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Consideraciones sobre la naturaleza de la escala de Likert a través de diferentes estructuraciones de los datos: Una aplicación para identificar estilos de aprendizaje en un entorno universitario

  • Autores: Ramón Álvarez Esteban Árbol académico, J. L. Vázquez Burguete Árbol académico
  • Localización: Revista internacional de sociología, ISSN 0034-9712, Vol. 82, Nº. 3, 2024
  • Idioma: español
  • DOI: 10.3989/ris.2024.82.3.M23-05
  • Títulos paralelos:
    • Considerations on the nature of the likert scale in different data structures: an application to identify learning styles in university students
  • Enlaces
  • Resumen
    • español

      La controversia sobre la consideración de los ítems Likert medidos en escala ordinal o de intervalo dentro de los cuestionarios, así como la construcción de una escala-Likert mediante agregación de estos ítems, sigue enfrentando a los investigadores. Técnicas de análisis de datos multidimensionales como el análisis de correspondencias múltiples (ACM) y el análisis de componentes principales (ACP) obtienen estructuras dependiendo de la naturaleza de los datos, mientras que el análisis factorial múltiple dual (AFMD) permite construir configuracio-nes comparables para cada grupo de una muestra. En este trabajo, se estudian los diferentes estilos de aprendizaje (EA) mediante un cuestionario. El objetivo radica en mostrar configuraciones multidimensionales estables con estas técnicas, compartiendo una estructura común que relaciona los estilos colaborativo, dependiente y participativo. Se evidencia que estos estilos se contra-ponen al elusivo. Igualmente, se obtienen algunas diferencias multidimensionales por sexo, considerado como variable ilustrativa (ACM y ACP) y también de forma activa (AFMD).

    • English

      The controversy regarding the consideration of Likert-scale items as either ordinal or interval within questionnaires, as well as the construction of a Likert scale by aggregating them, continues to challenge researchers. Multidimensional data analysis techniques such as Multiple Corres pondence Anal ysis (MCA) and Principal Component Analysis (PCA) provide structures depending on the nature of the data. Dual Multiple Factor Analysis (DMFA) allows constructing comparable configurations for each group in a sample. The paper analyses the different learning styles of a sample of university students using a questionnaire, showing stable multidimensional configurations using these techniques, revealing a common structure in which collaborative, dependent, and participative learning styles are related, in contrast to the avoidant style. Some multidimensional differences by gender were also found when considered as an illustrative variable (ACM and ACP) and also active (AFMD)

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