Articles producció científica> Psicologia

Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales: estimating latent trait scores

  • Datos identificativos

    Identificador: imarina:9286576
    Autores:
    Lorenzo-Seva, UrbanoVan Ginkel, Joost R.
    Resumen:
    Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Liken-type items, and the aim of the analysis is to estimate participants' scores on the corresponding latent traits. We propose a new approach to deal with missing responses in such a situation that is based on (1) multiple imputation of non-responses and (2) simultaneous rotation of the imputed datasets. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses, and a simulation study based on artificial datasets. The results show that our approach (specifically, Hot-Deck multiple imputation followed of Consensus Promin rotation) was able to successfully compute factor score estimates even for participants that have missing data.
  • Otros:

    Autor según el artículo: Lorenzo-Seva, Urbano; Van Ginkel, Joost R.;
    Departamento: Psicologia
    Autor/es de la URV: Lorenzo Seva, Urbano
    Palabras clave: Variables Predictive mean matching imputation Parameters Optimal agreement Oblique factor rotation Multiple imputation Missing data Loading matrices Hot-deck imputation Factor scores Exploratory factor analysis Consensus rotation
    Resumen: Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Liken-type items, and the aim of the analysis is to estimate participants' scores on the corresponding latent traits. We propose a new approach to deal with missing responses in such a situation that is based on (1) multiple imputation of non-responses and (2) simultaneous rotation of the imputed datasets. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses, and a simulation study based on artificial datasets. The results show that our approach (specifically, Hot-Deck multiple imputation followed of Consensus Promin rotation) was able to successfully compute factor score estimates even for participants that have missing data.
    Áreas temáticas: Saúde coletiva Revistas ciencias del comportamiento Psychology, multidisciplinary Psychology (miscellaneous) Psychology (all) Psychology Psicología Medicina i General psychology Educação Ciencias sociales
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: urbano.lorenzo@urv.cat
    Identificador del autor: 0000-0001-5369-3099
    Fecha de alta del registro: 2024-09-07
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://revistas.um.es/analesps/article/view/analesps.32.2.215161
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Anales De Psicologia. 32 (2): 596-608
    Referencia de l'ítem segons les normes APA: Lorenzo-Seva, Urbano; Van Ginkel, Joost R.; (2016). Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales: estimating latent trait scores. Anales De Psicologia, 32(2), 596-608. DOI: 10.6018/analesps.32.2.215161
    DOI del artículo: 10.6018/analesps.32.2.215161
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2016
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Psychology,Psychology (Miscellaneous),Psychology, Multidisciplinary
    Variables
    Predictive mean matching imputation
    Parameters
    Optimal agreement
    Oblique factor rotation
    Multiple imputation
    Missing data
    Loading matrices
    Hot-deck imputation
    Factor scores
    Exploratory factor analysis
    Consensus rotation
    Saúde coletiva
    Revistas ciencias del comportamiento
    Psychology, multidisciplinary
    Psychology (miscellaneous)
    Psychology (all)
    Psychology
    Psicología
    Medicina i
    General psychology
    Educação
    Ciencias sociales
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