juan_Analysis
Sebastian Robledo
3/9/2022
ArtÃculo 3 - LM
Tabla
Estimate | Standard Error | t value | Pr(>|t|) | ||
(Intercept) | 0.783 | 0.102 | 7.687 | 0.0000 | *** |
EA | 0.413 | 0.025 | 16.816 | 0.0000 | *** |
BL | 0.406 | 0.024 | 16.619 | 0.0000 | *** |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
Residual standard error: 0.5258 on 997 degrees of freedom | |||||
Multiple R-squared: 0.3638, Adjusted R-squared: 0.3625 | |||||
F-statistic: 285 on 997 and 2 DF, p-value: 0.0000 | |||||
Análisis
## We fitted a linear model (estimated using OLS) to predict DHP with EA and BL (formula: DHP ~ EA + BL). The model explains a statistically significant and substantial proportion of variance (R2 = 0.36, F(2, 997) = 285.02, p < .001, adj. R2 = 0.36). The model's intercept, corresponding to EA = 0 and BL = 0, is at 0.78 (95% CI [0.58, 0.98], t(997) = 7.69, p < .001). Within this model:
##
## - The effect of EA is statistically significant and positive (beta = 0.41, 95% CI [0.37, 0.46], t(997) = 16.82, p < .001; Std. beta = 0.42, 95% CI [0.38, 0.47])
## - The effect of BL is statistically significant and positive (beta = 0.41, 95% CI [0.36, 0.45], t(997) = 16.62, p < .001; Std. beta = 0.42, 95% CI [0.37, 0.47])
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using the Wald approximation.ArtÃculo 2 - GLM
Tabla
Estimate | Standard Error | z value | Pr(>|z|) | Signif. | |
(Intercept) | 7.204 | 0.602 | 11.965 | 0.0000 | *** |
EA | -1.459 | 0.139 | -10.479 | 0.0000 | *** |
BL | -1.423 | 0.136 | -10.468 | 0.0000 | *** |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
| |||||
(Dispersion parameter for binomial family taken to be 1) | |||||
Null deviance: 1203 on 999 degrees of freedom | |||||
Residual deviance: 942.9 on 997 degrees of freedom | |||||
Análisis
## We fitted a logistic model (estimated using ML) to predict DHP with EA and BL (formula: DHP ~ EA + BL). The model's explanatory power is moderate (Tjur's R2 = 0.25). The model's intercept, corresponding to EA = 0 and BL = 0, is at 7.20 (95% CI [6.05, 8.42], p < .001). Within this model:
##
## - The effect of EA is statistically significant and negative (beta = -1.46, 95% CI [-1.74, -1.19], p < .001; Std. beta = -0.99, 95% CI [-1.18, -0.81])
## - The effect of BL is statistically significant and negative (beta = -1.42, 95% CI [-1.70, -1.16], p < .001; Std. beta = -0.97, 95% CI [-1.16, -0.79])
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed usingArtÃculo 1 - SEM
SEM Análisis
Simulación de datos
DHP = Desarrollo de Habilidades de Pensamiento
EA = Estilos de Aprendizaje
BL = Blended Learning
El Desarrollo de Habilidades de Pensamiento es influenciada por la Estilos de Aprendizaje y por la Blended Learning
