Setting the sesion and loading the data
# Seteo de sesión
library(pacman)Warning: package 'pacman' was built under R version 4.4.3pacman::p_load(
dplyr, haven, sjlabelled, psych, purrr, tidyr, sjPlot, ggplot2,
parameters, table1, car, beeswarm, lme4, scales, ggrepel, corrplot,
ggtext, patchwork, lavaan, semTools, DT, knitr, kableExtra)
options(scipen = 999)
rm(list = ls())
# Cargamos los datos
pisa <- readRDS(here::here("input/data/proc_data/pisa22_proc.rds"))model_cfa <- '
gen_dse =~ search_info + evaluate_info + collect_data +
explain_content + share_content + pair_collab +
write_text + + create_media + change_settings
spec_dse =~ programming + develop_webpage
'
m_cfa <- cfa(model = model_cfa,
data = pisa,
estimator = "DWLS",
ordered = T,
std.lv = F)Warning: lavaan->lav_object_post_check():
some estimated ov variances are negativestandardizedsolution(m_cfa) %>%
filter(op == "=~") %>%
select(Factor = lhs, Indicator = rhs, `Loading (DWLS)` = est.std) Factor Indicator Loading..DWLS.
1 gen_dse search_info 0.909
2 gen_dse evaluate_info 0.891
3 gen_dse collect_data 0.834
4 gen_dse explain_content 0.887
5 gen_dse share_content 0.906
6 gen_dse pair_collab 0.889
7 gen_dse write_text 0.890
8 gen_dse create_media 0.844
9 gen_dse change_settings 0.809
10 spec_dse programming 0.674
11 spec_dse develop_webpage 1.002summary(m_cfa)lavaan 0.6-19 ended normally after 32 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 45
Used Total
Number of observations 247752 393607
Model Test User Model:
Test statistic 98875.541
Degrees of freedom 43
P-value (Chi-square) 0.000
Parameter Estimates:
Parameterization Delta
Standard errors Standard
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
gen_dse =~
search_info 1.000
evaluate_info 0.981 0.001 1347.797 0.000
collect_data 0.918 0.001 1333.543 0.000
explain_contnt 0.976 0.001 1473.984 0.000
share_content 0.998 0.001 1465.273 0.000
pair_collab 0.978 0.001 1458.512 0.000
write_text 0.980 0.001 1441.677 0.000
create_media 0.929 0.001 1345.320 0.000
change_settngs 0.890 0.001 1264.131 0.000
spec_dse =~
programming 1.000
develop_webpag 1.487 0.003 551.730 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
gen_dse ~~
spec_dse 0.437 0.001 590.982 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
search_info|t1 -1.534 0.004 -387.962 0.000
search_info|t2 -1.170 0.003 -359.253 0.000
search_info|t3 -0.379 0.003 -146.697 0.000
evaluate_nf|t1 -1.617 0.004 -387.958 0.000
evaluate_nf|t2 -1.111 0.003 -350.180 0.000
evaluate_nf|t3 -0.065 0.003 -25.915 0.000
collect_dat|t1 -1.439 0.004 -385.048 0.000
collect_dat|t2 -0.866 0.003 -299.383 0.000
collect_dat|t3 0.082 0.003 32.620 0.000
expln_cntnt|t1 -1.550 0.004 -388.137 0.000
expln_cntnt|t2 -1.035 0.003 -336.581 0.000
expln_cntnt|t3 -0.092 0.003 -36.492 0.000
share_cntnt|t1 -1.580 0.004 -388.244 0.000
share_cntnt|t2 -1.121 0.003 -351.733 0.000
share_cntnt|t3 -0.227 0.003 -89.494 0.000
pair_collab|t1 -1.629 0.004 -387.753 0.000
pair_collab|t2 -1.143 0.003 -355.314 0.000
pair_collab|t3 -0.230 0.003 -90.600 0.000
write_text|t1 -1.628 0.004 -387.776 0.000
write_text|t2 -1.130 0.003 -353.275 0.000
write_text|t3 -0.236 0.003 -92.620 0.000
create_medi|t1 -1.480 0.004 -386.691 0.000
