Exploratory structural equation modeling for event-related potential data-An all-in-one approach?

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  • Author(s): Scharf F;Scharf F; Nestler S; Nestler S
  • Source:
    Psychophysiology [Psychophysiology] 2019 Mar; Vol. 56 (3), pp. e13303. Date of Electronic Publication: 2018 Dec 07.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Blackwell Country of Publication: United States NLM ID: 0142657 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1540-5958 (Electronic) Linking ISSN: 00485772 NLM ISO Abbreviation: Psychophysiology Subsets: MEDLINE
    • Publication Information:
      Publication: Malden, MA : Blackwell
      Original Publication: Baltimore, Williams & Wilkins.
    • Subject Terms:
    • Abstract:
      ERP data are characterized by high dimensionality and a mixture of constituting signals and are thus challenging for researchers to analyze. To address these challenges, exploratory factor analysis (EFA) has been used to provide estimates of the unobserved factors and to use these estimates for further statistical analyses (e.g., analyses of group effects). However, the EFA approach is prone to biases due to assigning individual factor scores to each observation as an intermediate step and does not properly consider participants, electrodes, and groups/conditions as differentiable sources of factor variance, with the consequence that factor correlations are inaccurately estimated. Here, we suggest exploratory structural equation modeling (ESEM) as a potential approach to address these limitations. ESEM may handle the complexity of ERP data more appropriately because multiple sources of variance can be formally taken into consideration. We demonstrate the application of ESEM to ERP data (in comparison with EFA) with an illustrative example and report the results of a small simulation study in which ESEM clearly outperformed EFA with respect to accurate estimation of the population factor loadings, population factor correlations, and group differences. We discuss how robust statistical inference can be conducted within the ESEM approach. We conclude that ESEM naturally extends the current EFA approach for ERP data and that it can provide a coherent and flexible analysis framework for all kinds of ERP research questions.
      (© 2018 Society for Psychophysiological Research.)
    • Contributed Indexing:
      Keywords: ERP; exploratory factor analysis; exploratory structural equation modeling; principal component analysis
    • Publication Date:
      Date Created: 20181212 Date Completed: 20200416 Latest Revision: 20200416
    • Publication Date:
      20240105
    • Accession Number:
      10.1111/psyp.13303
    • Accession Number:
      30537207