isabel_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) | 0.501 | 0.116 | 4.326 | 0.0000 | *** |
FA | 0.463 | 0.027 | 16.880 | 0.0000 | *** |
ES | 0.427 | 0.027 | 15.636 | 0.0000 | *** |
Signif. codes: 0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05 < '.' < 0.1 < '' < 1 | |||||
Residual standard error: 0.5534 on 997 degrees of freedom | |||||
Multiple R-squared: 0.3481, Adjusted R-squared: 0.3468 | |||||
F-statistic: 266.2 on 997 and 2 DF, p-value: 0.0000 | |||||
Análisis
## We fitted a linear model (estimated using OLS) to predict IE with FA and ES (formula: IE ~ FA + ES). The model explains a statistically significant and substantial proportion of variance (R2 = 0.35, F(2, 997) = 266.22, p < .001, adj. R2 = 0.35). The model's intercept, corresponding to FA = 0 and ES = 0, is at 0.50 (95% CI [0.27, 0.73], t(997) = 4.33, p < .001). Within this model:
##
## - The effect of FA is statistically significant and positive (beta = 0.46, 95% CI [0.41, 0.52], t(997) = 16.88, p < .001; Std. beta = 0.43, 95% CI [0.38, 0.48])
## - The effect of ES is statistically significant and positive (beta = 0.43, 95% CI [0.37, 0.48], t(997) = 15.64, p < .001; Std. beta = 0.40, 95% CI [0.35, 0.45])
##
## 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) | 7.636 | 0.642 | 11.884 | 0.0000 | *** |
FA | -1.492 | 0.143 | -10.454 | 0.0000 | *** |
ES | -1.440 | 0.142 | -10.169 | 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: 1241 on 999 degrees of freedom | |||||
Residual deviance: 994 on 997 degrees of freedom | |||||
Análisis
## We fitted a logistic model (estimated using ML) to predict IE with FA and ES (formula: IE ~ FA + ES). The model's explanatory power is moderate (Tjur's R2 = 0.23). The model's intercept, corresponding to FA = 0 and ES = 0, is at 7.64 (95% CI [6.41, 8.93], p < .001). Within this model:
##
## - The effect of FA is statistically significant and negative (beta = -1.49, 95% CI [-1.78, -1.22], p < .001; Std. beta = -0.95, 95% CI [-1.13, -0.78])
## - The effect of ES is statistically significant and negative (beta = -1.44, 95% CI [-1.72, -1.17], p < .001; Std. beta = -0.92, 95% CI [-1.11, -0.75])