create_medi|t2 -0.896 0.003 -306.522 0.000
create_medi|t3 0.019 0.003 7.558 0.000
chng_sttngs|t1 -1.489 0.004 -386.968 0.000
chng_sttngs|t2 -0.944 0.003 -317.730 0.000
chng_sttngs|t3 -0.062 0.003 -24.742 0.000
programming|t1 -0.721 0.003 -260.298 0.000
programming|t2 -0.101 0.003 -40.187 0.000
programming|t3 0.668 0.003 244.277 0.000
develp_wbpg|t1 -1.139 0.003 -354.708 0.000
develp_wbpg|t2 -0.499 0.003 -189.306 0.000
develp_wbpg|t3 0.375 0.003 145.191 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.search_info 0.175
.evaluate_info 0.206
.collect_data 0.305
.explain_contnt 0.214
.share_content 0.179
.pair_collab 0.210
.write_text 0.207
.create_media 0.287
.change_settngs 0.346
.programming 0.546
.develop_webpag -0.004
gen_dse 0.825 0.001 1067.492 0.000
spec_dse 0.454 0.001 391.728 0.000fit_pisa <- fitMeasures(m_cfa,
c("chisq", "df", "pvalue", "cfi", "tli", "rmsea", "srmr"))
knitr::kable(fit_pisa, digits = 3)| x | |
|---|---|
| chisq | 98875.54115382 |
| df | 43.00000000 |
| pvalue | 0.00000000 |
| cfi | 0.99527934 |
| tli | 0.99396194 |
| rmsea | 0.09631813 |
| srmr | 0.05758094 |
multigroup_cfa <- '
gen_dse =~ search_info + evaluate_info + collect_data +
explain_content + share_content + pair_collab +
write_text + + create_media + change_settings
spec_dse =~ programming + develop_webpage
'
# Modelo configural
fitgroup <- cfa(model = multigroup_cfa,
data = pisa,
group = "CNT",
ordered = TRUE
)Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative# demora 2 minutos
# Modelo métrico (fijando las cargas)
fitgroup_metric <- cfa(model = multigroup_cfa,
data = pisa,
group = "CNT",
ordered = TRUE,
# missing = "fiml"
group.equal = "loadings")Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative# 3 minutos
# Modelo escalar (fijando cargas y interceptos)
memory.limit(size = 5000) # Si tienes 16 GB de RAM, por ejemploWarning: 'memory.limit()' is no longer supported[1] Inffitgroup_scalar <- cfa(model = multigroup_cfa,
data = pisa,
group = "CNT",
ordered = TRUE,
# missing = "fiml",
group.equal = c("loadings", "intercepts"))Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative# Extracción de indicadores de ajuste
fitmeasures_config <- fitMeasures(fitgroup,
c("chisq", "df", "pvalue", "cfi", "tli", "rmsea", "srmr"))Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negativefitmeasures_metric <- fitMeasures(fitgroup_metric,
c("chisq", "df", "pvalue", "cfi", "tli", "rmsea", "srmr"))Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negativefitmeasures_scalar <- fitMeasures(fitgroup_scalar,
c("chisq", "df", "pvalue", "cfi", "tli", "rmsea", "srmr"))
# Todo se demora aprox 2 minutos
# Tabla resumen (html y opción latex)
tabla_completa <- data.frame(
Modelo = c("1. Configural", "2. Métrico", "3. Escalar"),
chisq = c(fitmeasures_config["chisq"], fitmeasures_metric["chisq"], fitmeasures_scalar["chisq"]),
df = c(fitmeasures_config["df"], fitmeasures_metric["df"], fitmeasures_scalar["df"]),
CFI = c(fitmeasures_config["cfi"], fitmeasures_metric["cfi"], fitmeasures_scalar["cfi"]),
TLI = c(fitmeasures_config["tli"], fitmeasures_metric["tli"], fitmeasures_scalar["tli"]),
