janeth_Analysis
Sebastian Robledo
3/9/2022
ArtÃculo 4 - ASN
Cargando los datos
Cleaning data
Citation network
ArtÃculo 3 - LM
Tabla
Estimate | Standard Error | t value | Pr(>|t|) | ||
(Intercept) | 0.326 | 0.135 | 2.409 | 0.0162 | * |
EE | 0.438 | 0.030 | 14.497 | 0.0000 | *** |
CRP | 0.445 | 0.027 | 16.524 | 0.0000 | *** |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
Residual standard error: 0.5506 on 997 degrees of freedom | |||||
Multiple R-squared: 0.3056, Adjusted R-squared: 0.3042 | |||||
F-statistic: 219.3 on 997 and 2 DF, p-value: 0.0000 | |||||
Análisis
## We fitted a linear model (estimated using OLS) to predict PC with EE and CRP (formula: PC ~ EE + CRP). The model explains a statistically significant and substantial proportion of variance (R2 = 0.31, F(2, 997) = 219.34, p < .001, adj. R2 = 0.30). The model's intercept, corresponding to EE = 0 and CRP = 0, is at 0.33 (95% CI [0.06, 0.59], t(997) = 2.41, p = 0.016). Within this model:
##
## - The effect of EE is statistically significant and positive (beta = 0.44, 95% CI [0.38, 0.50], t(997) = 14.50, p < .001; Std. beta = 0.38, 95% CI [0.33, 0.44])
## - The effect of CRP is statistically significant and positive (beta = 0.44, 95% CI [0.39, 0.50], t(997) = 16.52, p < .001; Std. beta = 0.44, 95% CI [0.39, 0.49])
##
## 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) | -8.134 | 0.703 | -11.565 | 0.0000 | *** |
EE | 1.400 | 0.147 | 9.504 | 0.0000 | *** |
CRP | 1.481 | 0.137 | 10.842 | 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: 1248 on 999 degrees of freedom | |||||
Residual deviance: 1030 on 997 degrees of freedom | |||||
(2 observations deleted due to missingness) | |||||
Análisis
## We fitted a logistic model (estimated using ML) to predict PC with EE and CRP (formula: PC ~ EE + CRP). The model's explanatory power is moderate (Tjur's R2 = 0.21). The model's intercept, corresponding to EE = 0 and CRP = 0, is at -8.13 (95% CI [-9.55, -6.79], p < .001). Within this model:
##
## - The effect of EE is statistically significant and positive (beta = 1.40, 95% CI [1.12, 1.69], p < .001; Std. beta = 0.81, 95% CI [0.65, 0.98])
## - The effect of CRP is statistically significant and positive (beta = 1.48, 95% CI [1.22, 1.76], p < .001; Std. beta = 0.96, 95% CI [0.79, 1.14])
##
## 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
Simulación de datos
PC = Pensamiento CrÃtico
EE = Estrategias de Enseñanza
CRP = Capacidad de Resolución de Problemas
El Pensamiento CrÃtico es influenciada por la Estrategias de Enseñanza y por la Capacidad de Resolución de Problemas
