EFAfactors

Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) doi:10.1207/s15327906mbr0102_10, Kaiser-Guttman Criterion (KGC) by Guttman (1954) doi:10.1007/BF02289162 and Kaiser (1960) doi:10.1177/001316446002000116, and flexible Parallel Analysis (PA) by Horn (1965) doi:10.1007/BF02289447 based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) doi:10.1037/met0000074, Comparison Data (CD) by Ruscio and Roche (2012) doi:10.1037/a0025697, and Hull method by Lorenzo-Seva et al. (2011) doi:10.1080/00273171.2011.564527, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) doi:10.3758/s13428-023-02122-4 and Factor Forest (FF) by Goretzko and Buhner (2020) doi:10.1037/met0000262. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.

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