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) | 0.617 | 0.125 | 4.938 | 0.0000 | *** |
REL | 0.500 | 0.031 | 16.115 | 0.0000 | *** |
ES | 0.310 | 0.029 | 10.535 | 0.0000 | *** |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
Residual standard error: 0.5823 on 997 degrees of freedom | |||||
Multiple R-squared: 0.2785, Adjusted R-squared: 0.277 | |||||
F-statistic: 192.4 on 997 and 2 DF, p-value: 0.0000 | |||||
Análisis
## We fitted a linear model (estimated using OLS) to predict PL with REL and ES (formula: PL ~ REL + ES). The model explains a statistically significant and substantial proportion of variance (R2 = 0.28, F(2, 997) = 192.38, p < .001, adj. R2 = 0.28). The model's intercept, corresponding to REL = 0 and ES = 0, is at 0.62 (95% CI [0.37, 0.86], t(997) = 4.94, p < .001). Within this model:
##
## - The effect of REL is statistically significant and positive (beta = 0.50, 95% CI [0.44, 0.56], t(997) = 16.12, p < .001; Std. beta = 0.43, 95% CI [0.38, 0.49])
## - The effect of ES is statistically significant and positive (beta = 0.31, 95% CI [0.25, 0.37], t(997) = 10.53, p < .001; Std. beta = 0.28, 95% CI [0.23, 0.34])
##
## 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) | 7.993 | 0.612 | 13.058 | 0.0000 | *** |
REL | -1.440 | 0.131 | -11.028 | 0.0000 | *** |
ES | -1.266 | 0.127 | -10.006 | 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: 1372 on 999 degrees of freedom | |||||
Residual deviance: 1111 on 997 degrees of freedom | |||||
Análisis
## We fitted a logistic model (estimated using ML) to predict PL with REL and ES (formula: PL ~ REL + ES). The model's explanatory power is moderate (Tjur's R2 = 0.24). The model's intercept, corresponding to REL = 0 and ES = 0, is at 7.99 (95% CI [6.82, 9.23], p < .001). Within this model:
##
## - The effect of REL is statistically significant and negative (beta = -1.44, 95% CI [-1.70, -1.19], p < .001; Std. beta = -0.94, 95% CI [-1.11, -0.77])
## - The effect of ES is statistically significant and negative (beta = -1.27, 95% CI [-1.52, -1.02], p < .001; Std. beta = -0.82, 95% CI [-0.98, -0.66])