RMSEA = c(fitmeasures_config["rmsea"], fitmeasures_metric["rmsea"], fitmeasures_scalar["rmsea"]),
SRMR = c(fitmeasures_config["srmr"], fitmeasures_metric["srmr"], fitmeasures_scalar["srmr"])
)
# Cálculo deltas
tabla_completa$delta_chisq <- c(NA, diff(tabla_completa$chisq))
tabla_completa$delta_df <- c(NA, diff(tabla_completa$df))
tabla_completa$delta_CFI <- c(NA, diff(tabla_completa$CFI))
tabla_completa$delta_RMSEA <- c(NA, diff(tabla_completa$RMSEA))
# ANOVA para significancia
test_anova <- anova(fitgroup, fitgroup_metric, fitgroup_scalar)Warning: lavaan->lavTestLRT():
Some restricted models fit better than less restricted models; either
these models are not nested, or the less restricted model failed to reach
a global optimum.Smallest difference = -9182.17722022042.# 3-4 minutos
tabla_completa$p_value_delta_chisq <- test_anova$`Pr(>Chisq)`
invariance_table_cnt <- knitr::kable(
tabla_completa,
format = "html",
digits = 3,
caption = "Análisis de invarianza por países",
col.names = c(
"Modelo",
"χ<sup>2</sup>", # chi²
"df", "CFI", "TLI", "RMSEA", "SRMR",
"Δχ<sup>2</sup>", # Δχ²
"Δdf", # Δdf
"ΔCFI", # ΔCFI
"ΔRMSEA", # ΔRMSEA
"p-valor (Δχ<sup>2</sup>)" # p-valor (Δχ²)
),
booktabs = TRUE,
escape=FALSE
) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
invariance_table_cnt| Modelo | χ2 | df | CFI | TLI | RMSEA | SRMR | Δχ2 | Δdf | ΔCFI | ΔRMSEA | p-valor (Δχ2) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Configural | 113431.5 | 2236 | 0.995 | 0.994 | 0.102 | 0.062 | NA | NA | NA | NA | NA |
| 2. Métrico | 145381.1 | 2695 | 0.994 | 0.994 | 0.105 | 0.069 | 31949.649 | 459 | -0.001 | 0.003 | 0 |
| 3. Escalar | 136199.0 | 3715 | 0.995 | 0.996 | 0.087 | 0.062 | -9182.177 | 1020 | 0.000 | -0.019 | 1 |
# Obtenemos todos los parámetros
format(Sys.time(), tz = "Chile/Continental")[1] "2025-08-07 16:00:31"all_params <- parameterEstimates(fitgroup, standardized = TRUE)
# Operador =~
factor_loadings_all_groups <- all_params %>%
filter(op == "=~")
# Extraemos las labels
group_labels <- lavInspect(fitgroup, "group.label")
# Dataframe con todos los países
country_map <- data.frame(group = 1:length(group_labels), CNT = unlist(group_labels))
# Dataframe con ambas dimensiones
factor_loadings_all_groups <- factor_loadings_all_groups %>%
left_join(country_map, by = "group")
# Dataframe gen dse
loadings_gen_dse <- factor_loadings_all_groups %>%
filter(lhs == "gen_dse") %>%
select(CNT, item = rhs, loading = std.all, latent_factor = lhs)
# Etiquetas tres países con peor ajuste
labels_gen_dse <- loadings_gen_dse %>%
group_by(item) %>%
slice_min(order_by = loading, n = 3) %>%
ungroup()
# Dataframe spec dse
loadings_spec_dse <- factor_loadings_all_groups %>%
filter(lhs == "spec_dse") %>%
select(CNT, item = rhs, loading = std.all, latent_factor = lhs)
# Etiquetas tres países con menor ajuste
labels_spec_dse <- loadings_spec_dse %>%
group_by(item) %>%
slice_min(order_by = loading, n = 3) %>%
ungroup()
# Gráfico cleaveland autoeficacia general
library(forcats)Warning: package 'forcats' was built under R version 4.4.3
Attaching package: 'forcats'The following object is masked from 'package:sjlabelled':
as_factorplot_gen_dse <- ggplot(loadings_gen_dse,
aes(x = loading,
