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A tutorial on the use of temporal exploratory factor analysis in developmental ERP research – chances and challenges

Developmental researchers are often interested in event-related potentials (ERPs) as a measure of brain activity occurring time-locked to an event of interest. The traditional data-analytic approach for ERPs is based on the observed ERP and suffers from two major problems: First, the definition of analysis time windows as well as regions of interest often proceeds in a relatively arbitrary manner. Second, the observed signal at the scalp is a mixture of underlying signals generated in the brain. The temporal exploratory factor analysis (EFA) for ERP data, also known as temporal principal component analysis (PCA), aims to address these problems. However, its application in developmental ERP research settings comes with the unique challenge that the component structure differs between children and adults (so-called measurement non-invariance). Separate EFAs for the groups can be used to cope with this challenge and gain valuable insights into developmental processes. Here, we demonstrate how to make results from separate EFAs accessible for inferential statistics by re-scaling to original units. In addition, separate EFAs enable analyses of latency differences between groups. We propose the application of an established Jackknife approach to the factor solution for this purpose. With this tutorial, we want to enable readers with a focus on developmental research to conduct an EFA-based ERP analysis of amplitude and latency differences to address their research questions. We explain the benefits of an EFA-based approach, introduce the EFA model and demonstrate its application to a developmental research question step-by-step using real-data from a child and an adult group (code and data are made available). Finally, we discuss how to cope with typical challenges during the analysis and name potential limitations.