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Updated: Jan 24, 2026

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Múltiple Imputación de Datos Faltantes en Análisis Factorial Moderado

Joost R van Ginkel1, Dylan Molenaar2

  • 1Leiden University.

Multivariate behavioral research
|January 23, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Los datos faltantes en el análisis factorial moderado son un desafío. Un nuevo método de imputación múltiple maneja eficazmente los datos faltantes del moderador, superando la eliminación de casos y la coincidencia de medias predictivas en precisión y potencia.

Palabras clave:
máxima verosimilitud de información completaeliminación de casosdatos faltantesanálisis factorial moderadoimputación múltiplecoincidencia de medias predictivas

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Área de la Ciencia:

  • Psicometría
  • Modelado estadístico
  • Análisis de datos

Sus antecedentes:

  • El análisis factorial moderado extiende el modelo de factores comunes con una variable moderadora continua.
  • El manejo de datos faltantes en las variables indicadoras generalmente se realiza con máxima verosimilitud de información completa.
  • Los datos faltantes en la variable moderadora presentan un desafío significativo, a menudo requiriendo la eliminación de casos.

Objetivo del estudio:

  • Proponer y evaluar un procedimiento de imputación múltiple para el análisis factorial moderado con datos faltantes del moderador.
  • Comparar el rendimiento del método propuesto frente a la eliminación de casos y la coincidencia de medias predictivas.

Principales métodos:

  • Desarrollo de una técnica de imputación múltiple basada en el modelo de factores moderados.
  • Análisis comparativo utilizando datos simulados bajo diversas condiciones de datos faltantes.
  • Las métricas de evaluación incluyeron el sesgo de las estimaciones de parámetros y la potencia estadística.

Principales resultados:

  • El procedimiento de imputación múltiple propuesto maneja eficazmente los datos faltantes del moderador.
  • La eliminación de casos y la coincidencia de medias predictivas exhibieron menor potencia y mayor sesgo en las estimaciones de parámetros.
  • La imputación múltiple demostró un rendimiento superior en comparación con los métodos tradicionales.

Conclusiones:

  • La imputación múltiple es un enfoque robusto y recomendado para abordar los datos faltantes del moderador en el análisis factorial moderado.
  • El método propuesto ofrece una mayor precisión y potencia estadística que la eliminación de casos y la coincidencia de medias predictivas.
  • Este avance facilita análisis más confiables en presencia de patrones complejos de datos faltantes.