What is it about?
Understanding the structure and composition of data is an important undertaking for a wide range of scientific domains. An initial step in this endeavor is to determine how the data can be summarized into a smaller set of meaningful variables (i.e., dimensions). In this article, we extend a state-of-the-art network science approach, called exploratory graph analysis (EGA), used to identify the dimensions that exist in multivariate data. Using Monte Carlo methods, we compared EGA with several traditional eigenvalue-based approaches that are commonly used in the psychological literature including parallel analysis. Additionally, the simulation study evaluated the performance of new variants of the EGA method and considered a wider set of realistic conditions, such as unidimensional structures and variables of continuous and categorical levels of measurement. We found that EGA performed as well as or better than the most accurate traditional method (i.e., parallel analysis). Importantly, EGA offers a few advantages over traditional methods: (a) it provides an intuitive visual representation of the results, (b) this representation offers a more complex understanding of the data’s structure, and (c) the algorithm is deterministic meaning there are fewer researcher degrees of freedom. In sum, our study demonstrates that EGA can accurately identify the underlying structure of multivariate data, while retaining the complexity of the data’s structure. This implies that researchers can meaningfully summarize their data without sacrificing the finer details.
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Why is it important?
Understanding how psychological traits are organized is a central problem in many different areas of psychology. The techniques we are presenting in this paper help us to further advance the methodological area of psychology to better understand the organization of psychological traits.
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This page is a summary of: Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial., Psychological Methods, June 2020, American Psychological Association (APA),
DOI: 10.1037/met0000255.
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