daniel_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) | -0.172 | 0.127 | -1.351 | 0.1771 | |
EP | 0.428 | 0.028 | 15.284 | 0.0000 | *** |
MP | 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 REL with EP and MP (formula: REL ~ EP + MP). 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 EP = 0 and MP = 0, is at -0.17 (95% CI [-0.42, 0.08], t(997) = -1.35, p = 0.177). Within this model:
##
## - The effect of EP 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 MP 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 | *** |
MP | 1.640 | 0.140 | 11.745 | 0.0000 | *** |
RE | 1.169 | 0.124 | 9.420 | 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 REL with MP and RE (formula: REL ~ MP + RE). The model's explanatory power is moderate (Tjur's R2 = 0.23). The model's intercept, corresponding to MP = 0 and RE = 0, is at -8.42 (95% CI [-9.70, -7.21], p < .001). Within this model:
##
## - The effect of MP is statistically significant and positive (beta = 1.64, 95% CI [1.37, 1.92], p < .001; Std. beta = 1.01, 95% CI [0.84, 1.18])
## - The effect of RE is statistically significant and positive (beta = 1.17, 95% CI [0.93, 1.42], p < .001; Std. beta = 0.77, 95% CI [0.61, 0.93])
##
## 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
EP = Estilo Parental REL = Relación MP = Modelo Pedagógico
La relación es influenciada por el estilo parental y por el modelo pedagógico
REL ~ EP + MP
Análisis Exploratorio
factanal(danielSimData, factors = 3, rotation = "promax")##
## Call:
## factanal(x = danielSimData, factors = 3, rotation = "promax")
##
## Uniquenesses:
## i1 i2 i3 i4 i5 i6 i7 i8 i9
## 0.518 0.643 0.650 0.554 0.531 0.596 0.400 0.454 0.370
##
## Loadings:
## Factor1 Factor2 Factor3
## i1 0.741
## i2 0.578
## i3 0.552
## i4 0.659
## i5 0.694
## i6 0.634
## i7 0.719
## i8 0.671
## i9 0.848
##
## Factor1 Factor2 Factor3
## SS loadings 1.701 1.336 1.196
## Proportion Var 0.189 0.148 0.133
## Cumulative Var 0.189 0.337 0.470
##
## Factor Correlations:
## Factor1 Factor2 Factor3
## Factor1 1.000 0.5371 0.5385
## Factor2 0.537 1.0000 0.0146
## Factor3 0.539 0.0146 1.0000
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 8.16 on 12 degrees of freedom.
## The p-value is 0.773| Valor | Concepto |
|---|---|
| < 0.39 | pobre |
| .4 - .49 | Justo |
| .5 - .59 | Bueno |
| .6 - .69 | Muy bueno |
| .7 + | Excelente |
Modelo
Creamos el modelo
danielModel <-
"EP =~ i1 + i2 + i3
MP =~ i4 + i5 + i6
REL =~ i7 + i8 + i9
REL ~ EP
REL ~ MP"
daniel.fit <-
cfa(danielModel,
data = danielSimData)Revisamos las cargas
inspect(daniel.fit,
what = "std")$lambda## EP MP REL
## i1 0.657 0.000 0.000
## i2 0.610 0.000 0.000
## i3 0.608 0.000 0.000
## i4 0.000 0.675 0.000
## i5 0.000 0.678 0.000
## i6 0.000 0.634 0.000
## i7 0.000 0.000 0.784
## i8 0.000 0.000 0.747
## i9 0.000 0.000 0.769>.6
Evaluamos el modelo
summary(daniel.fit,
# standardized = TRUE,
fit.measures = TRUE)## lavaan 0.6-9 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 20.060
## Degrees of freedom 24
## P-value (Chi-square) 0.693
##
## Model Test Baseline Model:
##
## Test statistic 2359.259
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.003
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9510.951
## Loglikelihood unrestricted model (H1) -9500.921
##
## Akaike (AIC) 19063.901
## Bayesian (BIC) 19166.964
## Sample-size adjusted Bayesian (BIC) 19100.267
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.020
## 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|)
## EP =~
## i1 1.000
## i2 0.800 0.063 12.784 0.000
## i3 0.903 0.071 12.771 0.000
## MP =~
## i4 1.000
## i5 0.937 0.063 14.887 0.000
## i6 0.906 0.063 14.488 0.000
## REL =~
## i7 1.000
## i8 1.056 0.048 21.931 0.000
## i9 1.143 0.051 22.414 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## REL ~
## EP 0.557 0.050 11.096 0.000
## MP 0.560 0.045 12.390 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## EP ~~
## MP 0.005 0.014 0.354 0.724
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .i1 0.372 0.026 14.594 0.000
## .i2 0.305 0.019 16.298 0.000
## .i3 0.392 0.024 16.355 0.000
## .i4 0.396 0.026 15.286 0.000
## .i5 0.342 0.023 15.162 0.000
## .i6 0.404 0.024 16.711 0.000
## .i7 0.204 0.014 14.778 0.000
## .i8 0.288 0.018 16.387 0.000
## .i9 0.294 0.019 15.470 0.000
## EP 0.283 0.031 9.148 0.000
## MP 0.330 0.033 9.968 0.000
## .REL 0.132 0.015 8.703 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)
daniel_mean <-
danielSimData |>
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))
daniel_sd <-
danielSimData |>
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))
daniel_sd_mean <-
daniel_mean |>
bind_rows(daniel_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))
daniel_cronbach_RE <-
danielSimData |>
dplyr::select(i1, i2, i3) |>
cronbach.alpha()
daniel_cronbach_DE <-
danielSimData |>
dplyr::select(i4, i5, i6) |>
cronbach.alpha()
daniel_cronbach_CD <-
danielSimData |>
dplyr::select(i7, i8, i9) |>
cronbach.alpha()
daniel_cronbach <-
tibble(RE = daniel_cronbach_RE$alpha,
DE = daniel_cronbach_DE$alpha,
CD = daniel_cronbach_CD$alpha) |>
transpose_df() |>
rename(Variable_compuesta = rowname,
Cronbach = "1") |>
right_join(daniel_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(daniel_mean,
daniel_sd,
daniel_sd_mean)
daniel_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 | 3.37 | 0.81 |
| DE | 3 | 0.70 | 3.11 | 0.85 |
| CD | 3 | 0.81 | 2.54 | 0.73 |
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 <- danielSimData |>
rowwise() |>
mutate(RE = mean(i1, i2, i3))
DE <- danielSimData |> rowwise() |> mutate(DE = mean(i4, i5, i6))
CD <- danielSimData |> rowwise() |> mutate(CD = mean(i7, i8, i9))
daniel_latent <-
tibble(RE = RE$RE,
DE = DE$DE,
CD = CD$CD)
rcorr(as.matrix(daniel_latent))## RE DE CD
## RE 1.00 0.04 0.26
## DE 0.04 1.00 0.33
## CD 0.26 0.33 1.00
##
## n= 1000
##
##
## P
## RE DE CD
## RE 0.2514 0.0000
## DE 0.2514 0.0000
## CD 0.0000 0.0000Diagrama del modelo
semPaths(daniel.fit,
what = "est",
fade = FALSE,
residuals = FALSE,
edge.label.cex = 0.75)