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EFAfactors  

Determining the Number of Factors in Exploratory Factor Analysis
View on CRAN: Click here


Download and install EFAfactors package within the R console
Install from CRAN:
install.packages("EFAfactors")

Install from Github:
library("remotes")
install_github("cran/EFAfactors")

Install by package version:
library("remotes")
install_version("EFAfactors", "1.1.0")



Attach the package and use:
library("EFAfactors")
Maintained by
Haijiang Qin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-25
Latest Update: 2024-09-25
Description:
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.
How to cite:
Haijiang Qin (2024). EFAfactors: Determining the Number of Factors in Exploratory Factor Analysis. R package version 1.1.0, https://cran.r-project.org/web/packages/EFAfactors. Accessed 07 Nov. 2024.
Previous versions and publish date:
1.0.0 (2024-09-25 10:50)
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