natalia_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) | -0.278 | 0.136 | -2.047 | 0.0410 | * |
PP | 0.513 | 0.031 | 16.632 | 0.0000 | *** |
AA | 0.549 | 0.031 | 17.941 | 0.0000 | *** |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
Residual standard error: 0.6164 on 997 degrees of freedom | |||||
Multiple R-squared: 0.3758, Adjusted R-squared: 0.3745 | |||||
F-statistic: 300.1 on 997 and 2 DF, p-value: 0.0000 | |||||
Análisis
## We fitted a linear model (estimated using OLS) to predict FIM with PP and AA (formula: FIM ~ PP + AA). The model explains a statistically significant and substantial proportion of variance (R2 = 0.38, F(2, 997) = 300.11, p < .001, adj. R2 = 0.37). The model's intercept, corresponding to PP = 0 and AA = 0, is at -0.28 (95% CI [-0.54, -0.01], t(997) = -2.05, p = 0.041). Within this model:
##
## - The effect of PP is statistically significant and positive (beta = 0.51, 95% CI [0.45, 0.57], t(997) = 16.63, p < .001; Std. beta = 0.42, 95% CI [0.37, 0.47])
## - The effect of AA is statistically significant and positive (beta = 0.55, 95% CI [0.49, 0.61], t(997) = 17.94, p < .001; Std. beta = 0.45, 95% CI [0.40, 0.50])
##
## 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) | -9.047 | 0.683 | -13.251 | 0.0000 | *** |
PP | 1.542 | 0.139 | 11.063 | 0.0000 | *** |
AA | 1.577 | 0.141 | 11.167 | 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: 1337 on 999 degrees of freedom | |||||
Residual deviance: 1045 on 997 degrees of freedom | |||||
Análisis
## We fitted a logistic model (estimated using ML) to predict FIM with PP and AA (formula: FIM ~ PP + AA). The model's explanatory power is substantial (Tjur's R2 = 0.26). The model's intercept, corresponding to PP = 0 and AA = 0, is at -9.05 (95% CI [-10.42, -7.75], p < .001). Within this model:
##
## - The effect of PP is statistically significant and positive (beta = 1.54, 95% CI [1.27, 1.82], p < .001; Std. beta = 0.98, 95% CI [0.81, 1.15])
## - The effect of AA is statistically significant and positive (beta = 1.58, 95% CI [1.31, 1.86], p < .001; Std. beta = 1.00, 95% CI [0.83, 1.19])
##
## 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
FIM = Formación Inicial de Maestros
PP = Prácticas Pedagógicas
AA = Alfabetización académica
El Formación Inicial de Maestros es influenciada por la Prácticas Pedagógicas y por la Alfabetización académica
