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

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.

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