EFAfactors:Determining the Number of Factors in Exploratory Factor Analysis
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.