FIM ~ PP + AA
Análisis Factorial Exploratorio
factanal(nataliaSimData, factors = 3, rotation = "promax")##
## Call:
## factanal(x = nataliaSimData, factors = 3, rotation = "promax")
##
## Uniquenesses:
## i1 i2 i3 i4 i5 i6 i7 i8 i9
## 0.426 0.616 0.605 0.591 0.619 0.444 0.366 0.351 0.415
##
## Loadings:
## Factor1 Factor2 Factor3
## i1 -0.170 0.858
## i2 0.106 0.551
## i3 0.168 -0.122 0.509
## i4 0.631
## i5 0.620
## i6 0.725
## i7 0.677 0.123
## i8 0.844
## i9 0.764
##
## Factor1 Factor2 Factor3
## SS loadings 1.824 1.347 1.317
## Proportion Var 0.203 0.150 0.146
## Cumulative Var 0.203 0.352 0.499
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 -0.4703 0.5912
## Factor2 -0.470 1.0000 0.0867
## Factor3 0.591 0.0867 1.0000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 14.87 on 12 degrees of freedom.
## The p-value is 0.249| Valor | Concepto |
|---|---|
| < 0.39 | pobre |
| .4 - .49 | Justo |
| .5 - .59 | Bueno |
| .6 - .69 | Muy bueno |
| .7 + | Excelente |
Análisis Factorial Confirmatorio
Creamos el modelo
nataliaModel <-
"PP =~ i1 + i2 + i3
AA =~ i4 + i5 + i6
FIM =~ i7 + i8 + i9
FIM ~ PP
FIM ~ AA"
natalia.fit <-
cfa(nataliaModel,
data = nataliaSimData)Revisamos las cargas
inspect(natalia.fit,
what = "std")$lambda## PP AA FIM
## i1 0.676 0.000 0.000
## i2 0.646 0.000 0.000
## i3 0.659 0.000 0.000
## i4 0.000 0.639 0.000
## i5 0.000 0.607 0.000
## i6 0.000 0.755 0.000
## i7 0.000 0.000 0.811
## i8 0.000 0.000 0.786
## i9 0.000 0.000 0.761>.6
Evaluamos el modelo
summary(natalia.fit,
# standardized = TRUE,
fit.measures = TRUE)## lavaan 0.6-9 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 32.755
## Degrees of freedom 24
## P-value (Chi-square) 0.109
##
## Model Test Baseline Model:
##
## Test statistic 2730.420
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997
## Tucker-Lewis Index (TLI) 0.995
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9433.559
## Loglikelihood unrestricted model (H1) -9417.182
##
## Akaike (AIC) 18909.118
## Bayesian (BIC) 19012.181
## Sample-size adjusted Bayesian (BIC) 18945.484
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.019
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.034
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.019
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PP =~
## i1 1.000
## i2 0.971 0.065 14.878 0.000
## i3 1.119 0.075 15.015 0.000
## AA =~
## i4 1.000
## i5 0.970 0.068 14.355 0.000
## i6 1.128 0.072 15.616 0.000
## FIM =~
## i7 1.000
## i8 0.927 0.037 24.763 0.000
## i9 0.870 0.036 24.034 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## FIM ~
## PP 0.788 0.058 13.491 0.000
## AA 0.779 0.058 13.345 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PP ~~
## AA -0.010 0.012 -0.855 0.393
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .i1 0.316 0.020 15.521 0.000
## .i2 0.349 0.021 16.545 0.000
## .i3 0.434 0.027 16.136 0.000
## .i4 0.385 0.023 17.071 0.000
## .i5 0.427 0.024 17.936 0.000
## .i6 0.254 0.021 12.379 0.000
## .i7 0.239 0.016 14.560 0.000
## .i8 0.244 0.015 15.775 0.000
## .i9 0.253 0.015 16.795 0.000
## PP 0.266 0.026 10.101 0.000
## AA 0.265 0.028 9.614 0.000
## .FIM 0.147 0.019 7.933 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)
natalia_mean <-
nataliaSimData |>
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))
natalia_sd <-
nataliaSimData |>
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))
natalia_sd_mean <-
natalia_mean |>
bind_rows(natalia_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))
natalia_cronbach_RE <-
nataliaSimData |>
dplyr::select(i1, i2, i3) |>
cronbach.alpha()
natalia_cronbach_DE <-
nataliaSimData |>
dplyr::select(i4, i5, i6) |>
cronbach.alpha()
natalia_cronbach_CD <-
nataliaSimData |>
dplyr::select(i7, i8, i9) |>
cronbach.alpha()
natalia_cronbach <-
tibble(RE = natalia_cronbach_RE$alpha,
DE = natalia_cronbach_DE$alpha,
CD = natalia_cronbach_CD$alpha) |>
transpose_df() |>
rename(Variable_compuesta = rowname,
Cronbach = "1") |>
right_join(natalia_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(natalia_mean,
natalia_sd,
natalia_sd_mean)
natalia_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 | 3.51 | 0.76 |
| DE | 3 | 0.70 | 2.86 | 0.81 |
| CD | 3 | 0.83 | 2.66 | 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 <- nataliaSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3))
DE <- nataliaSimData |> rowwise() |> mutate(DE = mean(i4, i5, i6))
CD <- nataliaSimData |> rowwise() |> mutate(CD = mean(i7, i8, i9))
natalia_latent <-
tibble(RE = RE$RE,
DE = DE$DE,
CD = CD$CD)
rcorr(as.matrix(natalia_latent))## RE DE CD
## RE 1.00 -0.02 0.33
## DE -0.02 1.00 0.31
## CD 0.33 0.31 1.00
##
## n= 1000
##
##
## P
## RE DE CD
## RE 0.5189 0.0000
## DE 0.5189 0.0000
## CD 0.0000 0.0000Diagrama del modelo
semPaths(natalia.fit,
what = "est",
fade = FALSE,
residuals = FALSE,
edge.label.cex = 0.75)