Abelardo_Analysis
abelardo Robledo
3/9/2022
ArtÃculo 4 - ASN
Cargando los datos
Cleaning data
Citation network
Inferential statistic
Merging data
Linear Regression
Correlation between ego density and production
ArtÃculo 3 - LM
Tabla
Estimate | Standard Error | t value | Pr(>|t|) | ||
(Intercept) | -0.172 | 0.127 | -1.351 | 0.1771 | |
RE | 0.428 | 0.028 | 15.284 | 0.0000 | *** |
CD | 0.564 | 0.030 | 18.800 | 0.0000 | *** |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
Residual standard error: 0.5824 on 997 degrees of freedom | |||||
Multiple R-squared: 0.3659, Adjusted R-squared: 0.3646 | |||||
F-statistic: 287.6 on 997 and 2 DF, p-value: 0.0000 | |||||
Análisis
## We fitted a linear model (estimated using OLS) to predict DE with RE and CD (formula: DE ~ RE + CD). The model explains a statistically significant and substantial proportion of variance (R2 = 0.37, F(2, 997) = 287.64, p < .001, adj. R2 = 0.36). The model's intercept, corresponding to RE = 0 and CD = 0, is at -0.17 (95% CI [-0.42, 0.08], t(997) = -1.35, p = 0.177). Within this model:
##
## - The effect of RE is statistically significant and positive (beta = 0.43, 95% CI [0.37, 0.48], t(997) = 15.28, p < .001; Std. beta = 0.39, 95% CI [0.34, 0.44])
## - The effect of CD is statistically significant and positive (beta = 0.56, 95% CI [0.51, 0.62], t(997) = 18.80, p < .001; Std. beta = 0.47, 95% CI [0.42, 0.52])
##
## 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) | 8.418 | 0.635 | 13.247 | 0.0000 | *** |
RE | -1.169 | 0.124 | -9.420 | 0.0000 | *** |
CD | -1.640 | 0.140 | -11.745 | 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: 1383 on 999 degrees of freedom | |||||
Residual deviance: 1121 on 997 degrees of freedom | |||||
Análisis
## We fitted a logistic model (estimated using ML) to predict DE with RE and CD (formula: DE ~ RE + CD). The model's explanatory power is moderate (Tjur's R2 = 0.23). The model's intercept, corresponding to RE = 0 and CD = 0, is at 8.42 (95% CI [7.21, 9.70], p < .001). Within this model:
##
## - The effect of RE is statistically significant and negative (beta = -1.17, 95% CI [-1.42, -0.93], p < .001; Std. beta = -0.77, 95% CI [-0.93, -0.61])
## - The effect of CD is statistically significant and negative (beta = -1.64, 95% CI [-1.92, -1.37], p < .001; Std. beta = -1.01, 95% CI [-1.18, -0.84])
##
## 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 datosPC = Pensamiento CrÃtico
RE = Ritual Evaluativo DE = Deserción Escolar CD = Creencias de los docentes
La deserción escolar es influenciada por el ritual evaluativo y por la creencia de los docente