##
## 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
IE = Inteligencia Emocional
FA = Familia
ES = Escuela
La Inteligencia Emocional es influenciada por la Familia y por la Escuela
IE ~ FA + ES
Análisis Exploratorio
factanal(isabelSimData, factors = 3, rotation = "promax")##
## Call:
## factanal(x = isabelSimData, factors = 3, rotation = "promax")
##
## Uniquenesses:
## i1 i2 i3 i4 i5 i6 i7 i8 i9
## 0.606 0.542 0.648 0.510 0.652 0.515 0.340 0.426 0.417
##
## Loadings:
## Factor1 Factor2 Factor3
## i1 0.625
## i2 0.681
## i3 0.578
## i4 0.650
## i5 0.589
## i6 0.731
## i7 0.782
## i8 0.725
## i9 0.750
##
## Factor1 Factor2 Factor3
## SS loadings 1.711 1.306 1.198
## Proportion Var 0.190 0.145 0.133
## Cumulative Var 0.190 0.335 0.468
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 0.5246 0.5274
## Factor2 0.525 1.0000 -0.0663
## Factor3 0.527 -0.0663 1.0000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 16.13 on 12 degrees of freedom.
## The p-value is 0.185| Valor | Concepto |
|---|---|
| < 0.39 | pobre |
| .4 - .49 | Justo |
| .5 - .59 | Bueno |
| .6 - .69 | Muy bueno |
| .7 + | Excelente |
Modelo
Creamos el modelo
isabelModel <-
"FA =~ i1 + i2 + i3
ES =~ i4 + i5 + i6
IE =~ i7 + i8 + i9
IE ~ FA
IE ~ ES"
isabel.fit <-
cfa(isabelModel,
data = isabelSimData)Revisamos las cargas
inspect(isabel.fit,
what = "std")$lambda## FA ES IE
## i1 0.625 0.000 0.000
## i2 0.676 0.000 0.000
## i3 0.597 0.000 0.000
## i4 0.000 0.719 0.000
## i5 0.000 0.587 0.000
## i6 0.000 0.673 0.000
## i7 0.000 0.000 0.813
## i8 0.000 0.000 0.758
## i9 0.000 0.000 0.762>.6
Evaluamos el modelo
summary(isabel.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 20.741
## Degrees of freedom 24
## P-value (Chi-square) 0.654
##
## Model Test Baseline Model:
##
## Test statistic 2483.301
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.002
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9733.640
## Loglikelihood unrestricted model (H1) -9723.269
##
## Akaike (AIC) 19509.280
## Bayesian (BIC) 19612.343
## Sample-size adjusted Bayesian (BIC) 19545.646
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.021
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.013
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## FA =~
## i1 1.000
## i2 1.233 0.091 13.622 0.000
## i3 1.086 0.083 13.008 0.000
## ES =~
## i4 1.000
## i5 0.771 0.054 14.221 0.000
## i6 0.890 0.058 15.266 0.000
## IE =~
## i7 1.000
## i8 0.919 0.039 23.345 0.000
## i9 0.821 0.035 23.445 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## IE ~
## FA 0.847 0.070 12.121 0.000
## ES 0.703 0.054 13.059 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## FA ~~
## ES -0.015 0.013 -1.146 0.252
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .i1 0.367 0.022 16.351 0.000
## .i2 0.426 0.029 14.485 0.000
## .i3 0.501 0.029 17.209 0.000
## .i4 0.315 0.023 13.516 0.000
## .i5 0.382 0.021 18.101 0.000
## .i6 0.324 0.021 15.468 0.000
## .i7 0.257 0.019 13.829 0.000
## .i8 0.314 0.019 16.453 0.000
## .i9 0.245 0.015 16.306 0.000
## FA 0.235 0.026 8.951 0.000
## ES 0.338 0.032 10.692 0.000
## .IE 0.184 0.022 8.320 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)
isabel_mean <-
isabelSimData |>
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))
isabel_sd <-
isabelSimData |>
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))
isabel_sd_mean <-
isabel_mean |>
bind_rows(isabel_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))
isabel_cronbach_RE <-
isabelSimData |>
dplyr::select(i1, i2, i3) |>
cronbach.alpha()
isabel_cronbach_DE <-
isabelSimData |>
dplyr::select(i4, i5, i6) |>
cronbach.alpha()
isabel_cronbach_CD <-
isabelSimData |>
dplyr::select(i7, i8, i9) |>
cronbach.alpha()
isabel_cronbach <-
tibble(RE = isabel_cronbach_RE$alpha,
DE = isabel_cronbach_DE$alpha,
CD = isabel_cronbach_CD$alpha) |>
transpose_df() |>
rename(Variable_compuesta = rowname,
Cronbach = "1") |>
right_join(isabel_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(isabel_mean,
isabel_sd,
isabel_sd_mean)
isabel_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 | 0.66 | 3.51 | 0.78 |
| DE | 3 | 0.70 | 2.82 | 0.81 |
| CD | 3 | 0.82 | 3.07 | 0.87 |
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 <- isabelSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3))
DE <- isabelSimData |> rowwise() |> mutate(DE = mean(i4, i5, i6))
CD <- isabelSimData |> rowwise() |> mutate(CD = mean(i7, i8, i9))
isabel_latent <-
tibble(RE = RE$RE,
DE = DE$DE,
CD = CD$CD)
rcorr(as.matrix(isabel_latent))## RE DE CD
## RE 1.00 -0.05 0.28
## DE -0.05 1.00 0.34
## CD 0.28 0.34 1.00
##
## n= 1000
##
##
## P
## RE DE CD
## RE 0.1157 0.0000
## DE 0.1157 0.0000
## CD 0.0000 0.0000Diagrama del modelo
semPaths(isabel.fit,
what = "est",
fade = FALSE,
residuals = FALSE,
edge.label.cex = 0.75)