##
## 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(hildaSimData, factors = 3, rotation = "promax")##
## Call:
## factanal(x = hildaSimData, factors = 3, rotation = "promax")
##
## Uniquenesses:
## i1 i2 i3 i4 i5 i6 i7 i8 i9
## 0.673 0.511 0.678 0.715 0.575 0.539 0.457 0.362 0.452
##
## Loadings:
## Factor1 Factor2 Factor3
## i1 0.528
## i2 0.701
## i3 0.585
## i4 0.518
## i5 0.677
## i6 0.660
## i7 0.711
## i8 0.776
## i9 0.740
##
## Factor1 Factor2 Factor3
## SS loadings 1.671 1.166 1.118
## Proportion Var 0.186 0.130 0.124
## Cumulative Var 0.186 0.315 0.439
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 -0.4160 0.5830
## Factor2 -0.416 1.0000 -0.0573
## Factor3 0.583 -0.0573 1.0000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 11.43 on 12 degrees of freedom.
## The p-value is 0.492| Valor | Concepto |
|---|---|
| < 0.39 | pobre |
| .4 - .49 | Justo |
| .5 - .59 | Bueno |
| .6 - .69 | Muy bueno |
| .7 + | Excelente |
Modelo
Creamos el modelo
hildaModel <-
"EE =~ i1 + i2 + i3
CRP =~ i4 + i5 + i6
PC =~ i7 + i8 + i9
PC ~ EE
PC ~ CRP"
hilda.fit <-
cfa(hildaModel,
data = hildaSimData)Revisamos las cargas
inspect(hilda.fit,
what = "std")$lambda## EE CRP PC
## i1 0.591 0.000 0.000
## i2 0.689 0.000 0.000
## i3 0.555 0.000 0.000
## i4 0.000 0.537 0.000
## i5 0.000 0.619 0.000
## i6 0.000 0.702 0.000
## i7 0.000 0.000 0.739
## i8 0.000 0.000 0.801
## i9 0.000 0.000 0.735>.6
Evaluamos el modelo
summary(hilda.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 18.005
## Degrees of freedom 24
## P-value (Chi-square) 0.803
##
## Model Test Baseline Model:
##
## Test statistic 2088.054
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.004
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9719.132
## Loglikelihood unrestricted model (H1) -9710.129
##
## Akaike (AIC) 19480.264
## Bayesian (BIC) 19583.327
## Sample-size adjusted Bayesian (BIC) 19516.630
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.017
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.015
##
## 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 1.243 0.097 12.763 0.000
## i3 0.820 0.069 11.872 0.000
## CRP =~
## i4 1.000
## i5 1.177 0.102 11.506 0.000
## i6 1.264 0.110 11.450 0.000
## PC =~
## i7 1.000
## i8 1.210 0.057 21.360 0.000
## i9 1.085 0.053 20.399 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## PC ~
## EE 0.709 0.063 11.186 0.000
## CRP 0.485 0.059 8.253 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## EE ~~
## CRP 0.011 0.010 1.109 0.267
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .i1 0.410 0.024 16.958 0.000
## .i2 0.377 0.028 13.326 0.000
## .i3 0.332 0.019 17.929 0.000
## .i4 0.478 0.027 17.897 0.000
## .i5 0.431 0.028 15.152 0.000
## .i6 0.319 0.027 11.597 0.000
## .i7 0.260 0.016 16.229 0.000
## .i8 0.255 0.019 13.285 0.000
## .i9 0.312 0.019 16.367 0.000
## EE 0.220 0.027 8.273 0.000
## CRP 0.194 0.027 7.315 0.000
## .PC 0.148 0.016 9.013 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)
hilda_mean <-
hildaSimData |>
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))
hilda_sd <-
hildaSimData |>
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))
hilda_sd_mean <-
hilda_mean |>
bind_rows(hilda_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))
hilda_cronbach_RE <-
hildaSimData |>
dplyr::select(i1, i2, i3) |>
cronbach.alpha()
hilda_cronbach_DE <-
hildaSimData |>
dplyr::select(i4, i5, i6) |>
cronbach.alpha()
hilda_cronbach_CD <-
hildaSimData |>
dplyr::select(i7, i8, i9) |>
cronbach.alpha()
hilda_cronbach <-
tibble(RE = hilda_cronbach_RE$alpha,
DE = hilda_cronbach_DE$alpha,
CD = hilda_cronbach_CD$alpha) |>
transpose_df() |>
rename(Variable_compuesta = rowname,
Cronbach = "1") |>
right_join(hilda_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(hilda_mean,
hilda_sd,
hilda_sd_mean)
hilda_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 | 0.64 | 2.81 | 0.79 |
| DE | 3 | 0.65 | 3.10 | 0.82 |
| CD | 3 | 0.80 | 3.08 | 0.76 |
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 <- hildaSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3))
DE <- hildaSimData |> rowwise() |> mutate(DE = mean(i4, i5, i6))
CD <- hildaSimData |> rowwise() |> mutate(CD = mean(i7, i8, i9))
hilda_latent <-
tibble(RE = RE$RE,
DE = DE$DE,
CD = CD$CD)
rcorr(as.matrix(hilda_latent))## RE DE CD
## RE 1.00 0.06 0.27
## DE 0.06 1.00 0.20
## CD 0.27 0.20 1.00
##
## n= 1000
##
##
## P
## RE DE CD
## RE 0.0753 0.0000
## DE 0.0753 0.0000
## CD 0.0000 0.0000Diagrama del modelo
semPaths(hilda.fit,
what = "est",
fade = FALSE,
residuals = FALSE,
edge.label.cex = 0.75)