DE ~ RE + CD
Análisis Exploratorio
factanal(AbelardoSimData, factors = 3, rotation = "promax")##
## Call:
## factanal(x = AbelardoSimData, factors = 3, rotation = "promax")
##
## Uniquenesses:
## i1 i2 i3 i4 i5 i6 i7 i8 i9
## 0.610 0.572 0.652 0.582 0.627 0.511 0.364 0.427 0.345
##
## Loadings:
## Factor1 Factor2 Factor3
## i1 0.598
## i2 0.674
## i3 0.596
## i4 0.647
## i5 0.622
## i6 0.687
## i7 0.719
## i8 0.690
## i9 0.830
##
## Factor1 Factor2 Factor3
## SS loadings 1.687 1.287 1.176
## Proportion Var 0.187 0.143 0.131
## Cumulative Var 0.187 0.330 0.461
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 0.5077 0.5917
## Factor2 0.508 1.0000 -0.0271
## Factor3 0.592 -0.0271 1.0000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 12.06 on 12 degrees of freedom.
## The p-value is 0.441| Valor | Concepto |
|---|---|
| < 0.39 | pobre |
| .4 - .49 | Justo |
| .5 - .59 | Bueno |
| .6 - .69 | Muy bueno |
| .7 + | Excelente |
Modelo
Creamos el modelo
AbelardoModel <-
"RE =~ i1 + i2 + i3
DE =~ i4 + i5 + i6
CD =~ i7 + i8 + i9
DE ~ RE
DE ~ CD"
Abelardo.fit <-
cfa(AbelardoModel,
data = AbelardoSimData)Revisamos las cargas
inspect(Abelardo.fit,
what = "std")$lambda## RE DE CD
## i1 0.645 0.000 0.000
## i2 0.632 0.000 0.000
## i3 0.588 0.000 0.000
## i4 0.000 0.642 0.000
## i5 0.000 0.602 0.000
## i6 0.000 0.709 0.000
## i7 0.000 0.000 0.805
## i8 0.000 0.000 0.762
## i9 0.000 0.000 0.794>.6
Evaluamos el modelo
summary(Abelardo.fit,
# standardized = TRUE,
fit.measures = TRUE)## lavaan 0.6-9 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 22.292
## Degrees of freedom 24
## P-value (Chi-square) 0.562
##
## Model Test Baseline Model:
##
## Test statistic 2511.764
## 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) -9805.686
## Loglikelihood unrestricted model (H1) -9794.540
##
## Akaike (AIC) 19653.372
## Bayesian (BIC) 19756.435
## Sample-size adjusted Bayesian (BIC) 19689.737
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.024
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.018
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## RE =~
## i1 1.000
## i2 0.930 0.072 12.908 0.000
## i3 0.865 0.069 12.555 0.000
## DE =~
## i4 1.000
## i5 0.804 0.058 13.968 0.000
## i6 0.947 0.063 15.046 0.000
## CD =~
## i7 1.000
## i8 1.020 0.043 23.777 0.000
## i9 1.092 0.044 24.657 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## DE ~
## RE -0.448 0.061 -7.399 0.000
## CD 0.742 0.056 13.273 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## RE ~~
## CD 0.188 0.019 9.861 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .i1 0.460 0.030 15.107 0.000
## .i2 0.425 0.027 15.567 0.000
## .i3 0.464 0.027 17.024 0.000
## .i4 0.437 0.026 16.652 0.000
## .i5 0.349 0.020 17.790 0.000
## .i6 0.273 0.019 14.160 0.000
## .i7 0.224 0.015 14.746 0.000
## .i8 0.310 0.019 16.679 0.000
## .i9 0.287 0.019 15.275 0.000
## RE 0.327 0.036 9.004 0.000
## .DE 0.140 0.020 6.991 0.000
## CD 0.411 0.029 14.219 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)
abelardo_mean <-
AbelardoSimData |>
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))
abelardo_sd <-
AbelardoSimData |>
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))
abelardo_sd_mean <-
abelardo_mean |>
bind_rows(abelardo_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))
abelardo_cronbach_RE <-
AbelardoSimData |>
dplyr::select(i1, i2, i3) |>
cronbach.alpha()
abelardo_cronbach_DE <-
AbelardoSimData |>
dplyr::select(i4, i5, i6) |>
cronbach.alpha()
abelardo_cronbach_CD <-
AbelardoSimData |>
dplyr::select(i7, i8, i9) |>
cronbach.alpha()
abelardo_cronbach <-
tibble(RE = abelardo_cronbach_RE$alpha,
DE = abelardo_cronbach_DE$alpha,
CD = abelardo_cronbach_CD$alpha) |>
transpose_df() |>
rename(Variable_compuesta = rowname,
Cronbach = "1") |>
right_join(abelardo_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(abelardo_mean,
abelardo_sd,
abelardo_sd_mean)
abelardo_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 | 0.65 | 3.09 | 0.89 |
| DE | 3 | 0.69 | 3.02 | 0.86 |
| CD | 3 | 0.83 | 2.86 | 0.80 |
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 <- AbelardoSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3))
DE <- AbelardoSimData |> rowwise() |> mutate(DE = mean(i4, i5, i6))
CD <- AbelardoSimData |> rowwise() |> mutate(CD = mean(i7, i8, i9))
abelardo_latent <-
tibble(RE = RE$RE,
DE = DE$DE,
CD = CD$CD)
rcorr(as.matrix(abelardo_latent))## RE DE CD
## RE 1.00 0.02 0.29
## DE 0.02 1.00 0.32
## CD 0.29 0.32 1.00
##
## n= 1000
##
##
## P
## RE DE CD
## RE 0.5711 0.0000
## DE 0.5711 0.0000
## CD 0.0000 0.0000Diagrama del modelo
semPaths(Abelardo.fit,
what = "est",
fade = FALSE,
residuals = FALSE,
edge.label.cex = 0.75)