PC ~ EE + CRP
Análisis Exploratorio
factanal(janethSimData, factors = 3, rotation = "promax")##
## Call:
## factanal(x = janethSimData, factors = 3, rotation = "promax")
##
## Uniquenesses:
## i1 i2 i3 i4 i5 i6 i7 i8 i9
## 0.572 0.523 0.590 0.547 0.600 0.504 0.314 0.376 0.401
##
## Loadings:
## Factor1 Factor2 Factor3
## i1 0.656
## i2 0.708
## i3 0.608
## i4 0.679
## i5 0.633
## i6 0.699
## i7 0.812
## i8 0.789
## i9 0.693
##
## Factor1 Factor2 Factor3
## SS loadings 1.766 1.361 1.311
## Proportion Var 0.196 0.151 0.146
## Cumulative Var 0.196 0.347 0.493
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 -0.5155 0.5770
## Factor2 -0.515 1.0000 -0.0227
## Factor3 0.577 -0.0227 1.0000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 7.44 on 12 degrees of freedom.
## The p-value is 0.827| Valor | Concepto |
|---|---|
| < 0.39 | pobre |
| .4 - .49 | Justo |
| .5 - .59 | Bueno |
| .6 - .69 | Muy bueno |
| .7 + | Excelente |
Modelo
Creamos el modelo
janethModel <-
"EE =~ i1 + i2 + i3
CRP =~ i4 + i5 + i6
PC =~ i7 + i8 + i9
PC ~ EE
PC ~ CRP"
janeth.fit <-
cfa(janethModel,
data = janethSimData)Revisamos las cargas
inspect(janeth.fit,
what = "std")$lambda## EE CRP PC
## i1 0.655 0.000 0.000
## i2 0.678 0.000 0.000
## i3 0.648 0.000 0.000
## i4 0.000 0.666 0.000
## i5 0.000 0.630 0.000
## i6 0.000 0.711 0.000
## i7 0.000 0.000 0.822
## i8 0.000 0.000 0.784
## i9 0.000 0.000 0.781>.6
Evaluamos el modelo
summary(janeth.fit,
# standardized = TRUE,
fit.measures = TRUE)## lavaan 0.6-9 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 22.128
## Degrees of freedom 24
## P-value (Chi-square) 0.572
##
## Model Test Baseline Model:
##
## Test statistic 2743.064
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.001
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9597.764
## Loglikelihood unrestricted model (H1) -9586.701
##
## Akaike (AIC) 19237.529
## Bayesian (BIC) 19340.592
## Sample-size adjusted Bayesian (BIC) 19273.894
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.023
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.017
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## EE =~
## i1 1.000
## i2 1.042 0.070 14.956 0.000
## i3 1.100 0.075 14.672 0.000
## CRP =~
## i4 1.000
## i5 0.953 0.066 14.544 0.000
## i6 1.113 0.073 15.156 0.000
## PC =~
## i7 1.000
## i8 0.961 0.038 25.165 0.000
## i9 1.048 0.042 25.086 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## PC ~
## EE 0.815 0.062 13.223 0.000
## CRP 0.677 0.056 12.145 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## EE ~~
## CRP 0.008 0.012 0.659 0.510
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .i1 0.334 0.021 16.221 0.000
## .i2 0.320 0.021 15.395 0.000
## .i3 0.419 0.025 16.439 0.000
## .i4 0.334 0.021 15.687 0.000
## .i5 0.367 0.022 16.910 0.000
## .i6 0.324 0.023 13.877 0.000
## .i7 0.231 0.016 14.157 0.000
## .i8 0.278 0.017 16.071 0.000
## .i9 0.337 0.021 16.186 0.000
## EE 0.251 0.026 9.753 0.000
## CRP 0.267 0.027 9.883 0.000
## .PC 0.182 0.020 9.226 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)
janeth_mean <-
janethSimData |>
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))
janeth_sd <-
janethSimData |>
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))
janeth_sd_mean <-
janeth_mean |>
bind_rows(janeth_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))
janeth_cronbach_RE <-
janethSimData |>
dplyr::select(i1, i2, i3) |>
cronbach.alpha()
janeth_cronbach_DE <-
janethSimData |>
dplyr::select(i4, i5, i6) |>
cronbach.alpha()
janeth_cronbach_CD <-
janethSimData |>
dplyr::select(i7, i8, i9) |>
cronbach.alpha()
janeth_cronbach <-
tibble(RE = janeth_cronbach_RE$alpha,
DE = janeth_cronbach_DE$alpha,
CD = janeth_cronbach_CD$alpha) |>
transpose_df() |>
rename(Variable_compuesta = rowname,
Cronbach = "1") |>
right_join(janeth_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(janeth_mean,
janeth_sd,
janeth_sd_mean)
janeth_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 | 0.70 | 2.95 | 0.77 |
| DE | 3 | 0.71 | 2.87 | 0.78 |
| CD | 3 | 0.84 | 2.88 | 0.84 |
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 <- janethSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3))
DE <- janethSimData |> rowwise() |> mutate(DE = mean(i4, i5, i6))
CD <- janethSimData |> rowwise() |> mutate(CD = mean(i7, i8, i9))
janeth_latent <-
tibble(RE = RE$RE,
DE = DE$DE,
CD = CD$CD)
rcorr(as.matrix(janeth_latent))## RE DE CD
## RE 1.00 0.02 0.34
## DE 0.02 1.00 0.24
## CD 0.34 0.24 1.00
##
## n= 1000
##
##
## P
## RE DE CD
## RE 0.6324 0.0000
## DE 0.6324 0.0000
## CD 0.0000 0.0000Diagrama del modelo
semPaths(janeth.fit,
what = "est",
fade = FALSE,
residuals = FALSE,
edge.label.cex = 0.75)