y = fct_reorder(item, loading, .fun = median, .desc = FALSE))) +
geom_point(aes(color = '#B42012'), size = 3, alpha = 0.7) +
geom_text(data = labels_gen_dse,
aes(label = CNT),
nudge_y = 0.3,
size = 2.5,
color = "black",
check_overlap = TRUE) +
scale_color_identity(name = "Factor",
guide = "legend",
labels = c("General")) +
labs(title = "Cargas Factoriales: Autoeficacia General",
x = "Carga Factorial Estandarizada",
y = "Ítem") +
theme_minimal(base_size = 10) +
theme(axis.text.y = element_text(size = 8),
plot.title = element_text(hjust = 0.5),
legend.position = "top")
plot_gen_dse
# Cleaveland autoeficacia especializada
# Fijar el gráfico a x = .3
plot_spec_dse <- ggplot(loadings_spec_dse,
aes(x = loading,
y = fct_reorder(item, loading, .fun = median, .desc = FALSE))) +
geom_point(aes(color = '#E16462'), size = 3, alpha = 0.7) +
geom_text_repel(data = labels_spec_dse,
aes(label = CNT),
size = 2.5,
color = "black",
box.padding = 0.4,
point.padding = 0.2,
min.segment.length = 0,
max.overlaps = Inf) +
scale_color_identity(name = "Factor",
guide = "legend",
labels = c("Especializada")) +
labs(title = "Cargas Factoriales: Autoeficacia Especializada",
x = "Carga Factorial Estandarizada",
y = "Ítem") +
theme_minimal(base_size = 10) +
theme(axis.text.y = element_text(size = 8),
plot.title = element_text(hjust = 0.5),
legend.position = "top")
plot_spec_dse
# SEM por separado para cada país
lista_paises <- unique(unlist(pisa$CNT))
resultados_lista <- list()
summary_lista <- list()
# Iteramos por cada país con el mismo modelo
for (pais_actual in lista_paises) {
format(Sys.time(), tz = "Chile/Continental")
datos_pais <- pisa %>%
filter(CNT == pais_actual)
fit_pais <- tryCatch({
sem(model = multigroup_cfa,
data = datos_pais,
ordered = TRUE
# missing = "fiml"
)
}, error = function(e) {
cat(" -> ¡ERROR! No se pudo ajustar el modelo para", pais_actual, ". Razón:", conditionMessage(e), "\n")
return(NULL)
})
if (!is.null(fit_pais)) {
if (lavInspect(fit_pais, "converged")) {
medidas_ajuste <- fitMeasures(fit_pais)
resultados_lista[[pais_actual]] <- medidas_ajuste
} else {
cat(" -> ¡ADVERTENCIA! El modelo para", pais_actual, "no convergió. Se omitirán sus resultados.\n")
resultados_lista[[pais_actual]] <- rep(NA)
}
}
format(Sys.time(), tz = "Chile/Continental")
}Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative### Test con chile (o otros países)
datos_test <- pisa %>%
filter(CNT == "CHL")
test_model <- sem(model = multigroup_cfa,
data = datos_test,
ordered = TRUE)Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative#fitMeasures(test_model)
# Convertimos resultados_lista en dataframe y agregamos CNT para detectar país
fit_indices_por_pais <- do.call(rbind, resultados_lista)
fit_indices_por_pais <- as.data.frame(fit_indices_por_pais)
fit_indices_por_pais <- fit_indices_por_pais %>%
tibble::rownames_to_column(var = "CNT")
# Seleccionamos indicadores de interés
indices_ajuste <- fit_indices_por_pais %>%
select(CNT, chisq, df, rmsea, rmsea.ci.lower, rmsea.ci.upper, srmr, cfi, tli)
datatable(
indices_ajuste,
options = list(
pageLength = 10,
autoWidth = TRUE,
searchHighlight = TRUE
),
filter = 'top',
rownames = FALSE,
caption = "Índices de Ajuste del Modelo (Interactiva)"
)