DHP ~ EA + BL
Análisis Exploratorio
factanal(juanSimData, factors = 3, rotation = "promax")##
## Call:
## factanal(x = juanSimData, factors = 3, rotation = "promax")
##
## Uniquenesses:
## i1 i2 i3 i4 i5 i6 i7 i8 i9
## 0.626 0.417 0.592 0.578 0.556 0.529 0.364 0.422 0.429
##
## Loadings:
## Factor1 Factor2 Factor3
## i1 0.554
## i2 0.801
## i3 0.634
## i4 0.638
## i5 0.664
## i6 0.685
## i7 0.807
## i8 0.738
## i9 0.739
##
## Factor1 Factor2 Factor3
## SS loadings 1.759 1.355 1.328
## Proportion Var 0.195 0.151 0.148
## Cumulative Var 0.195 0.346 0.494
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 -0.48246 0.55811
## Factor2 -0.482 1.00000 -0.00953
## Factor3 0.558 -0.00953 1.00000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 13.78 on 12 degrees of freedom.
## The p-value is 0.315| Valor | Concepto |
|---|---|
| < 0.39 | pobre |
| .4 - .49 | Justo |
| .5 - .59 | Bueno |
| .6 - .69 | Muy bueno |
| .7 + | Excelente |
Modelo
Creamos el modelo
juanModel <-
"EA =~ i1 + i2 + i3
BL =~ i4 + i5 + i6
DHP =~ i7 + i8 + i9
DHP ~ EA
DHP ~ BL"
juan.fit <-
cfa(juanModel,
data = juanSimData)Revisamos las cargas
inspect(juan.fit,
what = "std")$lambda## EA BL DHP
## i1 0.624 0.000 0.000
## i2 0.735 0.000 0.000
## i3 0.648 0.000 0.000
## i4 0.000 0.657 0.000
## i5 0.000 0.665 0.000
## i6 0.000 0.680 0.000
## i7 0.000 0.000 0.790
## i8 0.000 0.000 0.762
## i9 0.000 0.000 0.760>.6
Evaluamos el modelo
summary(juan.fit,
# standardized = TRUE,
fit.measures = TRUE)## lavaan 0.6-9 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 24.612
## Degrees of freedom 24
## P-value (Chi-square) 0.427
##
## Model Test Baseline Model:
##
## Test statistic 2535.257
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9910.249
## Loglikelihood unrestricted model (H1) -9897.943
##
## Akaike (AIC) 19862.498
## Bayesian (BIC) 19965.561
## Sample-size adjusted Bayesian (BIC) 19898.864
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.005
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.026
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.016
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## EA =~
## i1 1.000
## i2 1.322 0.089 14.845 0.000
## i3 1.158 0.081 14.378 0.000
## BL =~
## i4 1.000
## i5 0.995 0.068 14.577 0.000
## i6 0.980 0.067 14.671 0.000
## DHP =~
## i7 1.000
## i8 0.913 0.041 22.496 0.000
## i9 0.871 0.039 22.460 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## DHP ~
## EA 0.738 0.062 11.871 0.000
## BL 0.587 0.051 11.571 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## EA ~~
## BL 0.012 0.013 0.919 0.358
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .i1 0.369 0.022 17.133 0.000
## .i2 0.351 0.027 12.818 0.000
## .i3 0.438 0.027 16.394 0.000
## .i4 0.441 0.028 15.822 0.000
## .i5 0.418 0.027 15.528 0.000
## .i6 0.375 0.025 14.960 0.000
## .i7 0.275 0.019 14.611 0.000
## .i8 0.275 0.017 15.896 0.000
## .i9 0.253 0.016 15.962 0.000
## EA 0.236 0.026 9.243 0.000
## BL 0.335 0.035 9.665 0.000
## .DHP 0.202 0.021 9.612 0.000| Medidas | Valor a revisar |
|---|---|
| chi-square | p < .05 |
| CFI - Comparative Fit Index | >.9 |
| TLI - Tuker-Lewis Index | >.9 |
| RMSEA | <.05 |
Tabla 1
Promedios, desviaciones estándar, confiabilidad interna y variables compuestas (N = 1000)
juan_mean <-
juanSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3),
DE = mean(i4, i5, i6),
CD = mean(i7, i8, i9)) |>
dplyr::select(RE, DE, CD) |>
ungroup() |>
summarise(across(everything(), mean))
juan_sd <-
juanSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3),
DE = mean(i4, i5, i6),
CD = mean(i7, i8, i9)) |>
dplyr::select(RE, DE, CD) |>
ungroup() |>
summarise(across(everything(), sd))
juan_sd_mean <-
juan_mean |>
bind_rows(juan_sd) |>
transpose_df() |>
rename(Variable_compuesta = rowname,
promedio = "1",
des_est = "2") |>
mutate(promedio = round(promedio, digits = 2),
des_est = round(des_est, digits = 2))
juan_cronbach_RE <-
juanSimData |>
dplyr::select(i1, i2, i3) |>
cronbach.alpha()
juan_cronbach_DE <-
juanSimData |>
dplyr::select(i4, i5, i6) |>
cronbach.alpha()
juan_cronbach_CD <-
juanSimData |>
dplyr::select(i7, i8, i9) |>
cronbach.alpha()
juan_cronbach <-
tibble(RE = juan_cronbach_RE$alpha,
DE = juan_cronbach_DE$alpha,
CD = juan_cronbach_CD$alpha) |>
transpose_df() |>
rename(Variable_compuesta = rowname,
Cronbach = "1") |>
right_join(juan_sd_mean) |>
dplyr::select(Variable_compuesta,
Cronbach,
promedio,
des_est) |>
mutate(Cronbach = round(Cronbach,
digits = 2),
No_items = 3) |>
dplyr::select(Variable_compuesta,
No_items,
Cronbach,
promedio,
des_est
)## Joining, by = "Variable_compuesta"rm(juan_mean,
juan_sd,
juan_sd_mean)
juan_cronbach |>
gt() |>
tab_header(
title = "Tabla 1 Promedios, desviaciones estándar, confiabilidad interna y variables compuestas (N = 1000)"
)| Tabla 1 Promedios, desviaciones estándar, confiabilidad interna y variables compuestas (N = 1000) | ||||
|---|---|---|---|---|
| Variable_compuesta | No_items | Cronbach | promedio | des_est |
| RE | 3 | 0.71 | 2.73 | 0.78 |
| DE | 3 | 0.71 | 3.00 | 0.88 |
| CD | 3 | 0.81 | 2.86 | 0.86 |
EstadÃsticas a tener en cuenta
| Cronbach Alpha | Concepto |
|---|---|
| <.6 | Inaceptable - Se eliminan items |
| 6 - .64 | Indeseable |
| .65 - .69 | Aceptable minimamente |
| .7 - .79 | Respetable |
| .8 - .89 | Muy bueno |
| .9> | Demasiado buenos - eliminar items |
Tabla 2
Matrix de correlaciones
RE <- juanSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3))
DE <- juanSimData |> rowwise() |> mutate(DE = mean(i4, i5, i6))
CD <- juanSimData |> rowwise() |> mutate(CD = mean(i7, i8, i9))
juan_latent <-
tibble(RE = RE$RE,
DE = DE$DE,
CD = CD$CD)
rcorr(as.matrix(juan_latent))## RE DE CD
## RE 1.00 0.02 0.28
## DE 0.02 1.00 0.29
## CD 0.28 0.29 1.00
##
## n= 1000
##
##
## P
## RE DE CD
## RE 0.431 0.000
## DE 0.431 0.000
## CD 0.000 0.000Diagrama del modelo
semPaths(juan.fit,
what = "est",
fade = FALSE,
residuals = FALSE,
edge.label.cex = 0.75)