Viabilidad de los Procedimientos de Aprendizaje Automático Automatizado para el Estudio de las Evidencias de la Validez de CriterioEl Caso del VIA-IS

  1. Mercedes Ovejero 1
  2. Jesús M. Alvarado 1
  3. Jaime Ballesteros 2
  1. 1 Universidad Complutense de Madrid, Facultad de Psicología
  2. 2 Sermes CRO, Departamento de Biometría.
Revista:
Revista iberoamericana de diagnóstico y evaluación psicológica

ISSN: 1135-3848

Año de publicación: 2022

Título del ejemplar: Avances en Medición en Psicología

Volumen: 5

Número: 66

Páginas: 117-126

Tipo: Artículo

DOI: 10.21865/RIDEP66.5.09 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de diagnóstico y evaluación psicológica

Resumen

El enfoque tradicional de la validez de criterio se basa en el uso de correlaciones y regresión lineal empleados en una única muestra. Los avances en la algoritmia del aprendizaje automático supervisado han permitido el desarrollo de herramientas automatizadas (AutoML) que suponen un ahorro de tiempo y de coste computacional de la exploración de la capacidad predictiva de las puntuaciones de un test sobre criterios relevantes para el modelo teórico. El presente trabajo aplicó el pipeline de AutoML al estudio de la capacidad predictiva del cuestionario VIA-IS de fortalezas personales sobre la satisfacción general con la vida. Los resultados muestran que el AutoML permite explorar de forma rápida y eficiente gran cantidad de algoritmos predictivos, obteniéndose mejores medidas generales de ajuste y que las fortalezas gratitud, curiosidad, esperanza, apreciación del a belleza y la bondad son las que contribuyen en mayor grado a pronosticar la satisfacción vital.

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Fuente de los datos: Dialnet