429 0.5626
i3 0.8401 0.8411 0.5845
i4 0.8422 0.8430 0.5601
i5 0.8427 0.8428 0.5550
i6 0.8422 0.8423 0.5584
i7 0.8433 0.8434 0.5452
i8 0.8420 0.8421 0.5606
i9 0.8465 0.8471 0.5005
i10 0.8451 0.8455 0.5194
i11 0.8503 0.8503 0.4548Mayor a 0.7 las variables manfiestas para acomodador son confiables.
RcmdrMisc::reliability(cov(yazmin %>%
select(i12 , i13 ,i14 , i15 , i16 , i17 , i18 , i19 , i20 , i21 , i22),
use ="complete.obs"))
Alpha reliability = 0.9393
Standardized alpha = 0.9398
Reliability deleting each item in turn:
Alpha Std.Alpha r(item, total)
i12 0.9335 0.9340 0.7434
i13 0.9327 0.9332 0.7643
i14 0.9338 0.9342 0.7370
i15 0.9337 0.9341 0.7398
i16 0.9338 0.9344 0.7347
i17 0.9342 0.9347 0.7250
i18 0.9334 0.9340 0.7431
i19 0.9330 0.9335 0.7551
i20 0.9332 0.9338 0.7493
i21 0.9345 0.9351 0.7174
i22 0.9339 0.9344 0.7329RcmdrMisc::reliability(cov(yazmin %>%
select(i23 , i24 , i25 , i26 , i27 , i28 , i29 , i30 , i31 , i32 , i33),
use ="complete.obs"))
Alpha reliability = 0.9355
Standardized alpha = 0.9361
Reliability deleting each item in turn:
Alpha Std.Alpha r(item, total)
i23 0.9303 0.9309 0.7084
i24 0.9307 0.9313 0.6999
i25 0.9291 0.9297 0.7393
i26 0.9296 0.9303 0.7250
i27 0.9284 0.9291 0.7523
i28 0.9295 0.9296 0.7399
i29 0.9306 0.9313 0.7002
i30 0.9288 0.9295 0.7445
i31 0.9290 0.9296 0.7425
i32 0.9284 0.9290 0.7542
i33 0.9302 0.9309 0.7104RcmdrMisc::reliability(cov(yazmin %>%
select(i34 , i35 ,i36 , i37 , i38 , i39 , i40 , i41 , i42 , i43 , i44),
use ="complete.obs"))
Alpha reliability = 0.9396
Standardized alpha = 0.9399
Reliability deleting each item in turn:
Alpha Std.Alpha r(item, total)
i34 0.9347 0.9350 0.7233
i35 0.9338 0.9339 0.7486
i36 0.9334 0.9337 0.7549
i37 0.9339 0.9342 0.7430
i38 0.9328 0.9332 0.7677
i39 0.9345 0.9347 0.7310
i40 0.9338 0.9341 0.7450
i41 0.9353 0.9356 0.7080
i42 0.9337 0.9340 0.7477
i43 0.9338 0.9341 0.7441
i44 0.9343 0.9346 0.7333RcmdrMisc::reliability(cov(yazmin %>%
select(i45 , i46 , i47 , i48),
use ="complete.obs"))
Alpha reliability = 0.7266
Standardized alpha = 0.7282
Reliability deleting each item in turn:
Alpha Std.Alpha r(item, total)
i45 0.6715 0.6744 0.5059
i46 0.6463 0.6499 0.5479
i47 0.6693 0.6705 0.5134
i48 0.6748 0.6757 0.5040yazmin_tidyed <-
yazmin %>%
rowwise() %>%
mutate(consistencia_1 = mean(c(i1, i2, i3, i4, i5, i6, i7, i8 , i9, i10, i11)),
consistencia_2 = mean(c(i12 , i13 ,i14 , i15 , i16 , i17 , i18 , i19 , i20 , i21 , i22)),
consistencia_3 = mean(c(i23 , i24 , i25 , i26 , i27 , i28 , i29 , i30 , i31 , i32 , i33)),
consistencia_4 = mean(c(i34 , i35 ,i36 , i37 , i38 , i39 , i40 , i41 , i42 , i43 , i44)),
desemp_acade = mean(c(i45 , i46 , i47 , i48))) %>%
select(desemp_acade, consistencia_1, consistencia_2, consistencia_3, consistencia_4) %>%
mutate(consistencia_1 = round(consistencia_1, 2),
consistencia_2 = round(consistencia_2, 2),
consistencia_3 = round(consistencia_3, 2),
consistencia_4 = round(consistencia_4, 2),
desemp_acade = round(desemp_acade, 2)) %>%
mutate(desemp_acade = ifelse(yazmin_tidyed >= 3, 1, 2))
Error: Problem with `mutate()` input `desemp_acade`.
x Input `desemp_acade` can't be recycled to size 1.
ℹ Input `desemp_acade` is `ifelse(yazmin_tidyed >= 3, 1, 2)`.
ℹ Input `desemp_acade` must be size 1, not 600.
ℹ Did you mean: `desemp_acade = list(ifelse(yazmin_tidyed >= 3, 1, 2))` ?
ℹ The error occurred in row 1.
Run `rlang::last_error()` to see where the error occurred.summary(yazmin_GLM)
Call:
glm(formula = desemp_acade ~ consistencia_1 + consistencia_2 +
consistencia_3 + consistencia_4, family = "binomial", data = yazmin_tidyed)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5147 -0.5931 0.2523 0.6820 2.1164
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.7987 1.0142 -11.633 < 2e-16 ***
consistencia_1 1.0729 0.2126 5.047 4.48e-07 ***
consistencia_2 1.1639 0.2180 5.338 9.40e-08 ***
consistencia_3 1.0335 0.2222 4.652 3.29e-06 ***
consistencia_4 0.9973 0.2226 4.480 7.46e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 801.60 on 599 degrees of freedom
Residual deviance: 508.15 on 595 degrees of freedom
AIC: 518.15
Number of Fisher Scoring iterations: 5anova(yazmin_GLM, test = "Chisq")
Analysis of Deviance Table
Model: binomial, link: logit
Response: desemp_acade
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 599 801.60
consistencia_1 1 161.756 598 639.84 < 2.2e-16 ***
consistencia_2 1 76.460 597 563.38 < 2.2e-16 ***
consistencia_3 1 33.309 596 530.07 7.862e-09 ***
consistencia_4 1 21.921 595 508.15 2.841e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1