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A Simple Two-Step Procedure for Fitting Fully Unrestricted Exploratory Factor Analytic Solutions with Correlated Residuals

  • Identification data

    Identifier: imarina:9333720
    Authors:
    Ferrando, Pere JHernandez-Dorado, AnaLorenzo-Seva, Urbano
    Abstract:
    A frequent criticism of exploratory factor analysis (EFA) is that it does not allow correlated residuals to be modelled, while they can be routinely specified in the confirmatory (CFA) model. In this article, we propose an EFA approach in which both the common factor solution and the residual matrix are unrestricted (i.e., the correlated residuals need not be specified a priori). The estimation procedures are two-stage and based on the unweighted least squares principle. Procedures for judging the solution appropriateness (including goodness of fit) are also proposed. The simulation studies and illustrative example suggest that the approach works quite well Although the proposal is based on existing results, most of the developments can be considered to be new contributions, and are expected to be particularly useful in the earlier stages of item calibration. The whole procedure has been implemented in both R language and a well-known non-commercial EFA program.
  • Others:

    Author, as appears in the article.: Ferrando, Pere J; Hernandez-Dorado, Ana; Lorenzo-Seva, Urbano
    Department: Psicologia
    URV's Author/s: Ferrando Piera, Pere Joan / Lorenzo Seva, Urbano
    Keywords: Number Monte-carlo Local dependence Linear and nonlinear factor analysis Item analysis Goodness-of-fit Exploratory factor analysis Correlated residuals
    Abstract: A frequent criticism of exploratory factor analysis (EFA) is that it does not allow correlated residuals to be modelled, while they can be routinely specified in the confirmatory (CFA) model. In this article, we propose an EFA approach in which both the common factor solution and the residual matrix are unrestricted (i.e., the correlated residuals need not be specified a priori). The estimation procedures are two-stage and based on the unweighted least squares principle. Procedures for judging the solution appropriateness (including goodness of fit) are also proposed. The simulation studies and illustrative example suggest that the approach works quite well Although the proposal is based on existing results, most of the developments can be considered to be new contributions, and are expected to be particularly useful in the earlier stages of item calibration. The whole procedure has been implemented in both R language and a well-known non-commercial EFA program.
    Thematic Areas: Sociology and political science Sociologia i política Social sciences, mathematical methods Psychology Psicología Modeling and simulation Mathematics, interdisciplinary applications Matemática / probabilidade e estatística General o multidisciplinar General economics,econometrics and finance General decision sciences Economics, econometrics and finance (miscellaneous) Economics, econometrics and finance (all) Economia Decision sciences (miscellaneous) Decision sciences (all) Ciencias sociales
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: urbano.lorenzo@urv.cat perejoan.ferrando@urv.cat
    Author identifier: 0000-0001-5369-3099 0000-0002-3133-5466
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.tandfonline.com/doi/full/10.1080/10705511.2023.2267181
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Structural Equation Modeling-A Multidisciplinary Journal. 31 (3): 420-428
    APA: Ferrando, Pere J; Hernandez-Dorado, Ana; Lorenzo-Seva, Urbano (2024). A Simple Two-Step Procedure for Fitting Fully Unrestricted Exploratory Factor Analytic Solutions with Correlated Residuals. Structural Equation Modeling-A Multidisciplinary Journal, 31(3), 420-428. DOI: 10.1080/10705511.2023.2267181
    Article's DOI: 10.1080/10705511.2023.2267181
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2024
    Publication Type: Journal Publications
  • Keywords:

    Decision Sciences (Miscellaneous),Economics, Econometrics and Finance (Miscellaneous),Mathematics, Interdisciplinary Applications,Modeling and Simulation,Social Sciences, Mathematical Methods,Sociology and Political Science
    Number
    Monte-carlo
    Local dependence
    Linear and nonlinear factor analysis
    Item analysis
    Goodness-of-fit
    Exploratory factor analysis
    Correlated residuals
    Sociology and political science
    Sociologia i política
    Social sciences, mathematical methods
    Psychology
    Psicología
    Modeling and simulation
    Mathematics, interdisciplinary applications
    Matemática / probabilidade e estatística
    General o multidisciplinar
    General economics,econometrics and finance
    General decision sciences
    Economics, econometrics and finance (miscellaneous)
    Economics, econometrics and finance (all)
    Economia
    Decision sciences (miscellaneous)
    Decision sciences (all)
    Ciencias sociales
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